From 1bcefe770ab1c456f4af5cd912fa2f2d41184cf8 Mon Sep 17 00:00:00 2001 From: Mark Hillebrand Date: Mon, 18 Jan 2016 09:35:54 +0100 Subject: [PATCH] .gitattributes: set proper configuration for text files (and normalize files) --- .gitattributes | 86 + .gitignore | 372 +- CNTK.sln | 1742 +- .../Other/Simple2d/AdditionalFiles/MakeData.m | 220 +- .../Other/Simple2d/Data/SimpleMapping.txt | 4 +- Examples/Other/Simple2d/README.md | 164 +- Examples/README.md | 40 +- Examples/Speech/AN4/Data/glob_0000.mlf | 110260 +++++++-------- Examples/Speech/AN4/Data/glob_0000.scp | 1896 +- Examples/Speech/AN4/Data/state.list | 264 +- .../TIMIT/AdditionalFiles/ComputeConfusion.m | 368 +- .../TIMIT/AdditionalFiles/ShowConfusions.m | 154 +- Examples/Text/PennTreebank/Config/rnn.config | 892 +- ...nal Network Toolkit_MSR-LA (2014-03-28).md | 296 +- Source/ActionsLib/ActionsLib.vcxproj | 358 +- Source/ActionsLib/ActionsLib.vcxproj.filters | 136 +- Source/CNTK/CNTK.cpp | 1454 +- Source/CNTK/CNTK.vcxproj | 482 +- Source/CNTK/CNTK.vcxproj.filters | 542 +- Source/CNTK/SynchronousExecutionEngine.h | 780 +- Source/Common/BestGpu.cpp | 1404 +- Source/Common/File.cpp | 2 +- Source/Common/Include/BestGpu.h | 54 +- Source/Common/Include/ProgressTracing.h | 212 +- Source/Common/Include/ScriptableObjects.h | 1376 +- Source/Common/Include/Sequences.h | 1854 +- Source/Common/Include/TimerUtility.h | 66 +- Source/Common/Include/stdafx.h | 12 +- Source/Common/fileutil.cpp | 4356 +- .../ComputationNetworkLib.vcxproj | 404 +- .../ComputationNetworkLib.vcxproj.filters | 300 +- .../ComputationNetworkLib/ComputationNode.cpp | 562 +- .../InputAndParamNodes.h | 1144 +- Source/EvalDll/EvalDll.vcxproj | 316 +- Source/EvalDll/EvalDll.vcxproj.filters | 144 +- Source/Math/CPUMatrix.cpp | 11632 +- Source/Math/CPUMatrix.h | 874 +- Source/Math/CPUSparseMatrix.h | 378 +- Source/Math/GPUMatrix.h | 1032 +- Source/Math/GPUMatrixCUDAKernels.cuh | 10118 +- Source/Math/Math.vcxproj | 442 +- Source/Math/Math.vcxproj.filters | 298 +- Source/Math/MathCUDA.vcxproj | 460 +- Source/Math/MathCUDA.vcxproj.filters | 306 +- Source/Math/Matrix.cpp | 10622 +- Source/Math/Matrix.h | 1022 +- Source/Math/NoGPU.cpp | 2698 +- Source/Math/ValueQuantizer.h | 442 +- Source/Math/cudabasetypes.h | 178 +- Source/Math/cudadevice.h | 76 +- Source/Math/cudalattice.cpp | 430 +- Source/Math/cudalattice.h | 236 +- Source/Math/cudalatticeops.cu | 54 +- Source/Math/cudalatticeops.cu.h | 908 +- Source/Math/cudalatticeops.h | 126 +- Source/Math/cudalib.cpp | 166 +- Source/Math/cudalib.h | 114 +- Source/Math/latticefunctionskernels.h | 1846 +- .../Readers/BinaryReader/BinaryReader.vcxproj | 302 +- .../BinaryReader/BinaryReader.vcxproj.filters | 108 +- Source/Readers/DSSMReader/DSSMReader.cpp | 1370 +- Source/Readers/DSSMReader/DSSMReader.h | 318 +- Source/Readers/DSSMReader/DSSMReader.vcxproj | 298 +- .../DSSMReader/DSSMReader.vcxproj.filters | 158 +- Source/Readers/DSSMReader/Exports.cpp | 62 +- Source/Readers/DSSMReader/dllmain.cpp | 38 +- Source/Readers/DSSMReader/stdafx.cpp | 16 +- Source/Readers/DSSMReader/stdafx.h | 34 +- Source/Readers/DSSMReader/targetver.h | 16 +- Source/Readers/HTKMLFReader/HTKMLFReader.cpp | 3860 +- Source/Readers/HTKMLFReader/HTKMLFReader.h | 356 +- .../Readers/HTKMLFReader/HTKMLFReader.vcxproj | 308 +- .../HTKMLFReader/HTKMLFReader.vcxproj.filters | 132 +- Source/Readers/HTKMLFReader/ReadMe.txt | 10 +- Source/Readers/HTKMLFReader/chunkevalsource.h | 744 +- Source/Readers/HTKMLFReader/htkfeatio.h | 2074 +- .../HTKMLFReader/minibatchsourcehelpers.h | 616 +- .../HTKMLFReader/rollingwindowsource.h | 1674 +- .../HTKMLFReader/utterancesourcemulti.h | 2892 +- .../Readers/ImageReader/ImageReader.vcxproj | 290 +- .../ImageReader/ImageReader.vcxproj.filters | 86 +- .../Kaldi2Reader/utterancesourcemulti.h | 2734 +- .../LMSequenceReader/LMSequenceReader.vcxproj | 318 +- .../LMSequenceReader/SequenceReader.cpp | 14 +- .../Readers/LMSequenceReader/SequenceReader.h | 10 +- .../LUSequenceReader/LUSequenceReader.vcxproj | 314 +- .../LUSequenceReader.vcxproj.filters | 116 +- Source/Readers/LibSVMBinaryReader/Exports.cpp | 62 +- .../LibSVMBinaryReader/LibSVMBinaryReader.cpp | 1688 +- .../LibSVMBinaryReader/LibSVMBinaryReader.h | 534 +- .../LibSVMBinaryReader.vcxproj | 310 +- .../LibSVMBinaryReader.vcxproj.filters | 118 +- Source/Readers/LibSVMBinaryReader/dllmain.cpp | 38 +- Source/Readers/LibSVMBinaryReader/stdafx.cpp | 16 +- Source/Readers/LibSVMBinaryReader/targetver.h | 20 +- .../SparsePCReader/SparsePCReader.vcxproj | 310 +- .../SparsePCReader.vcxproj.filters | 166 +- .../Readers/UCIFastReader/UCIFastReader.cpp | 2264 +- Source/Readers/UCIFastReader/UCIFastReader.h | 40 +- .../UCIFastReader/UCIFastReader.vcxproj | 304 +- .../UCIFastReader.vcxproj.filters | 114 +- Source/SGDLib/DataReaderHelpers.h | 1176 +- Source/SGDLib/MultiNetworksSGD.h | 2540 +- Source/SGDLib/SGD.cpp | 5512 +- Source/SGDLib/SGD.h | 1030 +- Source/SGDLib/SGDLib.vcxproj | 434 +- Source/SGDLib/SGDLib.vcxproj.filters | 382 +- .../SequenceTrainingLib.vcxproj | 166 +- .../SequenceTrainingLib.vcxproj.filters | 72 +- Source/SequenceTrainingLib/gammacalculation.h | 756 +- .../latticeforwardbackward.cpp | 2850 +- .../parallelforwardbackward.cpp | 1732 +- Tests/EndToEndTests/Image/Data/Test.txt | 200 +- Tests/EndToEndTests/Image/Data/Train.txt | 2000 +- .../Speech/Data/SimpleMapping.txt | 4 +- Tests/EndToEndTests/Speech/Data/glob_0000.mlf | 110260 +++++++-------- Tests/EndToEndTests/Speech/Data/glob_0000.scp | 1896 +- Tests/EndToEndTests/Speech/Data/state.list | 264 +- .../Speech/QuickE2E/baseline.windows.cpu.txt | 1456 +- .../Speech/QuickE2E/baseline.windows.gpu.txt | 2692 +- .../EndToEndTests/Speech/QuickE2E/cntk.config | 188 +- .../Speech/Simple/baseline.windows.cpu.txt | 9666 +- .../Speech/Simple/baseline.windows.gpu.txt | 9954 +- Tests/EndToEndTests/Speech/Simple/cntk.config | 194 +- Tests/UnitTests/EvalTest/EvalTest.vcxproj | 238 +- .../MathPerformanceTests.vcxproj | 234 +- Tests/UnitTests/MathTests/GPUMatrixTests.cpp | 1110 +- Tests/UnitTests/MathTests/MathTests.vcxproj | 320 +- .../MatrixDataSynchronizationTests.cpp | 240 +- .../UnitTests/ReaderTests/ReaderTests.vcxproj | 366 +- .../ReaderTests/ReaderTests.vcxproj.filters | 312 +- 131 files changed, 180378 insertions(+), 180292 deletions(-) diff --git a/.gitattributes b/.gitattributes index 5fc64846d7b7..f4dcd69564a6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,88 @@ +.gitattributes text +.gitignore text + +.clang-format text + +*.md text +*.txt text +*.html text +*.lyx text +*.bib text +KaldiReaderReadme text +Readme text + +*.bat text +*.py text +*.pl text +*.sh text eol=lf +build-and-test text eol=lf +configure text eol=lf +generate_build_info text eol=lf run-test text eol=lf run-test-common text eol=lf + +Makefile text +*.sln text +*.vcxproj text +*.vcxproj.filters text +*.vssettings text +*.csproj text + +*.h text +*.cpp text +*.cc text +*.cu text +*.cuh text + +*.cs text + +*.yml text + +*.config text +*.cntk text +*.bs text +*.ndl text +*.mel text +*.xml text +*.m text +*.mlf text +*.scp text +*.list text + +*.log text + +# Speech data +mean.363 text +var.363 text +prior.132 text + +# AMI-specific +Results text +40fbank.conf text +80fbank.conf text + +# SLU-specific +atis.*.simple + +# TIMIT-specific +*.fbank24_zda text +TIMIT*.100 text +TIMIT*.20 text +TIMIT*.arpa text +TIMIT*.cntk text +TIMIT*.dnn_map text +TIMIT*.fbank text +TIMIT*.fullpath text +TIMIT*.lookahead text +TIMIT*.rnn text +TIMIT*.statelist text +TIMIT*.tfsa text +TIMIT*.transitions text + +# Binary extensions: +*.vsdm binary +*.pdf binary +*.png binary +*.docx binary +*.chunk binary +*.pptx binary diff --git a/.gitignore b/.gitignore index b99e7e2320d2..3ba023f84e9a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,186 +1,186 @@ -## Ignore Visual Studio temporary files, build results, and -## files generated by popular Visual Studio add-ons. - -# User-specific files -*.suo -*.user -*.sln.docstates -*.orig -\#* -.\#* - -# Local build configuration -Config.make - -# Build results - -[Dd]ebug/ -[Rr]elease/ -x64/ -build/ -[Bb]in/ -[Oo]bj/ -.run-* -lib/ -bin/ - -# Enable "build/" folder in the NuGet Packages folder since NuGet packages use it for MSBuild targets -!packages/*/build/ - -# MSTest test Results -[Tt]est[Rr]esult*/ -[Bb]uild[Ll]og.* - -*_i.c -*_p.c -*.ilk -*.meta -*.obj -*.pch -*.pdb -*.pgc -*.pgd -*.rsp -*.sbr -*.tlb -*.tli -*.tlh -*.tmp -*.tmp_proj -*.log -*.vspscc -*.vssscc -.builds -*.pidb -*.log -*.scc -*.dep -*.o -*.d - -# Visual C++ cache files -ipch/ -*.aps -*.ncb -*.opensdf -*.sdf -*.cachefile - -# Visual Studio profiler -*.psess -*.vsp -*.vspx - -# Guidance Automation Toolkit -*.gpState - -# ReSharper is a .NET coding add-in -_ReSharper*/ -*.[Rr]e[Ss]harper - -# TeamCity is a build add-in -_TeamCity* - -# DotCover is a Code Coverage Tool -*.dotCover - -# NCrunch -*.ncrunch* -.*crunch*.local.xml - -# Installshield output folder -[Ee]xpress/ - -# DocProject is a documentation generator add-in -DocProject/buildhelp/ -DocProject/Help/*.HxT -DocProject/Help/*.HxC -DocProject/Help/*.hhc -DocProject/Help/*.hhk -DocProject/Help/*.hhp -DocProject/Help/Html2 -DocProject/Help/html - -# Click-Once directory -publish/ - -# Publish Web Output -*.Publish.xml - -# NuGet Packages Directory -## TODO: If you have NuGet Package Restore enabled, uncomment the next line -#packages/ - -# Windows Azure Build Output -csx -*.build.csdef - -# Windows Store app package directory -AppPackages/ - -# Others -sql/ -*.Cache -ClientBin/ -[Ss]tyle[Cc]op.* -~$* -*~ -*.dbmdl -*.[Pp]ublish.xml -*.pfx -*.publishsettings - -# RIA/Silverlight projects -Generated_Code/ - -# Backup & report files from converting an old project file to a newer -# Visual Studio version. Backup files are not needed, because we have git ;-) -_UpgradeReport_Files/ -Backup*/ -UpgradeLog*.XML -UpgradeLog*.htm - -# SQL Server files -App_Data/*.mdf -App_Data/*.ldf - - -#LightSwitch generated files -GeneratedArtifacts/ -_Pvt_Extensions/ -ModelManifest.xml - -# ========================= -# Windows detritus -# ========================= - -# Windows image file caches -Thumbs.db -ehthumbs.db - -# Folder config file -Desktop.ini - -# Recycle Bin used on file shares -$RECYCLE.BIN/ - -# Mac desktop service store files -.DS_Store - -*.lyx~ -*.bak - -bin/ -LOG -*.log -core -*.lyx# - -# ========================= -# prebuild file -# ========================= -Source/CNTK/buildinfo.h -Source/CNTK/buildinfo.h$$ - - -*.lyx# +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. + +# User-specific files +*.suo +*.user +*.sln.docstates +*.orig +\#* +.\#* + +# Local build configuration +Config.make + +# Build results + +[Dd]ebug/ +[Rr]elease/ +x64/ +build/ +[Bb]in/ +[Oo]bj/ +.run-* +lib/ +bin/ + +# Enable "build/" folder in the NuGet Packages folder since NuGet packages use it for MSBuild targets +!packages/*/build/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +*_i.c +*_p.c +*.ilk +*.meta +*.obj +*.pch +*.pdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.log +*.scc +*.dep +*.o +*.d + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opensdf +*.sdf +*.cachefile + +# Visual Studio profiler +*.psess +*.vsp +*.vspx + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# NCrunch +*.ncrunch* +.*crunch*.local.xml + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.Publish.xml + +# NuGet Packages Directory +## TODO: If you have NuGet Package Restore enabled, uncomment the next line +#packages/ + +# Windows Azure Build Output +csx +*.build.csdef + +# Windows Store app package directory +AppPackages/ + +# Others +sql/ +*.Cache +ClientBin/ +[Ss]tyle[Cc]op.* +~$* +*~ +*.dbmdl +*.[Pp]ublish.xml +*.pfx +*.publishsettings + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file to a newer +# Visual Studio version. Backup files are not needed, because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm + +# SQL Server files +App_Data/*.mdf +App_Data/*.ldf + + +#LightSwitch generated files +GeneratedArtifacts/ +_Pvt_Extensions/ +ModelManifest.xml + +# ========================= +# Windows detritus +# ========================= + +# Windows image file caches +Thumbs.db +ehthumbs.db + +# Folder config file +Desktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Mac desktop service store files +.DS_Store + +*.lyx~ +*.bak + +bin/ +LOG +*.log +core +*.lyx# + +# ========================= +# prebuild file +# ========================= +Source/CNTK/buildinfo.h +Source/CNTK/buildinfo.h$$ + + +*.lyx# diff --git a/CNTK.sln b/CNTK.sln index 461b51c62578..e8bbd5c77dee 100644 --- a/CNTK.sln +++ b/CNTK.sln @@ -1,871 +1,871 @@ - -Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio 2013 -VisualStudioVersion = 12.0.40629.0 -MinimumVisualStudioVersion = 10.0.40219.1 -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CNTK", "Source\CNTK\CNTK.vcxproj", "{E6F26F9A-FF64-4F0A-B749-CD309EE357EE}" - ProjectSection(ProjectDependencies) = postProject - {33D2FD22-DEF2-4507-A58A-368F641AEBE5} = {33D2FD22-DEF2-4507-A58A-368F641AEBE5} - {EB2BE26F-6BD4-4274-971F-86D080779DD1} = {EB2BE26F-6BD4-4274-971F-86D080779DD1} - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tests", "Tests", "{D45DF403-6781-444E-B654-A96868C5BE68}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Reader Plugins", "Reader Plugins", "{33EBFE78-A1A8-4961-8938-92A271941F94}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CNTK Core", "CNTK Core", "{DD043083-71A4-409A-AA91-F9C548DCF7EC}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Linux build files", "Linux build files", "{3ED0465D-23E7-4855-9694-F788717B6533}" - ProjectSection(SolutionItems) = preProject - configure = configure - Makefile = Makefile - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Documentation", "Documentation", "{065AF55D-AF02-448B-BFCD-52619FDA4BD0}" - ProjectSection(SolutionItems) = preProject - README.md = README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tutorial", "Tutorial", "{98D2C32B-0C1F-4E19-A626-65F7BA4600CF}" - ProjectSection(SolutionItems) = preProject - Documentation\Tutorial\CNTK-Tutorial-NIPS2015.pdf = Documentation\Tutorial\CNTK-Tutorial-NIPS2015.pdf - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CNTK-TechReport", "CNTK-TechReport", "{EA67F51F-1FE8-462D-9F3E-01161685AD59}" - ProjectSection(SolutionItems) = preProject - Documentation\CNTK-TechReport\lyx\CNTKBook-20151201.pdf = Documentation\CNTK-TechReport\lyx\CNTKBook-20151201.pdf - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Documents", "Documents", "{DE1A06BA-EC5C-4E0D-BCA8-3EA555310C58}" - ProjectSection(SolutionItems) = preProject - Documentation\Documents\Configuration Files.docx = Documentation\Documents\Configuration Files.docx - Documentation\Documents\External Buffer Behavior.docx = Documentation\Documents\External Buffer Behavior.docx - Documentation\Documents\Model Editing Language.docx = Documentation\Documents\Model Editing Language.docx - Documentation\Documents\Network Description Language.docx = Documentation\Documents\Network Description Language.docx - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "License", "License", "{63024704-A2D7-497E-AD4B-5C10C6AA1374}" - ProjectSection(SolutionItems) = preProject - license\MSR Computational Network Toolkit_MSR-LA (2014-03-28).md = license\MSR Computational Network Toolkit_MSR-LA (2014-03-28).md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "lyx", "lyx", "{F9BEB27E-8AF5-464E-8D45-0000D5AFA2D3}" - ProjectSection(SolutionItems) = preProject - Documentation\CNTK-TechReport\lyx\CNTKBook-master.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook-master.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_Abstract.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_Abstract.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_ASRDecoder_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_ASRDecoder_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_CN_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CN_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Adv_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Adv_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Programmer_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Programmer_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_ExampleSetup_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_ExampleSetup_Chapter.lyx - Documentation\CNTK-TechReport\lyx\CNTKBook_Introduction.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_Introduction.lyx - Documentation\CNTK-TechReport\lyx\references.bib = Documentation\CNTK-TechReport\lyx\references.bib - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "figures", "figures", "{889C1CCF-92B3-450B-B00D-FC9A9D5BE464}" - ProjectSection(SolutionItems) = preProject - Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.pdf = Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.pdf - Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.png = Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.png - Documentation\CNTK-TechReport\figures\CN-1HiddenNN.pdf = Documentation\CNTK-TechReport\figures\CN-1HiddenNN.pdf - Documentation\CNTK-TechReport\figures\CN-1HiddenNN.png = Documentation\CNTK-TechReport\figures\CN-1HiddenNN.png - Documentation\CNTK-TechReport\figures\CN-2Inputs.pdf = Documentation\CNTK-TechReport\figures\CN-2Inputs.pdf - Documentation\CNTK-TechReport\figures\CN-2Inputs.png = Documentation\CNTK-TechReport\figures\CN-2Inputs.png - Documentation\CNTK-TechReport\figures\CN-EfficientGradient.pdf = Documentation\CNTK-TechReport\figures\CN-EfficientGradient.pdf - Documentation\CNTK-TechReport\figures\CN-EfficientGradient.png = Documentation\CNTK-TechReport\figures\CN-EfficientGradient.png - Documentation\CNTK-TechReport\figures\CN-NaiveGradient.pdf = Documentation\CNTK-TechReport\figures\CN-NaiveGradient.pdf - Documentation\CNTK-TechReport\figures\CN-NaiveGradient.png = Documentation\CNTK-TechReport\figures\CN-NaiveGradient.png - Documentation\CNTK-TechReport\figures\CN-ShareWeight.pdf = Documentation\CNTK-TechReport\figures\CN-ShareWeight.pdf - Documentation\CNTK-TechReport\figures\CN-ShareWeight.png = Documentation\CNTK-TechReport\figures\CN-ShareWeight.png - Documentation\CNTK-TechReport\figures\CN-WithDelayNode.pdf = Documentation\CNTK-TechReport\figures\CN-WithDelayNode.pdf - Documentation\CNTK-TechReport\figures\CN-WithDelayNode.png = Documentation\CNTK-TechReport\figures\CN-WithDelayNode.png - Documentation\CNTK-TechReport\figures\CNNComputation.pdf = Documentation\CNTK-TechReport\figures\CNNComputation.pdf - Documentation\CNTK-TechReport\figures\CNNComputation.png = Documentation\CNTK-TechReport\figures\CNNComputation.png - Documentation\CNTK-TechReport\figures\CNTKArch.pdf = Documentation\CNTK-TechReport\figures\CNTKArch.pdf - Documentation\CNTK-TechReport\figures\CNTKArch.png = Documentation\CNTK-TechReport\figures\CNTKArch.png - Documentation\CNTK-TechReport\figures\ConfusionData1.png = Documentation\CNTK-TechReport\figures\ConfusionData1.png - Documentation\CNTK-TechReport\figures\ConfusionData100.png = Documentation\CNTK-TechReport\figures\ConfusionData100.png - Documentation\CNTK-TechReport\figures\SequenceBatch.pdf = Documentation\CNTK-TechReport\figures\SequenceBatch.pdf - Documentation\CNTK-TechReport\figures\SequenceBatch.png = Documentation\CNTK-TechReport\figures\SequenceBatch.png - Documentation\CNTK-TechReport\figures\SimpleDemoDataReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoDataReference.png - Documentation\CNTK-TechReport\figures\SimpleDemoErrorRateReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoErrorRateReference.png - Documentation\CNTK-TechReport\figures\SimpleDemoOutputReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoOutputReference.png - Documentation\CNTK-TechReport\figures\simpleRNN.png = Documentation\CNTK-TechReport\figures\simpleRNN.png - Documentation\CNTK-TechReport\figures\SpeechErrorRate.png = Documentation\CNTK-TechReport\figures\SpeechErrorRate.png - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", "{C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\README_Windows_Debug_commands.txt = Tests\EndToEndTests\Speech\README_Windows_Debug_commands.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "QuickE2E", "QuickE2E", "{4BBF2950-3DBD-469A-AD57-6CACBEBAF541}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\QuickE2E\baseline.cpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.cpu.txt - Tests\EndToEndTests\Speech\QuickE2E\baseline.gpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.gpu.txt - Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\QuickE2E\cntk.config = Tests\EndToEndTests\Speech\QuickE2E\cntk.config - Tests\EndToEndTests\Speech\QuickE2E\run-test = Tests\EndToEndTests\Speech\QuickE2E\run-test - Tests\EndToEndTests\Speech\QuickE2E\testcases.yml = Tests\EndToEndTests\Speech\QuickE2E\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{5F733BBA-FE83-4668-8F83-8B0E78A36619}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\Data\AN4.LICENSE.html = Tests\EndToEndTests\Speech\Data\AN4.LICENSE.html - Tests\EndToEndTests\Speech\Data\glob_0000.mlf = Tests\EndToEndTests\Speech\Data\glob_0000.mlf - Tests\EndToEndTests\Speech\Data\glob_0000.scp = Tests\EndToEndTests\Speech\Data\glob_0000.scp - Tests\EndToEndTests\Speech\Data\README.txt = Tests\EndToEndTests\Speech\Data\README.txt - Tests\EndToEndTests\Speech\Data\state.list = Tests\EndToEndTests\Speech\Data\state.list - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "LSTM", "LSTM", "{19EE975B-232D-49F0-94C7-6F1C6424FB53}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\LSTM\cntk.config = Tests\EndToEndTests\Speech\LSTM\cntk.config - Tests\EndToEndTests\Speech\LSTM\lstm.bs = Tests\EndToEndTests\Speech\LSTM\lstm.bs - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ComputationNetworkLib", "Source\ComputationNetworkLib\ComputationNetworkLib.vcxproj", "{928ABD1B-4D3B-4017-AEF1-0FA1B4467513}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - {EAD17188-072C-4726-B840-A769C36DAD1B} = {EAD17188-072C-4726-B840-A769C36DAD1B} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SGDLib", "Source\SGDLib\SGDLib.vcxproj", "{DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}" - ProjectSection(ProjectDependencies) = postProject - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ParallelTraining", "ParallelTraining", "{5E666C53-2D82-49C9-9127-3FDDC321C741}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\ParallelTraining\SimpleMultiGPU.config = Tests\EndToEndTests\ParallelTraining\SimpleMultiGPU.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{6D1353D6-F196-466F-B886-F16D48759B20}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\ParallelTraining\Data\SimpleDataTrain.txt = Tests\EndToEndTests\ParallelTraining\Data\SimpleDataTrain.txt - Tests\EndToEndTests\ParallelTraining\Data\SimpleMapping.txt = Tests\EndToEndTests\ParallelTraining\Data\SimpleMapping.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NoQuantization", "NoQuantization", "{B6725C9F-A6D2-4269-9B74-7888A90F7884}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SinglePrecision", "SinglePrecision", "{B27DD434-EECD-4EE0-A03B-1150EB87258E}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.cpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.gpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.cpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.gpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\run-test = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\run-test - Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\testcases.yml = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DoublePrecision", "DoublePrecision", "{A4884465-CFBB-4A64-A9DE-690E1A63EF7E}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.cpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.gpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.cpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.gpu.txt - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\run-test = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\run-test - Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\testcases.yml = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Kaldi2Reader", "Kaldi2Reader", "{C70E1572-20FF-496C-A0A9-10AA6755A07C}" - ProjectSection(SolutionItems) = preProject - Source\Readers\Kaldi2Reader\basetypes.h = Source\Readers\Kaldi2Reader\basetypes.h - Source\Readers\Kaldi2Reader\biggrowablevectors.h = Source\Readers\Kaldi2Reader\biggrowablevectors.h - Source\Readers\Kaldi2Reader\chunkevalsource.h = Source\Readers\Kaldi2Reader\chunkevalsource.h - Source\Readers\Kaldi2Reader\DataReader.cpp = Source\Readers\Kaldi2Reader\DataReader.cpp - Source\Readers\Kaldi2Reader\DataWriter.cpp = Source\Readers\Kaldi2Reader\DataWriter.cpp - Source\Readers\Kaldi2Reader\dllmain.cpp = Source\Readers\Kaldi2Reader\dllmain.cpp - Source\Readers\Kaldi2Reader\DOCUMENTATION.txt = Source\Readers\Kaldi2Reader\DOCUMENTATION.txt - Source\Readers\Kaldi2Reader\htkfeatio.h = Source\Readers\Kaldi2Reader\htkfeatio.h - Source\Readers\Kaldi2Reader\htkfeatio_utils.h = Source\Readers\Kaldi2Reader\htkfeatio_utils.h - Source\Readers\Kaldi2Reader\HTKMLFReader.cpp = Source\Readers\Kaldi2Reader\HTKMLFReader.cpp - Source\Readers\Kaldi2Reader\HTKMLFReader.h = Source\Readers\Kaldi2Reader\HTKMLFReader.h - Source\Readers\Kaldi2Reader\HTKMLFWriter.cpp = Source\Readers\Kaldi2Reader\HTKMLFWriter.cpp - Source\Readers\Kaldi2Reader\HTKMLFWriter.h = Source\Readers\Kaldi2Reader\HTKMLFWriter.h - Source\Readers\Kaldi2Reader\kaldi.h = Source\Readers\Kaldi2Reader\kaldi.h - Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.cpp = Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.cpp - Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.h = Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.h - Source\Readers\Kaldi2Reader\latticearchive.cpp = Source\Readers\Kaldi2Reader\latticearchive.cpp - Source\Readers\Kaldi2Reader\minibatchiterator.h = Source\Readers\Kaldi2Reader\minibatchiterator.h - Source\Readers\Kaldi2Reader\minibatchsourcehelpers.h = Source\Readers\Kaldi2Reader\minibatchsourcehelpers.h - Source\Readers\Kaldi2Reader\msra_mgram.h = Source\Readers\Kaldi2Reader\msra_mgram.h - Source\Readers\Kaldi2Reader\notes.txt = Source\Readers\Kaldi2Reader\notes.txt - Source\Readers\Kaldi2Reader\numahelpers.h = Source\Readers\Kaldi2Reader\numahelpers.h - Source\Readers\Kaldi2Reader\pplhelpers.h = Source\Readers\Kaldi2Reader\pplhelpers.h - Source\Readers\Kaldi2Reader\readaheadsource.h = Source\Readers\Kaldi2Reader\readaheadsource.h - Source\Readers\Kaldi2Reader\rollingwindowsource.h = Source\Readers\Kaldi2Reader\rollingwindowsource.h - Source\Readers\Kaldi2Reader\simplethread.h = Source\Readers\Kaldi2Reader\simplethread.h - Source\Readers\Kaldi2Reader\ssefloat4.h = Source\Readers\Kaldi2Reader\ssefloat4.h - Source\Readers\Kaldi2Reader\ssematrix.h = Source\Readers\Kaldi2Reader\ssematrix.h - Source\Readers\Kaldi2Reader\stdafx.cpp = Source\Readers\Kaldi2Reader\stdafx.cpp - Source\Readers\Kaldi2Reader\stdafx.h = Source\Readers\Kaldi2Reader\stdafx.h - Source\Readers\Kaldi2Reader\targetver.h = Source\Readers\Kaldi2Reader\targetver.h - Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.cpp = Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.cpp - Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.h = Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.h - Source\Readers\Kaldi2Reader\UtteranceDerivativeComputationInterface.h = Source\Readers\Kaldi2Reader\UtteranceDerivativeComputationInterface.h - Source\Readers\Kaldi2Reader\utterancesourcemulti.h = Source\Readers\Kaldi2Reader\utterancesourcemulti.h - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SequenceTrainingLib", "Source\SequenceTrainingLib\SequenceTrainingLib.vcxproj", "{EAD17188-072C-4726-B840-A769C36DAD1B}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Truncated", "Truncated", "{88F85A64-105D-4CDA-8199-B7A312FC8A27}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.cpu.txt - Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.gpu.txt - Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\LSTM\Truncated\run-test = Tests\EndToEndTests\Speech\LSTM\Truncated\run-test - Tests\EndToEndTests\Speech\LSTM\Truncated\testcases.yml = Tests\EndToEndTests\Speech\LSTM\Truncated\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "FullUtterance", "FullUtterance", "{8241108A-7824-4FF2-BECA-7521A9D89DCF}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.cpu.txt - Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.gpu.txt - Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\LSTM\FullUtterance\run-test = Tests\EndToEndTests\Speech\LSTM\FullUtterance\run-test - Tests\EndToEndTests\Speech\LSTM\FullUtterance\testcases.yml = Tests\EndToEndTests\Speech\LSTM\FullUtterance\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DNN", "DNN", "{6994C86D-A672-4254-824A-51F4DFEB807F}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\DNN\cntk.config = Tests\EndToEndTests\Speech\DNN\cntk.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Parallel1BitQuantization", "Parallel1BitQuantization", "{FD88A77E-A839-4513-AC5C-AD92447BE229}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.cpu.txt - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.gpu.txt - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\run-test = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\run-test - Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\testcases.yml = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ParallelNoQuantization", "ParallelNoQuantization", "{40F93928-5CA2-433A-A48C-C8E9A35D7079}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.cpu.txt - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.gpu.txt - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\run-test = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\run-test - Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\testcases.yml = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DiscriminativePreTraining", "DiscriminativePreTraining", "{39B9BB97-D0E8-439A-8A1B-8DB8E7CF73C3}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\add_layer.mel = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\add_layer.mel - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.cpu.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.gpu.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\cntk_dpt.config = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\cntk_dpt.config - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn_1layer.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn_1layer.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\run-test = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\run-test - Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\testcases.yml = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "UnitTests", "UnitTests", "{6F19321A-65E7-4829-B00C-3886CD6C6EDE}" -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MathTests", "Tests\UnitTests\MathTests\MathTests.vcxproj", "{4701E678-5E6F-470D-B348-9CD1A2C095D1}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ActionsLib", "Source\ActionsLib\ActionsLib.vcxproj", "{EB2BE26F-6BD4-4274-971F-86D080779DD1}" - ProjectSection(ProjectDependencies) = postProject - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} = {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SequenceTraining", "SequenceTraining", "{BB8B9FC5-C4B3-477F-80E2-665DC8E431BD}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\DNN\SequenceTraining\add_layer.mel = Tests\EndToEndTests\Speech\DNN\SequenceTraining\add_layer.mel - Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.gpu.txt - Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\DNN\SequenceTraining\cntk_sequence.config = Tests\EndToEndTests\Speech\DNN\SequenceTraining\cntk_sequence.config - Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn.txt - Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn_1layer.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn_1layer.txt - Tests\EndToEndTests\Speech\DNN\SequenceTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\macros.txt - Tests\EndToEndTests\Speech\DNN\SequenceTraining\replace_ce_with_sequence_criterion.mel = Tests\EndToEndTests\Speech\DNN\SequenceTraining\replace_ce_with_sequence_criterion.mel - Tests\EndToEndTests\Speech\DNN\SequenceTraining\run-test = Tests\EndToEndTests\Speech\DNN\SequenceTraining\run-test - Tests\EndToEndTests\Speech\DNN\SequenceTraining\testcases.yml = Tests\EndToEndTests\Speech\DNN\SequenceTraining\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{8071EF60-30F7-4A77-81AA-ADCA0E18B1E3}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{76F9323D-34A1-43A5-A594-C4798931FF21}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Image\Data\labelsmap.txt = Tests\EndToEndTests\Image\Data\labelsmap.txt - Tests\EndToEndTests\Image\Data\Test.txt = Tests\EndToEndTests\Image\Data\Test.txt - Tests\EndToEndTests\Image\Data\Train.txt = Tests\EndToEndTests\Image\Data\Train.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "QuickE2E", "QuickE2E", "{2A884EB5-037C-481E-8170-BCDC8B3EDD93}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Image\QuickE2E\baseline.linux.debug.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.linux.debug.gpu.txt - Tests\EndToEndTests\Image\QuickE2E\baseline.linux.release.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.linux.release.gpu.txt - Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.cpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.cpu.txt - Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.gpu.txt - Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.cpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.cpu.txt - Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.gpu.txt - Tests\EndToEndTests\Image\QuickE2E\cntk.config = Tests\EndToEndTests\Image\QuickE2E\cntk.config - Tests\EndToEndTests\Image\QuickE2E\Convolution.ndl = Tests\EndToEndTests\Image\QuickE2E\Convolution.ndl - Tests\EndToEndTests\Image\QuickE2E\Macros.ndl = Tests\EndToEndTests\Image\QuickE2E\Macros.ndl - Tests\EndToEndTests\Image\QuickE2E\run-test = Tests\EndToEndTests\Image\QuickE2E\run-test - Tests\EndToEndTests\Image\QuickE2E\testcases.yml = Tests\EndToEndTests\Image\QuickE2E\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SVD", "SVD", "{669B6203-9675-4950-B526-7CD72D55E5E1}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\SVD\baseline.cpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.cpu.txt - Tests\EndToEndTests\Speech\SVD\baseline.gpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.gpu.txt - Tests\EndToEndTests\Speech\SVD\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\SVD\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\SVD\cntk.config = Tests\EndToEndTests\Speech\SVD\cntk.config - Tests\EndToEndTests\Speech\SVD\run-test = Tests\EndToEndTests\Speech\SVD\run-test - Tests\EndToEndTests\Speech\SVD\testcases.yml = Tests\EndToEndTests\Speech\SVD\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple", "Simple", "{81AE014F-DD63-47C7-B6E2-DB1D2833DCD1}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\Speech\Simple\baseline.cpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.cpu.txt - Tests\EndToEndTests\Speech\Simple\baseline.gpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.gpu.txt - Tests\EndToEndTests\Speech\Simple\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.windows.cpu.txt - Tests\EndToEndTests\Speech\Simple\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.windows.gpu.txt - Tests\EndToEndTests\Speech\Simple\cntk.config = Tests\EndToEndTests\Speech\Simple\cntk.config - Tests\EndToEndTests\Speech\Simple\run-test = Tests\EndToEndTests\Speech\Simple\run-test - Tests\EndToEndTests\Speech\Simple\testcases.yml = Tests\EndToEndTests\Speech\Simple\testcases.yml - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Examples", "Examples", "{47755F2E-D674-4175-9E38-8EA053455072}" - ProjectSection(SolutionItems) = preProject - Examples\README.md = Examples\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{9BDFA4BE-790E-408F-915B-5979BB5078C6}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", 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postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "DSSMReader", "Source\Readers\DSSMReader\DSSMReader.vcxproj", "{014DA766-B37B-4581-BC26-963EA5507931}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "HTKMLFReader", "Source\Readers\HTKMLFReader\HTKMLFReader.vcxproj", "{33D2FD22-DEF2-4507-A58A-368F641AEBE5}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LMSequenceReader", "Source\Readers\LMSequenceReader\LMSequenceReader.vcxproj", "{9A2F2441-5972-4EA8-9215-4119FCE0FB68}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LUSequenceReader", "Source\Readers\LUSequenceReader\LUSequenceReader.vcxproj", "{62836DC1-DF77-4B98-BF2D-45C943B7DDC6}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SparsePCReader", "Source\Readers\SparsePCReader\SparsePCReader.vcxproj", "{CE429AA2-3778-4619-8FD1-49BA3B81197B}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "UCIFastReader", "Source\Readers\UCIFastReader\UCIFastReader.vcxproj", "{E6646FFE-3588-4276-8A15-8D65C22711C1}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ImageReader", "Source\Readers\ImageReader\ImageReader.vcxproj", "{9BD0A746-0BBD-45B6-B81C-053F03C26CFB}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "EvalTest", "Tests\UnitTests\EvalTest\EvalTest.vcxproj", "{731312A8-6DA3-4841-AFCD-57520BA1BF8E}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MathPerformanceTests", "Tests\UnitTests\MathPerformanceTests\MathPerformanceTests.vcxproj", "{668BEED5-AC07-4F35-B3AE-EE65A7F9C976}" - ProjectSection(ProjectDependencies) = postProject - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "EndToEndTests", "EndToEndTests", "{6E565B48-1923-49CE-9787-9BBB9D96F4C5}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\run-test-common = Tests\EndToEndTests\run-test-common - Tests\EndToEndTests\TestDriver.py = Tests\EndToEndTests\TestDriver.py - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "LM", "LM", "{3BF59CCE-D245-420A-9F17-73CE61E284C2}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "RNNLM", "RNNLM", "{811924DE-2F12-4EA0-BE58-E57BEF3B74D1}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CPU", "CPU", "{96012801-5187-4FAF-A54E-BF4B73C855F8}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\LM\RNNLM\CPU\global.config = Tests\EndToEndTests\LM\RNNLM\CPU\global.config - Tests\EndToEndTests\LM\RNNLM\CPU\rnnlm.config = Tests\EndToEndTests\LM\RNNLM\CPU\rnnlm.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "GPU", "GPU", "{2A1F0FB0-2304-4F35-87B3-66230C6E58F0}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\LM\RNNLM\GPU\rnnlm.config = Tests\EndToEndTests\LM\RNNLM\GPU\rnnlm.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{BFBC6BE1-C33E-4A80-B8F3-A33410EC00FC}" - ProjectSection(SolutionItems) = preProject - Tests\EndToEndTests\SLU\atis.dev.IOB.simple = Tests\EndToEndTests\SLU\atis.dev.IOB.simple - Tests\EndToEndTests\SLU\atis.test.apos.pred.pos.head.IOB.simple = Tests\EndToEndTests\SLU\atis.test.apos.pred.pos.head.IOB.simple - Tests\EndToEndTests\SLU\atis.train.apos.pred.pos.head.IOB.simple = Tests\EndToEndTests\SLU\atis.train.apos.pred.pos.head.IOB.simple - Tests\EndToEndTests\SLU\globals.config = Tests\EndToEndTests\SLU\globals.config - Tests\EndToEndTests\SLU\input.txt = Tests\EndToEndTests\SLU\input.txt - Tests\EndToEndTests\SLU\inputmap.txt = Tests\EndToEndTests\SLU\inputmap.txt - Tests\EndToEndTests\SLU\lstmNDL.txt = Tests\EndToEndTests\SLU\lstmNDL.txt - Tests\EndToEndTests\SLU\README.txt = Tests\EndToEndTests\SLU\README.txt - Tests\EndToEndTests\SLU\rnnlu.config = Tests\EndToEndTests\SLU\rnnlu.config - Tests\EndToEndTests\SLU\rnnlu.ndl.config = Tests\EndToEndTests\SLU\rnnlu.ndl.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "MNIST", "MNIST", "{FA33A61E-95C7-4049-8111-22058CE361A3}" - ProjectSection(SolutionItems) = preProject - Examples\Image\MNIST\README.md = Examples\Image\MNIST\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{F99E1E80-50D8-421C-AD94-8ED0DF08C355}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{ED57E827-B28F-4BEE-BFB7-398EF8D83357}" - ProjectSection(SolutionItems) = preProject - Examples\Image\MNIST\AdditionalFiles\mnist_convert.py = Examples\Image\MNIST\AdditionalFiles\mnist_convert.py - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{6E5A252C-ACCE-42E0-9819-FF4DEF6D739E}" - ProjectSection(SolutionItems) = preProject - Examples\Image\MNIST\Config\01_OneHidden.config = Examples\Image\MNIST\Config\01_OneHidden.config - Examples\Image\MNIST\Config\01_OneHidden.ndl = Examples\Image\MNIST\Config\01_OneHidden.ndl - Examples\Image\MNIST\Config\02_Convolution.config = Examples\Image\MNIST\Config\02_Convolution.config - Examples\Image\MNIST\Config\02_Convolution.ndl = Examples\Image\MNIST\Config\02_Convolution.ndl - Examples\Image\MNIST\Config\03_ConvBatchNorm.config = Examples\Image\MNIST\Config\03_ConvBatchNorm.config - Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl = Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl - Examples\Image\MNIST\Config\Macros.ndl = Examples\Image\MNIST\Config\Macros.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CIFAR-10", "CIFAR-10", "{77125562-3BF2-45D2-9B73-72CA8E03C78C}" - ProjectSection(SolutionItems) = preProject - Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config = Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config - Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl = Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl - Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config - Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel - Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl - Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py = Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py - Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.config = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.config - Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel - Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl - Examples\Image\Miscellaneous\CIFAR-10\16to32.txt = Examples\Image\Miscellaneous\CIFAR-10\16to32.txt - Examples\Image\Miscellaneous\CIFAR-10\32to64.txt = Examples\Image\Miscellaneous\CIFAR-10\32to64.txt - Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py = Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py - Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt = Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt - Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl = Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl - Examples\Image\Miscellaneous\CIFAR-10\readme.txt = Examples\Image\Miscellaneous\CIFAR-10\readme.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ImageNet", "ImageNet", "{EF710C5A-E616-442A-889D-C997D39AF2E1}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AlexNet", "AlexNet", "{D29DC402-98A3-40C7-B683-4CC84DEC5C18}" - ProjectSection(SolutionItems) = preProject - Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel - Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config - Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl - Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "VGG", "VGG", "{BC0D6DFF-80CF-4B41-AD55-6F561C6FD229}" - ProjectSection(SolutionItems) = preProject - Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel - Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml = Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml - Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.config - Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Other", "Other", "{CCD56F12-BA17-4753-B5EE-4995FE682995}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{D2A060F1-128E-42A1-A0D0-3E3E1DFBC427}" - ProjectSection(SolutionItems) = preProject - Examples\Other\Simple2d\README.md = Examples\Other\Simple2d\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NdlExamples", "NdlExamples", "{FC573A62-6DAE-40A4-8153-520C8571A007}" - ProjectSection(SolutionItems) = preProject - Examples\Other\NdlExamples\NDLExamples.ndl = Examples\Other\NdlExamples\NDLExamples.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{1E37CE40-556D-4693-B58C-F8D4CE349BB7}" - ProjectSection(SolutionItems) = preProject - Examples\Other\Simple2d\Config\Multigpu.config = Examples\Other\Simple2d\Config\Multigpu.config - Examples\Other\Simple2d\Config\Simple.config = Examples\Other\Simple2d\Config\Simple.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{BF1A621D-528B-4B84-AAFC-EF1455FC6830}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{8C128B1D-87E0-4643-AB93-2581589AE425}" -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AN4", "AN4", "{EDA80B25-B181-4F70-844E-FBB41D40EA34}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\AN4\README.md = Examples\Speech\AN4\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\AN4\Config\FeedForward.config = Examples\Speech\AN4\Config\FeedForward.config - Examples\Speech\AN4\Config\LSTM-NDL.config = Examples\Speech\AN4\Config\LSTM-NDL.config - Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl = Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{6FCDB701-C23D-4BD7-BD48-12D46DC51084}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html = Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AMI", "AMI", "{0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\AMI\Readme = Examples\Speech\Miscellaneous\AMI\Readme - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TIMIT", "TIMIT", "{FB65FA58-C47B-4A49-9566-40FD5D75FC59}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "cntk_config", "cntk_config", "{439F8840-309E-4B7F-BCEC-196E0EADE949}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf - Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf - Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config = Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config - Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config - Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl - Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel - Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "config", "config", "{1C7D222F-E17B-444F-A18C-6205DEEF27BA}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel = Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel - Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl = Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl - Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl = Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl - Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl = Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl - Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl = Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl - Examples\Speech\Miscellaneous\TIMIT\config\globals.config = Examples\Speech\Miscellaneous\TIMIT\config\globals.config - Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl = Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl - Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl - Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl - Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl = Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl - Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config - Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CPU", "CPU", "{5ED4F5DC-E016-4E10-BACD-6A760A0CDE89}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config - Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "GPU", "GPU", "{35CFD8E3-7206-4243-AB5C-AAF610109A5C}" - ProjectSection(SolutionItems) = preProject - Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config - Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "PennTreebank", "PennTreebank", "{6F4125B5-220F-4FB7-B6C4-85A966A0268C}" - ProjectSection(SolutionItems) = preProject - Examples\Text\PennTreebank\README.md = Examples\Text\PennTreebank\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{850008BC-36B0-4A0A-BD0C-B6D5C2184227}" - ProjectSection(SolutionItems) = preProject - Examples\Text\PennTreebank\Config\rnn.config = Examples\Text\PennTreebank\Config\rnn.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{E6DC3B7D-303D-4A54-B040-D8DCF8C56E17}" - ProjectSection(SolutionItems) = preProject - Examples\Text\Miscellaneous\SLU\lstmNDL.txt = Examples\Text\Miscellaneous\SLU\lstmNDL.txt - Examples\Text\Miscellaneous\SLU\README.txt = Examples\Text\Miscellaneous\SLU\README.txt - Examples\Text\Miscellaneous\SLU\rnnlu.config = Examples\Text\Miscellaneous\SLU\rnnlu.config - Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt = Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tools", "Tools", "{83BFF5BF-D054-4B3E-9769-B00BA707F116}" - ProjectSection(SolutionItems) = preProject - Tools\build-and-test = Tools\build-and-test - Tools\generate_build_info = Tools\generate_build_info - Tools\msvc_collect_coverage.py = Tools\msvc_collect_coverage.py - Tools\run_boost_unit_tests.py = Tools\run_boost_unit_tests.py - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ResNet", "ResNet", "{06D2C644-AE5F-4C30-A1F6-C78E2845AAB1}" - ProjectSection(SolutionItems) = preProject - Examples\Image\Miscellaneous\ImageNet\ResNet\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\ResNet\add_top5_layer.mel - Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl - Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py = Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py - Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.config = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.config - Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl - Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.config = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.config - Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl - EndProjectSection -EndProject -Global - GlobalSection(SolutionConfigurationPlatforms) = preSolution - Debug|x64 = Debug|x64 - Release|x64 = Release|x64 - EndGlobalSection - GlobalSection(ProjectConfigurationPlatforms) = postSolution - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Debug|x64.ActiveCfg = Debug|x64 - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Debug|x64.Build.0 = Debug|x64 - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Release|x64.ActiveCfg = Release|x64 - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Release|x64.Build.0 = Release|x64 - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Debug|x64.ActiveCfg = Debug|x64 - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Debug|x64.Build.0 = Debug|x64 - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Release|x64.ActiveCfg = Release|x64 - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Release|x64.Build.0 = Release|x64 - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Debug|x64.ActiveCfg = Debug|x64 - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Debug|x64.Build.0 = Debug|x64 - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Release|x64.ActiveCfg = Release|x64 - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Release|x64.Build.0 = Release|x64 - {EAD17188-072C-4726-B840-A769C36DAD1B}.Debug|x64.ActiveCfg = Debug|x64 - {EAD17188-072C-4726-B840-A769C36DAD1B}.Debug|x64.Build.0 = Debug|x64 - {EAD17188-072C-4726-B840-A769C36DAD1B}.Release|x64.ActiveCfg = Release|x64 - {EAD17188-072C-4726-B840-A769C36DAD1B}.Release|x64.Build.0 = Release|x64 - {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Debug|x64.ActiveCfg = Debug|x64 - {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Debug|x64.Build.0 = Debug|x64 - {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Release|x64.ActiveCfg = Release|x64 - {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Release|x64.Build.0 = Release|x64 - {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Debug|x64.ActiveCfg = Debug|x64 - {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Debug|x64.Build.0 = Debug|x64 - {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Release|x64.ActiveCfg = Release|x64 - {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Release|x64.Build.0 = Release|x64 - {A4FC3467-4787-43E8-BBC0-D79AE56B468D}.Debug|x64.ActiveCfg = Debug|x64 - {A4FC3467-4787-43E8-BBC0-D79AE56B468D}.Debug|x64.Build.0 = Debug|x64 - {A4FC3467-4787-43E8-BBC0-D79AE56B468D}.Release|x64.ActiveCfg = Release|x64 - {A4FC3467-4787-43E8-BBC0-D79AE56B468D}.Release|x64.Build.0 = Release|x64 - {482999D1-B7E2-466E-9F8D-2119F93EAFD9}.Debug|x64.ActiveCfg = Debug|x64 - {482999D1-B7E2-466E-9F8D-2119F93EAFD9}.Debug|x64.Build.0 = Debug|x64 - {482999D1-B7E2-466E-9F8D-2119F93EAFD9}.Release|x64.ActiveCfg = Release|x64 - {482999D1-B7E2-466E-9F8D-2119F93EAFD9}.Release|x64.Build.0 = Release|x64 - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}.Debug|x64.ActiveCfg = Debug|x64 - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}.Debug|x64.Build.0 = Debug|x64 - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}.Release|x64.ActiveCfg = Release|x64 - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}.Release|x64.Build.0 = Release|x64 - 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{014DA766-B37B-4581-BC26-963EA5507931}.Debug|x64.Build.0 = Debug|x64 - {014DA766-B37B-4581-BC26-963EA5507931}.Release|x64.ActiveCfg = Release|x64 - {014DA766-B37B-4581-BC26-963EA5507931}.Release|x64.Build.0 = Release|x64 - {33D2FD22-DEF2-4507-A58A-368F641AEBE5}.Debug|x64.ActiveCfg = Debug|x64 - {33D2FD22-DEF2-4507-A58A-368F641AEBE5}.Debug|x64.Build.0 = Debug|x64 - {33D2FD22-DEF2-4507-A58A-368F641AEBE5}.Release|x64.ActiveCfg = Release|x64 - {33D2FD22-DEF2-4507-A58A-368F641AEBE5}.Release|x64.Build.0 = Release|x64 - {9A2F2441-5972-4EA8-9215-4119FCE0FB68}.Debug|x64.ActiveCfg = Debug|x64 - {9A2F2441-5972-4EA8-9215-4119FCE0FB68}.Debug|x64.Build.0 = Debug|x64 - {9A2F2441-5972-4EA8-9215-4119FCE0FB68}.Release|x64.ActiveCfg = Release|x64 - {9A2F2441-5972-4EA8-9215-4119FCE0FB68}.Release|x64.Build.0 = Release|x64 - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6}.Debug|x64.ActiveCfg = Debug|x64 - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6}.Debug|x64.Build.0 = Debug|x64 - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6}.Release|x64.ActiveCfg = Release|x64 - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6}.Release|x64.Build.0 = Release|x64 - {CE429AA2-3778-4619-8FD1-49BA3B81197B}.Debug|x64.ActiveCfg = Debug|x64 - {CE429AA2-3778-4619-8FD1-49BA3B81197B}.Debug|x64.Build.0 = Debug|x64 - {CE429AA2-3778-4619-8FD1-49BA3B81197B}.Release|x64.ActiveCfg = Release|x64 - {CE429AA2-3778-4619-8FD1-49BA3B81197B}.Release|x64.Build.0 = Release|x64 - {E6646FFE-3588-4276-8A15-8D65C22711C1}.Debug|x64.ActiveCfg = Debug|x64 - {E6646FFE-3588-4276-8A15-8D65C22711C1}.Debug|x64.Build.0 = Debug|x64 - {E6646FFE-3588-4276-8A15-8D65C22711C1}.Release|x64.ActiveCfg = Release|x64 - {E6646FFE-3588-4276-8A15-8D65C22711C1}.Release|x64.Build.0 = Release|x64 - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB}.Debug|x64.ActiveCfg = Debug|x64 - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB}.Debug|x64.Build.0 = Debug|x64 - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB}.Release|x64.ActiveCfg = Release|x64 - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB}.Release|x64.Build.0 = Release|x64 - {731312A8-6DA3-4841-AFCD-57520BA1BF8E}.Debug|x64.ActiveCfg = Debug|x64 - {731312A8-6DA3-4841-AFCD-57520BA1BF8E}.Debug|x64.Build.0 = Debug|x64 - {731312A8-6DA3-4841-AFCD-57520BA1BF8E}.Release|x64.ActiveCfg = Release|x64 - {731312A8-6DA3-4841-AFCD-57520BA1BF8E}.Release|x64.Build.0 = Release|x64 - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976}.Debug|x64.ActiveCfg = Debug|x64 - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976}.Debug|x64.Build.0 = Debug|x64 - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976}.Release|x64.ActiveCfg = Release|x64 - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976}.Release|x64.Build.0 = Release|x64 - EndGlobalSection - GlobalSection(SolutionProperties) = preSolution - HideSolutionNode = FALSE - EndGlobalSection - GlobalSection(NestedProjects) = preSolution - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {3ED0465D-23E7-4855-9694-F788717B6533} = {83BFF5BF-D054-4B3E-9769-B00BA707F116} - {98D2C32B-0C1F-4E19-A626-65F7BA4600CF} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} - {EA67F51F-1FE8-462D-9F3E-01161685AD59} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} - {DE1A06BA-EC5C-4E0D-BCA8-3EA555310C58} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} - {63024704-A2D7-497E-AD4B-5C10C6AA1374} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} - {F9BEB27E-8AF5-464E-8D45-0000D5AFA2D3} = {EA67F51F-1FE8-462D-9F3E-01161685AD59} - {889C1CCF-92B3-450B-B00D-FC9A9D5BE464} = {EA67F51F-1FE8-462D-9F3E-01161685AD59} - {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} - {4BBF2950-3DBD-469A-AD57-6CACBEBAF541} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {5F733BBA-FE83-4668-8F83-8B0E78A36619} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {19EE975B-232D-49F0-94C7-6F1C6424FB53} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {5E666C53-2D82-49C9-9127-3FDDC321C741} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} - {6D1353D6-F196-466F-B886-F16D48759B20} = {5E666C53-2D82-49C9-9127-3FDDC321C741} - {B6725C9F-A6D2-4269-9B74-7888A90F7884} = {5E666C53-2D82-49C9-9127-3FDDC321C741} - {B27DD434-EECD-4EE0-A03B-1150EB87258E} = {B6725C9F-A6D2-4269-9B74-7888A90F7884} - {A4884465-CFBB-4A64-A9DE-690E1A63EF7E} = {B6725C9F-A6D2-4269-9B74-7888A90F7884} - {C70E1572-20FF-496C-A0A9-10AA6755A07C} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {EAD17188-072C-4726-B840-A769C36DAD1B} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {88F85A64-105D-4CDA-8199-B7A312FC8A27} = {19EE975B-232D-49F0-94C7-6F1C6424FB53} - {8241108A-7824-4FF2-BECA-7521A9D89DCF} = {19EE975B-232D-49F0-94C7-6F1C6424FB53} - {6994C86D-A672-4254-824A-51F4DFEB807F} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {FD88A77E-A839-4513-AC5C-AD92447BE229} = {6994C86D-A672-4254-824A-51F4DFEB807F} - {40F93928-5CA2-433A-A48C-C8E9A35D7079} = {6994C86D-A672-4254-824A-51F4DFEB807F} - {39B9BB97-D0E8-439A-8A1B-8DB8E7CF73C3} = {6994C86D-A672-4254-824A-51F4DFEB807F} - {6F19321A-65E7-4829-B00C-3886CD6C6EDE} = {D45DF403-6781-444E-B654-A96868C5BE68} - {4701E678-5E6F-470D-B348-9CD1A2C095D1} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} - {EB2BE26F-6BD4-4274-971F-86D080779DD1} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {BB8B9FC5-C4B3-477F-80E2-665DC8E431BD} = {6994C86D-A672-4254-824A-51F4DFEB807F} - {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} - {76F9323D-34A1-43A5-A594-C4798931FF21} = {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} - {2A884EB5-037C-481E-8170-BCDC8B3EDD93} = {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} - {669B6203-9675-4950-B526-7CD72D55E5E1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {81AE014F-DD63-47C7-B6E2-DB1D2833DCD1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} - {9BDFA4BE-790E-408F-915B-5979BB5078C6} = {47755F2E-D674-4175-9E38-8EA053455072} - {3CE841C0-02E5-46DB-B401-6F8784880173} = {47755F2E-D674-4175-9E38-8EA053455072} - {97AAB0C8-D553-49CB-A539-004FCD7FD59F} = {47755F2E-D674-4175-9E38-8EA053455072} - {A4FC3467-4787-43E8-BBC0-D79AE56B468D} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} - {482999D1-B7E2-466E-9F8D-2119F93EAFD9} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {B3DD765E-694E-4494-BAD7-37BBF2942517} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} - {D667AF32-028A-4A5D-BE19-F46776F0F6B2} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {1D5787D4-52E4-45DB-951B-82F220EE0C6A} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {014DA766-B37B-4581-BC26-963EA5507931} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {33D2FD22-DEF2-4507-A58A-368F641AEBE5} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {9A2F2441-5972-4EA8-9215-4119FCE0FB68} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {CE429AA2-3778-4619-8FD1-49BA3B81197B} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {E6646FFE-3588-4276-8A15-8D65C22711C1} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB} = {33EBFE78-A1A8-4961-8938-92A271941F94} - {731312A8-6DA3-4841-AFCD-57520BA1BF8E} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} - {6E565B48-1923-49CE-9787-9BBB9D96F4C5} = {D45DF403-6781-444E-B654-A96868C5BE68} - {3BF59CCE-D245-420A-9F17-73CE61E284C2} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} - {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} = {3BF59CCE-D245-420A-9F17-73CE61E284C2} - {96012801-5187-4FAF-A54E-BF4B73C855F8} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} - {2A1F0FB0-2304-4F35-87B3-66230C6E58F0} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} - {BFBC6BE1-C33E-4A80-B8F3-A33410EC00FC} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} - {FA33A61E-95C7-4049-8111-22058CE361A3} = {9BDFA4BE-790E-408F-915B-5979BB5078C6} - {F99E1E80-50D8-421C-AD94-8ED0DF08C355} = {9BDFA4BE-790E-408F-915B-5979BB5078C6} - {ED57E827-B28F-4BEE-BFB7-398EF8D83357} = {FA33A61E-95C7-4049-8111-22058CE361A3} - {6E5A252C-ACCE-42E0-9819-FF4DEF6D739E} = {FA33A61E-95C7-4049-8111-22058CE361A3} - {77125562-3BF2-45D2-9B73-72CA8E03C78C} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355} - {EF710C5A-E616-442A-889D-C997D39AF2E1} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355} - {D29DC402-98A3-40C7-B683-4CC84DEC5C18} = {EF710C5A-E616-442A-889D-C997D39AF2E1} - {BC0D6DFF-80CF-4B41-AD55-6F561C6FD229} = {EF710C5A-E616-442A-889D-C997D39AF2E1} - {CCD56F12-BA17-4753-B5EE-4995FE682995} = {47755F2E-D674-4175-9E38-8EA053455072} - {D2A060F1-128E-42A1-A0D0-3E3E1DFBC427} = {CCD56F12-BA17-4753-B5EE-4995FE682995} - {FC573A62-6DAE-40A4-8153-520C8571A007} = {CCD56F12-BA17-4753-B5EE-4995FE682995} - {1E37CE40-556D-4693-B58C-F8D4CE349BB7} = {D2A060F1-128E-42A1-A0D0-3E3E1DFBC427} - {BF1A621D-528B-4B84-AAFC-EF1455FC6830} = {3CE841C0-02E5-46DB-B401-6F8784880173} - {8C128B1D-87E0-4643-AB93-2581589AE425} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} - {EDA80B25-B181-4F70-844E-FBB41D40EA34} = {3CE841C0-02E5-46DB-B401-6F8784880173} - {B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49} = {EDA80B25-B181-4F70-844E-FBB41D40EA34} - {6FCDB701-C23D-4BD7-BD48-12D46DC51084} = {EDA80B25-B181-4F70-844E-FBB41D40EA34} - {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830} - {FB65FA58-C47B-4A49-9566-40FD5D75FC59} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830} - {439F8840-309E-4B7F-BCEC-196E0EADE949} = {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} - {1C7D222F-E17B-444F-A18C-6205DEEF27BA} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} - {5ED4F5DC-E016-4E10-BACD-6A760A0CDE89} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} - {35CFD8E3-7206-4243-AB5C-AAF610109A5C} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} - {6F4125B5-220F-4FB7-B6C4-85A966A0268C} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} - {850008BC-36B0-4A0A-BD0C-B6D5C2184227} = {6F4125B5-220F-4FB7-B6C4-85A966A0268C} - {E6DC3B7D-303D-4A54-B040-D8DCF8C56E17} = {8C128B1D-87E0-4643-AB93-2581589AE425} - {06D2C644-AE5F-4C30-A1F6-C78E2845AAB1} = {EF710C5A-E616-442A-889D-C997D39AF2E1} - EndGlobalSection -EndGlobal + +Microsoft Visual Studio Solution File, Format Version 12.00 +# Visual Studio 2013 +VisualStudioVersion = 12.0.40629.0 +MinimumVisualStudioVersion = 10.0.40219.1 +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CNTK", "Source\CNTK\CNTK.vcxproj", "{E6F26F9A-FF64-4F0A-B749-CD309EE357EE}" + ProjectSection(ProjectDependencies) = postProject + {33D2FD22-DEF2-4507-A58A-368F641AEBE5} = {33D2FD22-DEF2-4507-A58A-368F641AEBE5} + {EB2BE26F-6BD4-4274-971F-86D080779DD1} = {EB2BE26F-6BD4-4274-971F-86D080779DD1} + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tests", "Tests", "{D45DF403-6781-444E-B654-A96868C5BE68}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Reader Plugins", "Reader Plugins", "{33EBFE78-A1A8-4961-8938-92A271941F94}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CNTK Core", "CNTK Core", "{DD043083-71A4-409A-AA91-F9C548DCF7EC}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Linux build files", "Linux build files", "{3ED0465D-23E7-4855-9694-F788717B6533}" + ProjectSection(SolutionItems) = preProject + configure = configure + Makefile = Makefile + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Documentation", "Documentation", "{065AF55D-AF02-448B-BFCD-52619FDA4BD0}" + ProjectSection(SolutionItems) = preProject + README.md = README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tutorial", "Tutorial", "{98D2C32B-0C1F-4E19-A626-65F7BA4600CF}" + ProjectSection(SolutionItems) = preProject + Documentation\Tutorial\CNTK-Tutorial-NIPS2015.pdf = Documentation\Tutorial\CNTK-Tutorial-NIPS2015.pdf + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CNTK-TechReport", "CNTK-TechReport", "{EA67F51F-1FE8-462D-9F3E-01161685AD59}" + ProjectSection(SolutionItems) = preProject + Documentation\CNTK-TechReport\lyx\CNTKBook-20151201.pdf = Documentation\CNTK-TechReport\lyx\CNTKBook-20151201.pdf + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Documents", "Documents", "{DE1A06BA-EC5C-4E0D-BCA8-3EA555310C58}" + ProjectSection(SolutionItems) = preProject + Documentation\Documents\Configuration Files.docx = Documentation\Documents\Configuration Files.docx + Documentation\Documents\External Buffer Behavior.docx = Documentation\Documents\External Buffer Behavior.docx + Documentation\Documents\Model Editing Language.docx = Documentation\Documents\Model Editing Language.docx + Documentation\Documents\Network Description Language.docx = Documentation\Documents\Network Description Language.docx + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "License", "License", "{63024704-A2D7-497E-AD4B-5C10C6AA1374}" + ProjectSection(SolutionItems) = preProject + license\MSR Computational Network Toolkit_MSR-LA (2014-03-28).md = license\MSR Computational Network Toolkit_MSR-LA (2014-03-28).md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "lyx", "lyx", "{F9BEB27E-8AF5-464E-8D45-0000D5AFA2D3}" + ProjectSection(SolutionItems) = preProject + Documentation\CNTK-TechReport\lyx\CNTKBook-master.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook-master.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_Abstract.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_Abstract.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_ASRDecoder_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_ASRDecoder_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_CN_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CN_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Adv_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Adv_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Programmer_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_CNTK_Programmer_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_ExampleSetup_Chapter.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_ExampleSetup_Chapter.lyx + Documentation\CNTK-TechReport\lyx\CNTKBook_Introduction.lyx = Documentation\CNTK-TechReport\lyx\CNTKBook_Introduction.lyx + Documentation\CNTK-TechReport\lyx\references.bib = Documentation\CNTK-TechReport\lyx\references.bib + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "figures", "figures", "{889C1CCF-92B3-450B-B00D-FC9A9D5BE464}" + ProjectSection(SolutionItems) = preProject + Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.pdf = Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.pdf + Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.png = Documentation\CNTK-TechReport\figures\CN+TrainingCriterion.png + Documentation\CNTK-TechReport\figures\CN-1HiddenNN.pdf = Documentation\CNTK-TechReport\figures\CN-1HiddenNN.pdf + Documentation\CNTK-TechReport\figures\CN-1HiddenNN.png = Documentation\CNTK-TechReport\figures\CN-1HiddenNN.png + Documentation\CNTK-TechReport\figures\CN-2Inputs.pdf = Documentation\CNTK-TechReport\figures\CN-2Inputs.pdf + Documentation\CNTK-TechReport\figures\CN-2Inputs.png = Documentation\CNTK-TechReport\figures\CN-2Inputs.png + Documentation\CNTK-TechReport\figures\CN-EfficientGradient.pdf = Documentation\CNTK-TechReport\figures\CN-EfficientGradient.pdf + Documentation\CNTK-TechReport\figures\CN-EfficientGradient.png = Documentation\CNTK-TechReport\figures\CN-EfficientGradient.png + Documentation\CNTK-TechReport\figures\CN-NaiveGradient.pdf = Documentation\CNTK-TechReport\figures\CN-NaiveGradient.pdf + Documentation\CNTK-TechReport\figures\CN-NaiveGradient.png = Documentation\CNTK-TechReport\figures\CN-NaiveGradient.png + Documentation\CNTK-TechReport\figures\CN-ShareWeight.pdf = Documentation\CNTK-TechReport\figures\CN-ShareWeight.pdf + Documentation\CNTK-TechReport\figures\CN-ShareWeight.png = Documentation\CNTK-TechReport\figures\CN-ShareWeight.png + Documentation\CNTK-TechReport\figures\CN-WithDelayNode.pdf = Documentation\CNTK-TechReport\figures\CN-WithDelayNode.pdf + Documentation\CNTK-TechReport\figures\CN-WithDelayNode.png = Documentation\CNTK-TechReport\figures\CN-WithDelayNode.png + Documentation\CNTK-TechReport\figures\CNNComputation.pdf = Documentation\CNTK-TechReport\figures\CNNComputation.pdf + Documentation\CNTK-TechReport\figures\CNNComputation.png = Documentation\CNTK-TechReport\figures\CNNComputation.png + Documentation\CNTK-TechReport\figures\CNTKArch.pdf = Documentation\CNTK-TechReport\figures\CNTKArch.pdf + Documentation\CNTK-TechReport\figures\CNTKArch.png = Documentation\CNTK-TechReport\figures\CNTKArch.png + Documentation\CNTK-TechReport\figures\ConfusionData1.png = Documentation\CNTK-TechReport\figures\ConfusionData1.png + Documentation\CNTK-TechReport\figures\ConfusionData100.png = Documentation\CNTK-TechReport\figures\ConfusionData100.png + Documentation\CNTK-TechReport\figures\SequenceBatch.pdf = Documentation\CNTK-TechReport\figures\SequenceBatch.pdf + Documentation\CNTK-TechReport\figures\SequenceBatch.png = Documentation\CNTK-TechReport\figures\SequenceBatch.png + Documentation\CNTK-TechReport\figures\SimpleDemoDataReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoDataReference.png + Documentation\CNTK-TechReport\figures\SimpleDemoErrorRateReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoErrorRateReference.png + Documentation\CNTK-TechReport\figures\SimpleDemoOutputReference.png = Documentation\CNTK-TechReport\figures\SimpleDemoOutputReference.png + Documentation\CNTK-TechReport\figures\simpleRNN.png = Documentation\CNTK-TechReport\figures\simpleRNN.png + Documentation\CNTK-TechReport\figures\SpeechErrorRate.png = Documentation\CNTK-TechReport\figures\SpeechErrorRate.png + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", "{C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\README_Windows_Debug_commands.txt = Tests\EndToEndTests\Speech\README_Windows_Debug_commands.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "QuickE2E", "QuickE2E", "{4BBF2950-3DBD-469A-AD57-6CACBEBAF541}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\QuickE2E\baseline.cpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.cpu.txt + Tests\EndToEndTests\Speech\QuickE2E\baseline.gpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.gpu.txt + Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\QuickE2E\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\QuickE2E\cntk.config = Tests\EndToEndTests\Speech\QuickE2E\cntk.config + Tests\EndToEndTests\Speech\QuickE2E\run-test = Tests\EndToEndTests\Speech\QuickE2E\run-test + Tests\EndToEndTests\Speech\QuickE2E\testcases.yml = Tests\EndToEndTests\Speech\QuickE2E\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{5F733BBA-FE83-4668-8F83-8B0E78A36619}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\Data\AN4.LICENSE.html = Tests\EndToEndTests\Speech\Data\AN4.LICENSE.html + Tests\EndToEndTests\Speech\Data\glob_0000.mlf = Tests\EndToEndTests\Speech\Data\glob_0000.mlf + Tests\EndToEndTests\Speech\Data\glob_0000.scp = Tests\EndToEndTests\Speech\Data\glob_0000.scp + Tests\EndToEndTests\Speech\Data\README.txt = Tests\EndToEndTests\Speech\Data\README.txt + Tests\EndToEndTests\Speech\Data\state.list = Tests\EndToEndTests\Speech\Data\state.list + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "LSTM", "LSTM", "{19EE975B-232D-49F0-94C7-6F1C6424FB53}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\LSTM\cntk.config = Tests\EndToEndTests\Speech\LSTM\cntk.config + Tests\EndToEndTests\Speech\LSTM\lstm.bs = Tests\EndToEndTests\Speech\LSTM\lstm.bs + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ComputationNetworkLib", "Source\ComputationNetworkLib\ComputationNetworkLib.vcxproj", "{928ABD1B-4D3B-4017-AEF1-0FA1B4467513}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + {EAD17188-072C-4726-B840-A769C36DAD1B} = {EAD17188-072C-4726-B840-A769C36DAD1B} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SGDLib", "Source\SGDLib\SGDLib.vcxproj", "{DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}" + ProjectSection(ProjectDependencies) = postProject + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ParallelTraining", "ParallelTraining", "{5E666C53-2D82-49C9-9127-3FDDC321C741}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\ParallelTraining\SimpleMultiGPU.config = Tests\EndToEndTests\ParallelTraining\SimpleMultiGPU.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{6D1353D6-F196-466F-B886-F16D48759B20}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\ParallelTraining\Data\SimpleDataTrain.txt = Tests\EndToEndTests\ParallelTraining\Data\SimpleDataTrain.txt + Tests\EndToEndTests\ParallelTraining\Data\SimpleMapping.txt = Tests\EndToEndTests\ParallelTraining\Data\SimpleMapping.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NoQuantization", "NoQuantization", "{B6725C9F-A6D2-4269-9B74-7888A90F7884}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SinglePrecision", "SinglePrecision", "{B27DD434-EECD-4EE0-A03B-1150EB87258E}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.cpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.gpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.cpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\baseline.windows.gpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\run-test = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\run-test + Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\testcases.yml = Tests\EndToEndTests\ParallelTraining\NoQuantization\SinglePrecision\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DoublePrecision", "DoublePrecision", "{A4884465-CFBB-4A64-A9DE-690E1A63EF7E}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.cpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.gpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.cpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.cpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.gpu.txt = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\baseline.windows.gpu.txt + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\run-test = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\run-test + Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\testcases.yml = Tests\EndToEndTests\ParallelTraining\NoQuantization\DoublePrecision\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Kaldi2Reader", "Kaldi2Reader", "{C70E1572-20FF-496C-A0A9-10AA6755A07C}" + ProjectSection(SolutionItems) = preProject + Source\Readers\Kaldi2Reader\basetypes.h = Source\Readers\Kaldi2Reader\basetypes.h + Source\Readers\Kaldi2Reader\biggrowablevectors.h = Source\Readers\Kaldi2Reader\biggrowablevectors.h + Source\Readers\Kaldi2Reader\chunkevalsource.h = Source\Readers\Kaldi2Reader\chunkevalsource.h + Source\Readers\Kaldi2Reader\DataReader.cpp = Source\Readers\Kaldi2Reader\DataReader.cpp + Source\Readers\Kaldi2Reader\DataWriter.cpp = Source\Readers\Kaldi2Reader\DataWriter.cpp + Source\Readers\Kaldi2Reader\dllmain.cpp = Source\Readers\Kaldi2Reader\dllmain.cpp + Source\Readers\Kaldi2Reader\DOCUMENTATION.txt = Source\Readers\Kaldi2Reader\DOCUMENTATION.txt + Source\Readers\Kaldi2Reader\htkfeatio.h = Source\Readers\Kaldi2Reader\htkfeatio.h + Source\Readers\Kaldi2Reader\htkfeatio_utils.h = Source\Readers\Kaldi2Reader\htkfeatio_utils.h + Source\Readers\Kaldi2Reader\HTKMLFReader.cpp = Source\Readers\Kaldi2Reader\HTKMLFReader.cpp + Source\Readers\Kaldi2Reader\HTKMLFReader.h = Source\Readers\Kaldi2Reader\HTKMLFReader.h + Source\Readers\Kaldi2Reader\HTKMLFWriter.cpp = Source\Readers\Kaldi2Reader\HTKMLFWriter.cpp + Source\Readers\Kaldi2Reader\HTKMLFWriter.h = Source\Readers\Kaldi2Reader\HTKMLFWriter.h + Source\Readers\Kaldi2Reader\kaldi.h = Source\Readers\Kaldi2Reader\kaldi.h + Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.cpp = Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.cpp + Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.h = Source\Readers\Kaldi2Reader\KaldiSequenceTrainingDerivative.h + Source\Readers\Kaldi2Reader\latticearchive.cpp = Source\Readers\Kaldi2Reader\latticearchive.cpp + Source\Readers\Kaldi2Reader\minibatchiterator.h = Source\Readers\Kaldi2Reader\minibatchiterator.h + Source\Readers\Kaldi2Reader\minibatchsourcehelpers.h = Source\Readers\Kaldi2Reader\minibatchsourcehelpers.h + Source\Readers\Kaldi2Reader\msra_mgram.h = Source\Readers\Kaldi2Reader\msra_mgram.h + Source\Readers\Kaldi2Reader\notes.txt = Source\Readers\Kaldi2Reader\notes.txt + Source\Readers\Kaldi2Reader\numahelpers.h = Source\Readers\Kaldi2Reader\numahelpers.h + Source\Readers\Kaldi2Reader\pplhelpers.h = Source\Readers\Kaldi2Reader\pplhelpers.h + Source\Readers\Kaldi2Reader\readaheadsource.h = Source\Readers\Kaldi2Reader\readaheadsource.h + Source\Readers\Kaldi2Reader\rollingwindowsource.h = Source\Readers\Kaldi2Reader\rollingwindowsource.h + Source\Readers\Kaldi2Reader\simplethread.h = Source\Readers\Kaldi2Reader\simplethread.h + Source\Readers\Kaldi2Reader\ssefloat4.h = Source\Readers\Kaldi2Reader\ssefloat4.h + Source\Readers\Kaldi2Reader\ssematrix.h = Source\Readers\Kaldi2Reader\ssematrix.h + Source\Readers\Kaldi2Reader\stdafx.cpp = Source\Readers\Kaldi2Reader\stdafx.cpp + Source\Readers\Kaldi2Reader\stdafx.h = Source\Readers\Kaldi2Reader\stdafx.h + Source\Readers\Kaldi2Reader\targetver.h = Source\Readers\Kaldi2Reader\targetver.h + Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.cpp = Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.cpp + Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.h = Source\Readers\Kaldi2Reader\UtteranceDerivativeBuffer.h + Source\Readers\Kaldi2Reader\UtteranceDerivativeComputationInterface.h = Source\Readers\Kaldi2Reader\UtteranceDerivativeComputationInterface.h + Source\Readers\Kaldi2Reader\utterancesourcemulti.h = Source\Readers\Kaldi2Reader\utterancesourcemulti.h + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SequenceTrainingLib", "Source\SequenceTrainingLib\SequenceTrainingLib.vcxproj", "{EAD17188-072C-4726-B840-A769C36DAD1B}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Truncated", "Truncated", "{88F85A64-105D-4CDA-8199-B7A312FC8A27}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.cpu.txt + Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.gpu.txt + Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\LSTM\Truncated\run-test = Tests\EndToEndTests\Speech\LSTM\Truncated\run-test + Tests\EndToEndTests\Speech\LSTM\Truncated\testcases.yml = Tests\EndToEndTests\Speech\LSTM\Truncated\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "FullUtterance", "FullUtterance", "{8241108A-7824-4FF2-BECA-7521A9D89DCF}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.cpu.txt + Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.gpu.txt + Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtterance\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\LSTM\FullUtterance\run-test = Tests\EndToEndTests\Speech\LSTM\FullUtterance\run-test + Tests\EndToEndTests\Speech\LSTM\FullUtterance\testcases.yml = Tests\EndToEndTests\Speech\LSTM\FullUtterance\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DNN", "DNN", "{6994C86D-A672-4254-824A-51F4DFEB807F}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\DNN\cntk.config = Tests\EndToEndTests\Speech\DNN\cntk.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Parallel1BitQuantization", "Parallel1BitQuantization", "{FD88A77E-A839-4513-AC5C-AD92447BE229}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.cpu.txt + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.gpu.txt + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\run-test = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\run-test + Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\testcases.yml = Tests\EndToEndTests\Speech\DNN\Parallel1BitQuantization\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ParallelNoQuantization", "ParallelNoQuantization", "{40F93928-5CA2-433A-A48C-C8E9A35D7079}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.cpu.txt + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.gpu.txt + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\run-test = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\run-test + Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\testcases.yml = Tests\EndToEndTests\Speech\DNN\ParallelNoQuantization\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "DiscriminativePreTraining", "DiscriminativePreTraining", "{39B9BB97-D0E8-439A-8A1B-8DB8E7CF73C3}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\add_layer.mel = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\add_layer.mel + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.cpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.cpu.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.gpu.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\cntk_dpt.config = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\cntk_dpt.config + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn_1layer.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\dnn_1layer.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\macros.txt + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\run-test = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\run-test + Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\testcases.yml = Tests\EndToEndTests\Speech\DNN\DiscriminativePreTraining\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "UnitTests", "UnitTests", "{6F19321A-65E7-4829-B00C-3886CD6C6EDE}" +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MathTests", "Tests\UnitTests\MathTests\MathTests.vcxproj", "{4701E678-5E6F-470D-B348-9CD1A2C095D1}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ActionsLib", "Source\ActionsLib\ActionsLib.vcxproj", "{EB2BE26F-6BD4-4274-971F-86D080779DD1}" + ProjectSection(ProjectDependencies) = postProject + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} = {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SequenceTraining", "SequenceTraining", "{BB8B9FC5-C4B3-477F-80E2-665DC8E431BD}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\DNN\SequenceTraining\add_layer.mel = Tests\EndToEndTests\Speech\DNN\SequenceTraining\add_layer.mel + Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.gpu.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.gpu.txt + Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\DNN\SequenceTraining\cntk_sequence.config = Tests\EndToEndTests\Speech\DNN\SequenceTraining\cntk_sequence.config + Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn.txt + Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn_1layer.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\dnn_1layer.txt + Tests\EndToEndTests\Speech\DNN\SequenceTraining\macros.txt = Tests\EndToEndTests\Speech\DNN\SequenceTraining\macros.txt + Tests\EndToEndTests\Speech\DNN\SequenceTraining\replace_ce_with_sequence_criterion.mel = Tests\EndToEndTests\Speech\DNN\SequenceTraining\replace_ce_with_sequence_criterion.mel + Tests\EndToEndTests\Speech\DNN\SequenceTraining\run-test = Tests\EndToEndTests\Speech\DNN\SequenceTraining\run-test + Tests\EndToEndTests\Speech\DNN\SequenceTraining\testcases.yml = Tests\EndToEndTests\Speech\DNN\SequenceTraining\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{8071EF60-30F7-4A77-81AA-ADCA0E18B1E3}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{76F9323D-34A1-43A5-A594-C4798931FF21}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Image\Data\labelsmap.txt = Tests\EndToEndTests\Image\Data\labelsmap.txt + Tests\EndToEndTests\Image\Data\Test.txt = Tests\EndToEndTests\Image\Data\Test.txt + Tests\EndToEndTests\Image\Data\Train.txt = Tests\EndToEndTests\Image\Data\Train.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "QuickE2E", "QuickE2E", "{2A884EB5-037C-481E-8170-BCDC8B3EDD93}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Image\QuickE2E\baseline.linux.debug.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.linux.debug.gpu.txt + Tests\EndToEndTests\Image\QuickE2E\baseline.linux.release.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.linux.release.gpu.txt + Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.cpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.cpu.txt + Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.debug.gpu.txt + Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.cpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.cpu.txt + Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.gpu.txt = Tests\EndToEndTests\Image\QuickE2E\baseline.windows.release.gpu.txt + Tests\EndToEndTests\Image\QuickE2E\cntk.config = Tests\EndToEndTests\Image\QuickE2E\cntk.config + Tests\EndToEndTests\Image\QuickE2E\Convolution.ndl = Tests\EndToEndTests\Image\QuickE2E\Convolution.ndl + Tests\EndToEndTests\Image\QuickE2E\Macros.ndl = Tests\EndToEndTests\Image\QuickE2E\Macros.ndl + Tests\EndToEndTests\Image\QuickE2E\run-test = Tests\EndToEndTests\Image\QuickE2E\run-test + Tests\EndToEndTests\Image\QuickE2E\testcases.yml = Tests\EndToEndTests\Image\QuickE2E\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SVD", "SVD", "{669B6203-9675-4950-B526-7CD72D55E5E1}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\SVD\baseline.cpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.cpu.txt + Tests\EndToEndTests\Speech\SVD\baseline.gpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.gpu.txt + Tests\EndToEndTests\Speech\SVD\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\SVD\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\SVD\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\SVD\cntk.config = Tests\EndToEndTests\Speech\SVD\cntk.config + Tests\EndToEndTests\Speech\SVD\run-test = Tests\EndToEndTests\Speech\SVD\run-test + Tests\EndToEndTests\Speech\SVD\testcases.yml = Tests\EndToEndTests\Speech\SVD\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple", "Simple", "{81AE014F-DD63-47C7-B6E2-DB1D2833DCD1}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\Speech\Simple\baseline.cpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.cpu.txt + Tests\EndToEndTests\Speech\Simple\baseline.gpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.gpu.txt + Tests\EndToEndTests\Speech\Simple\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.windows.cpu.txt + Tests\EndToEndTests\Speech\Simple\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\Simple\baseline.windows.gpu.txt + Tests\EndToEndTests\Speech\Simple\cntk.config = Tests\EndToEndTests\Speech\Simple\cntk.config + Tests\EndToEndTests\Speech\Simple\run-test = Tests\EndToEndTests\Speech\Simple\run-test + Tests\EndToEndTests\Speech\Simple\testcases.yml = Tests\EndToEndTests\Speech\Simple\testcases.yml + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Examples", "Examples", "{47755F2E-D674-4175-9E38-8EA053455072}" + ProjectSection(SolutionItems) = preProject + Examples\README.md = Examples\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{9BDFA4BE-790E-408F-915B-5979BB5078C6}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", "{3CE841C0-02E5-46DB-B401-6F8784880173}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Text", "Text", "{97AAB0C8-D553-49CB-A539-004FCD7FD59F}" +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ReaderTests", "Tests\UnitTests\ReaderTests\ReaderTests.vcxproj", "{A4FC3467-4787-43E8-BBC0-D79AE56B468D}" + ProjectSection(ProjectDependencies) = postProject + {33D2FD22-DEF2-4507-A58A-368F641AEBE5} = {33D2FD22-DEF2-4507-A58A-368F641AEBE5} + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + {E6646FFE-3588-4276-8A15-8D65C22711C1} = {E6646FFE-3588-4276-8A15-8D65C22711C1} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "EvalDll", "Source\EvalDll\EvalDll.vcxproj", "{482999D1-B7E2-466E-9F8D-2119F93EAFD9}" + ProjectSection(ProjectDependencies) = postProject + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "Math", "Source\Math\Math.vcxproj", "{60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}" + ProjectSection(ProjectDependencies) = postProject + {B3DD765E-694E-4494-BAD7-37BBF2942517} = {B3DD765E-694E-4494-BAD7-37BBF2942517} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MathCUDA", "Source\Math\MathCUDA.vcxproj", "{B3DD765E-694E-4494-BAD7-37BBF2942517}" +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LibSVMBinaryReader", "Source\Readers\LibSVMBinaryReader\LibSVMBinaryReader.vcxproj", "{D667AF32-028A-4A5D-BE19-F46776F0F6B2}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "BinaryReader", "Source\Readers\BinaryReader\BinaryReader.vcxproj", "{1D5787D4-52E4-45DB-951B-82F220EE0C6A}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "DSSMReader", "Source\Readers\DSSMReader\DSSMReader.vcxproj", "{014DA766-B37B-4581-BC26-963EA5507931}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "HTKMLFReader", "Source\Readers\HTKMLFReader\HTKMLFReader.vcxproj", "{33D2FD22-DEF2-4507-A58A-368F641AEBE5}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LMSequenceReader", "Source\Readers\LMSequenceReader\LMSequenceReader.vcxproj", "{9A2F2441-5972-4EA8-9215-4119FCE0FB68}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LUSequenceReader", "Source\Readers\LUSequenceReader\LUSequenceReader.vcxproj", "{62836DC1-DF77-4B98-BF2D-45C943B7DDC6}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "SparsePCReader", "Source\Readers\SparsePCReader\SparsePCReader.vcxproj", "{CE429AA2-3778-4619-8FD1-49BA3B81197B}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "UCIFastReader", "Source\Readers\UCIFastReader\UCIFastReader.vcxproj", "{E6646FFE-3588-4276-8A15-8D65C22711C1}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ImageReader", "Source\Readers\ImageReader\ImageReader.vcxproj", "{9BD0A746-0BBD-45B6-B81C-053F03C26CFB}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "EvalTest", "Tests\UnitTests\EvalTest\EvalTest.vcxproj", "{731312A8-6DA3-4841-AFCD-57520BA1BF8E}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "MathPerformanceTests", "Tests\UnitTests\MathPerformanceTests\MathPerformanceTests.vcxproj", "{668BEED5-AC07-4F35-B3AE-EE65A7F9C976}" + ProjectSection(ProjectDependencies) = postProject + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "EndToEndTests", "EndToEndTests", "{6E565B48-1923-49CE-9787-9BBB9D96F4C5}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\run-test-common = Tests\EndToEndTests\run-test-common + Tests\EndToEndTests\TestDriver.py = Tests\EndToEndTests\TestDriver.py + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "LM", "LM", "{3BF59CCE-D245-420A-9F17-73CE61E284C2}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "RNNLM", "RNNLM", "{811924DE-2F12-4EA0-BE58-E57BEF3B74D1}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CPU", "CPU", "{96012801-5187-4FAF-A54E-BF4B73C855F8}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\LM\RNNLM\CPU\global.config = Tests\EndToEndTests\LM\RNNLM\CPU\global.config + Tests\EndToEndTests\LM\RNNLM\CPU\rnnlm.config = Tests\EndToEndTests\LM\RNNLM\CPU\rnnlm.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "GPU", "GPU", "{2A1F0FB0-2304-4F35-87B3-66230C6E58F0}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\LM\RNNLM\GPU\rnnlm.config = Tests\EndToEndTests\LM\RNNLM\GPU\rnnlm.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{BFBC6BE1-C33E-4A80-B8F3-A33410EC00FC}" + ProjectSection(SolutionItems) = preProject + Tests\EndToEndTests\SLU\atis.dev.IOB.simple = Tests\EndToEndTests\SLU\atis.dev.IOB.simple + Tests\EndToEndTests\SLU\atis.test.apos.pred.pos.head.IOB.simple = Tests\EndToEndTests\SLU\atis.test.apos.pred.pos.head.IOB.simple + Tests\EndToEndTests\SLU\atis.train.apos.pred.pos.head.IOB.simple = Tests\EndToEndTests\SLU\atis.train.apos.pred.pos.head.IOB.simple + Tests\EndToEndTests\SLU\globals.config = Tests\EndToEndTests\SLU\globals.config + Tests\EndToEndTests\SLU\input.txt = Tests\EndToEndTests\SLU\input.txt + Tests\EndToEndTests\SLU\inputmap.txt = Tests\EndToEndTests\SLU\inputmap.txt + Tests\EndToEndTests\SLU\lstmNDL.txt = Tests\EndToEndTests\SLU\lstmNDL.txt + Tests\EndToEndTests\SLU\README.txt = Tests\EndToEndTests\SLU\README.txt + Tests\EndToEndTests\SLU\rnnlu.config = Tests\EndToEndTests\SLU\rnnlu.config + Tests\EndToEndTests\SLU\rnnlu.ndl.config = Tests\EndToEndTests\SLU\rnnlu.ndl.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "MNIST", "MNIST", "{FA33A61E-95C7-4049-8111-22058CE361A3}" + ProjectSection(SolutionItems) = preProject + Examples\Image\MNIST\README.md = Examples\Image\MNIST\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{F99E1E80-50D8-421C-AD94-8ED0DF08C355}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{ED57E827-B28F-4BEE-BFB7-398EF8D83357}" + ProjectSection(SolutionItems) = preProject + Examples\Image\MNIST\AdditionalFiles\mnist_convert.py = Examples\Image\MNIST\AdditionalFiles\mnist_convert.py + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{6E5A252C-ACCE-42E0-9819-FF4DEF6D739E}" + ProjectSection(SolutionItems) = preProject + Examples\Image\MNIST\Config\01_OneHidden.config = Examples\Image\MNIST\Config\01_OneHidden.config + Examples\Image\MNIST\Config\01_OneHidden.ndl = Examples\Image\MNIST\Config\01_OneHidden.ndl + Examples\Image\MNIST\Config\02_Convolution.config = Examples\Image\MNIST\Config\02_Convolution.config + Examples\Image\MNIST\Config\02_Convolution.ndl = Examples\Image\MNIST\Config\02_Convolution.ndl + Examples\Image\MNIST\Config\03_ConvBatchNorm.config = Examples\Image\MNIST\Config\03_ConvBatchNorm.config + Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl = Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl + Examples\Image\MNIST\Config\Macros.ndl = Examples\Image\MNIST\Config\Macros.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CIFAR-10", "CIFAR-10", "{77125562-3BF2-45D2-9B73-72CA8E03C78C}" + ProjectSection(SolutionItems) = preProject + Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config = Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config + Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl = Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl + Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config + Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel + Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl + Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py = Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py + Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.config = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.config + Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel + Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl + Examples\Image\Miscellaneous\CIFAR-10\16to32.txt = Examples\Image\Miscellaneous\CIFAR-10\16to32.txt + Examples\Image\Miscellaneous\CIFAR-10\32to64.txt = Examples\Image\Miscellaneous\CIFAR-10\32to64.txt + Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py = Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py + Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt = Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt + Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl = Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl + Examples\Image\Miscellaneous\CIFAR-10\readme.txt = Examples\Image\Miscellaneous\CIFAR-10\readme.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ImageNet", "ImageNet", "{EF710C5A-E616-442A-889D-C997D39AF2E1}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AlexNet", "AlexNet", "{D29DC402-98A3-40C7-B683-4CC84DEC5C18}" + ProjectSection(SolutionItems) = preProject + Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel + Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config + Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl + Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "VGG", "VGG", "{BC0D6DFF-80CF-4B41-AD55-6F561C6FD229}" + ProjectSection(SolutionItems) = preProject + Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel + Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml = Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml + Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.config + Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Other", "Other", "{CCD56F12-BA17-4753-B5EE-4995FE682995}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{D2A060F1-128E-42A1-A0D0-3E3E1DFBC427}" + ProjectSection(SolutionItems) = preProject + Examples\Other\Simple2d\README.md = Examples\Other\Simple2d\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NdlExamples", "NdlExamples", "{FC573A62-6DAE-40A4-8153-520C8571A007}" + ProjectSection(SolutionItems) = preProject + Examples\Other\NdlExamples\NDLExamples.ndl = Examples\Other\NdlExamples\NDLExamples.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{1E37CE40-556D-4693-B58C-F8D4CE349BB7}" + ProjectSection(SolutionItems) = preProject + Examples\Other\Simple2d\Config\Multigpu.config = Examples\Other\Simple2d\Config\Multigpu.config + Examples\Other\Simple2d\Config\Simple.config = Examples\Other\Simple2d\Config\Simple.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{BF1A621D-528B-4B84-AAFC-EF1455FC6830}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{8C128B1D-87E0-4643-AB93-2581589AE425}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AN4", "AN4", "{EDA80B25-B181-4F70-844E-FBB41D40EA34}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\AN4\README.md = Examples\Speech\AN4\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\AN4\Config\FeedForward.config = Examples\Speech\AN4\Config\FeedForward.config + Examples\Speech\AN4\Config\LSTM-NDL.config = Examples\Speech\AN4\Config\LSTM-NDL.config + Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl = Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{6FCDB701-C23D-4BD7-BD48-12D46DC51084}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html = Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AMI", "AMI", "{0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\AMI\Readme = Examples\Speech\Miscellaneous\AMI\Readme + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TIMIT", "TIMIT", "{FB65FA58-C47B-4A49-9566-40FD5D75FC59}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "cntk_config", "cntk_config", "{439F8840-309E-4B7F-BCEC-196E0EADE949}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf + Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf + Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config = Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config + Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config + Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl + Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel + Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "config", "config", "{1C7D222F-E17B-444F-A18C-6205DEEF27BA}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel = Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel + Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl = Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl + Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl = Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl + Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl = Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl + Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl = Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl + Examples\Speech\Miscellaneous\TIMIT\config\globals.config = Examples\Speech\Miscellaneous\TIMIT\config\globals.config + Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl = Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl + Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl + Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl + Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl = Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl + Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config + Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CPU", "CPU", "{5ED4F5DC-E016-4E10-BACD-6A760A0CDE89}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config + Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "GPU", "GPU", "{35CFD8E3-7206-4243-AB5C-AAF610109A5C}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config + Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "PennTreebank", "PennTreebank", "{6F4125B5-220F-4FB7-B6C4-85A966A0268C}" + ProjectSection(SolutionItems) = preProject + Examples\Text\PennTreebank\README.md = Examples\Text\PennTreebank\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{850008BC-36B0-4A0A-BD0C-B6D5C2184227}" + ProjectSection(SolutionItems) = preProject + Examples\Text\PennTreebank\Config\rnn.config = Examples\Text\PennTreebank\Config\rnn.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{E6DC3B7D-303D-4A54-B040-D8DCF8C56E17}" + ProjectSection(SolutionItems) = preProject + Examples\Text\Miscellaneous\SLU\lstmNDL.txt = Examples\Text\Miscellaneous\SLU\lstmNDL.txt + Examples\Text\Miscellaneous\SLU\README.txt = Examples\Text\Miscellaneous\SLU\README.txt + Examples\Text\Miscellaneous\SLU\rnnlu.config = Examples\Text\Miscellaneous\SLU\rnnlu.config + Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt = Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tools", "Tools", "{83BFF5BF-D054-4B3E-9769-B00BA707F116}" + ProjectSection(SolutionItems) = preProject + Tools\build-and-test = Tools\build-and-test + Tools\generate_build_info = Tools\generate_build_info + Tools\msvc_collect_coverage.py = Tools\msvc_collect_coverage.py + Tools\run_boost_unit_tests.py = Tools\run_boost_unit_tests.py + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ResNet", "ResNet", "{06D2C644-AE5F-4C30-A1F6-C78E2845AAB1}" + ProjectSection(SolutionItems) = preProject + Examples\Image\Miscellaneous\ImageNet\ResNet\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\ResNet\add_top5_layer.mel + Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl + Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py = Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py + Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.config = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.config + Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl + Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.config = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.config + Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl + EndProjectSection +EndProject +Global + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug|x64 = Debug|x64 + Release|x64 = Release|x64 + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Debug|x64.ActiveCfg = Debug|x64 + {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Debug|x64.Build.0 = Debug|x64 + {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Release|x64.ActiveCfg = Release|x64 + {E6F26F9A-FF64-4F0A-B749-CD309EE357EE}.Release|x64.Build.0 = Release|x64 + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Debug|x64.ActiveCfg = Debug|x64 + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Debug|x64.Build.0 = Debug|x64 + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Release|x64.ActiveCfg = Release|x64 + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}.Release|x64.Build.0 = Release|x64 + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Debug|x64.ActiveCfg = Debug|x64 + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Debug|x64.Build.0 = Debug|x64 + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Release|x64.ActiveCfg = Release|x64 + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937}.Release|x64.Build.0 = Release|x64 + {EAD17188-072C-4726-B840-A769C36DAD1B}.Debug|x64.ActiveCfg = Debug|x64 + {EAD17188-072C-4726-B840-A769C36DAD1B}.Debug|x64.Build.0 = Debug|x64 + {EAD17188-072C-4726-B840-A769C36DAD1B}.Release|x64.ActiveCfg = Release|x64 + {EAD17188-072C-4726-B840-A769C36DAD1B}.Release|x64.Build.0 = Release|x64 + {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Debug|x64.ActiveCfg = Debug|x64 + {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Debug|x64.Build.0 = Debug|x64 + {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Release|x64.ActiveCfg = Release|x64 + {4701E678-5E6F-470D-B348-9CD1A2C095D1}.Release|x64.Build.0 = Release|x64 + {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Debug|x64.ActiveCfg = Debug|x64 + {EB2BE26F-6BD4-4274-971F-86D080779DD1}.Debug|x64.Build.0 = Debug|x64 + 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{3ED0465D-23E7-4855-9694-F788717B6533} = {83BFF5BF-D054-4B3E-9769-B00BA707F116} + {98D2C32B-0C1F-4E19-A626-65F7BA4600CF} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} + {EA67F51F-1FE8-462D-9F3E-01161685AD59} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} + {DE1A06BA-EC5C-4E0D-BCA8-3EA555310C58} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} + {63024704-A2D7-497E-AD4B-5C10C6AA1374} = {065AF55D-AF02-448B-BFCD-52619FDA4BD0} + {F9BEB27E-8AF5-464E-8D45-0000D5AFA2D3} = {EA67F51F-1FE8-462D-9F3E-01161685AD59} + {889C1CCF-92B3-450B-B00D-FC9A9D5BE464} = {EA67F51F-1FE8-462D-9F3E-01161685AD59} + {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} + {4BBF2950-3DBD-469A-AD57-6CACBEBAF541} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {5F733BBA-FE83-4668-8F83-8B0E78A36619} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {19EE975B-232D-49F0-94C7-6F1C6424FB53} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {5E666C53-2D82-49C9-9127-3FDDC321C741} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} + {6D1353D6-F196-466F-B886-F16D48759B20} = {5E666C53-2D82-49C9-9127-3FDDC321C741} + {B6725C9F-A6D2-4269-9B74-7888A90F7884} = {5E666C53-2D82-49C9-9127-3FDDC321C741} + {B27DD434-EECD-4EE0-A03B-1150EB87258E} = {B6725C9F-A6D2-4269-9B74-7888A90F7884} + {A4884465-CFBB-4A64-A9DE-690E1A63EF7E} = {B6725C9F-A6D2-4269-9B74-7888A90F7884} + {C70E1572-20FF-496C-A0A9-10AA6755A07C} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {EAD17188-072C-4726-B840-A769C36DAD1B} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {88F85A64-105D-4CDA-8199-B7A312FC8A27} = {19EE975B-232D-49F0-94C7-6F1C6424FB53} + {8241108A-7824-4FF2-BECA-7521A9D89DCF} = {19EE975B-232D-49F0-94C7-6F1C6424FB53} + {6994C86D-A672-4254-824A-51F4DFEB807F} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {FD88A77E-A839-4513-AC5C-AD92447BE229} = {6994C86D-A672-4254-824A-51F4DFEB807F} + {40F93928-5CA2-433A-A48C-C8E9A35D7079} = {6994C86D-A672-4254-824A-51F4DFEB807F} + {39B9BB97-D0E8-439A-8A1B-8DB8E7CF73C3} = {6994C86D-A672-4254-824A-51F4DFEB807F} + {6F19321A-65E7-4829-B00C-3886CD6C6EDE} = {D45DF403-6781-444E-B654-A96868C5BE68} + {4701E678-5E6F-470D-B348-9CD1A2C095D1} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} + {EB2BE26F-6BD4-4274-971F-86D080779DD1} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {BB8B9FC5-C4B3-477F-80E2-665DC8E431BD} = {6994C86D-A672-4254-824A-51F4DFEB807F} + {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} + {76F9323D-34A1-43A5-A594-C4798931FF21} = {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} + {2A884EB5-037C-481E-8170-BCDC8B3EDD93} = {8071EF60-30F7-4A77-81AA-ADCA0E18B1E3} + {669B6203-9675-4950-B526-7CD72D55E5E1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {81AE014F-DD63-47C7-B6E2-DB1D2833DCD1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} + {9BDFA4BE-790E-408F-915B-5979BB5078C6} = {47755F2E-D674-4175-9E38-8EA053455072} + {3CE841C0-02E5-46DB-B401-6F8784880173} = {47755F2E-D674-4175-9E38-8EA053455072} + {97AAB0C8-D553-49CB-A539-004FCD7FD59F} = {47755F2E-D674-4175-9E38-8EA053455072} + {A4FC3467-4787-43E8-BBC0-D79AE56B468D} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} + {482999D1-B7E2-466E-9F8D-2119F93EAFD9} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {B3DD765E-694E-4494-BAD7-37BBF2942517} = {DD043083-71A4-409A-AA91-F9C548DCF7EC} + {D667AF32-028A-4A5D-BE19-F46776F0F6B2} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {1D5787D4-52E4-45DB-951B-82F220EE0C6A} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {014DA766-B37B-4581-BC26-963EA5507931} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {33D2FD22-DEF2-4507-A58A-368F641AEBE5} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {9A2F2441-5972-4EA8-9215-4119FCE0FB68} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {62836DC1-DF77-4B98-BF2D-45C943B7DDC6} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {CE429AA2-3778-4619-8FD1-49BA3B81197B} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {E6646FFE-3588-4276-8A15-8D65C22711C1} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {9BD0A746-0BBD-45B6-B81C-053F03C26CFB} = {33EBFE78-A1A8-4961-8938-92A271941F94} + {731312A8-6DA3-4841-AFCD-57520BA1BF8E} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} + {668BEED5-AC07-4F35-B3AE-EE65A7F9C976} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE} + {6E565B48-1923-49CE-9787-9BBB9D96F4C5} = {D45DF403-6781-444E-B654-A96868C5BE68} + {3BF59CCE-D245-420A-9F17-73CE61E284C2} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} + {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} = {3BF59CCE-D245-420A-9F17-73CE61E284C2} + {96012801-5187-4FAF-A54E-BF4B73C855F8} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} + {2A1F0FB0-2304-4F35-87B3-66230C6E58F0} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1} + {BFBC6BE1-C33E-4A80-B8F3-A33410EC00FC} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5} + {FA33A61E-95C7-4049-8111-22058CE361A3} = {9BDFA4BE-790E-408F-915B-5979BB5078C6} + {F99E1E80-50D8-421C-AD94-8ED0DF08C355} = {9BDFA4BE-790E-408F-915B-5979BB5078C6} + {ED57E827-B28F-4BEE-BFB7-398EF8D83357} = {FA33A61E-95C7-4049-8111-22058CE361A3} + {6E5A252C-ACCE-42E0-9819-FF4DEF6D739E} = {FA33A61E-95C7-4049-8111-22058CE361A3} + {77125562-3BF2-45D2-9B73-72CA8E03C78C} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355} + {EF710C5A-E616-442A-889D-C997D39AF2E1} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355} + {D29DC402-98A3-40C7-B683-4CC84DEC5C18} = {EF710C5A-E616-442A-889D-C997D39AF2E1} + {BC0D6DFF-80CF-4B41-AD55-6F561C6FD229} = {EF710C5A-E616-442A-889D-C997D39AF2E1} + {CCD56F12-BA17-4753-B5EE-4995FE682995} = {47755F2E-D674-4175-9E38-8EA053455072} + {D2A060F1-128E-42A1-A0D0-3E3E1DFBC427} = {CCD56F12-BA17-4753-B5EE-4995FE682995} + {FC573A62-6DAE-40A4-8153-520C8571A007} = {CCD56F12-BA17-4753-B5EE-4995FE682995} + {1E37CE40-556D-4693-B58C-F8D4CE349BB7} = {D2A060F1-128E-42A1-A0D0-3E3E1DFBC427} + {BF1A621D-528B-4B84-AAFC-EF1455FC6830} = {3CE841C0-02E5-46DB-B401-6F8784880173} + {8C128B1D-87E0-4643-AB93-2581589AE425} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} + {EDA80B25-B181-4F70-844E-FBB41D40EA34} = {3CE841C0-02E5-46DB-B401-6F8784880173} + {B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49} = {EDA80B25-B181-4F70-844E-FBB41D40EA34} + {6FCDB701-C23D-4BD7-BD48-12D46DC51084} = {EDA80B25-B181-4F70-844E-FBB41D40EA34} + {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830} + {FB65FA58-C47B-4A49-9566-40FD5D75FC59} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830} + {439F8840-309E-4B7F-BCEC-196E0EADE949} = {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} + {1C7D222F-E17B-444F-A18C-6205DEEF27BA} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} + {5ED4F5DC-E016-4E10-BACD-6A760A0CDE89} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} + {35CFD8E3-7206-4243-AB5C-AAF610109A5C} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59} + {6F4125B5-220F-4FB7-B6C4-85A966A0268C} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} + {850008BC-36B0-4A0A-BD0C-B6D5C2184227} = {6F4125B5-220F-4FB7-B6C4-85A966A0268C} + {E6DC3B7D-303D-4A54-B040-D8DCF8C56E17} = {8C128B1D-87E0-4643-AB93-2581589AE425} + {06D2C644-AE5F-4C30-A1F6-C78E2845AAB1} = {EF710C5A-E616-442A-889D-C997D39AF2E1} + EndGlobalSection +EndGlobal diff --git a/Examples/Other/Simple2d/AdditionalFiles/MakeData.m b/Examples/Other/Simple2d/AdditionalFiles/MakeData.m index f7d98d9639c2..c55dd309fbd2 100644 --- a/Examples/Other/Simple2d/AdditionalFiles/MakeData.m +++ b/Examples/Other/Simple2d/AdditionalFiles/MakeData.m @@ -1,110 +1,110 @@ -%% -% Create training and test sets for the simple CNTK demo. Plot the results -% Create 2-dimensional data for the testing the CNTK Toolkit - -N = 10000; -x = 2*(rand(N,1) - 0.5); % Uniform from -1 to 1 -y = 2*(rand(N,1) - 0.5); % Uniform from -1 to 1 - -label = 0.25*sin(2*pi*0.5*x) > y; % Sinusoidal decision boundary - -%% -% Plot the training data -figure(1); -plot(x(label), y(label), 'rx', ... - x(~label), y(~label), 'bo'); -xlabel('X axis'); -ylabel('Y axis'); -title('Simple Data Training Plot'); - -print -dpng SimpleDemoData - -%% -% Dump the data to a file. Label needs to be an integer so we can't just -% dump the entire array at once. -fp = fopen('SimpleDataTrain.txt','w'); -for i=1:N - fprintf(fp, '%g %g %d\n', x(i), y(i), label(i)); -end -fclose(fp); - -%% -% Create a uniform grid of test data. This is easier to plot than the -% training data. -testDelta = 0.01; -testMax = 1; -testPoints = [-testMax:testDelta:testMax]; -testN = length(testPoints); -[testX, testY] = meshgrid(testPoints, testPoints); -fp = fopen('SimpleDataTest.txt','w'); -for i=1:length(testX(:)) - fprintf(fp, '%g %g %d\n', testX(i), testY(i), 0); -end -fclose(fp); - -%% -% -% Run CNTK here. -% The rest of the command in this file plot the results. - -%% -load SimpleDataTrain.txt -load SimpleDataTest.txt -load SimpleOutput.ScaledLogLikelihood - -%% -figure(2); -if 0 - % Plot each test point. - pos = SimpleOutput(:,1)>SimpleOutput(:,2); - plot(SimpleDataTest(pos,1), SimpleDataTest(pos,2), 'rx', ... - SimpleDataTest(~pos,1), SimpleDataTest(~pos,2), 'bo') -else - data=reshape(SimpleOutput(:,1), testN, testN); - imagesc(testPoints, testPoints, data); - axis xy - colorbar - title('Output 2 from DNN'); - [m,i] = min(data.^2); - hold on; plot(testPoints, testPoints(i), '--'); hold off -end -print -dpng SimpleDemoOutput - -%% -% Capture the training error rate information from the log file. -fp = fopen('Demo_Simple_Demo_Simple_Demo_Output.log', 'r'); -if fp - clear trainingError - while true - theLine = fgets(fp); - if isempty(theLine) || theLine(1) == -1 - break; - end - % Look for the message at the end of each epoch. - if strncmp(theLine, 'Finished ', length('Finished ')) - try - % Pick out the epoch number and training error - strToks = regexprep(theLine, ... - '.*Epoch\[(\d*)].*EvalErr Per Sample = (.*) +Ave Learn.*', ... - '$1 $2'); - numericToks = str2num(strToks); - trainingError(numericToks(1)) = numericToks(2); - catch e - % Ignore lines we can't read - end - end - end - fclose(fp); -else - fprintf('Can not find the demo training log\n'); -end - -%% -figure(3); -loglog(trainingError) -title('Performance of Simple Demo'); -xlabel('Epoch Number'); -ylabel('Average Error Rate (training data)'); - -print -dpng SimpleDemoErrorRate - +%% +% Create training and test sets for the simple CNTK demo. Plot the results +% Create 2-dimensional data for the testing the CNTK Toolkit + +N = 10000; +x = 2*(rand(N,1) - 0.5); % Uniform from -1 to 1 +y = 2*(rand(N,1) - 0.5); % Uniform from -1 to 1 + +label = 0.25*sin(2*pi*0.5*x) > y; % Sinusoidal decision boundary + +%% +% Plot the training data +figure(1); +plot(x(label), y(label), 'rx', ... + x(~label), y(~label), 'bo'); +xlabel('X axis'); +ylabel('Y axis'); +title('Simple Data Training Plot'); + +print -dpng SimpleDemoData + +%% +% Dump the data to a file. Label needs to be an integer so we can't just +% dump the entire array at once. +fp = fopen('SimpleDataTrain.txt','w'); +for i=1:N + fprintf(fp, '%g %g %d\n', x(i), y(i), label(i)); +end +fclose(fp); + +%% +% Create a uniform grid of test data. This is easier to plot than the +% training data. +testDelta = 0.01; +testMax = 1; +testPoints = [-testMax:testDelta:testMax]; +testN = length(testPoints); +[testX, testY] = meshgrid(testPoints, testPoints); +fp = fopen('SimpleDataTest.txt','w'); +for i=1:length(testX(:)) + fprintf(fp, '%g %g %d\n', testX(i), testY(i), 0); +end +fclose(fp); + +%% +% +% Run CNTK here. +% The rest of the command in this file plot the results. + +%% +load SimpleDataTrain.txt +load SimpleDataTest.txt +load SimpleOutput.ScaledLogLikelihood + +%% +figure(2); +if 0 + % Plot each test point. + pos = SimpleOutput(:,1)>SimpleOutput(:,2); + plot(SimpleDataTest(pos,1), SimpleDataTest(pos,2), 'rx', ... + SimpleDataTest(~pos,1), SimpleDataTest(~pos,2), 'bo') +else + data=reshape(SimpleOutput(:,1), testN, testN); + imagesc(testPoints, testPoints, data); + axis xy + colorbar + title('Output 2 from DNN'); + [m,i] = min(data.^2); + hold on; plot(testPoints, testPoints(i), '--'); hold off +end +print -dpng SimpleDemoOutput + +%% +% Capture the training error rate information from the log file. +fp = fopen('Demo_Simple_Demo_Simple_Demo_Output.log', 'r'); +if fp + clear trainingError + while true + theLine = fgets(fp); + if isempty(theLine) || theLine(1) == -1 + break; + end + % Look for the message at the end of each epoch. + if strncmp(theLine, 'Finished ', length('Finished ')) + try + % Pick out the epoch number and training error + strToks = regexprep(theLine, ... + '.*Epoch\[(\d*)].*EvalErr Per Sample = (.*) +Ave Learn.*', ... + '$1 $2'); + numericToks = str2num(strToks); + trainingError(numericToks(1)) = numericToks(2); + catch e + % Ignore lines we can't read + end + end + end + fclose(fp); +else + fprintf('Can not find the demo training log\n'); +end + +%% +figure(3); +loglog(trainingError) +title('Performance of Simple Demo'); +xlabel('Epoch Number'); +ylabel('Average Error Rate (training data)'); + +print -dpng SimpleDemoErrorRate + diff --git a/Examples/Other/Simple2d/Data/SimpleMapping.txt b/Examples/Other/Simple2d/Data/SimpleMapping.txt index b564feae0716..0d66ea1aee95 100644 --- a/Examples/Other/Simple2d/Data/SimpleMapping.txt +++ b/Examples/Other/Simple2d/Data/SimpleMapping.txt @@ -1,2 +1,2 @@ -0 -1 +0 +1 diff --git a/Examples/Other/Simple2d/README.md b/Examples/Other/Simple2d/README.md index 4f7961a79766..ef9143177004 100644 --- a/Examples/Other/Simple2d/README.md +++ b/Examples/Other/Simple2d/README.md @@ -1,82 +1,82 @@ -# CNTK example: Simple2d - -## Overview - -|Data |Two dimensional synthetic data -|:--------|:--- -|Purpose |Showcase how to train a simple CNTK network (CPU and GPU) and how to use it for scoring (decoding) -|Network |SimpleNetworkBuilder, 2 hidden layers with 50 sigmoid nodes each, cross entropy with softmax -|Training |Stochastic gradient descent with momentum -|Comments |There are two config files: Simple.config uses a single CPU or GPU, Multigpu.config uses data-parallel SGD for training on multiple GPUs - -## Running the example - -### Getting the data - -The data for this example is already contained in the folder Simple2d/Data/. - -### Setup - -Compile the sources to generate the cntk executable (not required if you downloaded the binaries). - -__Windows:__ Add the folder of the cntk executable to your path -(e.g. `set PATH=%PATH%;c:/src/cntk/x64/Debug/;`) -or prefix the call to the cntk executable with the corresponding folder. - -__Linux:__ Add the folder of the cntk executable to your path -(e.g. `export PATH=$PATH:$HOME/src/cntk/build/debug/bin/`) -or prefix the call to the cntk executable with the corresponding folder. - -### Run - -Run the example from the Simple2d/Data folder using: - -`cntk configFile=../Config/Simple.config` - -or run from any folder and specify the Data folder as the `currentDirectory`, -e.g. running from the Simple2d folder using: - -`cntk configFile=Config/Simple.config currentDirectory=Data` - -The output folder will be created inside Simple2d/. - -## Details - -### Config files - -The config files define a `RootDir` variable and sevearal other variables for directories. -The `ConfigDir` and `ModelDir` variables define the folders for additional config files and for model files. -These variables will be overwritten when running on the Philly cluster. -__It is therefore recommended to generally use `ConfigDir` and `ModelDir` in all config files.__ -To run on CPU set `deviceId = -1`, to run on GPU set deviceId to "auto" or a specific value >= 0. - -Both config files are nearly identical. -Multigpu.config has some additional parameters for parallel training (see parallelTrain in the file). -Both files define the following three commands: train, test and output. -By default only train and test are executed: - -`command=Simple_Demo_Train:Simple_Demo_Test` - -The prediction error on the test data is written to stdout. -The trained models for each epoch are stored in the output models folder. -In the case of the Multigpu config the console output is written to a file `stderr = DemoOut`. - -### Additional files - -The 'AdditionalFiles' folder contains the Matlab script that generates the -training and test data as well as the plots that are provided in the folder. -The data is synthetic 2d data representing two classes that are separated by a sinusoidal boundary. -SimpleDemoDataReference.png shows a plot of the training data. - -## Using a trained model - -The Test (Simple_Demo_Test) and the Output (Simple_Demo_Output) commands -specified in the config files use the trained model to compute labels for data -specified in the SimpleDataTest.txt file. The Test command computes prediction -error, cross entropy and perplexity for the test set and outputs them to the -console. The Output command writes for each test instance the likelihood per -label to a file `outputPath = $OutputDir$/SimpleOutput`. -To use the Output command either set `command=Simple_Demo_Output` in the config -file or add it to the command line. The model that is used to compute the labels -in these commands is defined in the modelPath variable at the beginning of the -file `modelPath=$modelDir$/simple.dnn`. +# CNTK example: Simple2d + +## Overview + +|Data |Two dimensional synthetic data +|:--------|:--- +|Purpose |Showcase how to train a simple CNTK network (CPU and GPU) and how to use it for scoring (decoding) +|Network |SimpleNetworkBuilder, 2 hidden layers with 50 sigmoid nodes each, cross entropy with softmax +|Training |Stochastic gradient descent with momentum +|Comments |There are two config files: Simple.config uses a single CPU or GPU, Multigpu.config uses data-parallel SGD for training on multiple GPUs + +## Running the example + +### Getting the data + +The data for this example is already contained in the folder Simple2d/Data/. + +### Setup + +Compile the sources to generate the cntk executable (not required if you downloaded the binaries). + +__Windows:__ Add the folder of the cntk executable to your path +(e.g. `set PATH=%PATH%;c:/src/cntk/x64/Debug/;`) +or prefix the call to the cntk executable with the corresponding folder. + +__Linux:__ Add the folder of the cntk executable to your path +(e.g. `export PATH=$PATH:$HOME/src/cntk/build/debug/bin/`) +or prefix the call to the cntk executable with the corresponding folder. + +### Run + +Run the example from the Simple2d/Data folder using: + +`cntk configFile=../Config/Simple.config` + +or run from any folder and specify the Data folder as the `currentDirectory`, +e.g. running from the Simple2d folder using: + +`cntk configFile=Config/Simple.config currentDirectory=Data` + +The output folder will be created inside Simple2d/. + +## Details + +### Config files + +The config files define a `RootDir` variable and sevearal other variables for directories. +The `ConfigDir` and `ModelDir` variables define the folders for additional config files and for model files. +These variables will be overwritten when running on the Philly cluster. +__It is therefore recommended to generally use `ConfigDir` and `ModelDir` in all config files.__ +To run on CPU set `deviceId = -1`, to run on GPU set deviceId to "auto" or a specific value >= 0. + +Both config files are nearly identical. +Multigpu.config has some additional parameters for parallel training (see parallelTrain in the file). +Both files define the following three commands: train, test and output. +By default only train and test are executed: + +`command=Simple_Demo_Train:Simple_Demo_Test` + +The prediction error on the test data is written to stdout. +The trained models for each epoch are stored in the output models folder. +In the case of the Multigpu config the console output is written to a file `stderr = DemoOut`. + +### Additional files + +The 'AdditionalFiles' folder contains the Matlab script that generates the +training and test data as well as the plots that are provided in the folder. +The data is synthetic 2d data representing two classes that are separated by a sinusoidal boundary. +SimpleDemoDataReference.png shows a plot of the training data. + +## Using a trained model + +The Test (Simple_Demo_Test) and the Output (Simple_Demo_Output) commands +specified in the config files use the trained model to compute labels for data +specified in the SimpleDataTest.txt file. The Test command computes prediction +error, cross entropy and perplexity for the test set and outputs them to the +console. The Output command writes for each test instance the likelihood per +label to a file `outputPath = $OutputDir$/SimpleOutput`. +To use the Output command either set `command=Simple_Demo_Output` in the config +file or add it to the command line. The model that is used to compute the labels +in these commands is defined in the modelPath variable at the beginning of the +file `modelPath=$modelDir$/simple.dnn`. diff --git a/Examples/README.md b/Examples/README.md index 77cf32c299ff..95d81bdb4c00 100644 --- a/Examples/README.md +++ b/Examples/README.md @@ -1,20 +1,20 @@ -# CNTK Examples - -This folder contains demos and examples to get started with CNTK. -The examples are structured by topic into Image, Speech, Text and Other. -The individual folders contain on the first level at least one self-contained example, -which cover different types of networks including FF, CNN, RNN and LSTM. -Further examples for for each category are provided in the corresponding Miscellaneous subfolder. -Each folder contains a Readme file that explains how to run the example on Windows and Linux. -How to run the examples on Philly (https://philly) is explained in the Philly portal wiki -(Philly is an internal GPU cluster for Microsoft production runs). - -The examples shown in the table below provide a good introduction to CNTK. -Please refer to the Readme file in the corresponding folder for further details. - -|Folder | Domain | Network types | -|:------------------------|:-------------------------------------------------|:----------------| -|Other/Simple2d | Synthetic 2d data | FF (CPU and GPU) -|Speech/AN4 | Speech data (CMU AN4) | FF and LSTM -|Image/MNIST | Image data (MNIST handwritten digit recognition) | CNN -|Text/PennTreebank | Text data (penn treebank) | RNN +# CNTK Examples + +This folder contains demos and examples to get started with CNTK. +The examples are structured by topic into Image, Speech, Text and Other. +The individual folders contain on the first level at least one self-contained example, +which cover different types of networks including FF, CNN, RNN and LSTM. +Further examples for for each category are provided in the corresponding Miscellaneous subfolder. +Each folder contains a Readme file that explains how to run the example on Windows and Linux. +How to run the examples on Philly (https://philly) is explained in the Philly portal wiki +(Philly is an internal GPU cluster for Microsoft production runs). + +The examples shown in the table below provide a good introduction to CNTK. +Please refer to the Readme file in the corresponding folder for further details. + +|Folder | Domain | Network types | +|:------------------------|:-------------------------------------------------|:----------------| +|Other/Simple2d | Synthetic 2d data | FF (CPU and GPU) +|Speech/AN4 | Speech data (CMU AN4) | FF and LSTM +|Image/MNIST | Image data (MNIST handwritten digit recognition) | CNN +|Text/PennTreebank | Text data (penn treebank) | RNN diff --git a/Examples/Speech/AN4/Data/glob_0000.mlf b/Examples/Speech/AN4/Data/glob_0000.mlf index cb86a72c8afc..d4362d67c79a 100644 --- a/Examples/Speech/AN4/Data/glob_0000.mlf +++ b/Examples/Speech/AN4/Data/glob_0000.mlf @@ -1,55130 +1,55130 @@ -#!MLF!# 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+An4/586/586/cen4-mjhp-b.mfc=000000000.chunk[249320,249647] +An4/133/133/cen2-flmm2-b.mfc=000000000.chunk[249648,249845] +An4/30/30/cen3-fclc-b.mfc=000000000.chunk[249846,250033] +An4/383/383/cen7-mdcs-b.mfc=000000000.chunk[250034,250381] +An4/34/34/cen7-fclc-b.mfc=000000000.chunk[250382,250679] +An4/851/851/an97-mskh-b.mfc=000000000.chunk[250680,250817] +An4/147/147/cen3-flrp-b.mfc=000000000.chunk[250818,250975] +An4/550/550/cen6-mjdr-b.mfc=000000000.chunk[250976,251143] +An4/407/407/cen5-mdmc-b.mfc=000000000.chunk[251144,251521] +An4/587/587/cen5-mjhp-b.mfc=000000000.chunk[251522,251799] +An4/22/22/cen8-fbbh-b.mfc=000000000.chunk[251800,252077] +An4/138/138/cen8-flmm2-b.mfc=000000000.chunk[252078,252655] +An4/361/361/an233-mcsc-b.mfc=000000000.chunk[252656,252733] diff --git a/Examples/Speech/AN4/Data/state.list b/Examples/Speech/AN4/Data/state.list index a2cc83e9f294..485e37948de0 100644 --- a/Examples/Speech/AN4/Data/state.list +++ b/Examples/Speech/AN4/Data/state.list @@ -1,132 +1,132 @@ -_ah_[2] -_ah_[3] -_ah_[4] -_hmm_[2] -_hmm_[3] -_hmm_[4] -_noise_[2] -_noise_[3] -_noise_[4] -aa_s2_1 -aa_s3_1 -aa_s4_1 -ae_s2_1 -ae_s3_1 -ae_s4_1 -ah_s2_1 -ah_s3_1 -ah_s4_1 -ao_s2_1 -ao_s3_1 -ao_s4_1 -aw_s2_1 -aw_s3_1 -aw_s4_1 -ax_s2_1 -ax_s3_1 -ax_s4_1 -ay_s2_1 -ay_s3_1 -ay_s4_1 -b_s2_1 -b_s3_1 -b_s4_1 -ch_s2_1 -ch_s3_1 -ch_s4_1 -d_s2_1 -d_s3_1 -d_s4_1 -dh_s2_1 -dh_s3_1 -dh_s4_1 -eh_s2_1 -eh_s3_1 -eh_s4_1 -er_s2_1 -er_s3_1 -er_s4_1 -ey_s2_1 -ey_s3_1 -ey_s4_1 -f_s2_1 -f_s3_1 -f_s4_1 -g_s2_1 -g_s3_1 -g_s4_1 -hh_s2_1 -hh_s3_1 -hh_s4_1 -ih_s2_1 -ih_s3_1 -ih_s4_1 -iy_s2_1 -iy_s3_1 -iy_s4_1 -jh_s2_1 -jh_s3_1 -jh_s4_1 -k_s2_1 -k_s3_1 -k_s4_1 -l_s2_1 -l_s3_1 -l_s4_1 -m_s2_1 -m_s3_1 -m_s4_1 -n_s2_1 -n_s3_1 -n_s4_1 -ng_s2_1 -ng_s3_1 -ng_s4_1 -ow_s2_1 -ow_s3_1 -ow_s4_1 -oy_s2_1 -oy_s3_1 -oy_s4_1 -p_s2_1 -p_s3_1 -p_s4_1 -r_s2_1 -r_s3_1 -r_s4_1 -s_s2_1 -s_s3_1 -s_s4_1 -sh_s2_1 -sh_s3_1 -sh_s4_1 -sil[2] -sil[3] -sil[4] -t_s2_1 -t_s3_1 -t_s4_1 -th_s2_1 -th_s3_1 -th_s4_1 -uh_s2_1 -uh_s3_1 -uh_s4_1 -uw_s2_1 -uw_s3_1 -uw_s4_1 -v_s2_1 -v_s3_1 -v_s4_1 -w_s2_1 -w_s3_1 -w_s4_1 -y_s2_1 -y_s3_1 -y_s4_1 -z_s2_1 -z_s3_1 -z_s4_1 -zh_s2_1 -zh_s3_1 -zh_s4_1 +_ah_[2] +_ah_[3] +_ah_[4] +_hmm_[2] +_hmm_[3] +_hmm_[4] +_noise_[2] +_noise_[3] +_noise_[4] +aa_s2_1 +aa_s3_1 +aa_s4_1 +ae_s2_1 +ae_s3_1 +ae_s4_1 +ah_s2_1 +ah_s3_1 +ah_s4_1 +ao_s2_1 +ao_s3_1 +ao_s4_1 +aw_s2_1 +aw_s3_1 +aw_s4_1 +ax_s2_1 +ax_s3_1 +ax_s4_1 +ay_s2_1 +ay_s3_1 +ay_s4_1 +b_s2_1 +b_s3_1 +b_s4_1 +ch_s2_1 +ch_s3_1 +ch_s4_1 +d_s2_1 +d_s3_1 +d_s4_1 +dh_s2_1 +dh_s3_1 +dh_s4_1 +eh_s2_1 +eh_s3_1 +eh_s4_1 +er_s2_1 +er_s3_1 +er_s4_1 +ey_s2_1 +ey_s3_1 +ey_s4_1 +f_s2_1 +f_s3_1 +f_s4_1 +g_s2_1 +g_s3_1 +g_s4_1 +hh_s2_1 +hh_s3_1 +hh_s4_1 +ih_s2_1 +ih_s3_1 +ih_s4_1 +iy_s2_1 +iy_s3_1 +iy_s4_1 +jh_s2_1 +jh_s3_1 +jh_s4_1 +k_s2_1 +k_s3_1 +k_s4_1 +l_s2_1 +l_s3_1 +l_s4_1 +m_s2_1 +m_s3_1 +m_s4_1 +n_s2_1 +n_s3_1 +n_s4_1 +ng_s2_1 +ng_s3_1 +ng_s4_1 +ow_s2_1 +ow_s3_1 +ow_s4_1 +oy_s2_1 +oy_s3_1 +oy_s4_1 +p_s2_1 +p_s3_1 +p_s4_1 +r_s2_1 +r_s3_1 +r_s4_1 +s_s2_1 +s_s3_1 +s_s4_1 +sh_s2_1 +sh_s3_1 +sh_s4_1 +sil[2] +sil[3] +sil[4] +t_s2_1 +t_s3_1 +t_s4_1 +th_s2_1 +th_s3_1 +th_s4_1 +uh_s2_1 +uh_s3_1 +uh_s4_1 +uw_s2_1 +uw_s3_1 +uw_s4_1 +v_s2_1 +v_s3_1 +v_s4_1 +w_s2_1 +w_s3_1 +w_s4_1 +y_s2_1 +y_s3_1 +y_s4_1 +z_s2_1 +z_s3_1 +z_s4_1 +zh_s2_1 +zh_s3_1 +zh_s4_1 diff --git a/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ComputeConfusion.m b/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ComputeConfusion.m index ca07e17185d3..e5c39aa1ad85 100644 --- a/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ComputeConfusion.m +++ b/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ComputeConfusion.m @@ -1,184 +1,184 @@ -function confusionData = ComputeConfusion(mlfFile) -% function confusionData = ComputeConfusion(mlfFile) -% Compute all the confusions for one experiment. Read in the TIMIT MLF file -% so we know which utterances we have. For each utterance, read in the -% CNTK output and compute the confusion matrix. Sum them all together. -if nargin < 1 - mlfFile = 'TimitLabels.mlf'; -end - -%% -% Parse the Timit MLF file because it tells us the true phonetic labels -% for each segment of each utterance. -fp = fopen(mlfFile,'r'); - -segmentLabels = []; -scale=1e-7; -numberOfUtterances = 0; -confusionData = 0; -while 1 - theLine = fgets(fp); - if isempty(theLine) || theLine(1) == -1 - break; - end - if strncmp(theLine, '#!MLF!#', 7) - continue; % Ignore the header - end - if theLine(1) == '"' % Look for file name indication - numberOfUtterances = numberOfUtterances + 1; - fileName = strtok(theLine); - fileName = fileName(2:end-1); - segmentLabels = []; - end - if theLine(1) >= '0' && theLine(1) <= '9' - % Got a speech segment with times and phoneme label. Parse it. - c = textscan(theLine, '%d %d %s '); - b = double(c{1}(1)); e = double(c{2}); l = c{3}{1}; - if isempty(segmentLabels) - clear segmentLabels; - segmentLabels(1000) = struct('begin', b, 'end', e, 'label', l); - segmentCount = 0; - end - segmentCount = segmentCount + 1; - % Add a new entry in the list of segments. - segmentLabels(segmentCount) = struct('begin', b*scale, 'end', e*scale, 'label', l); - end - if theLine(1) == '.' - % Found the end of the speech transcription. Process the new data. - c = ComputeConfusionOnce(fileName, segmentLabels(1:segmentCount)); - confusionData = confusionData + c; - segmentLabels = []; - end -end -fclose(fp); - -function Confusions = ComputeConfusionOnce(utteranceName, segmentLabels) -% function Confusions = ComputeConfusionOnce(utteranceName, labelData) -% Compute the confusion matrix for one TIMIT utterance. This routine takes -% the segment data (from the TIMIT label file) and a feature-file name. It -% transforms the feature file into a CNTK output file. It reads in the -% CNTK output file, and tabulates a confusion matrix. We do this one -% segment at a time, since TIMIT segments are variable length, and the CNTK -% output is sampled at regular intervals (10ms). -likelihoodName = strrep(strrep(utteranceName, 'Features/', 'Output/'), ... - 'fbank_zda', 'log'); -try - [likelihood,~] = htkread(likelihoodName); -catch me - fprintf('Can''t read %s using htkread. Ignoring.\n', likelihoodName); - Confusions = 0; - return -end - -nStates = 183; % Preordained. -frameRate = 100; % Preordained -Confusions = zeros(nStates, nStates); -for i=1:size(segmentLabels, 2) - % Go through each entry in the MLF file for one utterance. Each entry - % lists the beginning and each of each speech state. - % Compare the true label with the winner of the maximum likelihood from - % CNTK. - beginIndex = max(1, round(segmentLabels(i).begin*frameRate)); - endIndex = min(size(likelihood,1), round(segmentLabels(i).end*frameRate)); - curIndices = beginIndex:endIndex; - [~,winners] = max(likelihood(curIndices,:),[], 2); - correctLabel = FindLabelNumber(segmentLabels(i).label); - for w=winners(:)' % increment one at a time - Confusions(correctLabel, w) = Confusions(correctLabel, w) + 1; - end -end - -function labelNumber = FindLabelNumber(labelName) -% For each label name, turn the name into an index. The labels are listed, -% in order, in the TimitStateList file. -persistent stateList -if isempty(stateList) - stateList = ReadStateList('TimitStateList.txt'); -end -for labelNumber=1:size(stateList,1) - if strcmp(labelName, stateList{labelNumber}) - return; - end -end -labelNumber = []; - -function stateList = ReadStateList(stateListFile) -% Read in the state list file. This file contains an ordered list of -% states, each corresponding to one label (and one output in the CNTK -% network.) -fp = fopen(stateListFile); -nStates = 183; % Preordained -stateList = cell(nStates, 1); -stateIndex = 1; -while true - theLine = fgets(fp); - if isempty(theLine) || theLine(1) == -1 - break; - end - stateList{stateIndex} = theLine(1:end-1); - stateIndex = stateIndex + 1; -end -fclose(fp); - - -function [ DATA, HTKCode ] = htkread( Filename ) -% [ DATA, HTKCode ] = htkread( Filename ) -% -% Read DATA from possibly compressed HTK format file. -% -% Filename (string) - Name of the file to read from -% DATA (nSamp x NUMCOFS) - Output data array -% HTKCode - HTKCode describing file contents -% -% Compression is handled using the algorithm in 5.10 of the HTKBook. -% CRC is not implemented. -% -% Mark Hasegawa-Johnson -% July 3, 2002 -% Based on function mfcc_read written by Alexis Bernard -% Found at: https://raw.githubusercontent.com/ronw/matlab_htk/master/htkread.m -% - -fid=fopen(Filename,'r','b'); -if fid<0, - error(sprintf('Unable to read from file %s',Filename)); -end - -% Read number of frames -nSamp = fread(fid,1,'int32'); - -% Read sampPeriod -sampPeriod = fread(fid,1,'int32'); - -% Read sampSize -sampSize = fread(fid,1,'int16'); - -% Read HTK Code -HTKCode = fread(fid,1,'int16'); - -%%%%%%%%%%%%%%%%% -% Read the data -if bitget(HTKCode, 11), - DIM=sampSize/2; - nSamp = nSamp-4; - %disp(sprintf('htkread: Reading %d frames, dim %d, compressed, from %s',nSamp,DIM,Filename)); - - % Read the compression parameters - A = fread(fid,[1 DIM],'float'); -B = fread(fid,[1 DIM],'float'); - - % Read and uncompress the data - DATA = fread(fid, [DIM nSamp], 'int16')'; - DATA = (repmat(B, [nSamp 1]) + DATA) ./ repmat(A, [nSamp 1]); - - -else - DIM=sampSize/4; - %disp(sprintf('htkread: Reading %d frames, dim %d, uncompressed, from %s',nSamp,DIM,Filename)); - - % If not compressed: Read floating point data - DATA = fread(fid, [DIM nSamp], 'float')'; -end - -fclose(fid); - +function confusionData = ComputeConfusion(mlfFile) +% function confusionData = ComputeConfusion(mlfFile) +% Compute all the confusions for one experiment. Read in the TIMIT MLF file +% so we know which utterances we have. For each utterance, read in the +% CNTK output and compute the confusion matrix. Sum them all together. +if nargin < 1 + mlfFile = 'TimitLabels.mlf'; +end + +%% +% Parse the Timit MLF file because it tells us the true phonetic labels +% for each segment of each utterance. +fp = fopen(mlfFile,'r'); + +segmentLabels = []; +scale=1e-7; +numberOfUtterances = 0; +confusionData = 0; +while 1 + theLine = fgets(fp); + if isempty(theLine) || theLine(1) == -1 + break; + end + if strncmp(theLine, '#!MLF!#', 7) + continue; % Ignore the header + end + if theLine(1) == '"' % Look for file name indication + numberOfUtterances = numberOfUtterances + 1; + fileName = strtok(theLine); + fileName = fileName(2:end-1); + segmentLabels = []; + end + if theLine(1) >= '0' && theLine(1) <= '9' + % Got a speech segment with times and phoneme label. Parse it. + c = textscan(theLine, '%d %d %s '); + b = double(c{1}(1)); e = double(c{2}); l = c{3}{1}; + if isempty(segmentLabels) + clear segmentLabels; + segmentLabels(1000) = struct('begin', b, 'end', e, 'label', l); + segmentCount = 0; + end + segmentCount = segmentCount + 1; + % Add a new entry in the list of segments. + segmentLabels(segmentCount) = struct('begin', b*scale, 'end', e*scale, 'label', l); + end + if theLine(1) == '.' + % Found the end of the speech transcription. Process the new data. + c = ComputeConfusionOnce(fileName, segmentLabels(1:segmentCount)); + confusionData = confusionData + c; + segmentLabels = []; + end +end +fclose(fp); + +function Confusions = ComputeConfusionOnce(utteranceName, segmentLabels) +% function Confusions = ComputeConfusionOnce(utteranceName, labelData) +% Compute the confusion matrix for one TIMIT utterance. This routine takes +% the segment data (from the TIMIT label file) and a feature-file name. It +% transforms the feature file into a CNTK output file. It reads in the +% CNTK output file, and tabulates a confusion matrix. We do this one +% segment at a time, since TIMIT segments are variable length, and the CNTK +% output is sampled at regular intervals (10ms). +likelihoodName = strrep(strrep(utteranceName, 'Features/', 'Output/'), ... + 'fbank_zda', 'log'); +try + [likelihood,~] = htkread(likelihoodName); +catch me + fprintf('Can''t read %s using htkread. Ignoring.\n', likelihoodName); + Confusions = 0; + return +end + +nStates = 183; % Preordained. +frameRate = 100; % Preordained +Confusions = zeros(nStates, nStates); +for i=1:size(segmentLabels, 2) + % Go through each entry in the MLF file for one utterance. Each entry + % lists the beginning and each of each speech state. + % Compare the true label with the winner of the maximum likelihood from + % CNTK. + beginIndex = max(1, round(segmentLabels(i).begin*frameRate)); + endIndex = min(size(likelihood,1), round(segmentLabels(i).end*frameRate)); + curIndices = beginIndex:endIndex; + [~,winners] = max(likelihood(curIndices,:),[], 2); + correctLabel = FindLabelNumber(segmentLabels(i).label); + for w=winners(:)' % increment one at a time + Confusions(correctLabel, w) = Confusions(correctLabel, w) + 1; + end +end + +function labelNumber = FindLabelNumber(labelName) +% For each label name, turn the name into an index. The labels are listed, +% in order, in the TimitStateList file. +persistent stateList +if isempty(stateList) + stateList = ReadStateList('TimitStateList.txt'); +end +for labelNumber=1:size(stateList,1) + if strcmp(labelName, stateList{labelNumber}) + return; + end +end +labelNumber = []; + +function stateList = ReadStateList(stateListFile) +% Read in the state list file. This file contains an ordered list of +% states, each corresponding to one label (and one output in the CNTK +% network.) +fp = fopen(stateListFile); +nStates = 183; % Preordained +stateList = cell(nStates, 1); +stateIndex = 1; +while true + theLine = fgets(fp); + if isempty(theLine) || theLine(1) == -1 + break; + end + stateList{stateIndex} = theLine(1:end-1); + stateIndex = stateIndex + 1; +end +fclose(fp); + + +function [ DATA, HTKCode ] = htkread( Filename ) +% [ DATA, HTKCode ] = htkread( Filename ) +% +% Read DATA from possibly compressed HTK format file. +% +% Filename (string) - Name of the file to read from +% DATA (nSamp x NUMCOFS) - Output data array +% HTKCode - HTKCode describing file contents +% +% Compression is handled using the algorithm in 5.10 of the HTKBook. +% CRC is not implemented. +% +% Mark Hasegawa-Johnson +% July 3, 2002 +% Based on function mfcc_read written by Alexis Bernard +% Found at: https://raw.githubusercontent.com/ronw/matlab_htk/master/htkread.m +% + +fid=fopen(Filename,'r','b'); +if fid<0, + error(sprintf('Unable to read from file %s',Filename)); +end + +% Read number of frames +nSamp = fread(fid,1,'int32'); + +% Read sampPeriod +sampPeriod = fread(fid,1,'int32'); + +% Read sampSize +sampSize = fread(fid,1,'int16'); + +% Read HTK Code +HTKCode = fread(fid,1,'int16'); + +%%%%%%%%%%%%%%%%% +% Read the data +if bitget(HTKCode, 11), + DIM=sampSize/2; + nSamp = nSamp-4; + %disp(sprintf('htkread: Reading %d frames, dim %d, compressed, from %s',nSamp,DIM,Filename)); + + % Read the compression parameters + A = fread(fid,[1 DIM],'float'); +B = fread(fid,[1 DIM],'float'); + + % Read and uncompress the data + DATA = fread(fid, [DIM nSamp], 'int16')'; + DATA = (repmat(B, [nSamp 1]) + DATA) ./ repmat(A, [nSamp 1]); + + +else + DIM=sampSize/4; + %disp(sprintf('htkread: Reading %d frames, dim %d, uncompressed, from %s',nSamp,DIM,Filename)); + + % If not compressed: Read floating point data + DATA = fread(fid, [DIM nSamp], 'float')'; +end + +fclose(fid); + diff --git a/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ShowConfusions.m b/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ShowConfusions.m index e76050b4b05d..b23ee3ad4bc5 100644 --- a/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ShowConfusions.m +++ b/Examples/Speech/Miscellaneous/TIMIT/AdditionalFiles/ShowConfusions.m @@ -1,77 +1,77 @@ -function ShowConfusions(confusionData, squeeze) -% function ShowConfusions(confusionData) -% Average the three-state confusion data into monophone confusions. Then -% display the data. A graphical interface lets you interrogate the data, -% by moving the mouse, and clicking at various points. The phonetic labels -% are shown on the graph. -confusionSmall = ( ... - confusionData(1:3:end,1:3:end) + confusionData(2:3:end, 1:3:end) + confusionData(3:3:end, 1:3:end) + ... - confusionData(1:3:end,2:3:end) + confusionData(2:3:end, 2:3:end) + confusionData(3:3:end, 2:3:end) + ... - confusionData(1:3:end,3:3:end) + confusionData(2:3:end, 3:3:end) + confusionData(3:3:end, 3:3:end))/9; - -if nargin < 2 - squeeze = 1; -end - -imagesc(confusionSmall .^ squeeze) -axis ij -axis square -ylabel('True Label'); -xlabel('CNTK Prediction'); - -%% -stateList = ReadStateList(); - -h = []; -fprintf('Select a point with the mouse, type return to end...\n'); -while true - [x,y] = ginput(1); - if isempty(x) || isempty(y) - break; - end - - if ~isempty(h) - delete(h); - h = []; - end - try - trueLabel = stateList{(round(x)-1)*3+1}; - catch - trueLabel = 'Unknown'; - end - try - likelihoodLabel = stateList{(round(y)-1)*3+1}; - catch - likelihoodLabel = 'Unknown'; - end - h = text(40, -2, sprintf('%s -> %s', trueLabel, likelihoodLabel)); - % h = text(40, -2, sprintf('%g -> %g', x, y)); -end - -function stateList = ReadStateList(stateListFile) -% Read in the state list file. This file contains an ordered list of -% states, each corresponding to one label (and one output in the CNTK -% network.) -if nargin < 1 - stateListFile = 'TimitStateList.txt'; -end -% Read in the state list file. -fp = fopen(stateListFile); -nStates = 183; % Preordained -stateList = cell(nStates, 1); -stateIndex = 1; -while true - theLine = fgets(fp); - if isempty(theLine) || theLine(1) == -1 - break; - end - f = find(theLine == '_'); - if ~isempty(f) - label = theLine(1:f(1)-1); - else - label = theLine(1:end-1); - end - stateList{stateIndex} = label; - stateIndex = stateIndex + 1; -end -fclose(fp); +function ShowConfusions(confusionData, squeeze) +% function ShowConfusions(confusionData) +% Average the three-state confusion data into monophone confusions. Then +% display the data. A graphical interface lets you interrogate the data, +% by moving the mouse, and clicking at various points. The phonetic labels +% are shown on the graph. +confusionSmall = ( ... + confusionData(1:3:end,1:3:end) + confusionData(2:3:end, 1:3:end) + confusionData(3:3:end, 1:3:end) + ... + confusionData(1:3:end,2:3:end) + confusionData(2:3:end, 2:3:end) + confusionData(3:3:end, 2:3:end) + ... + confusionData(1:3:end,3:3:end) + confusionData(2:3:end, 3:3:end) + confusionData(3:3:end, 3:3:end))/9; + +if nargin < 2 + squeeze = 1; +end + +imagesc(confusionSmall .^ squeeze) +axis ij +axis square +ylabel('True Label'); +xlabel('CNTK Prediction'); + +%% +stateList = ReadStateList(); + +h = []; +fprintf('Select a point with the mouse, type return to end...\n'); +while true + [x,y] = ginput(1); + if isempty(x) || isempty(y) + break; + end + + if ~isempty(h) + delete(h); + h = []; + end + try + trueLabel = stateList{(round(x)-1)*3+1}; + catch + trueLabel = 'Unknown'; + end + try + likelihoodLabel = stateList{(round(y)-1)*3+1}; + catch + likelihoodLabel = 'Unknown'; + end + h = text(40, -2, sprintf('%s -> %s', trueLabel, likelihoodLabel)); + % h = text(40, -2, sprintf('%g -> %g', x, y)); +end + +function stateList = ReadStateList(stateListFile) +% Read in the state list file. This file contains an ordered list of +% states, each corresponding to one label (and one output in the CNTK +% network.) +if nargin < 1 + stateListFile = 'TimitStateList.txt'; +end +% Read in the state list file. +fp = fopen(stateListFile); +nStates = 183; % Preordained +stateList = cell(nStates, 1); +stateIndex = 1; +while true + theLine = fgets(fp); + if isempty(theLine) || theLine(1) == -1 + break; + end + f = find(theLine == '_'); + if ~isempty(f) + label = theLine(1:f(1)-1); + else + label = theLine(1:end-1); + end + stateList{stateIndex} = label; + stateIndex = stateIndex + 1; +end +fclose(fp); diff --git a/Examples/Text/PennTreebank/Config/rnn.config b/Examples/Text/PennTreebank/Config/rnn.config index 43a2d0d8594e..c63552c2ebc3 100644 --- a/Examples/Text/PennTreebank/Config/rnn.config +++ b/Examples/Text/PennTreebank/Config/rnn.config @@ -1,446 +1,446 @@ -# Parameters can be overwritten on the command line -# for example: cntk configFile=myConfigFile RootDir=../.. -# For running from Visual Studio add -# currentDirectory=$(SolutionDir)/ -RootDir = ".." - -ConfigDir = "$RootDir$/Config" -DataDir = "$RootDir$/Data" -OutputDir = "$RootDir$/Output" -ModelDir = "$OutputDir$/Models" - -# deviceId=-1 for CPU, >=0 for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available -deviceId = "auto" - -command = writeWordAndClassInfo:train:test - -precision = "float" -traceLevel = 1 -modelPath = "$ModelDir$/rnn.dnn" - -# uncomment the following line to write logs to a file -#stderr=$OutputDir$/rnnOutput - -type = double -numCPUThreads = 1 - -confVocabSize = 10000 -confClassSize = 50 - -trainFile = "ptb.train.cntk.txt" -validFile = "ptb.valid.cntk.txt" -testFile = "ptb.test.cntk.txt" - -writeWordAndClassInfo = [ - action = "writeWordAndClass" - inputFile = "$DataDir$/$trainFile$" - outputVocabFile = "$ModelDir$/vocab.txt" - outputWord2Cls = "$ModelDir$/word2cls.txt" - outputCls2Index = "$ModelDir$/cls2idx.txt" - vocabSize = "$confVocabSize$" - nbrClass = "$confClassSize$" - cutoff = 0 - printValues = true -] - -####################################### -# TRAINING CONFIG # -####################################### - -train = [ - action = "train" - minibatchSize = 2048 - traceLevel = 1 - epochSize = 0 - recurrentLayer = 1 - defaultHiddenActivity = 0.1 - useValidation = true - rnnType = "CLASSLSTM" - - SimpleNetworkBuilder = [ - trainingCriterion = "classCrossEntropyWithSoftmax" - evalCriterion = "classCrossEntropyWithSoftmax" - nodeType = "sigmoid" - initValueScale = 6.0 - layerSizes = "$confVocabSize$:150:200:10000" - addPrior = false - addDropoutNodes = false - applyMeanVarNorm = false - uniformInit = true - lookupTableOrder = 1 - - # these are for the class information for class-based language modeling - vocabSize = "$confVocabSize$" - nbrClass = "$confClassSize$" - ] - - SGD = [ - learningRatesPerSample = 0.1 - momentumPerMB = 0 - gradientClippingWithTruncation = true - clippingThresholdPerSample = 15.0 - maxEpochs = 16 - unroll = false - numMBsToShowResult = 100 - gradUpdateType = "none" - loadBestModel = true - - # settings for Auto Adjust Learning Rate - AutoAdjust = [ - autoAdjustLR = "adjustAfterEpoch" - reduceLearnRateIfImproveLessThan = 0.001 - continueReduce = false - increaseLearnRateIfImproveMoreThan = 1000000000 - learnRateDecreaseFactor = 0.5 - learnRateIncreaseFactor = 1.382 - numMiniBatch4LRSearch = 100 - numPrevLearnRates = 5 - numBestSearchEpoch = 1 - ] - - dropoutRate = 0.0 - ] - - reader = [ - readerType = "LMSequenceReader" - randomize = "none" - nbruttsineachrecurrentiter = 10 - - # word class info - wordclass = "$ModelDir$/vocab.txt" - - # if writerType is set, we will cache to a binary file - # if the binary file exists, we will use it instead of parsing this file - # writerType=BinaryReader - - # write definition - wfile = "$OutputDir$/sequenceSentence.bin" - - # wsize - inital size of the file in MB - # if calculated size would be bigger, that is used instead - wsize = 256 - - # wrecords - number of records we should allocate space for in the file - # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file - wrecords = 1000 - - # windowSize - number of records we should include in BinaryWriter window - windowSize = "$confVocabSize$" - - file = "$DataDir$/$trainFile$" - - # additional features sections - # for now store as expanded category data (including label in) - features = [ - # sentence has no features, so need to set dimension to zero - dim = 0 - # write definition - sectionType = "data" - ] - - # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops - sequence = [ - dim = 1 - wrecords = 2 - # write definition - sectionType = "data" - ] - - #labels sections - labelIn = [ - dim = 1 - labelType = "Category" - beginSequence = "" - endSequence = "" - - # vocabulary size - labelDim = "$confVocabSize$" - labelMappingFile = "$OutputDir$/sentenceLabels.txt" - - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 11 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 11 - # elementSize = sizeof(ElemType) is default - sectionType = "categoryLabels" - ] - ] - - # labels sections - labels = [ - dim = 1 - labelType = "NextWord" - beginSequence = "O" - endSequence = "O" - - # vocabulary size - labelDim = "$confVocabSize$" - labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" - - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 3 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 3 - # elementSize = sizeof(ElemType) is default - sectionType = categoryLabels - ] - ] - ] - - cvReader = [ - # reader to use - readerType = "LMSequenceReader" - randomize = "none" - - # word class info - wordclass = "$ModelDir$/vocab.txt" - - # if writerType is set, we will cache to a binary file - # if the binary file exists, we will use it instead of parsing this file - # writerType = "BinaryReader" - - # write definition - wfile = "$OutputDir$/sequenceSentence.valid.bin" - - # wsize - inital size of the file in MB - # if calculated size would be bigger, that is used instead - wsize = 256 - - # wrecords - number of records we should allocate space for in the file - # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file - wrecords = 1000 - - # windowSize - number of records we should include in BinaryWriter window - windowSize = "$confVocabSize$" - - file = "$DataDir$/$validFile$" - - # additional features sections - # for now store as expanded category data (including label in) - features = [ - # sentence has no features, so need to set dimension to zero - dim = 0 - # write definition - sectionType = "data" - ] - - # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops - sequence = [ - dim = 1 - wrecords = 2 - # write definition - sectionType = "data" - ] - - # labels sections - # it should be the same as that in the training set - labelIn = [ - dim = 1 - - # vocabulary size - labelDim = "$confVocabSize$" - labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" - - labelType = "Category" - beginSequence = "" - endSequence = "" - - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 11 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 11 - # elementSize = sizeof(ElemType) is default - sectionType = "categoryLabels" - ] - ] - - #labels sections - labels = [ - dim = 1 - - labelType = "NextWord" - beginSequence = "O" - endSequence = "O" - - # vocabulary size - labelDim = "$confVocabSize$" - labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" - - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 3 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 3 - # elementSize = sizeof(ElemType) is default - sectionType = "categoryLabels" - ] - ] - ] -] - -####################################### -# TEST CONFIG # -####################################### - -test = [ - action = "eval" - - # correspond to the number of words/characteres to train in a minibatch - minibatchSize = 1 - # need to be small since models are updated for each minibatch - traceLevel = 1 - epochSize = 0 - recurrentLayer = 1 - defaultHiddenActivity = 0.1 - useValidation = true - rnnType = "CLASSLSTM" - - reader = [ - # reader to use - readerType = "LMSequenceReader" - randomize = "none" - - # word class info - wordclass = "$ModelDir$/vocab.txt" - - # if writerType is set, we will cache to a binary file - # if the binary file exists, we will use it instead of parsing this file - # writerType = "BinaryReader" - - # write definition - wfile = "$OutputDir$/sequenceSentence.bin" - # wsize - inital size of the file in MB - # if calculated size would be bigger, that is used instead - wsize = 256 - - # wrecords - number of records we should allocate space for in the file - # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file - wrecords = 1000 - - # windowSize - number of records we should include in BinaryWriter window - windowSize = "$confVocabSize$" - - file = "$DataDir$/$testFile$" - - # additional features sections - # for now store as expanded category data (including label in) - features = [ - # sentence has no features, so need to set dimension to zero - dim = 0 - # write definition - sectionType = "data" - ] - - # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops - sequence = [ - dim = 1 - wrecords = 2 - # write definition - sectionType = "data" - ] - - #labels sections - labelIn = [ - dim = 1 - - # vocabulary size - labelDim = "$confVocabSize$" - labelMappingFile = "$OutputDir$/sentenceLabels.txt" - - labelType = "Category" - beginSequence = "" - endSequence = "" - - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 11 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 11 - # elementSize = sizeof(ElemType) is default - sectionType = "categoryLabels" - ] - ] - - #labels sections - labels = [ - dim = 1 - labelType = "NextWord" - beginSequence = "O" - endSequence = "O" - - # vocabulary size - labelDim = "$confVocabSize$" - - labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" - # Write definition - # sizeof(unsigned) which is the label index type - elementSize = 4 - sectionType = "labels" - - mapping = [ - # redefine number of records for this section, since we don't need to save it for each data record - wrecords = 3 - # variable size so use an average string size - elementSize = 10 - sectionType = "labelMapping" - ] - - category = [ - dim = 3 - # elementSize = sizeof(ElemType) is default - sectionType = "categoryLabels" - ] - ] - ] -] +# Parameters can be overwritten on the command line +# for example: cntk configFile=myConfigFile RootDir=../.. +# For running from Visual Studio add +# currentDirectory=$(SolutionDir)/ +RootDir = ".." + +ConfigDir = "$RootDir$/Config" +DataDir = "$RootDir$/Data" +OutputDir = "$RootDir$/Output" +ModelDir = "$OutputDir$/Models" + +# deviceId=-1 for CPU, >=0 for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available +deviceId = "auto" + +command = writeWordAndClassInfo:train:test + +precision = "float" +traceLevel = 1 +modelPath = "$ModelDir$/rnn.dnn" + +# uncomment the following line to write logs to a file +#stderr=$OutputDir$/rnnOutput + +type = double +numCPUThreads = 1 + +confVocabSize = 10000 +confClassSize = 50 + +trainFile = "ptb.train.cntk.txt" +validFile = "ptb.valid.cntk.txt" +testFile = "ptb.test.cntk.txt" + +writeWordAndClassInfo = [ + action = "writeWordAndClass" + inputFile = "$DataDir$/$trainFile$" + outputVocabFile = "$ModelDir$/vocab.txt" + outputWord2Cls = "$ModelDir$/word2cls.txt" + outputCls2Index = "$ModelDir$/cls2idx.txt" + vocabSize = "$confVocabSize$" + nbrClass = "$confClassSize$" + cutoff = 0 + printValues = true +] + +####################################### +# TRAINING CONFIG # +####################################### + +train = [ + action = "train" + minibatchSize = 2048 + traceLevel = 1 + epochSize = 0 + recurrentLayer = 1 + defaultHiddenActivity = 0.1 + useValidation = true + rnnType = "CLASSLSTM" + + SimpleNetworkBuilder = [ + trainingCriterion = "classCrossEntropyWithSoftmax" + evalCriterion = "classCrossEntropyWithSoftmax" + nodeType = "sigmoid" + initValueScale = 6.0 + layerSizes = "$confVocabSize$:150:200:10000" + addPrior = false + addDropoutNodes = false + applyMeanVarNorm = false + uniformInit = true + lookupTableOrder = 1 + + # these are for the class information for class-based language modeling + vocabSize = "$confVocabSize$" + nbrClass = "$confClassSize$" + ] + + SGD = [ + learningRatesPerSample = 0.1 + momentumPerMB = 0 + gradientClippingWithTruncation = true + clippingThresholdPerSample = 15.0 + maxEpochs = 16 + unroll = false + numMBsToShowResult = 100 + gradUpdateType = "none" + loadBestModel = true + + # settings for Auto Adjust Learning Rate + AutoAdjust = [ + autoAdjustLR = "adjustAfterEpoch" + reduceLearnRateIfImproveLessThan = 0.001 + continueReduce = false + increaseLearnRateIfImproveMoreThan = 1000000000 + learnRateDecreaseFactor = 0.5 + learnRateIncreaseFactor = 1.382 + numMiniBatch4LRSearch = 100 + numPrevLearnRates = 5 + numBestSearchEpoch = 1 + ] + + dropoutRate = 0.0 + ] + + reader = [ + readerType = "LMSequenceReader" + randomize = "none" + nbruttsineachrecurrentiter = 10 + + # word class info + wordclass = "$ModelDir$/vocab.txt" + + # if writerType is set, we will cache to a binary file + # if the binary file exists, we will use it instead of parsing this file + # writerType=BinaryReader + + # write definition + wfile = "$OutputDir$/sequenceSentence.bin" + + # wsize - inital size of the file in MB + # if calculated size would be bigger, that is used instead + wsize = 256 + + # wrecords - number of records we should allocate space for in the file + # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file + wrecords = 1000 + + # windowSize - number of records we should include in BinaryWriter window + windowSize = "$confVocabSize$" + + file = "$DataDir$/$trainFile$" + + # additional features sections + # for now store as expanded category data (including label in) + features = [ + # sentence has no features, so need to set dimension to zero + dim = 0 + # write definition + sectionType = "data" + ] + + # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops + sequence = [ + dim = 1 + wrecords = 2 + # write definition + sectionType = "data" + ] + + #labels sections + labelIn = [ + dim = 1 + labelType = "Category" + beginSequence = "" + endSequence = "" + + # vocabulary size + labelDim = "$confVocabSize$" + labelMappingFile = "$OutputDir$/sentenceLabels.txt" + + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 11 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 11 + # elementSize = sizeof(ElemType) is default + sectionType = "categoryLabels" + ] + ] + + # labels sections + labels = [ + dim = 1 + labelType = "NextWord" + beginSequence = "O" + endSequence = "O" + + # vocabulary size + labelDim = "$confVocabSize$" + labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" + + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 3 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 3 + # elementSize = sizeof(ElemType) is default + sectionType = categoryLabels + ] + ] + ] + + cvReader = [ + # reader to use + readerType = "LMSequenceReader" + randomize = "none" + + # word class info + wordclass = "$ModelDir$/vocab.txt" + + # if writerType is set, we will cache to a binary file + # if the binary file exists, we will use it instead of parsing this file + # writerType = "BinaryReader" + + # write definition + wfile = "$OutputDir$/sequenceSentence.valid.bin" + + # wsize - inital size of the file in MB + # if calculated size would be bigger, that is used instead + wsize = 256 + + # wrecords - number of records we should allocate space for in the file + # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file + wrecords = 1000 + + # windowSize - number of records we should include in BinaryWriter window + windowSize = "$confVocabSize$" + + file = "$DataDir$/$validFile$" + + # additional features sections + # for now store as expanded category data (including label in) + features = [ + # sentence has no features, so need to set dimension to zero + dim = 0 + # write definition + sectionType = "data" + ] + + # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops + sequence = [ + dim = 1 + wrecords = 2 + # write definition + sectionType = "data" + ] + + # labels sections + # it should be the same as that in the training set + labelIn = [ + dim = 1 + + # vocabulary size + labelDim = "$confVocabSize$" + labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" + + labelType = "Category" + beginSequence = "" + endSequence = "" + + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 11 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 11 + # elementSize = sizeof(ElemType) is default + sectionType = "categoryLabels" + ] + ] + + #labels sections + labels = [ + dim = 1 + + labelType = "NextWord" + beginSequence = "O" + endSequence = "O" + + # vocabulary size + labelDim = "$confVocabSize$" + labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" + + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 3 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 3 + # elementSize = sizeof(ElemType) is default + sectionType = "categoryLabels" + ] + ] + ] +] + +####################################### +# TEST CONFIG # +####################################### + +test = [ + action = "eval" + + # correspond to the number of words/characteres to train in a minibatch + minibatchSize = 1 + # need to be small since models are updated for each minibatch + traceLevel = 1 + epochSize = 0 + recurrentLayer = 1 + defaultHiddenActivity = 0.1 + useValidation = true + rnnType = "CLASSLSTM" + + reader = [ + # reader to use + readerType = "LMSequenceReader" + randomize = "none" + + # word class info + wordclass = "$ModelDir$/vocab.txt" + + # if writerType is set, we will cache to a binary file + # if the binary file exists, we will use it instead of parsing this file + # writerType = "BinaryReader" + + # write definition + wfile = "$OutputDir$/sequenceSentence.bin" + # wsize - inital size of the file in MB + # if calculated size would be bigger, that is used instead + wsize = 256 + + # wrecords - number of records we should allocate space for in the file + # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file + wrecords = 1000 + + # windowSize - number of records we should include in BinaryWriter window + windowSize = "$confVocabSize$" + + file = "$DataDir$/$testFile$" + + # additional features sections + # for now store as expanded category data (including label in) + features = [ + # sentence has no features, so need to set dimension to zero + dim = 0 + # write definition + sectionType = "data" + ] + + # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops + sequence = [ + dim = 1 + wrecords = 2 + # write definition + sectionType = "data" + ] + + #labels sections + labelIn = [ + dim = 1 + + # vocabulary size + labelDim = "$confVocabSize$" + labelMappingFile = "$OutputDir$/sentenceLabels.txt" + + labelType = "Category" + beginSequence = "" + endSequence = "" + + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 11 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 11 + # elementSize = sizeof(ElemType) is default + sectionType = "categoryLabels" + ] + ] + + #labels sections + labels = [ + dim = 1 + labelType = "NextWord" + beginSequence = "O" + endSequence = "O" + + # vocabulary size + labelDim = "$confVocabSize$" + + labelMappingFile = "$OutputDir$/sentenceLabels.out.txt" + # Write definition + # sizeof(unsigned) which is the label index type + elementSize = 4 + sectionType = "labels" + + mapping = [ + # redefine number of records for this section, since we don't need to save it for each data record + wrecords = 3 + # variable size so use an average string size + elementSize = 10 + sectionType = "labelMapping" + ] + + category = [ + dim = 3 + # elementSize = sizeof(ElemType) is default + sectionType = "categoryLabels" + ] + ] + ] +] diff --git a/License/MSR Computational Network Toolkit_MSR-LA (2014-03-28).md b/License/MSR Computational Network Toolkit_MSR-LA (2014-03-28).md index 071a2392a290..f8e9f70b1c35 100644 --- a/License/MSR Computational Network Toolkit_MSR-LA (2014-03-28).md +++ b/License/MSR Computational Network Toolkit_MSR-LA (2014-03-28).md @@ -1,148 +1,148 @@ -# Microsoft Research License Terms -## for Computational Network Toolkit (CNTK) - -These license terms are an agreement between Microsoft Corporation -(or based on where you live, one of its affiliates) and you. Please read them. -They apply to the software named above, which includes the media on which you -received, if any. The terms also apply to any Microsoft: - -* updates, -* supplements, -* Internet-based services, and -* support services - -for this software, unless other terms accompany those items. If so, those terms -apply. - -__By using the software you accept these terms.__ -__If you do not accept them, do not use the software.__ - -______________________________________________________ - -__If you comply with these license terms, you have the rights below.__ - -### 1. SCOPE OF RIGHTS: - -a. __License Grant.__ You may use, copy, modify, creative derivative works, -and distribute the Software for non-commercial purposes, subject to the -restrictions in this MSR-LA. Examples of non-commercial uses are teaching, -academic research, public demonstrations and personal experimentation. - -b. __Publication.__ You may publish (or present papers or articles) on your -results from using the software. - -c. __Third Party Programs.__ The software may include third party programs -that Microsoft, not the third party, licenses to you under this agreement. -Notices, if any, for the third party program are included for your -information only. - -### 2. DISTRIBUTION REQUIREMENTS: - -a. If you distribute the Software or any derivative works of the Software, -you will distribute them under the same terms and conditions as in this -license, and you will not grant other rights to the Software or derivative -works that are different from those provided by this MSR-LA. - -b. If you have created derivative works of the software, and distribute -such derivative works, you will cause the modified files to carry prominent -notices so that recipients know that they are not receiving the original -software. Such notices must state: (i) that you have changed the software; -and (ii) the date of any changes. - -### 3. DISTRIBUTION RESTRICTIONS: YOU MAY NOT: - -a. alter any copyright, trademark or patent notice in the software; - -b. use Microsoft's trademarks in your programs' names or in a way that -suggests your derivative works or modifications come from or are endorsed -by Microsoft; - -c. include the software in malicious, deceptive or unlawful programs; - -______________________________________________________ - -__In return, we simply require that you agree:__ - -### 4. LICENSE TO MICROSOFT. - -In the event you contribute code or other material to the Computational Network -Toolkit project, Microsoft is granted back, without any restrictions or -limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable -and sub-licensable license, to reproduce, publicly perform or display, install, -use, modify, post, distribute, make and have made, sell and transfer any such -contributions. - -### 5. TERM; TERMINATION. - -The term of this Agreement will commence upon your acceptance of these license -terms and will continue indefinitely unless terminated as provided herein. If -you breach this agreement or if you sue Microsoft or any other party over -patents that you think may apply to or read on the software or anyone's use of -the software, this agreement (and your license and rights obtained herein) -terminate automatically. If this agreement is terminated, you must cease using -and distributing any derivative works or modifications of the Software. Any -sections that are intended to survive termination of this agreement shall -survive. - -### 6. FEEDBACK. - -That any feedback about the Software provided by you to us is voluntarily -given, and Microsoft shall be free to use the feedback as it sees fit without -obligation or restriction of any kind, even if the feedback is designated by -you as confidential. - -### 7. SCOPE OF LICENSE. - -The software is licensed, not sold. This agreement only gives you some rights -to use the software. Microsoft reserves all other rights. The patent rights, -if any, granted to you in this MSR-LA only apply to the Software, not to any -derivative works you make. In using the Software, you must comply with any -technical limitations in the software that only allow you to use it in certain -ways. You may not: - -a. make more copies of the software than specified in this agreement or -allowed by applicable law, despite this limitation; - -b. rent, lease or lend the software; - -c. transfer the software or this agreement to any third party; or - -d. use the software for commercial software hosting services. - -### 8. EXPORT RESTRICTIONS. - -The software is subject to United States export laws and regulations. You must -comply with all domestic and international export laws and regulations that -apply to the software. These laws include restrictions on destinations, end -users and end use. For additional information, see www.microsoft.com/exporting. - -### 9. ENTIRE AGREEMENT. - -This agreement, and the terms for supplements, updates, Internet-based services -and support services that you use, are the entire agreement for the software -and support services. - -### 10. GOVERNING LAW AND VENUE. - -This Agreement is governed by and construed in accordance with the laws of the -state of Washington, without reference to its choice of law principles to the -contrary. Each party hereby consents to the jurisdiction and venue of the state -and federal courts located in King County, Washington, with regard to any suit -or claim arising under or by reason of this Agreement. - -### 11. LEGAL EFFECT. - -This agreement describes certain legal rights. You may have other rights under -the laws of your country. You may also have rights with respect to the party -from whom you acquired the dataset. This agreement does not change your rights -under the laws of your country if the laws of your country do not permit it to -do so. - -### 12. DISCLAIMER OF WARRANTY. - -THE SOFTWARE IS LICENSED "AS-IS". YOU BEAR THE RISK OF USING IT. MICROSOFT -GIVES NO EXPRESS WARRANTIES, GUARANTEES OR CONDITIONS. YOU MAY HAVE ADDITIONAL -CONSUMER RIGHTS OR STATUTORY GUARANTEES UNDER YOUR LOCAL LAWS WHICH THIS -AGREEMENT CANNOT CHANGE. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAWS, -MICROSOFT EXCLUDES THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A -PARTICULAR PURPOSE AND NON-INFRINGEMENT. +# Microsoft Research License Terms +## for Computational Network Toolkit (CNTK) + +These license terms are an agreement between Microsoft Corporation +(or based on where you live, one of its affiliates) and you. Please read them. +They apply to the software named above, which includes the media on which you +received, if any. The terms also apply to any Microsoft: + +* updates, +* supplements, +* Internet-based services, and +* support services + +for this software, unless other terms accompany those items. If so, those terms +apply. + +__By using the software you accept these terms.__ +__If you do not accept them, do not use the software.__ + +______________________________________________________ + +__If you comply with these license terms, you have the rights below.__ + +### 1. SCOPE OF RIGHTS: + +a. __License Grant.__ You may use, copy, modify, creative derivative works, +and distribute the Software for non-commercial purposes, subject to the +restrictions in this MSR-LA. Examples of non-commercial uses are teaching, +academic research, public demonstrations and personal experimentation. + +b. __Publication.__ You may publish (or present papers or articles) on your +results from using the software. + +c. __Third Party Programs.__ The software may include third party programs +that Microsoft, not the third party, licenses to you under this agreement. +Notices, if any, for the third party program are included for your +information only. + +### 2. DISTRIBUTION REQUIREMENTS: + +a. If you distribute the Software or any derivative works of the Software, +you will distribute them under the same terms and conditions as in this +license, and you will not grant other rights to the Software or derivative +works that are different from those provided by this MSR-LA. + +b. If you have created derivative works of the software, and distribute +such derivative works, you will cause the modified files to carry prominent +notices so that recipients know that they are not receiving the original +software. Such notices must state: (i) that you have changed the software; +and (ii) the date of any changes. + +### 3. DISTRIBUTION RESTRICTIONS: YOU MAY NOT: + +a. alter any copyright, trademark or patent notice in the software; + +b. use Microsoft's trademarks in your programs' names or in a way that +suggests your derivative works or modifications come from or are endorsed +by Microsoft; + +c. include the software in malicious, deceptive or unlawful programs; + +______________________________________________________ + +__In return, we simply require that you agree:__ + +### 4. LICENSE TO MICROSOFT. + +In the event you contribute code or other material to the Computational Network +Toolkit project, Microsoft is granted back, without any restrictions or +limitations, a non-exclusive, perpetual, irrevocable, royalty-free, assignable +and sub-licensable license, to reproduce, publicly perform or display, install, +use, modify, post, distribute, make and have made, sell and transfer any such +contributions. + +### 5. TERM; TERMINATION. + +The term of this Agreement will commence upon your acceptance of these license +terms and will continue indefinitely unless terminated as provided herein. If +you breach this agreement or if you sue Microsoft or any other party over +patents that you think may apply to or read on the software or anyone's use of +the software, this agreement (and your license and rights obtained herein) +terminate automatically. If this agreement is terminated, you must cease using +and distributing any derivative works or modifications of the Software. Any +sections that are intended to survive termination of this agreement shall +survive. + +### 6. FEEDBACK. + +That any feedback about the Software provided by you to us is voluntarily +given, and Microsoft shall be free to use the feedback as it sees fit without +obligation or restriction of any kind, even if the feedback is designated by +you as confidential. + +### 7. SCOPE OF LICENSE. + +The software is licensed, not sold. This agreement only gives you some rights +to use the software. Microsoft reserves all other rights. The patent rights, +if any, granted to you in this MSR-LA only apply to the Software, not to any +derivative works you make. In using the Software, you must comply with any +technical limitations in the software that only allow you to use it in certain +ways. You may not: + +a. make more copies of the software than specified in this agreement or +allowed by applicable law, despite this limitation; + +b. rent, lease or lend the software; + +c. transfer the software or this agreement to any third party; or + +d. use the software for commercial software hosting services. + +### 8. EXPORT RESTRICTIONS. + +The software is subject to United States export laws and regulations. You must +comply with all domestic and international export laws and regulations that +apply to the software. These laws include restrictions on destinations, end +users and end use. For additional information, see www.microsoft.com/exporting. + +### 9. ENTIRE AGREEMENT. + +This agreement, and the terms for supplements, updates, Internet-based services +and support services that you use, are the entire agreement for the software +and support services. + +### 10. GOVERNING LAW AND VENUE. + +This Agreement is governed by and construed in accordance with the laws of the +state of Washington, without reference to its choice of law principles to the +contrary. Each party hereby consents to the jurisdiction and venue of the state +and federal courts located in King County, Washington, with regard to any suit +or claim arising under or by reason of this Agreement. + +### 11. LEGAL EFFECT. + +This agreement describes certain legal rights. You may have other rights under +the laws of your country. You may also have rights with respect to the party +from whom you acquired the dataset. This agreement does not change your rights +under the laws of your country if the laws of your country do not permit it to +do so. + +### 12. DISCLAIMER OF WARRANTY. + +THE SOFTWARE IS LICENSED "AS-IS". YOU BEAR THE RISK OF USING IT. MICROSOFT +GIVES NO EXPRESS WARRANTIES, GUARANTEES OR CONDITIONS. YOU MAY HAVE ADDITIONAL +CONSUMER RIGHTS OR STATUTORY GUARANTEES UNDER YOUR LOCAL LAWS WHICH THIS +AGREEMENT CANNOT CHANGE. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAWS, +MICROSOFT EXCLUDES THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NON-INFRINGEMENT. diff --git a/Source/ActionsLib/ActionsLib.vcxproj b/Source/ActionsLib/ActionsLib.vcxproj index 510ea46b5b6d..13d33a8da48d 100644 --- a/Source/ActionsLib/ActionsLib.vcxproj +++ b/Source/ActionsLib/ActionsLib.vcxproj @@ -1,180 +1,180 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {EB2BE26F-6BD4-4274-971F-86D080779DD1} - - - - - - - - - Win32Proj - CNTK - ActionsLib - - - - StaticLibrary - true - v120 - Unicode - - - StaticLibrary - false - v120 - true - Unicode - - - - - - - - - - - - true - ..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\CNTK;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - false - ..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\CNTK;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(ExecutablePath) - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - - - - Level4 - Disabled - _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - true - /bigobj %(AdditionalOptions) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - Math.dll; nvml.dll; cudart64_70.dll - 100000000 - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt - - - true - Copy content files to target directory - - - - - - - - - Level4 - - - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - Speed - /d2Zi+ %(AdditionalOptions) - true - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - true - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - true - Math.dll; nvml.dll; cudart64_70.dll - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - - - - - - - true - - - - - - - - - - - - - - - - - - - - - - - NotUsing - - - NotUsing - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {EB2BE26F-6BD4-4274-971F-86D080779DD1} + + + + + + + + + Win32Proj + CNTK + ActionsLib + + + + StaticLibrary + true + v120 + Unicode + + + StaticLibrary + false + v120 + true + Unicode + + + + + + + + + + + + true + ..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\CNTK;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + false + ..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\CNTK;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(ExecutablePath) + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + + + + Level4 + Disabled + _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + true + /bigobj %(AdditionalOptions) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + Math.dll; nvml.dll; cudart64_70.dll + 100000000 + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt + + + true + Copy content files to target directory + + + + + + + + + Level4 + + + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + Speed + /d2Zi+ %(AdditionalOptions) + true + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + true + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + true + Math.dll; nvml.dll; cudart64_70.dll + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + + + + + + + true + + + + + + + + + + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + + + + + + + \ No newline at end of file diff --git a/Source/ActionsLib/ActionsLib.vcxproj.filters b/Source/ActionsLib/ActionsLib.vcxproj.filters index 3db7ccac9acb..38cc00b65315 100644 --- a/Source/ActionsLib/ActionsLib.vcxproj.filters +++ b/Source/ActionsLib/ActionsLib.vcxproj.filters @@ -1,69 +1,69 @@ - - - - - Common - - - Common - - - Common - - - Actions - - - Actions - - - Actions - - - Actions - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Actions - - - - - {b3d05c7b-7bcf-4b12-bcb5-dced86717202} - - - {85226dda-87ba-4da6-af04-563d0ce23b94} - - - {db4af610-ad50-4fc4-a494-2d4a3bcc6809} - - + + + + + Common + + + Common + + + Common + + + Actions + + + Actions + + + Actions + + + Actions + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Actions + + + + + {b3d05c7b-7bcf-4b12-bcb5-dced86717202} + + + {85226dda-87ba-4da6-af04-563d0ce23b94} + + + {db4af610-ad50-4fc4-a494-2d4a3bcc6809} + + \ No newline at end of file diff --git a/Source/CNTK/CNTK.cpp b/Source/CNTK/CNTK.cpp index 16b6bd1058ea..5ebc945d5c02 100644 --- a/Source/CNTK/CNTK.cpp +++ b/Source/CNTK/CNTK.cpp @@ -1,727 +1,727 @@ -// CNTK.cpp : Defines the entry point for the console application. -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#define _CRT_NONSTDC_NO_DEPRECATE // make VS accept POSIX functions without _ - -#include "stdafx.h" -#include "Basics.h" -#include "Actions.h" -#include "ComputationNetwork.h" -#include "ComputationNode.h" -#include "DataReader.h" -#include "DataWriter.h" -#include "SimpleNetworkBuilder.h" -#include "NDLNetworkBuilder.h" -#include "SynchronousExecutionEngine.h" -#include "ModelEditLanguage.h" -#include "CPUMatrix.h" // used for SetNumThreads() -#include "SGD.h" -#include "MPIWrapper.h" -#include "Config.h" -#include "MultiNetworksSGD.h" -#include "SimpleEvaluator.h" -#include "SimpleOutputWriter.h" -#include "MultiNetworksEvaluator.h" -#include "BestGpu.h" -#include "ProgressTracing.h" -#include "fileutil.h" -#include "ScriptableObjects.h" -#include "BrainScriptEvaluator.h" -#include "BrainScriptParser.h" - -#include -#include -#include -#if defined(_WIN32) -#include "io.h" -#endif -#include "buildinfo.h" -#include "hostname.h" -#ifdef LEAKDETECT -#include "vld.h" // for memory leak detection -#endif -#include -#include -#include -#include -#include - -#ifndef let -#define let const auto -#endif - -// TODO: Get rid of these globals -Microsoft::MSR::CNTK::MPIWrapper *g_mpi = nullptr; - -// TODO: Temporary mechanism to enable memory sharing for -// node output value matrices. This will go away when the -// sharing is ready to be enabled by default -bool g_shareNodeValueMatrices = false; - -using namespace std; -using namespace Microsoft::MSR; -using namespace Microsoft::MSR::CNTK; - -// internal test routine forward declaration -template -void TestCn(const ConfigParameters& config); - -void RedirectStdErr(wstring logpath) -{ - fprintf(stderr, "Redirecting stderr to file %S\n", logpath.c_str()); - auto f = make_shared(logpath.c_str(), fileOptionsWrite | fileOptionsText); - if (dup2(fileno(*f), 2) == -1) - { - RuntimeError("unexpected failure to redirect stderr to log file"); - } - setvbuf(stderr, NULL, _IONBF, 16384); // unbuffer it - static auto fKept = f; // keep it around (until it gets changed) -} - -std::string WCharToString(const wchar_t* wst) -{ - std::wstring ws(wst); - std::string s(ws.begin(), ws.end()); - s.assign(ws.begin(), ws.end()); - return s; -} - -template -void DumpNodeInfo(const ConfigParameters& config) -{ - wstring modelPath = config(L"modelPath"); - wstring nodeName = config(L"nodeName", L"__AllNodes__"); - wstring nodeNameRegexStr = config(L"nodeNameRegex", L""); - wstring defOutFilePath = modelPath + L"." + nodeName + L".txt"; - wstring outputFile = config(L"outputFile", defOutFilePath); - bool printValues = config(L"printValues", true); - - ComputationNetwork net(-1); //always use CPU - net.Load(modelPath); - net.DumpNodeInfoToFile(nodeName, printValues, outputFile, nodeNameRegexStr); -} - -size_t GetMaxEpochs(const ConfigParameters& configParams) -{ - ConfigParameters configSGD(configParams("SGD")); - size_t maxEpochs = configSGD("maxEpochs"); - - return maxEpochs; -} - -// special temporary function to guard against a now invalid usage of "truncated" which exists in some IPG production setups -static void DisableLegacyTruncationSettings(const ConfigParameters& TopLevelConfig, const ConfigParameters& commandConfig) -{ - if (TopLevelConfig.ExistsCurrent(L"Truncated")) - { - return; - } - - // if any of the action has set a reader/SGD section and has different Truncated value for reader and SGD section - ConfigArray actions = commandConfig(L"action"); - for (size_t i = 0; i < actions.size(); i++) - { - if (actions[i] == "train" || actions[i] == "trainRNN") - { - ConfigParameters sgd = ConfigParameters(commandConfig(L"SGD")); - ConfigParameters reader = ConfigParameters(commandConfig(L"reader")); - // reader and SGD sections are two must-have sections in train/trainRNN - if (reader.ExistsCurrent(L"Truncated") && !sgd.ExistsCurrent(L"Truncated")) - { - InvalidArgument("DisableLegacyUsage: setting Truncated only in reader section are not allowed. Please move Truncated=true/false to the top level section."); - } - } - } -} -static void DisableLegacyUsage(const ConfigParameters& TopLevelConfig, const ConfigArray& commands) -{ - for (size_t i = 0; i < commands.size(); i++) - { - ConfigParameters cfgParameters(TopLevelConfig(commands[i])); - DisableLegacyTruncationSettings(TopLevelConfig, cfgParameters); - } -} - -// process the command -template -void DoCommands(const ConfigParameters& config) -{ - ConfigArray command = config(L"command", "train"); - - int numCPUThreads = config(L"numCPUThreads", "0"); - numCPUThreads = CPUMatrix::SetNumThreads(numCPUThreads); - - if (numCPUThreads>0) - { - std::cerr << "Using " << numCPUThreads << " CPU threads" << endl; - } - - bool progressTracing = config(L"progressTracing", false); - - // temporary hack to prevent users from failling for a small breaking change related to the "truncated" flag (will be redone bigger and better some day) - DisableLegacyUsage(config, command); - - // summarize command info upfront in the log and stdout - size_t fullTotalMaxEpochs = 0; - for (int i = 0; i < command.size(); i++) - { - //get the configuration parameters that match the command - ConfigParameters commandParams(config(command[i])); - ConfigArray action = commandParams("action", "train"); - - // determine the action to perform, and do it - for (int j = 0; j < action.size(); j++) - { - if (action[j] == "train" || action[j] == "trainRNN" -#if 0 - || action[j] == "trainSequence" || action[j] == "trainSequenceRNN" -#endif - ) - { - wstring modelPath = commandParams("modelPath"); - std::wcerr << "CNTKModelPath: " << modelPath << endl; - size_t maxEpochs = GetMaxEpochs(commandParams); - std::cerr << "CNTKCommandTrainInfo: " + command[i] << " : " << maxEpochs << endl; - fullTotalMaxEpochs += maxEpochs; - } - } - } - std::cerr << "CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : " << fullTotalMaxEpochs << endl; - - // set up progress tracing for compute cluster management - if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) - { - ProgressTracing::TraceTotalNumberOfSteps(fullTotalMaxEpochs); // enable tracing, using this as the total number of epochs - } - - size_t fullEpochsOffset = 0; - - // execute the commands - for (int i = 0; i < command.size(); i++) - { - //get the configuration parameters that match the command - ConfigParameters commandParams(config(command[i])); - ConfigArray action = commandParams("action", "train"); - - if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) - { - ProgressTracing::SetStepOffset(fullEpochsOffset); // this is the epoch number that SGD will log relative to - } - - // determine the action to perform, and do it - for (int j = 0; j < action.size(); j++) - { - if (action[j] == "train" || action[j] == "trainRNN") - { - std::cerr << "CNTKCommandTrainBegin: " + command[i] << endl; - DoTrain(commandParams); - std::cerr << "CNTKCommandTrainEnd: " + command[i] << endl; - fullEpochsOffset += GetMaxEpochs(commandParams); - } -#if 0 - else if (action[j] == "trainSequence" || action[j] == "trainSequenceRNN") - { - std::cerr << "CNTKCommandTrainBegin: " + command[i] << endl; - DoSequenceTrain(commandParams); - std::cerr << "CNTKCommandTrainEnd: " + command[i] << endl; - fullEpochsOffset += GetMaxEpochs(commandParams); - } -#endif - else if (action[j] == "adapt") - { - DoAdapt(commandParams); - } - else if (action[j] == "test" || action[j] == "eval") - { - DoEval(commandParams); - } - else if (action[j] == "testunroll") - { - DoEvalUnroll(commandParams); - } - else if (action[j] == "edit") - { - DoEdit(commandParams); - } - else if (action[j] == "cv") - { - DoCrossValidate(commandParams); - } - else if (action[j] == "write") - { - DoWriteOutput(commandParams); - } - else if (action[j] == "devtest") - { - TestCn(config); // for "devtest" action pass the root config instead - } - else if (action[j] == "dumpnode") - { - DumpNodeInfo(commandParams); - } - else if (action[j] == "convertdbn") - { - DoConvertFromDbn(commandParams); - } - else if (action[j] == "createLabelMap") - { - DoCreateLabelMap(commandParams); - } - else if (action[j] == "writeWordAndClass") - { - DoWriteWordAndClassInfo(commandParams); - } - else if (action[j] == "plot") - { - DoTopologyPlot(commandParams); - } - else if (action[j] == "SVD") - { - DoParameterSVD(commandParams); - } - else if (action[j] == "trainEncoderDecoder") - { - DoEncoderDecoder(commandParams); - } - else if (action[j] == "testEncoderDecoder") - { - DoEvalEncodingBeamSearchDecoding(commandParams); - } - else if (action[j] == "trainBidirectionEncoderDecoder") - { - DoBidirectionEncoderDecoder(commandParams); - } - else if (action[j] == "beamSearch") - { - DoBeamSearchDecoding(commandParams); - } - else - { - RuntimeError("unknown action: %s in command set: %s", action[j].c_str(), command[i].c_str()); - } - - NDLScript ndlScript; - ndlScript.ClearGlobal(); // clear global macros between commands - } - } -} - -std::string TimeDateStamp() -{ -#if 0 // "safe" version for Windows, not needed it seems - __time64_t localtime; - - _time64(&localtime);// get current time and date - struct tm now; - _localtime64_s(&now, &localtime); // convert -#else - time_t t = time(NULL); - struct tm now = *localtime(&t); -#endif - char buf[30]; - sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now.tm_year + 1900, now.tm_mon + 1, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec); - return buf; -} - -void PrintBuiltInfo() -{ - fprintf(stderr, "-------------------------------------------------------------------\n"); - fprintf(stderr, "Build info: \n\n"); - fprintf(stderr, "\t\tBuilt time: %s %s\n", __DATE__, __TIME__); - fprintf(stderr, "\t\tLast modified date: %s\n", __TIMESTAMP__); -#ifdef _BUILDTYPE_ - fprintf(stderr, "\t\tBuild type: %s\n", _BUILDTYPE_); -#endif -#ifdef _MATHLIB_ - fprintf(stderr, "\t\tMath lib: %s\n", _MATHLIB_); -#endif -#ifdef _CUDA_PATH_ - fprintf(stderr, "\t\tCUDA_PATH: %s\n", _CUDA_PATH_); -#endif -#ifdef _CUB_PATH_ - fprintf(stderr, "\t\tCUB_PATH: %s\n", _CUB_PATH_); -#endif -#ifdef _CUDNN_PATH_ - fprintf(stderr, "\t\tCUDNN_PATH: %s\n", _CUDNN_PATH_); -#endif -#ifdef _GIT_EXIST - fprintf(stderr, "\t\tBuild Branch: %s\n", _BUILDBRANCH_); - fprintf(stderr, "\t\tBuild SHA1: %s\n", _BUILDSHA1_); -#endif -#ifdef _BUILDER_ - fprintf(stderr, "\t\tBuilt by %s on %s\n", _BUILDER_, _BUILDMACHINE_); -#endif -#ifdef _BUILDPATH_ - fprintf(stderr, "\t\tBuild Path: %s\n", _BUILDPATH_); -#endif - fprintf(stderr, "-------------------------------------------------------------------\n"); -} - -void PrintUsageInfo() -{ - fprintf(stderr, "-------------------------------------------------------------------\n"); - fprintf(stderr, "Usage: cntk configFile=yourConfigFile\n"); - fprintf(stderr, "For detailed information please consult the CNTK book\n"); - fprintf(stderr, "\"An Introduction to Computational Networks and the Computational Network Toolkit\"\n"); - fprintf(stderr, "-------------------------------------------------------------------\n"); -} - -// --------------------------------------------------------------------------- -// main() for use with BrainScript -// --------------------------------------------------------------------------- - -wstring ConsumeArg(vector & args) -{ - if (args.empty()) - InvalidArgument("Unexpected end of command line."); - wstring arg = args.front(); - args.erase(args.begin()); - return arg; -} -template -static void Append(vector & toWhat, const WHAT & what) { toWhat.insert(toWhat.end(), what.begin(), what.end()); } -static wstring PathToBSStringLiteral(const wstring & path) // quote a pathname for BS -{ - let hasSingleQuote = path.find(path, L'\'') != wstring::npos; - let hasDoubleQuote = path.find(path, L'"') != wstring::npos; - if (hasSingleQuote && hasDoubleQuote) - InvalidArgument("Pathname cannot contain both single (') and double (\") quote at the same time: %ls", path.c_str()); - else if (hasSingleQuote) - return L"\"" + path + L"\""; - else - return L'"' + path + L'"'; -} - -// TODO: decide where these should go. Also, do we need three variables? -extern wstring standardFunctions; -extern wstring commonMacros; -extern wstring computationNodes; - -int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & reports Win32 exceptions -{ - vector args(argv, argv+argc); - let exePath = ConsumeArg(args); - - // startup message - // In case of a redirect of stderr, this will be printed twice, once upfront, and once again after the redirect so that it goes into the log file - wstring startupMessage = msra::strfun::wstrprintf(L"running on %ls at %ls\n", msra::strfun::utf16(GetHostName()).c_str(), msra::strfun::utf16(TimeDateStamp()).c_str()); - startupMessage += msra::strfun::wstrprintf(L"command line: %ls", exePath.c_str()); - for (const auto & arg : args) - startupMessage += L" " + arg; - - fprintf(stderr, "%ls\n", startupMessage.c_str()); - - // parse command-line options - vector sourceFiles; - vector includePaths; - vector overrides; - wstring workingDir; - while (!args.empty()) - { - let option = ConsumeArg(args); - if (option == L"-f" || option == L"--file") // -f defines source files - Append(sourceFiles, msra::strfun::split(ConsumeArg(args), L";")); - else if (option == L"-I") // -I declares an include search path - Append(includePaths, msra::strfun::split(ConsumeArg(args), L";")); - else if (option == L"-D") // -D defines variables inline on the command line (which may override BS) - overrides.push_back(ConsumeArg(args)); - else if (option == L"--cd") // --cd sets the working directory - workingDir = ConsumeArg(args); - else - InvalidArgument("Invalid command-line option '%ls'.", option.c_str()); - } - - // change working directory - if (workingDir != L"") - _wchdir(workingDir.c_str()); - - // compile the BrainScript - wstring bs = L"[\n"; - bs += standardFunctions + computationNodes + commonMacros + L"\n"; // start with standard macros - for (const auto & sourceFile : sourceFiles) - bs += L"include " + PathToBSStringLiteral(sourceFile) + L"\n"; - bs += L"\n]\n"; - for (const auto & over : overrides) - bs += L"with [ " + over + L" ]\n"; - - fprintf(stderr, "\n\nBrainScript -->\n\n%ls\n\n", bs.c_str()); - - let expr = BS::ParseConfigExpression(bs, move(includePaths)); // parse - let valp = BS::Evaluate(expr); // evaluate parse into a dictionary - let & config = valp.AsRef(); // this is the dictionary - - // legacy parameters that have changed spelling - if (config.Find(L"DoneFile")) // variables follow camel case (start with lower-case letters) - InvalidArgument("Legacy spelling of 'DoneFile' no longer allowed. Use 'doneFile'."); - if (config.Find(L"command")) // spelling error, should be plural. Using 'actions' instead to match the data type. - InvalidArgument("Legacy spelling of 'command' no longer allowed. Use 'actions'."); - if (config.Find(L"type")) - InvalidArgument("Legacy name 'type' no longer allowed. Use 'precision'."); - - // parallel training - g_mpi = nullptr; - bool paralleltrain = config(L"parallelTrain", false); - if (paralleltrain) - g_mpi = new MPIWrapper(); - - g_shareNodeValueMatrices = config(L"shareNodeValueMatrices", false); - - // logging - wstring logpath = config(L"stderr", L""); - if (logpath != L"") - { - logpath += L"_actions"; // TODO: for old CNTK, this was a concatenation of all action names, which we no longer know - logpath += L".log"; // TODO: why do we need to append this here? - - if (paralleltrain) - logpath += msra::strfun::wstrprintf(L"rank%d", (int)g_mpi->CurrentNodeRank()); - - RedirectStdErr(logpath); - fprintf(stderr, "%ls\n", startupMessage.c_str()); - } - - // echo config info to log - PrintBuiltInfo(); - - // execute the actions - //std::string type = config(L"precision", "float"); - int numCPUThreads = config(L"numCPUThreads", 0); - numCPUThreads = CPUMatrix::SetNumThreads(numCPUThreads); - if (numCPUThreads > 0) - fprintf(stderr, "Using %d CPU threads.\n", numCPUThreads); - - bool progressTracing = config(L"progressTracing", false); - size_t fullTotalMaxEpochs = 1; // BUGBUG: BS does not allow me to read out the max epochs parameters, as that would instantiate and thus execute the objects - // set up progress tracing for compute cluster management - if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) - ProgressTracing::TraceTotalNumberOfSteps(fullTotalMaxEpochs); // enable tracing, using this as the total number of epochs - - // MAIN LOOP that executes the actions - auto actionsVal = config[L"actions"]; - // Note: weird behavior. If 'actions' is a scalar value (rather than an array) then it will have been resolved already after the above call. That means, it has already completed its action! - // Not pretty, but a direct consequence of the lazy evaluation. The only good solution would be to have a syntax for arrays including length 0 and 1. - // Since this in the end behaves indistinguishable from the array loop below, we will keep it for now. - if (actionsVal.Is()) - { - const ScriptableObjects::ConfigArray & actions = actionsVal; - for (int i = actions.GetIndexRange().first; i <= actions.GetIndexRange().second; i++) - { - actions.At(i, [](const wstring &){}); // this will evaluate and thus execute the action - } - } - // else action has already been executed, see comment above - - // write a doneFile if requested - wstring doneFile = config(L"doneFile", L""); - if (doneFile != L"") - { - FILE* fp = fopenOrDie(doneFile.c_str(), L"w"); - fprintf(fp, "successfully finished at %s on %s\n", TimeDateStamp().c_str(), GetHostName().c_str()); - fcloseOrDie(fp); - } - fprintf(stderr, "COMPLETED\n"), fflush(stderr); - - delete g_mpi; - return EXIT_SUCCESS; -} - -// --------------------------------------------------------------------------- -// main() for old CNTK config language -// --------------------------------------------------------------------------- - -int wmainOldCNTKConfig(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & repots Win32 exceptions -{ - ConfigParameters config; - std::string rawConfigString = ConfigParameters::ParseCommandLine(argc, argv, config); - - // get the command param set they want - wstring logpath = config(L"stderr", L""); - - // [1/26/2015 erw, add done file so that it can be used on HPC] - wstring DoneFile = config(L"DoneFile", L""); - ConfigArray command = config(L"command", "train"); - - // paralleltrain training - g_mpi = nullptr; - bool paralleltrain = config(L"parallelTrain", "false"); - if (paralleltrain) - { - g_mpi = new MPIWrapper(); - } - - g_shareNodeValueMatrices = config(L"shareNodeValueMatrices", false); - - if (logpath != L"") - { - for (int i = 0; i < command.size(); i++) - { - logpath += L"_"; - logpath += (wstring)command[i]; - } - logpath += L".log"; - - if (paralleltrain) - { - std::wostringstream oss; - oss << g_mpi->CurrentNodeRank(); - logpath += L"rank" + oss.str(); - } - RedirectStdErr(logpath); - } - - PrintBuiltInfo(); // this one goes to log file - std::string timestamp = TimeDateStamp(); - - //dump config info - fprintf(stderr, "running on %s at %s\n", GetHostName().c_str(), timestamp.c_str()); - fprintf(stderr, "command line: \n"); - for (int i = 0; i < argc; i++) - { - fprintf(stderr, "%s ", WCharToString(argv[i]).c_str()); - } - - // This simply merges all the different config parameters specified (eg, via config files or via command line directly), - // and prints it. - fprintf(stderr, "\n\n>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>\n"); - fprintf(stderr, "%s\n", rawConfigString.c_str()); - fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<\n"); - - // Same as above, but all variables are resolved. If a parameter is set multiple times (eg, set in config, overriden at command line), - // All of these assignments will appear, even though only the last assignment matters. - fprintf(stderr, "\n>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>\n"); - fprintf(stderr, "%s\n", config.ResolveVariables(rawConfigString).c_str()); - fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<\n"); - - // This outputs the final value each variable/parameter is assigned to in config (so if a parameter is set multiple times, only the last - // value it is set to will appear). - fprintf(stderr, "\n>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>\n"); - config.dumpWithResolvedVariables(); - fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<\n"); - - fprintf(stderr, "command: "); - for (int i = 0; i < command.size(); i++) - { - fprintf(stderr, "%s ", command[i].c_str()); - } - - //run commands - std::string type = config(L"precision", "float"); - // accept old precision key for backward compatibility - if (config.Exists("type")) - { - type = config(L"type", "float"); - } - - fprintf(stderr, "\nprecision = %s\n", type.c_str()); - if (type == "float") - { - DoCommands(config); - } - else if (type == "double") - { - DoCommands(config); - } - else - { - RuntimeError("invalid precision specified: %s", type.c_str()); - } - - // still here , write a DoneFile if necessary - if (!DoneFile.empty()) - { - FILE* fp = fopenOrDie(DoneFile.c_str(), L"w"); - fprintf(fp, "successfully finished at %s on %s\n", TimeDateStamp().c_str(), GetHostName().c_str()); - fcloseOrDie(fp); - } - fprintf(stderr, "COMPLETED\n"), fflush(stderr); - - delete g_mpi; - return EXIT_SUCCESS; -} - - -// --------------------------------------------------------------------------- -// main wrapper that catches C++ exceptions and prints them -// --------------------------------------------------------------------------- - -int wmain1(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & reports Win32 exceptions -{ - try - { - PrintBuiltInfo(); // print build info directly in case that user provides zero argument (convenient for checking build type) - if (argc <= 1) - InvalidArgument("No command-line argument given."); - // detect legacy CNTK configuration - bool isOldCNTKConfig = false; - for (int i = 0; i < argc && !isOldCNTKConfig; i++) - isOldCNTKConfig |= !_wcsnicmp(L"configFile=", argv[i], 11); - if (isOldCNTKConfig) - return wmainOldCNTKConfig(argc, argv); - // run from BrainScript - return wmainWithBS(argc, argv); - } - catch (const ScriptableObjects::ScriptingException &err) - { - fprintf(stderr, "EXCEPTION occurred: %s\n", err.what()); - err.PrintError(); - return EXIT_FAILURE; - } - catch (const std::exception &err) - { - fprintf(stderr, "EXCEPTION occurred: %s\n", err.what()); - PrintUsageInfo(); - return EXIT_FAILURE; - } - catch (...) - { - fprintf(stderr, "Unknown ERROR occurred"); - PrintUsageInfo(); - return EXIT_FAILURE; - } -} - -#ifdef __WINDOWS__ -void terminate_this() { fprintf(stderr, "terminate_this: aborting\n"), fflush(stderr); exit(EXIT_FAILURE); } - -int wmain(int argc, wchar_t* argv[]) // wmain wrapper that reports Win32 exceptions -{ - set_terminate (terminate_this); // insert a termination handler to ensure stderr gets flushed before actually terminating - _set_error_mode(_OUT_TO_STDERR); // make sure there are no CRT prompts when CNTK is executing - - // Note: this does not seem to work--processes with this seem to just hang instead of terminating - __try - { - return wmain1 (argc, argv); - } - __except (1/*EXCEPTION_EXECUTE_HANDLER, see excpt.h--not using constant to avoid Windows header in here*/) - { - fprintf (stderr, "CNTK: Win32 exception caught (such an access violation or a stack overflow)\n"); // TODO: separate out these two into a separate message - fflush (stderr); - exit (EXIT_FAILURE); - } -} -#endif - -#ifdef __UNIX__ -/// UNIX main function converts arguments in UTF-8 encoding and passes to Visual-Studio style wmain() which takes wchar_t strings. -int main(int argc, char* argv[]) -{ - // TODO: change to STL containers - wchar_t **wargs = new wchar_t*[argc]; - for (int i = 0; i < argc; ++i) - { - wargs[i] = new wchar_t[strlen(argv[i]) + 1]; - size_t ans = ::mbstowcs(wargs[i], argv[i], strlen(argv[i]) + 1); - assert(ans == strlen(argv[i])); - } - int ret = wmain1(argc, wargs); - for (int i = 0; i < argc; ++i) - delete[] wargs[i]; - delete[] wargs; - return ret; -} -#endif +// CNTK.cpp : Defines the entry point for the console application. +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#define _CRT_NONSTDC_NO_DEPRECATE // make VS accept POSIX functions without _ + +#include "stdafx.h" +#include "Basics.h" +#include "Actions.h" +#include "ComputationNetwork.h" +#include "ComputationNode.h" +#include "DataReader.h" +#include "DataWriter.h" +#include "SimpleNetworkBuilder.h" +#include "NDLNetworkBuilder.h" +#include "SynchronousExecutionEngine.h" +#include "ModelEditLanguage.h" +#include "CPUMatrix.h" // used for SetNumThreads() +#include "SGD.h" +#include "MPIWrapper.h" +#include "Config.h" +#include "MultiNetworksSGD.h" +#include "SimpleEvaluator.h" +#include "SimpleOutputWriter.h" +#include "MultiNetworksEvaluator.h" +#include "BestGpu.h" +#include "ProgressTracing.h" +#include "fileutil.h" +#include "ScriptableObjects.h" +#include "BrainScriptEvaluator.h" +#include "BrainScriptParser.h" + +#include +#include +#include +#if defined(_WIN32) +#include "io.h" +#endif +#include "buildinfo.h" +#include "hostname.h" +#ifdef LEAKDETECT +#include "vld.h" // for memory leak detection +#endif +#include +#include +#include +#include +#include + +#ifndef let +#define let const auto +#endif + +// TODO: Get rid of these globals +Microsoft::MSR::CNTK::MPIWrapper *g_mpi = nullptr; + +// TODO: Temporary mechanism to enable memory sharing for +// node output value matrices. This will go away when the +// sharing is ready to be enabled by default +bool g_shareNodeValueMatrices = false; + +using namespace std; +using namespace Microsoft::MSR; +using namespace Microsoft::MSR::CNTK; + +// internal test routine forward declaration +template +void TestCn(const ConfigParameters& config); + +void RedirectStdErr(wstring logpath) +{ + fprintf(stderr, "Redirecting stderr to file %S\n", logpath.c_str()); + auto f = make_shared(logpath.c_str(), fileOptionsWrite | fileOptionsText); + if (dup2(fileno(*f), 2) == -1) + { + RuntimeError("unexpected failure to redirect stderr to log file"); + } + setvbuf(stderr, NULL, _IONBF, 16384); // unbuffer it + static auto fKept = f; // keep it around (until it gets changed) +} + +std::string WCharToString(const wchar_t* wst) +{ + std::wstring ws(wst); + std::string s(ws.begin(), ws.end()); + s.assign(ws.begin(), ws.end()); + return s; +} + +template +void DumpNodeInfo(const ConfigParameters& config) +{ + wstring modelPath = config(L"modelPath"); + wstring nodeName = config(L"nodeName", L"__AllNodes__"); + wstring nodeNameRegexStr = config(L"nodeNameRegex", L""); + wstring defOutFilePath = modelPath + L"." + nodeName + L".txt"; + wstring outputFile = config(L"outputFile", defOutFilePath); + bool printValues = config(L"printValues", true); + + ComputationNetwork net(-1); //always use CPU + net.Load(modelPath); + net.DumpNodeInfoToFile(nodeName, printValues, outputFile, nodeNameRegexStr); +} + +size_t GetMaxEpochs(const ConfigParameters& configParams) +{ + ConfigParameters configSGD(configParams("SGD")); + size_t maxEpochs = configSGD("maxEpochs"); + + return maxEpochs; +} + +// special temporary function to guard against a now invalid usage of "truncated" which exists in some IPG production setups +static void DisableLegacyTruncationSettings(const ConfigParameters& TopLevelConfig, const ConfigParameters& commandConfig) +{ + if (TopLevelConfig.ExistsCurrent(L"Truncated")) + { + return; + } + + // if any of the action has set a reader/SGD section and has different Truncated value for reader and SGD section + ConfigArray actions = commandConfig(L"action"); + for (size_t i = 0; i < actions.size(); i++) + { + if (actions[i] == "train" || actions[i] == "trainRNN") + { + ConfigParameters sgd = ConfigParameters(commandConfig(L"SGD")); + ConfigParameters reader = ConfigParameters(commandConfig(L"reader")); + // reader and SGD sections are two must-have sections in train/trainRNN + if (reader.ExistsCurrent(L"Truncated") && !sgd.ExistsCurrent(L"Truncated")) + { + InvalidArgument("DisableLegacyUsage: setting Truncated only in reader section are not allowed. Please move Truncated=true/false to the top level section."); + } + } + } +} +static void DisableLegacyUsage(const ConfigParameters& TopLevelConfig, const ConfigArray& commands) +{ + for (size_t i = 0; i < commands.size(); i++) + { + ConfigParameters cfgParameters(TopLevelConfig(commands[i])); + DisableLegacyTruncationSettings(TopLevelConfig, cfgParameters); + } +} + +// process the command +template +void DoCommands(const ConfigParameters& config) +{ + ConfigArray command = config(L"command", "train"); + + int numCPUThreads = config(L"numCPUThreads", "0"); + numCPUThreads = CPUMatrix::SetNumThreads(numCPUThreads); + + if (numCPUThreads>0) + { + std::cerr << "Using " << numCPUThreads << " CPU threads" << endl; + } + + bool progressTracing = config(L"progressTracing", false); + + // temporary hack to prevent users from failling for a small breaking change related to the "truncated" flag (will be redone bigger and better some day) + DisableLegacyUsage(config, command); + + // summarize command info upfront in the log and stdout + size_t fullTotalMaxEpochs = 0; + for (int i = 0; i < command.size(); i++) + { + //get the configuration parameters that match the command + ConfigParameters commandParams(config(command[i])); + ConfigArray action = commandParams("action", "train"); + + // determine the action to perform, and do it + for (int j = 0; j < action.size(); j++) + { + if (action[j] == "train" || action[j] == "trainRNN" +#if 0 + || action[j] == "trainSequence" || action[j] == "trainSequenceRNN" +#endif + ) + { + wstring modelPath = commandParams("modelPath"); + std::wcerr << "CNTKModelPath: " << modelPath << endl; + size_t maxEpochs = GetMaxEpochs(commandParams); + std::cerr << "CNTKCommandTrainInfo: " + command[i] << " : " << maxEpochs << endl; + fullTotalMaxEpochs += maxEpochs; + } + } + } + std::cerr << "CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : " << fullTotalMaxEpochs << endl; + + // set up progress tracing for compute cluster management + if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) + { + ProgressTracing::TraceTotalNumberOfSteps(fullTotalMaxEpochs); // enable tracing, using this as the total number of epochs + } + + size_t fullEpochsOffset = 0; + + // execute the commands + for (int i = 0; i < command.size(); i++) + { + //get the configuration parameters that match the command + ConfigParameters commandParams(config(command[i])); + ConfigArray action = commandParams("action", "train"); + + if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) + { + ProgressTracing::SetStepOffset(fullEpochsOffset); // this is the epoch number that SGD will log relative to + } + + // determine the action to perform, and do it + for (int j = 0; j < action.size(); j++) + { + if (action[j] == "train" || action[j] == "trainRNN") + { + std::cerr << "CNTKCommandTrainBegin: " + command[i] << endl; + DoTrain(commandParams); + std::cerr << "CNTKCommandTrainEnd: " + command[i] << endl; + fullEpochsOffset += GetMaxEpochs(commandParams); + } +#if 0 + else if (action[j] == "trainSequence" || action[j] == "trainSequenceRNN") + { + std::cerr << "CNTKCommandTrainBegin: " + command[i] << endl; + DoSequenceTrain(commandParams); + std::cerr << "CNTKCommandTrainEnd: " + command[i] << endl; + fullEpochsOffset += GetMaxEpochs(commandParams); + } +#endif + else if (action[j] == "adapt") + { + DoAdapt(commandParams); + } + else if (action[j] == "test" || action[j] == "eval") + { + DoEval(commandParams); + } + else if (action[j] == "testunroll") + { + DoEvalUnroll(commandParams); + } + else if (action[j] == "edit") + { + DoEdit(commandParams); + } + else if (action[j] == "cv") + { + DoCrossValidate(commandParams); + } + else if (action[j] == "write") + { + DoWriteOutput(commandParams); + } + else if (action[j] == "devtest") + { + TestCn(config); // for "devtest" action pass the root config instead + } + else if (action[j] == "dumpnode") + { + DumpNodeInfo(commandParams); + } + else if (action[j] == "convertdbn") + { + DoConvertFromDbn(commandParams); + } + else if (action[j] == "createLabelMap") + { + DoCreateLabelMap(commandParams); + } + else if (action[j] == "writeWordAndClass") + { + DoWriteWordAndClassInfo(commandParams); + } + else if (action[j] == "plot") + { + DoTopologyPlot(commandParams); + } + else if (action[j] == "SVD") + { + DoParameterSVD(commandParams); + } + else if (action[j] == "trainEncoderDecoder") + { + DoEncoderDecoder(commandParams); + } + else if (action[j] == "testEncoderDecoder") + { + DoEvalEncodingBeamSearchDecoding(commandParams); + } + else if (action[j] == "trainBidirectionEncoderDecoder") + { + DoBidirectionEncoderDecoder(commandParams); + } + else if (action[j] == "beamSearch") + { + DoBeamSearchDecoding(commandParams); + } + else + { + RuntimeError("unknown action: %s in command set: %s", action[j].c_str(), command[i].c_str()); + } + + NDLScript ndlScript; + ndlScript.ClearGlobal(); // clear global macros between commands + } + } +} + +std::string TimeDateStamp() +{ +#if 0 // "safe" version for Windows, not needed it seems + __time64_t localtime; + + _time64(&localtime);// get current time and date + struct tm now; + _localtime64_s(&now, &localtime); // convert +#else + time_t t = time(NULL); + struct tm now = *localtime(&t); +#endif + char buf[30]; + sprintf(buf, "%04d/%02d/%02d %02d:%02d:%02d", now.tm_year + 1900, now.tm_mon + 1, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec); + return buf; +} + +void PrintBuiltInfo() +{ + fprintf(stderr, "-------------------------------------------------------------------\n"); + fprintf(stderr, "Build info: \n\n"); + fprintf(stderr, "\t\tBuilt time: %s %s\n", __DATE__, __TIME__); + fprintf(stderr, "\t\tLast modified date: %s\n", __TIMESTAMP__); +#ifdef _BUILDTYPE_ + fprintf(stderr, "\t\tBuild type: %s\n", _BUILDTYPE_); +#endif +#ifdef _MATHLIB_ + fprintf(stderr, "\t\tMath lib: %s\n", _MATHLIB_); +#endif +#ifdef _CUDA_PATH_ + fprintf(stderr, "\t\tCUDA_PATH: %s\n", _CUDA_PATH_); +#endif +#ifdef _CUB_PATH_ + fprintf(stderr, "\t\tCUB_PATH: %s\n", _CUB_PATH_); +#endif +#ifdef _CUDNN_PATH_ + fprintf(stderr, "\t\tCUDNN_PATH: %s\n", _CUDNN_PATH_); +#endif +#ifdef _GIT_EXIST + fprintf(stderr, "\t\tBuild Branch: %s\n", _BUILDBRANCH_); + fprintf(stderr, "\t\tBuild SHA1: %s\n", _BUILDSHA1_); +#endif +#ifdef _BUILDER_ + fprintf(stderr, "\t\tBuilt by %s on %s\n", _BUILDER_, _BUILDMACHINE_); +#endif +#ifdef _BUILDPATH_ + fprintf(stderr, "\t\tBuild Path: %s\n", _BUILDPATH_); +#endif + fprintf(stderr, "-------------------------------------------------------------------\n"); +} + +void PrintUsageInfo() +{ + fprintf(stderr, "-------------------------------------------------------------------\n"); + fprintf(stderr, "Usage: cntk configFile=yourConfigFile\n"); + fprintf(stderr, "For detailed information please consult the CNTK book\n"); + fprintf(stderr, "\"An Introduction to Computational Networks and the Computational Network Toolkit\"\n"); + fprintf(stderr, "-------------------------------------------------------------------\n"); +} + +// --------------------------------------------------------------------------- +// main() for use with BrainScript +// --------------------------------------------------------------------------- + +wstring ConsumeArg(vector & args) +{ + if (args.empty()) + InvalidArgument("Unexpected end of command line."); + wstring arg = args.front(); + args.erase(args.begin()); + return arg; +} +template +static void Append(vector & toWhat, const WHAT & what) { toWhat.insert(toWhat.end(), what.begin(), what.end()); } +static wstring PathToBSStringLiteral(const wstring & path) // quote a pathname for BS +{ + let hasSingleQuote = path.find(path, L'\'') != wstring::npos; + let hasDoubleQuote = path.find(path, L'"') != wstring::npos; + if (hasSingleQuote && hasDoubleQuote) + InvalidArgument("Pathname cannot contain both single (') and double (\") quote at the same time: %ls", path.c_str()); + else if (hasSingleQuote) + return L"\"" + path + L"\""; + else + return L'"' + path + L'"'; +} + +// TODO: decide where these should go. Also, do we need three variables? +extern wstring standardFunctions; +extern wstring commonMacros; +extern wstring computationNodes; + +int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & reports Win32 exceptions +{ + vector args(argv, argv+argc); + let exePath = ConsumeArg(args); + + // startup message + // In case of a redirect of stderr, this will be printed twice, once upfront, and once again after the redirect so that it goes into the log file + wstring startupMessage = msra::strfun::wstrprintf(L"running on %ls at %ls\n", msra::strfun::utf16(GetHostName()).c_str(), msra::strfun::utf16(TimeDateStamp()).c_str()); + startupMessage += msra::strfun::wstrprintf(L"command line: %ls", exePath.c_str()); + for (const auto & arg : args) + startupMessage += L" " + arg; + + fprintf(stderr, "%ls\n", startupMessage.c_str()); + + // parse command-line options + vector sourceFiles; + vector includePaths; + vector overrides; + wstring workingDir; + while (!args.empty()) + { + let option = ConsumeArg(args); + if (option == L"-f" || option == L"--file") // -f defines source files + Append(sourceFiles, msra::strfun::split(ConsumeArg(args), L";")); + else if (option == L"-I") // -I declares an include search path + Append(includePaths, msra::strfun::split(ConsumeArg(args), L";")); + else if (option == L"-D") // -D defines variables inline on the command line (which may override BS) + overrides.push_back(ConsumeArg(args)); + else if (option == L"--cd") // --cd sets the working directory + workingDir = ConsumeArg(args); + else + InvalidArgument("Invalid command-line option '%ls'.", option.c_str()); + } + + // change working directory + if (workingDir != L"") + _wchdir(workingDir.c_str()); + + // compile the BrainScript + wstring bs = L"[\n"; + bs += standardFunctions + computationNodes + commonMacros + L"\n"; // start with standard macros + for (const auto & sourceFile : sourceFiles) + bs += L"include " + PathToBSStringLiteral(sourceFile) + L"\n"; + bs += L"\n]\n"; + for (const auto & over : overrides) + bs += L"with [ " + over + L" ]\n"; + + fprintf(stderr, "\n\nBrainScript -->\n\n%ls\n\n", bs.c_str()); + + let expr = BS::ParseConfigExpression(bs, move(includePaths)); // parse + let valp = BS::Evaluate(expr); // evaluate parse into a dictionary + let & config = valp.AsRef(); // this is the dictionary + + // legacy parameters that have changed spelling + if (config.Find(L"DoneFile")) // variables follow camel case (start with lower-case letters) + InvalidArgument("Legacy spelling of 'DoneFile' no longer allowed. Use 'doneFile'."); + if (config.Find(L"command")) // spelling error, should be plural. Using 'actions' instead to match the data type. + InvalidArgument("Legacy spelling of 'command' no longer allowed. Use 'actions'."); + if (config.Find(L"type")) + InvalidArgument("Legacy name 'type' no longer allowed. Use 'precision'."); + + // parallel training + g_mpi = nullptr; + bool paralleltrain = config(L"parallelTrain", false); + if (paralleltrain) + g_mpi = new MPIWrapper(); + + g_shareNodeValueMatrices = config(L"shareNodeValueMatrices", false); + + // logging + wstring logpath = config(L"stderr", L""); + if (logpath != L"") + { + logpath += L"_actions"; // TODO: for old CNTK, this was a concatenation of all action names, which we no longer know + logpath += L".log"; // TODO: why do we need to append this here? + + if (paralleltrain) + logpath += msra::strfun::wstrprintf(L"rank%d", (int)g_mpi->CurrentNodeRank()); + + RedirectStdErr(logpath); + fprintf(stderr, "%ls\n", startupMessage.c_str()); + } + + // echo config info to log + PrintBuiltInfo(); + + // execute the actions + //std::string type = config(L"precision", "float"); + int numCPUThreads = config(L"numCPUThreads", 0); + numCPUThreads = CPUMatrix::SetNumThreads(numCPUThreads); + if (numCPUThreads > 0) + fprintf(stderr, "Using %d CPU threads.\n", numCPUThreads); + + bool progressTracing = config(L"progressTracing", false); + size_t fullTotalMaxEpochs = 1; // BUGBUG: BS does not allow me to read out the max epochs parameters, as that would instantiate and thus execute the objects + // set up progress tracing for compute cluster management + if (progressTracing && ((g_mpi == nullptr) || g_mpi->IsMainNode())) + ProgressTracing::TraceTotalNumberOfSteps(fullTotalMaxEpochs); // enable tracing, using this as the total number of epochs + + // MAIN LOOP that executes the actions + auto actionsVal = config[L"actions"]; + // Note: weird behavior. If 'actions' is a scalar value (rather than an array) then it will have been resolved already after the above call. That means, it has already completed its action! + // Not pretty, but a direct consequence of the lazy evaluation. The only good solution would be to have a syntax for arrays including length 0 and 1. + // Since this in the end behaves indistinguishable from the array loop below, we will keep it for now. + if (actionsVal.Is()) + { + const ScriptableObjects::ConfigArray & actions = actionsVal; + for (int i = actions.GetIndexRange().first; i <= actions.GetIndexRange().second; i++) + { + actions.At(i, [](const wstring &){}); // this will evaluate and thus execute the action + } + } + // else action has already been executed, see comment above + + // write a doneFile if requested + wstring doneFile = config(L"doneFile", L""); + if (doneFile != L"") + { + FILE* fp = fopenOrDie(doneFile.c_str(), L"w"); + fprintf(fp, "successfully finished at %s on %s\n", TimeDateStamp().c_str(), GetHostName().c_str()); + fcloseOrDie(fp); + } + fprintf(stderr, "COMPLETED\n"), fflush(stderr); + + delete g_mpi; + return EXIT_SUCCESS; +} + +// --------------------------------------------------------------------------- +// main() for old CNTK config language +// --------------------------------------------------------------------------- + +int wmainOldCNTKConfig(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & repots Win32 exceptions +{ + ConfigParameters config; + std::string rawConfigString = ConfigParameters::ParseCommandLine(argc, argv, config); + + // get the command param set they want + wstring logpath = config(L"stderr", L""); + + // [1/26/2015 erw, add done file so that it can be used on HPC] + wstring DoneFile = config(L"DoneFile", L""); + ConfigArray command = config(L"command", "train"); + + // paralleltrain training + g_mpi = nullptr; + bool paralleltrain = config(L"parallelTrain", "false"); + if (paralleltrain) + { + g_mpi = new MPIWrapper(); + } + + g_shareNodeValueMatrices = config(L"shareNodeValueMatrices", false); + + if (logpath != L"") + { + for (int i = 0; i < command.size(); i++) + { + logpath += L"_"; + logpath += (wstring)command[i]; + } + logpath += L".log"; + + if (paralleltrain) + { + std::wostringstream oss; + oss << g_mpi->CurrentNodeRank(); + logpath += L"rank" + oss.str(); + } + RedirectStdErr(logpath); + } + + PrintBuiltInfo(); // this one goes to log file + std::string timestamp = TimeDateStamp(); + + //dump config info + fprintf(stderr, "running on %s at %s\n", GetHostName().c_str(), timestamp.c_str()); + fprintf(stderr, "command line: \n"); + for (int i = 0; i < argc; i++) + { + fprintf(stderr, "%s ", WCharToString(argv[i]).c_str()); + } + + // This simply merges all the different config parameters specified (eg, via config files or via command line directly), + // and prints it. + fprintf(stderr, "\n\n>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>\n"); + fprintf(stderr, "%s\n", rawConfigString.c_str()); + fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<\n"); + + // Same as above, but all variables are resolved. If a parameter is set multiple times (eg, set in config, overriden at command line), + // All of these assignments will appear, even though only the last assignment matters. + fprintf(stderr, "\n>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>\n"); + fprintf(stderr, "%s\n", config.ResolveVariables(rawConfigString).c_str()); + fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<\n"); + + // This outputs the final value each variable/parameter is assigned to in config (so if a parameter is set multiple times, only the last + // value it is set to will appear). + fprintf(stderr, "\n>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>\n"); + config.dumpWithResolvedVariables(); + fprintf(stderr, "<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<\n"); + + fprintf(stderr, "command: "); + for (int i = 0; i < command.size(); i++) + { + fprintf(stderr, "%s ", command[i].c_str()); + } + + //run commands + std::string type = config(L"precision", "float"); + // accept old precision key for backward compatibility + if (config.Exists("type")) + { + type = config(L"type", "float"); + } + + fprintf(stderr, "\nprecision = %s\n", type.c_str()); + if (type == "float") + { + DoCommands(config); + } + else if (type == "double") + { + DoCommands(config); + } + else + { + RuntimeError("invalid precision specified: %s", type.c_str()); + } + + // still here , write a DoneFile if necessary + if (!DoneFile.empty()) + { + FILE* fp = fopenOrDie(DoneFile.c_str(), L"w"); + fprintf(fp, "successfully finished at %s on %s\n", TimeDateStamp().c_str(), GetHostName().c_str()); + fcloseOrDie(fp); + } + fprintf(stderr, "COMPLETED\n"), fflush(stderr); + + delete g_mpi; + return EXIT_SUCCESS; +} + + +// --------------------------------------------------------------------------- +// main wrapper that catches C++ exceptions and prints them +// --------------------------------------------------------------------------- + +int wmain1(int argc, wchar_t* argv[]) // called from wmain which is a wrapper that catches & reports Win32 exceptions +{ + try + { + PrintBuiltInfo(); // print build info directly in case that user provides zero argument (convenient for checking build type) + if (argc <= 1) + InvalidArgument("No command-line argument given."); + // detect legacy CNTK configuration + bool isOldCNTKConfig = false; + for (int i = 0; i < argc && !isOldCNTKConfig; i++) + isOldCNTKConfig |= !_wcsnicmp(L"configFile=", argv[i], 11); + if (isOldCNTKConfig) + return wmainOldCNTKConfig(argc, argv); + // run from BrainScript + return wmainWithBS(argc, argv); + } + catch (const ScriptableObjects::ScriptingException &err) + { + fprintf(stderr, "EXCEPTION occurred: %s\n", err.what()); + err.PrintError(); + return EXIT_FAILURE; + } + catch (const std::exception &err) + { + fprintf(stderr, "EXCEPTION occurred: %s\n", err.what()); + PrintUsageInfo(); + return EXIT_FAILURE; + } + catch (...) + { + fprintf(stderr, "Unknown ERROR occurred"); + PrintUsageInfo(); + return EXIT_FAILURE; + } +} + +#ifdef __WINDOWS__ +void terminate_this() { fprintf(stderr, "terminate_this: aborting\n"), fflush(stderr); exit(EXIT_FAILURE); } + +int wmain(int argc, wchar_t* argv[]) // wmain wrapper that reports Win32 exceptions +{ + set_terminate (terminate_this); // insert a termination handler to ensure stderr gets flushed before actually terminating + _set_error_mode(_OUT_TO_STDERR); // make sure there are no CRT prompts when CNTK is executing + + // Note: this does not seem to work--processes with this seem to just hang instead of terminating + __try + { + return wmain1 (argc, argv); + } + __except (1/*EXCEPTION_EXECUTE_HANDLER, see excpt.h--not using constant to avoid Windows header in here*/) + { + fprintf (stderr, "CNTK: Win32 exception caught (such an access violation or a stack overflow)\n"); // TODO: separate out these two into a separate message + fflush (stderr); + exit (EXIT_FAILURE); + } +} +#endif + +#ifdef __UNIX__ +/// UNIX main function converts arguments in UTF-8 encoding and passes to Visual-Studio style wmain() which takes wchar_t strings. +int main(int argc, char* argv[]) +{ + // TODO: change to STL containers + wchar_t **wargs = new wchar_t*[argc]; + for (int i = 0; i < argc; ++i) + { + wargs[i] = new wchar_t[strlen(argv[i]) + 1]; + size_t ans = ::mbstowcs(wargs[i], argv[i], strlen(argv[i]) + 1); + assert(ans == strlen(argv[i])); + } + int ret = wmain1(argc, wargs); + for (int i = 0; i < argc; ++i) + delete[] wargs[i]; + delete[] wargs; + return ret; +} +#endif diff --git a/Source/CNTK/CNTK.vcxproj b/Source/CNTK/CNTK.vcxproj index 06edb8feb552..cf2ca3d3b392 100644 --- a/Source/CNTK/CNTK.vcxproj +++ b/Source/CNTK/CNTK.vcxproj @@ -1,242 +1,242 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {E6F26F9A-FF64-4F0A-B749-CD309EE357EE} - - - - - - - - - Win32Proj - CNTK - CNTK - - - - Application - true - v120 - Unicode - - - Application - false - v120 - true - Unicode - - - - - - - - - - - - true - ..\ActionsLib;..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath); - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\ActionsLib;..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath); - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(ExecutablePath) - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - - - Level4 - Disabled - _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - true - /bigobj %(AdditionalOptions) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - ActionsLib.lib; SGDLib.lib; ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; SequenceTrainingLib.lib; %(AdditionalDependencies) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - Math.dll; msmpi.dll; nvml.dll; cudart64_70.dll - 100000000 - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt - - - true - Copy content files to target directory - - - prebuild.bat - - - - - Level4 - - - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - Speed - /d2Zi+ %(AdditionalOptions) - true - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - true - true - ActionsLib.lib; SGDLib.lib; ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; SequenceTrainingLib.lib; %(AdditionalDependencies) - true - Math.dll; msmpi.dll; nvml.dll; cudart64_70.dll - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - 100000000 - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - - - - - - - true - - - - - prebuild.bat - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - - - NotUsing - - - NotUsing - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {E6F26F9A-FF64-4F0A-B749-CD309EE357EE} + + + + + + + + + Win32Proj + CNTK + CNTK + + + + Application + true + v120 + Unicode + + + Application + false + v120 + true + Unicode + + + + + + + + + + + + true + ..\ActionsLib;..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath); + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\ActionsLib;..\SequenceTrainingLib;..\SGDLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath); + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(ExecutablePath) + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + + + Level4 + Disabled + _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + true + /bigobj %(AdditionalOptions) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + ActionsLib.lib; SGDLib.lib; ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; SequenceTrainingLib.lib; %(AdditionalDependencies) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + Math.dll; msmpi.dll; nvml.dll; cudart64_70.dll + 100000000 + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt + + + true + Copy content files to target directory + + + prebuild.bat + + + + + Level4 + + + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + Speed + /d2Zi+ %(AdditionalOptions) + true + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + true + true + ActionsLib.lib; SGDLib.lib; ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; SequenceTrainingLib.lib; %(AdditionalDependencies) + true + Math.dll; msmpi.dll; nvml.dll; cudart64_70.dll + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + 100000000 + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + + + + + + + true + + + + + prebuild.bat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + + + NotUsing + + + NotUsing + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Source/CNTK/CNTK.vcxproj.filters b/Source/CNTK/CNTK.vcxproj.filters index 39b063809389..e00025801910 100644 --- a/Source/CNTK/CNTK.vcxproj.filters +++ b/Source/CNTK/CNTK.vcxproj.filters @@ -1,272 +1,272 @@ - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - Model Editing - - - Misc - - - Misc - - - Common - - - - Model Building, from old NDL - - - Model Building, from old NDL - - - Model Building, Standard Models - - - MPI Interfacing - - - Common\Include - - - Common - - - BrainScript - - - BrainScript - - - BrainScript - - - BrainScript - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Model Building, from old NDL - - - Model Editing - - - Model Building, from old NDL - - - Misc - - - Misc - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Model Building, from old NDL - - - Model Building, from old NDL - - - Model Building, from old NDL - - - Model Building, Standard Models - - - from Math - - - from Math - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - BrainScript - - - BrainScript - - - from SGDLib\SGD - - - from SGDLib\SGD - - - from SGDLib\SGD - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - - - Model Editing - - - Model Editing - - - Misc - - - BrainScript\Doc - - - - - {b3d05c7b-7bcf-4b12-bcb5-dced86717202} - - - {85226dda-87ba-4da6-af04-563d0ce23b94} - - - {53c3735f-1374-4044-ab58-8a646c95a5e8} - - - {3c119a92-ffb2-4850-adae-01778324974d} - - - {7b4cb3e8-272f-413d-badd-d437779b1aeb} - - - {0b366814-48b2-4619-bf92-85ee24e3cbc1} - - - {498bb2e9-53de-4955-970e-813e3f21025b} - - - {d3d5900a-8c5e-45f1-a2b7-f82f0e31994d} - - - {4f06ac18-7b30-490c-b801-128bdaa99450} - - - {3ddfc109-3a90-45f5-91e8-1930759cfe9d} - - - {f474b73c-05f9-43e6-997f-3ec83805c655} - - - {ebc74fe7-4a25-46e7-87a8-121881ef9124} - - - {6f6d67fe-2cc6-40ca-a349-cb5e32201ec9} - - - {fe2443a1-6323-449f-96be-cbd0f608f382} - - - {23e7cd74-fd60-4fb4-a925-c3dea584f176} - - - - - Misc - - - BrainScript\Doc - - + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + Model Editing + + + Misc + + + Misc + + + Common + + + + Model Building, from old NDL + + + Model Building, from old NDL + + + Model Building, Standard Models + + + MPI Interfacing + + + Common\Include + + + Common + + + BrainScript + + + BrainScript + + + BrainScript + + + BrainScript + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Model Building, from old NDL + + + Model Editing + + + Model Building, from old NDL + + + Misc + + + Misc + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Model Building, from old NDL + + + Model Building, from old NDL + + + Model Building, from old NDL + + + Model Building, Standard Models + + + from Math + + + from Math + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + BrainScript + + + BrainScript + + + from SGDLib\SGD + + + from SGDLib\SGD + + + from SGDLib\SGD + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + + + Model Editing + + + Model Editing + + + Misc + + + BrainScript\Doc + + + + + {b3d05c7b-7bcf-4b12-bcb5-dced86717202} + + + {85226dda-87ba-4da6-af04-563d0ce23b94} + + + {53c3735f-1374-4044-ab58-8a646c95a5e8} + + + {3c119a92-ffb2-4850-adae-01778324974d} + + + {7b4cb3e8-272f-413d-badd-d437779b1aeb} + + + {0b366814-48b2-4619-bf92-85ee24e3cbc1} + + + {498bb2e9-53de-4955-970e-813e3f21025b} + + + {d3d5900a-8c5e-45f1-a2b7-f82f0e31994d} + + + {4f06ac18-7b30-490c-b801-128bdaa99450} + + + {3ddfc109-3a90-45f5-91e8-1930759cfe9d} + + + {f474b73c-05f9-43e6-997f-3ec83805c655} + + + {ebc74fe7-4a25-46e7-87a8-121881ef9124} + + + {6f6d67fe-2cc6-40ca-a349-cb5e32201ec9} + + + {fe2443a1-6323-449f-96be-cbd0f608f382} + + + {23e7cd74-fd60-4fb4-a925-c3dea584f176} + + + + + Misc + + + BrainScript\Doc + + \ No newline at end of file diff --git a/Source/CNTK/SynchronousExecutionEngine.h b/Source/CNTK/SynchronousExecutionEngine.h index 808a7a122039..c19c4b7ec18f 100644 --- a/Source/CNTK/SynchronousExecutionEngine.h +++ b/Source/CNTK/SynchronousExecutionEngine.h @@ -1,390 +1,390 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#pragma once - -#include "IExecutionEngine.h" -#include "ComputationNetwork.h" -#include "ComputationNetworkBuilder.h" -#include "fileutil.h" // for fexists() - -namespace Microsoft { namespace MSR { namespace CNTK { - -// SynchronousNodeEvaluator -// Process the Network Description Language into a Computation Network useable -// by SynchronousExecutionEngine. -template -class SynchronousNodeEvaluator : public NDLNodeEvaluator -{ - typedef shared_ptr> ComputationNodePtr; -public: - // Constructor - create evaluator - SynchronousNodeEvaluator(ComputationNetworkPtr cn) : m_net(cn) - { } - - // Evaluate - evaluate a node and translate into underlying - // node - node we are evaluating - // baseName - base name for all symbols at this level - // pass - NDLPass through the evaluation (0-initial, 1-resolve variables, 2-final) - virtual void Evaluate(NDLNode* node, const wstring& baseName, const NDLPass pass); - -#ifdef LATER - // EvaluateDotName - Evaluate a dot name and resolve to target node - // node - NDLNode of the script - // nodeParam - NDLNode parameter we are evaluating - // baseName - name of the base node - // pass - which pass through the NDL nodes - // returns: the node that is the evaluated parameter - virtual NDLNode* EvaluateDotName(NDLNode* node, NDLNode* nodeParam, const std::wstring& baseNameP, const NDLPass pass) - - { - if (pass > ndlPassInitial && evaluateNode) - { - std::string name = nodeParam->GetName(); - std::wstring wname = msra::strfun::utf16(name); - if (nodeParam->GetType() == ndlTypeDotParameter) - { - // When we see a variable of the form "A.B" in a macro, we need to resolve it to an actual node, by first constructing it's - // fully-qualified name. There are 2 possibilities: - // 1) "A" was defined locally within the macro. In this case, we must find the fully-qualified name of the node that this macro - // call is being assigned to (eg, "C" in the example "C=Macro(X)"), and concatenate it's name with "A.B" (eg, "C.A.B"). - // 2) "A" was passed in as a parameter to a macro. In this case, we must find the fully-qualified name of the node that - // was passed in as "A", and replace the "A" and "A.B" with this name. - - // Consider the following example: - // NdlBLob=[ - // P=MacroCall1(...) - // C=MacroCall2(P) - // ] - // # MacroDefinition - // MacroCall2(X) - // { - // A=MacroCall3(...) - // D=Times(A.B,X.B)} - // } - // - - // In this example, in the call D=Times(A.B,X.B), we need to resolve A.B and X.B appropriately. - // Specifically, "A.B" must be resolved to the fully qualified name "C.A.B", whereas "X.B" must be resolved to the fully qualified name "P.B". - // We then use this fully-qualified name to look up this node in the model (using "m_net->GetNodeFromName"). - - std::size_t firstDotPos = name.find_first_of("."); - if (firstDotPos == std::string::npos) - { - LogicError("nodeParam of type \"ndlTypeDotParameter\" doesn't have a dot in its name: %s", name.c_str()); - } - - std::string nameBeforeDot = name.substr(0, firstDotPos); - std::string nameAfterDot = name.substr(firstDotPos + 1, name.size() - (firstDotPos + 1)); - - // look up if "nameBeforeDot" was a parameter to the macro. - NDLNode* resolvedParam = nodeParam->GetParentScript()->FindSymbol(nameBeforeDot); - if (resolvedParam != nullptr && resolvedParam->GetType() == ndlTypeMacroCall) - { - // if "nameBeforeDot" was a parameter to the macro, builds it's fully qualified name by - // replacing "nameBeforeDot" with the fully qualified name of the node passed in as the parameter. - NDLScript* parentScript = resolvedParam->GetParentScript(); - baseName = parentScript->GetBaseName(); - std::wstring resolvedParamName = msra::strfun::utf16(resolvedParam->GetName()); - wname = baseName.empty() ? - resolvedParamName + L"." + msra::strfun::utf16(nameAfterDot) : - baseName + L"." + resolvedParamName + L"." + msra::strfun::utf16(nameAfterDot); - } - else if (!baseName.empty()) - { - // else, "nameBeforeDot" wasn't a parameter to the macro, so treat it as a local variable. - wname = baseName + L"." + wname; - } - } - else if (!baseName.empty()) - { - wname = baseName + L"." + wname; - } - - // fully qualified names can be looked up in the model - if (m_net->NodeNameExists(wname)) - { - void* np = (void*)m_net->GetNodeFromName(wname); - nodeParam->SetEvalValue(np); - } - // NOTE: there is a bug here, we allow an abbreviated node reference (i.e. L1.BFF) based on return values in NDL - // when the actual full node reference that the computational network uses would be L1.BFF.FF.P, so that is what CN sees - // can we do the normal find symbol here to allow abbreviated node references? - - // if we still didn't get a value, throw an error - if (nodeParam->GetEvalValue() == nullptr) - { - LogicError("Dot name could not be resolved '%s': should have a node named '%ls' in computational network\n", nodeParam->GetName().c_str(), name.c_str()); - } - } - return nodeParam; - } -#endif - - // EvaluateParameter - Evaluate a parameter of a call - // node - NDLNode of the script - // nodeParam - NDLNode parameter we are evaluating - // baseName - name of the base node - // pass - which pass through the NDL nodes - // returns: the node that is the evaluated parameter - virtual NDLNode* EvaluateParameter(NDLNode* node, NDLNode* nodeParam, const std::wstring& baseNameP, const NDLPass pass ) - { - // get the parent script that includes the symbol table we are interested in - NDLScript* script = node->GetParentScript(); - wstring baseName = baseNameP; - if (script == NULL) - { - std::wstring name = baseName + L"." + msra::strfun::utf16(node->GetName()); - LogicError("no script for a parameter node in call to %ls\n", name.c_str()); - } - - // evaluate the parameter if we haven't yet, or if we are in the resolve pass (need to set the inputs) - bool evaluateNode = nodeParam->GetEvalValue() == NULL || pass == ndlPassResolve; - switch (nodeParam->GetType()) - { - // if the node is a parameter then look it up in the symbol table - case ndlTypeUndetermined: // an undetermined parameter needs to be looked up again in the symbol table - case ndlTypeParameter: - { - // lookup the parameter - NDLNode* nodeResolve = script->FindSymbol(nodeParam->GetName()); - - // if we have resolved the name, no need to continue evaluation - if (!(pass == ndlPassResolve && nodeResolve && nodeParam->GetEvalValue() == nullptr)) - { - break; - } - if (pass > ndlPassInitial && evaluateNode && nodeResolve) - { - std::string name = nodeResolve->GetName(); - // we need to start from the parent script, because that is the namespace of the parameter being passed in - NDLScript* parentScript = nodeResolve->GetParentScript(); - nodeResolve = parentScript->FindSymbol(name); - - // if we still didn't get a value - if (nodeResolve == nullptr || nodeResolve->GetEvalValue() == nullptr) - { - // check for the fully quantified name in the computation network - // this is needed for MEL processing, since CN nodes names can be used as parameters in MEL - std::wstring wname = msra::strfun::utf16(name); - if (m_net->NodeNameExists(wname)) - { - void* np = (void*)m_net->GetNodeFromName(wname).get(); - // if we don't have a resolve node, it's because the name didn't exist in NDL - if (!nodeResolve) - nodeResolve = nodeParam; - nodeResolve->SetEvalValue(np); - } - else - { - RuntimeError("Parameter name could not be resolved '%s'\n", name.c_str()); - } - } - } - nodeParam = nodeResolve; - break; - } - case ndlTypeFunction: - if (evaluateNode) - Evaluate(nodeParam, baseName, pass); - break; - case ndlTypeMacroCall: - if (evaluateNode) - nodeParam->EvaluateMacro(*this, baseName, pass); - break; - // constants and variables are good as is - case ndlTypeConstant: - case ndlTypeVariable: - break; - // everything else is illegal as a parameter - default: - { - std::wstring name = baseName + L"." + msra::strfun::utf16(node->GetName()); - RuntimeError("Invalid parameter (macro definitions and arrays not allowed), see call to %ls\n", name.c_str()); - } - break; - } - return nodeParam; - } - - // EvaluateParameters - Evaluate the parameters of a call - // node - NDLNode we are evaluating paramters for - // baseName - baseName for the current node - // nodeParamStart - starting parameter that contains a node - // nodeParamCount - ending parameter that contains a node - // pass - NDL pass we are evaluating - // returns: vector of eval pointers, which are ComputationNodePtr for CNEvaluator - virtual std::vector EvaluateParameters(NDLNode* node, const wstring& baseName, int nodeParamStart, int nodeParamCount, const NDLPass pass) - { - std::vector inputs; - std::vector*> parameter = node->GetParameters(); - ConfigArray paramString = node->GetParamString(); - - if (parameter.size() < 1) - { - return inputs; - } - if (nodeParamStart + nodeParamCount > parameter.size()) - LogicError("EvaluateParmeters: nodeParamters specified that do not exist"); - size_t numChildren = nodeParamCount; - for (size_t i=0; i < numChildren;++i) - { - int index = i+nodeParamStart; - NDLNode* nodeParam = parameter[index]; - std::wstring paramS = paramString[index]; - - // default base is same as current - std::wstring baseSymbol = baseName; - - NDLNode* nodeResult = EvaluateParameter(node, nodeParam, baseSymbol, pass); - // look for a prefix here and set baseName appropriately - - if (pass == ndlPassResolve) - { - void* np = nodeResult->GetEvalValue(); - assert(np != nullptr); - inputs.push_back((void*)np); - } - else if (pass == ndlPassInitial) // for initial pass we are only interested in resolved nodes (to get constant values) - { - inputs.push_back((void*)nodeResult); - } - // NOTE: in final pass inputs are always NULL - } - - // now return the vector - return inputs; - } - - // ProcessOptionalParameters - Process the optional parameters of a node - virtual void ProcessOptionalParameters(NDLNode* node) - { - vector*> params = node->GetParameters(true); // get all the optional parameters only - auto compNode = ComputationNode::FromVoidPtr(node->GetEvalValue()); - std::string empty; - - // loop through all the optional parameters processing them as necessary - for (NDLNode* param : params) - { - // make sure it's a "tag" optional parameter, that's all we process currently - if (_stricmp(param->GetName().c_str(), "tag")) - continue; - - std::string value = param->GetValue(); - if (!_stricmp(value.c_str(), "feature")) - { - SetOutputNode(m_net->FeatureNodes(), compNode); - } - else if (!_stricmp(value.c_str(), "label")) - { - SetOutputNode(m_net->LabelNodes(), compNode); - } - else if (!_stricmp(value.c_str(), "criterion") || !_stricmp(value.c_str(), "criteria")) - { - SetOutputNode(m_net->FinalCriterionNodes(), compNode); - } - else if (!_stricmp(value.c_str(), "multiSeq")) - { - fprintf(stderr, "'multiSeq' tag is defunct.\n"); - } - else if (!_strnicmp(value.c_str(), "eval", 4)) // only compare the first 4 characters. Yikes!! - { - SetOutputNode(m_net->EvaluationNodes(), compNode); - } - else if (!_stricmp(value.c_str(), "output")) - { - SetOutputNode(m_net->OutputNodes(), compNode); - } - } - - } - - // SetOutputNode - Set the output node, checks to see if it already exists first - // nodeGroup - group vector to add to - // compNode - computation node to add - // TODO: It seems that this is also applied to other tyoes of nodes, so the name of this function is wrong. - static void SetOutputNode(std::vector & nodeGroup, ComputationNodePtr compNode) - { - for (const auto & node : nodeGroup) - { - if (node == compNode) - return; - } - nodeGroup.push_back(compNode); - } - - // FindSymbol - Search the nodes for a fully quantified symbol - // symbol - name of the symbol fully quantified name with "dots" - // returns - pointer to the matching EvalValue for that node, of NULL if not found - virtual void* FindSymbol(const wstring& symbol) - { - if (m_net->NodeNameExists(symbol)) - return m_net->GetNodeFromName(symbol).get(); - return nullptr; - } - - virtual ~SynchronousNodeEvaluator() { } - -protected: - TensorShape ProcessTensorShapeParameters(const NDLNode* node, const vector & params, size_t & i, bool isImage, const wstring & cnNodeType/*for error messages only*/); - -private: - ComputationNetworkPtr m_net; - void operator=(const SynchronousNodeEvaluator&); -}; - -// SynchronousExecutionEngine -// TODO JC Refactor eligible methods and members into abstract base class. -template -class SynchronousExecutionEngine : public IExecutionEngine -{ -public: - SynchronousExecutionEngine(DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, unsigned long randomSeedOffset=0) - { - m_computationNetwork = make_shared(deviceId); - m_computationNetwork->SetRandomSeedOffset(randomSeedOffset); - m_nodeEvaluator = new SynchronousNodeEvaluator(m_computationNetwork); - } - - SynchronousExecutionEngine(ComputationNetworkPtr computationNetwork) - { - m_computationNetwork = computationNetwork; - m_nodeEvaluator = new SynchronousNodeEvaluator(m_computationNetwork); - } - - virtual ~SynchronousExecutionEngine() - { - delete m_nodeEvaluator; - } - - ComputationNetworkPtr GetComputationNetwork() - { - return m_computationNetwork; - } - - NDLNodeEvaluator& GetNodeEvaluator() - { - return *m_nodeEvaluator; - } - -private: - ComputationNetworkPtr m_computationNetwork; - SynchronousNodeEvaluator* m_nodeEvaluator; -protected: - // Copy constructor, should never be called. - SynchronousExecutionEngine(const SynchronousExecutionEngine& /*deepCopyFrom*/) - { - LogicError("'SynchronousExecutionEngine(const SynchronousExecutionEngine& deepCopyFrom)' should never be called."); - } - - // Assignment operator, should never be called. - SynchronousExecutionEngine& operator=(const SynchronousExecutionEngine& /*deepCopyFrom*/) - { - LogicError("'SynchronousExecutionEngine& operator=(const SynchronousExecutionEngine& deepCopyFrom)' should never be called."); - } -}; - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#pragma once + +#include "IExecutionEngine.h" +#include "ComputationNetwork.h" +#include "ComputationNetworkBuilder.h" +#include "fileutil.h" // for fexists() + +namespace Microsoft { namespace MSR { namespace CNTK { + +// SynchronousNodeEvaluator +// Process the Network Description Language into a Computation Network useable +// by SynchronousExecutionEngine. +template +class SynchronousNodeEvaluator : public NDLNodeEvaluator +{ + typedef shared_ptr> ComputationNodePtr; +public: + // Constructor - create evaluator + SynchronousNodeEvaluator(ComputationNetworkPtr cn) : m_net(cn) + { } + + // Evaluate - evaluate a node and translate into underlying + // node - node we are evaluating + // baseName - base name for all symbols at this level + // pass - NDLPass through the evaluation (0-initial, 1-resolve variables, 2-final) + virtual void Evaluate(NDLNode* node, const wstring& baseName, const NDLPass pass); + +#ifdef LATER + // EvaluateDotName - Evaluate a dot name and resolve to target node + // node - NDLNode of the script + // nodeParam - NDLNode parameter we are evaluating + // baseName - name of the base node + // pass - which pass through the NDL nodes + // returns: the node that is the evaluated parameter + virtual NDLNode* EvaluateDotName(NDLNode* node, NDLNode* nodeParam, const std::wstring& baseNameP, const NDLPass pass) + + { + if (pass > ndlPassInitial && evaluateNode) + { + std::string name = nodeParam->GetName(); + std::wstring wname = msra::strfun::utf16(name); + if (nodeParam->GetType() == ndlTypeDotParameter) + { + // When we see a variable of the form "A.B" in a macro, we need to resolve it to an actual node, by first constructing it's + // fully-qualified name. There are 2 possibilities: + // 1) "A" was defined locally within the macro. In this case, we must find the fully-qualified name of the node that this macro + // call is being assigned to (eg, "C" in the example "C=Macro(X)"), and concatenate it's name with "A.B" (eg, "C.A.B"). + // 2) "A" was passed in as a parameter to a macro. In this case, we must find the fully-qualified name of the node that + // was passed in as "A", and replace the "A" and "A.B" with this name. + + // Consider the following example: + // NdlBLob=[ + // P=MacroCall1(...) + // C=MacroCall2(P) + // ] + // # MacroDefinition + // MacroCall2(X) + // { + // A=MacroCall3(...) + // D=Times(A.B,X.B)} + // } + // + + // In this example, in the call D=Times(A.B,X.B), we need to resolve A.B and X.B appropriately. + // Specifically, "A.B" must be resolved to the fully qualified name "C.A.B", whereas "X.B" must be resolved to the fully qualified name "P.B". + // We then use this fully-qualified name to look up this node in the model (using "m_net->GetNodeFromName"). + + std::size_t firstDotPos = name.find_first_of("."); + if (firstDotPos == std::string::npos) + { + LogicError("nodeParam of type \"ndlTypeDotParameter\" doesn't have a dot in its name: %s", name.c_str()); + } + + std::string nameBeforeDot = name.substr(0, firstDotPos); + std::string nameAfterDot = name.substr(firstDotPos + 1, name.size() - (firstDotPos + 1)); + + // look up if "nameBeforeDot" was a parameter to the macro. + NDLNode* resolvedParam = nodeParam->GetParentScript()->FindSymbol(nameBeforeDot); + if (resolvedParam != nullptr && resolvedParam->GetType() == ndlTypeMacroCall) + { + // if "nameBeforeDot" was a parameter to the macro, builds it's fully qualified name by + // replacing "nameBeforeDot" with the fully qualified name of the node passed in as the parameter. + NDLScript* parentScript = resolvedParam->GetParentScript(); + baseName = parentScript->GetBaseName(); + std::wstring resolvedParamName = msra::strfun::utf16(resolvedParam->GetName()); + wname = baseName.empty() ? + resolvedParamName + L"." + msra::strfun::utf16(nameAfterDot) : + baseName + L"." + resolvedParamName + L"." + msra::strfun::utf16(nameAfterDot); + } + else if (!baseName.empty()) + { + // else, "nameBeforeDot" wasn't a parameter to the macro, so treat it as a local variable. + wname = baseName + L"." + wname; + } + } + else if (!baseName.empty()) + { + wname = baseName + L"." + wname; + } + + // fully qualified names can be looked up in the model + if (m_net->NodeNameExists(wname)) + { + void* np = (void*)m_net->GetNodeFromName(wname); + nodeParam->SetEvalValue(np); + } + // NOTE: there is a bug here, we allow an abbreviated node reference (i.e. L1.BFF) based on return values in NDL + // when the actual full node reference that the computational network uses would be L1.BFF.FF.P, so that is what CN sees + // can we do the normal find symbol here to allow abbreviated node references? + + // if we still didn't get a value, throw an error + if (nodeParam->GetEvalValue() == nullptr) + { + LogicError("Dot name could not be resolved '%s': should have a node named '%ls' in computational network\n", nodeParam->GetName().c_str(), name.c_str()); + } + } + return nodeParam; + } +#endif + + // EvaluateParameter - Evaluate a parameter of a call + // node - NDLNode of the script + // nodeParam - NDLNode parameter we are evaluating + // baseName - name of the base node + // pass - which pass through the NDL nodes + // returns: the node that is the evaluated parameter + virtual NDLNode* EvaluateParameter(NDLNode* node, NDLNode* nodeParam, const std::wstring& baseNameP, const NDLPass pass ) + { + // get the parent script that includes the symbol table we are interested in + NDLScript* script = node->GetParentScript(); + wstring baseName = baseNameP; + if (script == NULL) + { + std::wstring name = baseName + L"." + msra::strfun::utf16(node->GetName()); + LogicError("no script for a parameter node in call to %ls\n", name.c_str()); + } + + // evaluate the parameter if we haven't yet, or if we are in the resolve pass (need to set the inputs) + bool evaluateNode = nodeParam->GetEvalValue() == NULL || pass == ndlPassResolve; + switch (nodeParam->GetType()) + { + // if the node is a parameter then look it up in the symbol table + case ndlTypeUndetermined: // an undetermined parameter needs to be looked up again in the symbol table + case ndlTypeParameter: + { + // lookup the parameter + NDLNode* nodeResolve = script->FindSymbol(nodeParam->GetName()); + + // if we have resolved the name, no need to continue evaluation + if (!(pass == ndlPassResolve && nodeResolve && nodeParam->GetEvalValue() == nullptr)) + { + break; + } + if (pass > ndlPassInitial && evaluateNode && nodeResolve) + { + std::string name = nodeResolve->GetName(); + // we need to start from the parent script, because that is the namespace of the parameter being passed in + NDLScript* parentScript = nodeResolve->GetParentScript(); + nodeResolve = parentScript->FindSymbol(name); + + // if we still didn't get a value + if (nodeResolve == nullptr || nodeResolve->GetEvalValue() == nullptr) + { + // check for the fully quantified name in the computation network + // this is needed for MEL processing, since CN nodes names can be used as parameters in MEL + std::wstring wname = msra::strfun::utf16(name); + if (m_net->NodeNameExists(wname)) + { + void* np = (void*)m_net->GetNodeFromName(wname).get(); + // if we don't have a resolve node, it's because the name didn't exist in NDL + if (!nodeResolve) + nodeResolve = nodeParam; + nodeResolve->SetEvalValue(np); + } + else + { + RuntimeError("Parameter name could not be resolved '%s'\n", name.c_str()); + } + } + } + nodeParam = nodeResolve; + break; + } + case ndlTypeFunction: + if (evaluateNode) + Evaluate(nodeParam, baseName, pass); + break; + case ndlTypeMacroCall: + if (evaluateNode) + nodeParam->EvaluateMacro(*this, baseName, pass); + break; + // constants and variables are good as is + case ndlTypeConstant: + case ndlTypeVariable: + break; + // everything else is illegal as a parameter + default: + { + std::wstring name = baseName + L"." + msra::strfun::utf16(node->GetName()); + RuntimeError("Invalid parameter (macro definitions and arrays not allowed), see call to %ls\n", name.c_str()); + } + break; + } + return nodeParam; + } + + // EvaluateParameters - Evaluate the parameters of a call + // node - NDLNode we are evaluating paramters for + // baseName - baseName for the current node + // nodeParamStart - starting parameter that contains a node + // nodeParamCount - ending parameter that contains a node + // pass - NDL pass we are evaluating + // returns: vector of eval pointers, which are ComputationNodePtr for CNEvaluator + virtual std::vector EvaluateParameters(NDLNode* node, const wstring& baseName, int nodeParamStart, int nodeParamCount, const NDLPass pass) + { + std::vector inputs; + std::vector*> parameter = node->GetParameters(); + ConfigArray paramString = node->GetParamString(); + + if (parameter.size() < 1) + { + return inputs; + } + if (nodeParamStart + nodeParamCount > parameter.size()) + LogicError("EvaluateParmeters: nodeParamters specified that do not exist"); + size_t numChildren = nodeParamCount; + for (size_t i=0; i < numChildren;++i) + { + int index = i+nodeParamStart; + NDLNode* nodeParam = parameter[index]; + std::wstring paramS = paramString[index]; + + // default base is same as current + std::wstring baseSymbol = baseName; + + NDLNode* nodeResult = EvaluateParameter(node, nodeParam, baseSymbol, pass); + // look for a prefix here and set baseName appropriately + + if (pass == ndlPassResolve) + { + void* np = nodeResult->GetEvalValue(); + assert(np != nullptr); + inputs.push_back((void*)np); + } + else if (pass == ndlPassInitial) // for initial pass we are only interested in resolved nodes (to get constant values) + { + inputs.push_back((void*)nodeResult); + } + // NOTE: in final pass inputs are always NULL + } + + // now return the vector + return inputs; + } + + // ProcessOptionalParameters - Process the optional parameters of a node + virtual void ProcessOptionalParameters(NDLNode* node) + { + vector*> params = node->GetParameters(true); // get all the optional parameters only + auto compNode = ComputationNode::FromVoidPtr(node->GetEvalValue()); + std::string empty; + + // loop through all the optional parameters processing them as necessary + for (NDLNode* param : params) + { + // make sure it's a "tag" optional parameter, that's all we process currently + if (_stricmp(param->GetName().c_str(), "tag")) + continue; + + std::string value = param->GetValue(); + if (!_stricmp(value.c_str(), "feature")) + { + SetOutputNode(m_net->FeatureNodes(), compNode); + } + else if (!_stricmp(value.c_str(), "label")) + { + SetOutputNode(m_net->LabelNodes(), compNode); + } + else if (!_stricmp(value.c_str(), "criterion") || !_stricmp(value.c_str(), "criteria")) + { + SetOutputNode(m_net->FinalCriterionNodes(), compNode); + } + else if (!_stricmp(value.c_str(), "multiSeq")) + { + fprintf(stderr, "'multiSeq' tag is defunct.\n"); + } + else if (!_strnicmp(value.c_str(), "eval", 4)) // only compare the first 4 characters. Yikes!! + { + SetOutputNode(m_net->EvaluationNodes(), compNode); + } + else if (!_stricmp(value.c_str(), "output")) + { + SetOutputNode(m_net->OutputNodes(), compNode); + } + } + + } + + // SetOutputNode - Set the output node, checks to see if it already exists first + // nodeGroup - group vector to add to + // compNode - computation node to add + // TODO: It seems that this is also applied to other tyoes of nodes, so the name of this function is wrong. + static void SetOutputNode(std::vector & nodeGroup, ComputationNodePtr compNode) + { + for (const auto & node : nodeGroup) + { + if (node == compNode) + return; + } + nodeGroup.push_back(compNode); + } + + // FindSymbol - Search the nodes for a fully quantified symbol + // symbol - name of the symbol fully quantified name with "dots" + // returns - pointer to the matching EvalValue for that node, of NULL if not found + virtual void* FindSymbol(const wstring& symbol) + { + if (m_net->NodeNameExists(symbol)) + return m_net->GetNodeFromName(symbol).get(); + return nullptr; + } + + virtual ~SynchronousNodeEvaluator() { } + +protected: + TensorShape ProcessTensorShapeParameters(const NDLNode* node, const vector & params, size_t & i, bool isImage, const wstring & cnNodeType/*for error messages only*/); + +private: + ComputationNetworkPtr m_net; + void operator=(const SynchronousNodeEvaluator&); +}; + +// SynchronousExecutionEngine +// TODO JC Refactor eligible methods and members into abstract base class. +template +class SynchronousExecutionEngine : public IExecutionEngine +{ +public: + SynchronousExecutionEngine(DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, unsigned long randomSeedOffset=0) + { + m_computationNetwork = make_shared(deviceId); + m_computationNetwork->SetRandomSeedOffset(randomSeedOffset); + m_nodeEvaluator = new SynchronousNodeEvaluator(m_computationNetwork); + } + + SynchronousExecutionEngine(ComputationNetworkPtr computationNetwork) + { + m_computationNetwork = computationNetwork; + m_nodeEvaluator = new SynchronousNodeEvaluator(m_computationNetwork); + } + + virtual ~SynchronousExecutionEngine() + { + delete m_nodeEvaluator; + } + + ComputationNetworkPtr GetComputationNetwork() + { + return m_computationNetwork; + } + + NDLNodeEvaluator& GetNodeEvaluator() + { + return *m_nodeEvaluator; + } + +private: + ComputationNetworkPtr m_computationNetwork; + SynchronousNodeEvaluator* m_nodeEvaluator; +protected: + // Copy constructor, should never be called. + SynchronousExecutionEngine(const SynchronousExecutionEngine& /*deepCopyFrom*/) + { + LogicError("'SynchronousExecutionEngine(const SynchronousExecutionEngine& deepCopyFrom)' should never be called."); + } + + // Assignment operator, should never be called. + SynchronousExecutionEngine& operator=(const SynchronousExecutionEngine& /*deepCopyFrom*/) + { + LogicError("'SynchronousExecutionEngine& operator=(const SynchronousExecutionEngine& deepCopyFrom)' should never be called."); + } +}; + +}}} diff --git a/Source/Common/BestGpu.cpp b/Source/Common/BestGpu.cpp index df1b93089711..f5e7f7dbc1e1 100644 --- a/Source/Common/BestGpu.cpp +++ b/Source/Common/BestGpu.cpp @@ -1,702 +1,702 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -// This file requires the NVML library. Unfortunately, this library does not install an environment variable for locating it. -// On Windows, the SDK gets installed to "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml" (/include, /lib). -// On Linux, you need to install the deployment kit from https://developer.nvidia.com/gpu-deployment-kit and -// set NVML_INCLUDE = /the path you installed deployment kit/usr/include/nvidia/gdk - -// From the SDK documentation: -// "The NVML library can be found at: %ProgramW6432%\"NVIDIA Corporation"\NVSMI\ on Windows, but will not be added to the path. To dynamically link to NVML, add this path to the PATH environmental variable. To dynamically load NVML, call LoadLibrary with this path." -// "On Linux the NVML library will be found on the standard library path. For 64-bit Linux, both the 32-bit and 64-bit NVML libraries will be installed." - -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings -#include "Platform.h" -#include "BestGpu.h" -#include "Config.h" // for ConfigParameters -#include "ScriptableObjects.h" -#include "DebugUtil.h" -#ifndef CPUONLY -#pragma comment (lib, "cudart.lib") -#include -#include // note: expected at "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" (Windows) and /the path you installed deployment kit/usr/include/nvidia/gdk (Linux) -#pragma comment (lib, "nvml.lib") // note: expected at "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" (Windows) and /the path you installed deployment kit/usr/include/nvidia/gdk (Linux) -#include -#else -int bestGPUDummy = 42; // put something into this CPP, as to avoid a linker warning -#endif -#include "CommonMatrix.h" // for CPUDEVICE and AUTOPLACEMATRIX - -#ifndef CPUONLY // #define this to disable GPUs - -// CUDA-C includes -#include -#ifdef __WINDOWS__ -#include -#include -#include -#define PATH_DELIMITER '\\' -#elif defined(__UNIX__) -#define PATH_DELIMITER '/' -#endif//__WINDOWS__ -#include -#include - -#include -#include "CrossProcessMutex.h" - -// --------------------------------------------------------------------------- -// BestGpu class -// --------------------------------------------------------------------------- - -namespace Microsoft { namespace MSR { namespace CNTK { - -struct ProcessorData -{ - int cores; - nvmlMemory_t memory; - nvmlUtilization_t utilization; - cudaDeviceProp deviceProp; - size_t cudaFreeMem; - size_t cudaTotalMem; - bool dbnFound; - bool cnFound; - int deviceId; // the deviceId (cuda side) for this processor -}; - -enum BestGpuFlags -{ - bestGpuNormal = 0, - bestGpuAvoidSharing = 1, // don't share with other known machine learning Apps (cl.exe/dbn.exe) - bestGpuFavorMemory = 2, // favor memory - bestGpuFavorUtilization = 4, // favor low utilization - bestGpuFavorSpeed = 8, // favor fastest processor - bestGpuExclusiveLock = 16, // obtain mutex for selected GPU - bestGpuRequery = 256, // rerun the last query, updating statistics -}; - -class BestGpu -{ - std::map> m_GPUMutex; -private: - bool m_initialized; // initialized - bool m_nvmlData; // nvml Data is valid - bool m_cudaData; // cuda Data is valid - int m_deviceCount; // how many devices are available? - int m_queryCount; // how many times have we queried the usage counters? - BestGpuFlags m_lastFlags; // flag state at last query - int m_lastCount; // count of devices (with filtering of allowed Devices) - std::vector m_procData; - int m_allowedDevices; // bitfield of allowed devices - void GetCudaProperties(); - void GetNvmlData(); - void QueryNvmlData(); - -public: - BestGpu() : m_initialized(false), m_nvmlData(false), m_cudaData(false), m_deviceCount(0), m_queryCount(0), - m_lastFlags(bestGpuNormal), m_lastCount(0), m_allowedDevices(-1) - { - Init(); - } - ~BestGpu(); - void Init(); - void SetAllowedDevices(const std::vector& devices); // only allow certain GPUs - bool DeviceAllowed(int device); - void DisallowDevice(int device) { m_allowedDevices &= ~(1 << device); } - void AllowAll(); // reset to allow all GPUs (no allowed list) - bool UseMultiple(); // using multiple GPUs? - int GetDevice(BestGpuFlags flags = bestGpuNormal); // get a single device - static const int AllDevices = -1; // can be used to specify all GPUs in GetDevices() call - static const int RequeryDevices = -2; // Requery refreshing statistics and picking the same number as last query - std::vector GetDevices(int number = AllDevices, BestGpuFlags flags = bestGpuNormal ); // get multiple devices -private: - bool LockDevice(int deviceId, bool trial = true); -}; - -// DeviceFromConfig - Parse 'deviceId' config parameter to determine what type of behavior is desired -//Symbol - Meaning -// 'auto' - automatically pick a single GPU based on ?BestGpu? score -// 'cpu' - use the CPU -// 0 - or some other single number, use a single GPU with CUDA ID same as the number -// This can only be called with the same parameters each time, and 'auto' is determined upon first call. -static DEVICEID_TYPE SelectDevice(DEVICEID_TYPE deviceId, bool bLockGPU) -{ - // This can only be called with the same parameter. - static DEVICEID_TYPE lastDeviceId = DEVICEID_NOTYETDETERMINED; - if (lastDeviceId == DEVICEID_NOTYETDETERMINED) - lastDeviceId = deviceId; - else if (lastDeviceId != deviceId) - InvalidArgument("SelectDevice: Attempted to change device selection from %d to %d (%d means 'auto').", (int)lastDeviceId, (int)deviceId, (int)DEVICEID_AUTO); - - if (deviceId == DEVICEID_AUTO) - { - static DEVICEID_TYPE bestDeviceId = DEVICEID_NOTYETDETERMINED; - if (bestDeviceId == DEVICEID_NOTYETDETERMINED) // we only choose once - { - // GPU device to be auto-selected, so init our class - static BestGpu* g_bestGpu = nullptr; - if (g_bestGpu == nullptr) - g_bestGpu = new BestGpu(); - deviceId = (DEVICEID_TYPE) - g_bestGpu->GetDevice(BestGpuFlags(bLockGPU ? (bestGpuAvoidSharing | bestGpuExclusiveLock) : bestGpuAvoidSharing)); - bestDeviceId = deviceId; - } - else // already chosen - deviceId = bestDeviceId; - } - // route the result through EnforceOneGPUOnly() which only lets the first choice through (see comment there) - return EnforceOneGPUOnly(deviceId); -} -//#ifdef MATH_EXPORTS -//__declspec(dllexport) -//#endif -DEVICEID_TYPE DeviceFromConfig(const ScriptableObjects::IConfigRecord & config) -{ - bool bLockGPU = config(L"lockGPU", true); - // we need to deal with the old CNTK config semantics where 'deviceId' can be either a string or an int - auto valpp = config.Find(L"deviceId"); - if (!valpp) - return SelectDevice(DEVICEID_AUTO, bLockGPU); // not given at all: default - auto valp = *valpp; // (the type is not determined at this point) - if (valp.Is()) - { - wstring val = valp; - if (val == L"cpu") - return SelectDevice(CPUDEVICE, false); - else if (val == L"auto") - return SelectDevice(DEVICEID_AUTO, bLockGPU); - else - InvalidArgument("Invalid value '%ls' for deviceId parameter. Allowed are 'auto' and 'cpu' (case-sensitive).", val.c_str()); - } - else - return SelectDevice(valp, bLockGPU); -} -// legacy version for old CNTK config -//#ifdef MATH_EXPORTS -//__declspec(dllexport) -//#endif -DEVICEID_TYPE DeviceFromConfig(const ConfigParameters& config) -{ - ConfigValue val = config("deviceId", "auto"); - bool bLockGPU = config(L"lockGPU", true); - - if (!_stricmp(val.c_str(), "cpu")) - return SelectDevice(CPUDEVICE, false); - else if (!_stricmp(val.c_str(), "auto")) - return SelectDevice(DEVICEID_AUTO, bLockGPU); - else - return SelectDevice((int)val, bLockGPU); -} - -// !!!!This is from helper_cuda.h which comes with CUDA samples!!!! Consider if it is beneficial to just include all helper_cuda.h -// TODO: This is duplicated in GPUMatrix.cu -// Beginning of GPU Architecture definitions -inline int _ConvertSMVer2Cores(int major, int minor) -{ - // Defines for GPU Architecture types (using the SM version to determine the # of cores per SM - typedef struct - { - int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version - int Cores; - } sSMtoCores; - - sSMtoCores nGpuArchCoresPerSM[] = - { - { 0x10, 8 }, // Tesla Generation (SM 1.0) G80 class - { 0x11, 8 }, // Tesla Generation (SM 1.1) G8x class - { 0x12, 8 }, // Tesla Generation (SM 1.2) G9x class - { 0x13, 8 }, // Tesla Generation (SM 1.3) GT200 class - { 0x20, 32 }, // Fermi Generation (SM 2.0) GF100 class - { 0x21, 48 }, // Fermi Generation (SM 2.1) GF10x class - { 0x30, 192}, // Kepler Generation (SM 3.0) GK10x class - { 0x35, 192}, // Kepler Generation (SM 3.5) GK11x class - { -1, -1 } - }; - - int index = 0; - - while (nGpuArchCoresPerSM[index].SM != -1) - { - if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor)) - { - return nGpuArchCoresPerSM[index].Cores; - } - - index++; - } - - return nGpuArchCoresPerSM[7].Cores; -} - -void BestGpu::GetCudaProperties() -{ - if (m_cudaData) - return; - - int dev = 0; - - for (ProcessorData* pd : m_procData) - { - cudaSetDevice(dev); - pd->deviceId = dev; - cudaGetDeviceProperties(&pd->deviceProp, dev); - size_t free; - size_t total; - cudaMemGetInfo(&free, &total); - pd->cores = _ConvertSMVer2Cores(pd->deviceProp.major, pd->deviceProp.minor) * pd->deviceProp.multiProcessorCount; - pd->cudaFreeMem = free; - pd->cudaTotalMem = total; - dev++; - cudaDeviceReset(); - } - m_cudaData = m_procData.size() > 0; -} - -void BestGpu::Init() -{ - if (m_initialized) - return; - - //get the count of objects - cudaError_t err = - cudaGetDeviceCount(&m_deviceCount); - // TODO: use CUDA_CALL here - if (err != cudaSuccess) - { - const char* errmsg = cudaGetErrorString(err); - fprintf(stderr, "!!!!!!!!CUDA EXCEPTION: %s\n", errmsg); - RuntimeError("%s", errmsg); - } - - ProcessorData pdEmpty = { 0 }; - for (int i = 0; i < m_deviceCount; i++) - { - ProcessorData* data = new ProcessorData(); - *data = pdEmpty; - m_procData.push_back(data); - } - - if (m_deviceCount > 0) - { - GetCudaProperties(); - GetNvmlData(); - } - m_initialized = true; -} - - -BestGpu::~BestGpu() -{ - for (ProcessorData* data : m_procData) - { - delete data; - } - m_procData.clear(); - - if (m_nvmlData) - { - nvmlShutdown(); - } -} - -// GetNvmlData - Get data from the Nvidia Management Library -void BestGpu::GetNvmlData() -{ - // if we already did this, or we couldn't initialize the CUDA data, skip it - if (m_nvmlData || !m_cudaData) - return; - - // First initialize NVML library - nvmlReturn_t result = nvmlInit(); - if (NVML_SUCCESS != result) - { - return; - } - - QueryNvmlData(); -} - -// GetDevice - Determine the best device ID to use -// bestFlags - flags that modify how the score is calculated -int BestGpu::GetDevice(BestGpuFlags bestFlags) -{ - std::vector best = GetDevices(1, bestFlags); - return best[0]; -} - -// SetAllowedDevices - set the allowed devices array up -// devices - vector of allowed devices -void BestGpu::SetAllowedDevices(const std::vector& devices) -{ - m_allowedDevices = 0; - for (int device : devices) - { - m_allowedDevices |= (1 << device); - } -} - -// DeviceAllowed - is a particular device allowed? -// returns: true if the device is allowed, otherwise false -bool BestGpu::DeviceAllowed(int device) -{ - return !!(m_allowedDevices & (1< 1; -} - -// GetDevices - Determine the best device IDs to use -// number - how many devices do we want? -// bestFlags - flags that modify how the score is calculated -std::vector BestGpu::GetDevices(int number, BestGpuFlags p_bestFlags) -{ - BestGpuFlags bestFlags = p_bestFlags; - - // if they want all devices give them eveything we have - if (number == AllDevices) - number = max(m_deviceCount, 1); - else if (number == RequeryDevices) - { - number = m_lastCount; - } - - // create the initial array, initialized to all CPU - std::vector best(m_deviceCount, -1); - std::vector scores(m_deviceCount, -1.0); - - // if no GPUs were found, we should use the CPU - if (m_procData.size() == 0) - { - best.clear(); - best.push_back(-1); // default to CPU - return best; - } - - // get latest data - QueryNvmlData(); - - double utilGpuW = 0.15; - double utilMemW = 0.1; - double speedW = 0.2; - double freeMemW = 0.2; - double mlAppRunningW = 0.2; - - // if it's a requery, just use the same flags as last time - if (bestFlags & bestGpuRequery) - bestFlags = m_lastFlags; - - // adjust weights if necessary - if (bestFlags&bestGpuAvoidSharing) - { - mlAppRunningW *= 3; - } - if (bestFlags&bestGpuFavorMemory) // favor memory - { - freeMemW *= 2; - } - if (bestFlags&bestGpuFavorUtilization) // favor low utilization - { - utilGpuW *= 2; - utilMemW *= 2; - } - if (bestFlags&bestGpuFavorSpeed) // favor fastest processor - { - speedW *= 2; - } - - for (ProcessorData* pd : m_procData) - { - double score = 0.0; - - if (!DeviceAllowed(pd->deviceId)) - continue; - - // GPU utilization score - score = (1.0 - pd->utilization.gpu / 75.0f) * utilGpuW; - score += (1.0 - pd->utilization.memory / 60.0f) * utilMemW; - score += pd->cores / 1000.0f * speedW; - double mem = pd->memory.total > 0 ? pd->memory.free / (double)pd->memory.total : 1000000; // I saw this to be 0 when remoted in - // if it's not a tcc driver, then it's WDDM driver and values will be off because windows allocates all the memory from the nvml point of view - if (!pd->deviceProp.tccDriver || pd->memory.total == 0) - mem = pd->cudaFreeMem / (double)pd->cudaTotalMem; - score += mem * freeMemW; - score += ((pd->cnFound || pd->dbnFound) ? 0 : 1)*mlAppRunningW; - for (int i = 0; i < best.size(); i++) - { - // look for a better score - if (score > scores[i]) - { - // make room for this score in the correct location (insertion sort) - for (int j = (int)best.size() - 1; j > i; --j) - { - scores[j] = scores[j - 1]; - best[j] = best[j - 1]; - } - scores[i] = score; - best[i] = pd->deviceId; - break; - } - } - } - - // now get rid of any extra empty slots and disallowed devices - for (int j = (int)best.size() - 1; j > 0; --j) - { - // if this device is not allowed, or never was set remove it - if (best[j] == -1) - best.pop_back(); - else - break; - } - - // this code allows only one process to run concurrently on a machine - CrossProcessMutex deviceAllocationLock("DBN.exe GPGPU querying lock"); - - if (!deviceAllocationLock.Acquire((bestFlags & bestGpuExclusiveLock) != 0)) // failure --this should not really happen - RuntimeError("DeviceFromConfig: unexpected failure"); - - { - // even if user do not want to lock the GPU, we still need to check whether a particular GPU is locked or not, - // to respect other users' exclusive lock. - - vector bestAndAvaialbe; - for (auto i : best) - { - if (LockDevice(i, true)) - { - // available - bestAndAvaialbe.push_back(i); - } - } - best = bestAndAvaialbe; - if (best.size() > number) - { - best.resize(number); - } - } - - // save off the last values for future requeries - m_lastFlags = bestFlags; - m_lastCount = (int)best.size(); - - // if we eliminated all GPUs, use CPU - if (best.size() == 0) - { - best.push_back(-1); - } - - for (int z = 0; zdeviceProp.pciBusID == (int)pci.bus) - { - curPd = pd; - break; - } - } - - if (curPd == NULL) - continue; - - // Get the memory usage, will only work for TCC drivers - result = nvmlDeviceGetMemoryInfo(device, &memory); - if (NVML_SUCCESS != result) - { - return; - } - curPd->memory = memory; - - // Get the memory usage, will only work for TCC drivers - result = nvmlDeviceGetUtilizationRates(device, &utilization); - if (NVML_SUCCESS != result) - { - return; - } - if (m_queryCount) - { - // average, slightly overweighting the most recent query - curPd->utilization.gpu = (curPd->utilization.gpu*m_queryCount + utilization.gpu * 2) / (m_queryCount + 2); - curPd->utilization.memory = (curPd->utilization.memory*m_queryCount + utilization.memory * 2) / (m_queryCount + 2); - } - else - { - curPd->utilization = utilization; - } - m_queryCount++; - - unsigned size = 0; - result = nvmlDeviceGetComputeRunningProcesses(device, &size, NULL); - if (size > 0) - { - std::vector processInfo(size); - processInfo.resize(size); - for (nvmlProcessInfo_t info : processInfo) - info.usedGpuMemory = 0; - result = nvmlDeviceGetComputeRunningProcesses(device, &size, &processInfo[0]); - if (NVML_SUCCESS != result) - { - return; - } - bool cnFound = false; - bool dbnFound = false; - for (nvmlProcessInfo_t info : processInfo) - { - std::string name; - name.resize(256); - unsigned len = (unsigned)name.length(); - nvmlSystemGetProcessName(info.pid, (char*)name.data(), len); - name.resize(strlen(name.c_str())); - size_t pos = name.find_last_of(PATH_DELIMITER); - if (pos != std::string::npos) - name = name.substr(pos + 1); - if (GetCurrentProcessId() == info.pid || name.length() == 0) - continue; - cnFound = (cnFound || (!name.compare("cn.exe"))); - dbnFound = (dbnFound || (!name.compare("dbn.exe"))); - } - // set values to save - curPd->cnFound = cnFound; - curPd->dbnFound = dbnFound; - } - } - m_nvmlData = true; - return; -} - -bool BestGpu::LockDevice(int deviceId, bool trial) -{ - if (deviceId < 0) // don't lock CPU, always return true - { - return true; - } - // ported from dbn.exe, not perfect but it works in practice - char buffer[80]; - sprintf (buffer, "DBN.exe GPGPU exclusive lock for device %d", deviceId); - std::unique_ptr mutex(new CrossProcessMutex(buffer)); - if (!mutex->Acquire(false)) // failure --this should not really happen - { - fprintf(stderr, "LockDevice: Failed to lock GPU %d for exclusive use.\n", deviceId); - return false; - } - else - { - fprintf(stderr, "LockDevice: Locked GPU %d %s.\n", deviceId, trial ? "to test availability" : "for exclusive use"); - if (!trial) - m_GPUMutex[deviceId] = std::move(mutex); - else - fprintf(stderr, "LockDevice: Unlocked GPU %d after testing.\n", deviceId); - } - return true; -} - -#ifdef _WIN32 - -#if 0 -// --------------------------------------------------------------------------- -// some interfacing with the Windows DLL system to ensure clean shutdown vs. Delay loading of CUDA DLLs -// --------------------------------------------------------------------------- - -// The "notify hook" gets called for every call to the -// Delay load helper. This allows a user to hook every call and -// skip the Delay load helper entirely. -// -// dliNotify == { dliStartProcessing | dliNotePreLoadLibrary | dliNotePreGetProc | dliNoteEndProcessing } on this call. - -extern "C" INT_PTR WINAPI DelayLoadNofify( - unsigned dliNotify, - PDelayLoadInfo pdli - ) -{ - // load the library from an alternate path - if (dliNotify == dliNotePreLoadLibrary && !strcmp(pdli->szDll, "nvml.dll")) - { - WCHAR *path; - WCHAR nvmlPath[MAX_PATH] = { 0 }; - HRESULT hr = SHGetKnownFolderPath(FOLDERID_ProgramFiles, 0, NULL, &path); - lstrcpy(nvmlPath, path); - CoTaskMemFree(path); - if (SUCCEEDED(hr)) - { - HMODULE module = NULL; - WCHAR* dllName = L"\\NVIDIA Corporation\\NVSMI\\nvml.dll"; - lstrcat(nvmlPath, dllName); - module = LoadLibraryEx(nvmlPath, NULL, LOAD_WITH_ALTERED_SEARCH_PATH); - return (INT_PTR)module; - } - } - // check for failed GetProc, old version of the driver - if (dliNotify == dliFailGetProc && !strcmp(pdli->szDll, "nvml.dll")) - { - char name[256]; - size_t len = strlen(pdli->dlp.szProcName); - strcpy_s(name, pdli->dlp.szProcName); - // if the version 2 APIs are not supported, truncate "_v2" - if (len>3 && name[len-1] == '2') - name[len-3] = 0; - FARPROC pfnRet = ::GetProcAddress(pdli->hmodCur, name); - return (INT_PTR)pfnRet; - } - - return NULL; -} - -ExternC -PfnDliHook __pfnDliNotifyHook2 = (PfnDliHook)DelayLoadNofify; -// This is the failure hook, dliNotify = {dliFailLoadLib|dliFailGetProc} -ExternC -PfnDliHook __pfnDliFailureHook2 = (PfnDliHook)DelayLoadNofify; -#endif // _WIN32 -#endif - -}}} - -#endif // CPUONLY +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +// This file requires the NVML library. Unfortunately, this library does not install an environment variable for locating it. +// On Windows, the SDK gets installed to "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml" (/include, /lib). +// On Linux, you need to install the deployment kit from https://developer.nvidia.com/gpu-deployment-kit and +// set NVML_INCLUDE = /the path you installed deployment kit/usr/include/nvidia/gdk + +// From the SDK documentation: +// "The NVML library can be found at: %ProgramW6432%\"NVIDIA Corporation"\NVSMI\ on Windows, but will not be added to the path. To dynamically link to NVML, add this path to the PATH environmental variable. To dynamically load NVML, call LoadLibrary with this path." +// "On Linux the NVML library will be found on the standard library path. For 64-bit Linux, both the 32-bit and 64-bit NVML libraries will be installed." + +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings +#include "Platform.h" +#include "BestGpu.h" +#include "Config.h" // for ConfigParameters +#include "ScriptableObjects.h" +#include "DebugUtil.h" +#ifndef CPUONLY +#pragma comment (lib, "cudart.lib") +#include +#include // note: expected at "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" (Windows) and /the path you installed deployment kit/usr/include/nvidia/gdk (Linux) +#pragma comment (lib, "nvml.lib") // note: expected at "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" (Windows) and /the path you installed deployment kit/usr/include/nvidia/gdk (Linux) +#include +#else +int bestGPUDummy = 42; // put something into this CPP, as to avoid a linker warning +#endif +#include "CommonMatrix.h" // for CPUDEVICE and AUTOPLACEMATRIX + +#ifndef CPUONLY // #define this to disable GPUs + +// CUDA-C includes +#include +#ifdef __WINDOWS__ +#include +#include +#include +#define PATH_DELIMITER '\\' +#elif defined(__UNIX__) +#define PATH_DELIMITER '/' +#endif//__WINDOWS__ +#include +#include + +#include +#include "CrossProcessMutex.h" + +// --------------------------------------------------------------------------- +// BestGpu class +// --------------------------------------------------------------------------- + +namespace Microsoft { namespace MSR { namespace CNTK { + +struct ProcessorData +{ + int cores; + nvmlMemory_t memory; + nvmlUtilization_t utilization; + cudaDeviceProp deviceProp; + size_t cudaFreeMem; + size_t cudaTotalMem; + bool dbnFound; + bool cnFound; + int deviceId; // the deviceId (cuda side) for this processor +}; + +enum BestGpuFlags +{ + bestGpuNormal = 0, + bestGpuAvoidSharing = 1, // don't share with other known machine learning Apps (cl.exe/dbn.exe) + bestGpuFavorMemory = 2, // favor memory + bestGpuFavorUtilization = 4, // favor low utilization + bestGpuFavorSpeed = 8, // favor fastest processor + bestGpuExclusiveLock = 16, // obtain mutex for selected GPU + bestGpuRequery = 256, // rerun the last query, updating statistics +}; + +class BestGpu +{ + std::map> m_GPUMutex; +private: + bool m_initialized; // initialized + bool m_nvmlData; // nvml Data is valid + bool m_cudaData; // cuda Data is valid + int m_deviceCount; // how many devices are available? + int m_queryCount; // how many times have we queried the usage counters? + BestGpuFlags m_lastFlags; // flag state at last query + int m_lastCount; // count of devices (with filtering of allowed Devices) + std::vector m_procData; + int m_allowedDevices; // bitfield of allowed devices + void GetCudaProperties(); + void GetNvmlData(); + void QueryNvmlData(); + +public: + BestGpu() : m_initialized(false), m_nvmlData(false), m_cudaData(false), m_deviceCount(0), m_queryCount(0), + m_lastFlags(bestGpuNormal), m_lastCount(0), m_allowedDevices(-1) + { + Init(); + } + ~BestGpu(); + void Init(); + void SetAllowedDevices(const std::vector& devices); // only allow certain GPUs + bool DeviceAllowed(int device); + void DisallowDevice(int device) { m_allowedDevices &= ~(1 << device); } + void AllowAll(); // reset to allow all GPUs (no allowed list) + bool UseMultiple(); // using multiple GPUs? + int GetDevice(BestGpuFlags flags = bestGpuNormal); // get a single device + static const int AllDevices = -1; // can be used to specify all GPUs in GetDevices() call + static const int RequeryDevices = -2; // Requery refreshing statistics and picking the same number as last query + std::vector GetDevices(int number = AllDevices, BestGpuFlags flags = bestGpuNormal ); // get multiple devices +private: + bool LockDevice(int deviceId, bool trial = true); +}; + +// DeviceFromConfig - Parse 'deviceId' config parameter to determine what type of behavior is desired +//Symbol - Meaning +// 'auto' - automatically pick a single GPU based on ?BestGpu? score +// 'cpu' - use the CPU +// 0 - or some other single number, use a single GPU with CUDA ID same as the number +// This can only be called with the same parameters each time, and 'auto' is determined upon first call. +static DEVICEID_TYPE SelectDevice(DEVICEID_TYPE deviceId, bool bLockGPU) +{ + // This can only be called with the same parameter. + static DEVICEID_TYPE lastDeviceId = DEVICEID_NOTYETDETERMINED; + if (lastDeviceId == DEVICEID_NOTYETDETERMINED) + lastDeviceId = deviceId; + else if (lastDeviceId != deviceId) + InvalidArgument("SelectDevice: Attempted to change device selection from %d to %d (%d means 'auto').", (int)lastDeviceId, (int)deviceId, (int)DEVICEID_AUTO); + + if (deviceId == DEVICEID_AUTO) + { + static DEVICEID_TYPE bestDeviceId = DEVICEID_NOTYETDETERMINED; + if (bestDeviceId == DEVICEID_NOTYETDETERMINED) // we only choose once + { + // GPU device to be auto-selected, so init our class + static BestGpu* g_bestGpu = nullptr; + if (g_bestGpu == nullptr) + g_bestGpu = new BestGpu(); + deviceId = (DEVICEID_TYPE) + g_bestGpu->GetDevice(BestGpuFlags(bLockGPU ? (bestGpuAvoidSharing | bestGpuExclusiveLock) : bestGpuAvoidSharing)); + bestDeviceId = deviceId; + } + else // already chosen + deviceId = bestDeviceId; + } + // route the result through EnforceOneGPUOnly() which only lets the first choice through (see comment there) + return EnforceOneGPUOnly(deviceId); +} +//#ifdef MATH_EXPORTS +//__declspec(dllexport) +//#endif +DEVICEID_TYPE DeviceFromConfig(const ScriptableObjects::IConfigRecord & config) +{ + bool bLockGPU = config(L"lockGPU", true); + // we need to deal with the old CNTK config semantics where 'deviceId' can be either a string or an int + auto valpp = config.Find(L"deviceId"); + if (!valpp) + return SelectDevice(DEVICEID_AUTO, bLockGPU); // not given at all: default + auto valp = *valpp; // (the type is not determined at this point) + if (valp.Is()) + { + wstring val = valp; + if (val == L"cpu") + return SelectDevice(CPUDEVICE, false); + else if (val == L"auto") + return SelectDevice(DEVICEID_AUTO, bLockGPU); + else + InvalidArgument("Invalid value '%ls' for deviceId parameter. Allowed are 'auto' and 'cpu' (case-sensitive).", val.c_str()); + } + else + return SelectDevice(valp, bLockGPU); +} +// legacy version for old CNTK config +//#ifdef MATH_EXPORTS +//__declspec(dllexport) +//#endif +DEVICEID_TYPE DeviceFromConfig(const ConfigParameters& config) +{ + ConfigValue val = config("deviceId", "auto"); + bool bLockGPU = config(L"lockGPU", true); + + if (!_stricmp(val.c_str(), "cpu")) + return SelectDevice(CPUDEVICE, false); + else if (!_stricmp(val.c_str(), "auto")) + return SelectDevice(DEVICEID_AUTO, bLockGPU); + else + return SelectDevice((int)val, bLockGPU); +} + +// !!!!This is from helper_cuda.h which comes with CUDA samples!!!! Consider if it is beneficial to just include all helper_cuda.h +// TODO: This is duplicated in GPUMatrix.cu +// Beginning of GPU Architecture definitions +inline int _ConvertSMVer2Cores(int major, int minor) +{ + // Defines for GPU Architecture types (using the SM version to determine the # of cores per SM + typedef struct + { + int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version + int Cores; + } sSMtoCores; + + sSMtoCores nGpuArchCoresPerSM[] = + { + { 0x10, 8 }, // Tesla Generation (SM 1.0) G80 class + { 0x11, 8 }, // Tesla Generation (SM 1.1) G8x class + { 0x12, 8 }, // Tesla Generation (SM 1.2) G9x class + { 0x13, 8 }, // Tesla Generation (SM 1.3) GT200 class + { 0x20, 32 }, // Fermi Generation (SM 2.0) GF100 class + { 0x21, 48 }, // Fermi Generation (SM 2.1) GF10x class + { 0x30, 192}, // Kepler Generation (SM 3.0) GK10x class + { 0x35, 192}, // Kepler Generation (SM 3.5) GK11x class + { -1, -1 } + }; + + int index = 0; + + while (nGpuArchCoresPerSM[index].SM != -1) + { + if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor)) + { + return nGpuArchCoresPerSM[index].Cores; + } + + index++; + } + + return nGpuArchCoresPerSM[7].Cores; +} + +void BestGpu::GetCudaProperties() +{ + if (m_cudaData) + return; + + int dev = 0; + + for (ProcessorData* pd : m_procData) + { + cudaSetDevice(dev); + pd->deviceId = dev; + cudaGetDeviceProperties(&pd->deviceProp, dev); + size_t free; + size_t total; + cudaMemGetInfo(&free, &total); + pd->cores = _ConvertSMVer2Cores(pd->deviceProp.major, pd->deviceProp.minor) * pd->deviceProp.multiProcessorCount; + pd->cudaFreeMem = free; + pd->cudaTotalMem = total; + dev++; + cudaDeviceReset(); + } + m_cudaData = m_procData.size() > 0; +} + +void BestGpu::Init() +{ + if (m_initialized) + return; + + //get the count of objects + cudaError_t err = + cudaGetDeviceCount(&m_deviceCount); + // TODO: use CUDA_CALL here + if (err != cudaSuccess) + { + const char* errmsg = cudaGetErrorString(err); + fprintf(stderr, "!!!!!!!!CUDA EXCEPTION: %s\n", errmsg); + RuntimeError("%s", errmsg); + } + + ProcessorData pdEmpty = { 0 }; + for (int i = 0; i < m_deviceCount; i++) + { + ProcessorData* data = new ProcessorData(); + *data = pdEmpty; + m_procData.push_back(data); + } + + if (m_deviceCount > 0) + { + GetCudaProperties(); + GetNvmlData(); + } + m_initialized = true; +} + + +BestGpu::~BestGpu() +{ + for (ProcessorData* data : m_procData) + { + delete data; + } + m_procData.clear(); + + if (m_nvmlData) + { + nvmlShutdown(); + } +} + +// GetNvmlData - Get data from the Nvidia Management Library +void BestGpu::GetNvmlData() +{ + // if we already did this, or we couldn't initialize the CUDA data, skip it + if (m_nvmlData || !m_cudaData) + return; + + // First initialize NVML library + nvmlReturn_t result = nvmlInit(); + if (NVML_SUCCESS != result) + { + return; + } + + QueryNvmlData(); +} + +// GetDevice - Determine the best device ID to use +// bestFlags - flags that modify how the score is calculated +int BestGpu::GetDevice(BestGpuFlags bestFlags) +{ + std::vector best = GetDevices(1, bestFlags); + return best[0]; +} + +// SetAllowedDevices - set the allowed devices array up +// devices - vector of allowed devices +void BestGpu::SetAllowedDevices(const std::vector& devices) +{ + m_allowedDevices = 0; + for (int device : devices) + { + m_allowedDevices |= (1 << device); + } +} + +// DeviceAllowed - is a particular device allowed? +// returns: true if the device is allowed, otherwise false +bool BestGpu::DeviceAllowed(int device) +{ + return !!(m_allowedDevices & (1< 1; +} + +// GetDevices - Determine the best device IDs to use +// number - how many devices do we want? +// bestFlags - flags that modify how the score is calculated +std::vector BestGpu::GetDevices(int number, BestGpuFlags p_bestFlags) +{ + BestGpuFlags bestFlags = p_bestFlags; + + // if they want all devices give them eveything we have + if (number == AllDevices) + number = max(m_deviceCount, 1); + else if (number == RequeryDevices) + { + number = m_lastCount; + } + + // create the initial array, initialized to all CPU + std::vector best(m_deviceCount, -1); + std::vector scores(m_deviceCount, -1.0); + + // if no GPUs were found, we should use the CPU + if (m_procData.size() == 0) + { + best.clear(); + best.push_back(-1); // default to CPU + return best; + } + + // get latest data + QueryNvmlData(); + + double utilGpuW = 0.15; + double utilMemW = 0.1; + double speedW = 0.2; + double freeMemW = 0.2; + double mlAppRunningW = 0.2; + + // if it's a requery, just use the same flags as last time + if (bestFlags & bestGpuRequery) + bestFlags = m_lastFlags; + + // adjust weights if necessary + if (bestFlags&bestGpuAvoidSharing) + { + mlAppRunningW *= 3; + } + if (bestFlags&bestGpuFavorMemory) // favor memory + { + freeMemW *= 2; + } + if (bestFlags&bestGpuFavorUtilization) // favor low utilization + { + utilGpuW *= 2; + utilMemW *= 2; + } + if (bestFlags&bestGpuFavorSpeed) // favor fastest processor + { + speedW *= 2; + } + + for (ProcessorData* pd : m_procData) + { + double score = 0.0; + + if (!DeviceAllowed(pd->deviceId)) + continue; + + // GPU utilization score + score = (1.0 - pd->utilization.gpu / 75.0f) * utilGpuW; + score += (1.0 - pd->utilization.memory / 60.0f) * utilMemW; + score += pd->cores / 1000.0f * speedW; + double mem = pd->memory.total > 0 ? pd->memory.free / (double)pd->memory.total : 1000000; // I saw this to be 0 when remoted in + // if it's not a tcc driver, then it's WDDM driver and values will be off because windows allocates all the memory from the nvml point of view + if (!pd->deviceProp.tccDriver || pd->memory.total == 0) + mem = pd->cudaFreeMem / (double)pd->cudaTotalMem; + score += mem * freeMemW; + score += ((pd->cnFound || pd->dbnFound) ? 0 : 1)*mlAppRunningW; + for (int i = 0; i < best.size(); i++) + { + // look for a better score + if (score > scores[i]) + { + // make room for this score in the correct location (insertion sort) + for (int j = (int)best.size() - 1; j > i; --j) + { + scores[j] = scores[j - 1]; + best[j] = best[j - 1]; + } + scores[i] = score; + best[i] = pd->deviceId; + break; + } + } + } + + // now get rid of any extra empty slots and disallowed devices + for (int j = (int)best.size() - 1; j > 0; --j) + { + // if this device is not allowed, or never was set remove it + if (best[j] == -1) + best.pop_back(); + else + break; + } + + // this code allows only one process to run concurrently on a machine + CrossProcessMutex deviceAllocationLock("DBN.exe GPGPU querying lock"); + + if (!deviceAllocationLock.Acquire((bestFlags & bestGpuExclusiveLock) != 0)) // failure --this should not really happen + RuntimeError("DeviceFromConfig: unexpected failure"); + + { + // even if user do not want to lock the GPU, we still need to check whether a particular GPU is locked or not, + // to respect other users' exclusive lock. + + vector bestAndAvaialbe; + for (auto i : best) + { + if (LockDevice(i, true)) + { + // available + bestAndAvaialbe.push_back(i); + } + } + best = bestAndAvaialbe; + if (best.size() > number) + { + best.resize(number); + } + } + + // save off the last values for future requeries + m_lastFlags = bestFlags; + m_lastCount = (int)best.size(); + + // if we eliminated all GPUs, use CPU + if (best.size() == 0) + { + best.push_back(-1); + } + + for (int z = 0; zdeviceProp.pciBusID == (int)pci.bus) + { + curPd = pd; + break; + } + } + + if (curPd == NULL) + continue; + + // Get the memory usage, will only work for TCC drivers + result = nvmlDeviceGetMemoryInfo(device, &memory); + if (NVML_SUCCESS != result) + { + return; + } + curPd->memory = memory; + + // Get the memory usage, will only work for TCC drivers + result = nvmlDeviceGetUtilizationRates(device, &utilization); + if (NVML_SUCCESS != result) + { + return; + } + if (m_queryCount) + { + // average, slightly overweighting the most recent query + curPd->utilization.gpu = (curPd->utilization.gpu*m_queryCount + utilization.gpu * 2) / (m_queryCount + 2); + curPd->utilization.memory = (curPd->utilization.memory*m_queryCount + utilization.memory * 2) / (m_queryCount + 2); + } + else + { + curPd->utilization = utilization; + } + m_queryCount++; + + unsigned size = 0; + result = nvmlDeviceGetComputeRunningProcesses(device, &size, NULL); + if (size > 0) + { + std::vector processInfo(size); + processInfo.resize(size); + for (nvmlProcessInfo_t info : processInfo) + info.usedGpuMemory = 0; + result = nvmlDeviceGetComputeRunningProcesses(device, &size, &processInfo[0]); + if (NVML_SUCCESS != result) + { + return; + } + bool cnFound = false; + bool dbnFound = false; + for (nvmlProcessInfo_t info : processInfo) + { + std::string name; + name.resize(256); + unsigned len = (unsigned)name.length(); + nvmlSystemGetProcessName(info.pid, (char*)name.data(), len); + name.resize(strlen(name.c_str())); + size_t pos = name.find_last_of(PATH_DELIMITER); + if (pos != std::string::npos) + name = name.substr(pos + 1); + if (GetCurrentProcessId() == info.pid || name.length() == 0) + continue; + cnFound = (cnFound || (!name.compare("cn.exe"))); + dbnFound = (dbnFound || (!name.compare("dbn.exe"))); + } + // set values to save + curPd->cnFound = cnFound; + curPd->dbnFound = dbnFound; + } + } + m_nvmlData = true; + return; +} + +bool BestGpu::LockDevice(int deviceId, bool trial) +{ + if (deviceId < 0) // don't lock CPU, always return true + { + return true; + } + // ported from dbn.exe, not perfect but it works in practice + char buffer[80]; + sprintf (buffer, "DBN.exe GPGPU exclusive lock for device %d", deviceId); + std::unique_ptr mutex(new CrossProcessMutex(buffer)); + if (!mutex->Acquire(false)) // failure --this should not really happen + { + fprintf(stderr, "LockDevice: Failed to lock GPU %d for exclusive use.\n", deviceId); + return false; + } + else + { + fprintf(stderr, "LockDevice: Locked GPU %d %s.\n", deviceId, trial ? "to test availability" : "for exclusive use"); + if (!trial) + m_GPUMutex[deviceId] = std::move(mutex); + else + fprintf(stderr, "LockDevice: Unlocked GPU %d after testing.\n", deviceId); + } + return true; +} + +#ifdef _WIN32 + +#if 0 +// --------------------------------------------------------------------------- +// some interfacing with the Windows DLL system to ensure clean shutdown vs. Delay loading of CUDA DLLs +// --------------------------------------------------------------------------- + +// The "notify hook" gets called for every call to the +// Delay load helper. This allows a user to hook every call and +// skip the Delay load helper entirely. +// +// dliNotify == { dliStartProcessing | dliNotePreLoadLibrary | dliNotePreGetProc | dliNoteEndProcessing } on this call. + +extern "C" INT_PTR WINAPI DelayLoadNofify( + unsigned dliNotify, + PDelayLoadInfo pdli + ) +{ + // load the library from an alternate path + if (dliNotify == dliNotePreLoadLibrary && !strcmp(pdli->szDll, "nvml.dll")) + { + WCHAR *path; + WCHAR nvmlPath[MAX_PATH] = { 0 }; + HRESULT hr = SHGetKnownFolderPath(FOLDERID_ProgramFiles, 0, NULL, &path); + lstrcpy(nvmlPath, path); + CoTaskMemFree(path); + if (SUCCEEDED(hr)) + { + HMODULE module = NULL; + WCHAR* dllName = L"\\NVIDIA Corporation\\NVSMI\\nvml.dll"; + lstrcat(nvmlPath, dllName); + module = LoadLibraryEx(nvmlPath, NULL, LOAD_WITH_ALTERED_SEARCH_PATH); + return (INT_PTR)module; + } + } + // check for failed GetProc, old version of the driver + if (dliNotify == dliFailGetProc && !strcmp(pdli->szDll, "nvml.dll")) + { + char name[256]; + size_t len = strlen(pdli->dlp.szProcName); + strcpy_s(name, pdli->dlp.szProcName); + // if the version 2 APIs are not supported, truncate "_v2" + if (len>3 && name[len-1] == '2') + name[len-3] = 0; + FARPROC pfnRet = ::GetProcAddress(pdli->hmodCur, name); + return (INT_PTR)pfnRet; + } + + return NULL; +} + +ExternC +PfnDliHook __pfnDliNotifyHook2 = (PfnDliHook)DelayLoadNofify; +// This is the failure hook, dliNotify = {dliFailLoadLib|dliFailGetProc} +ExternC +PfnDliHook __pfnDliFailureHook2 = (PfnDliHook)DelayLoadNofify; +#endif // _WIN32 +#endif + +}}} + +#endif // CPUONLY diff --git a/Source/Common/File.cpp b/Source/Common/File.cpp index 4e626266b192..81bff6ed32d6 100644 --- a/Source/Common/File.cpp +++ b/Source/Common/File.cpp @@ -109,7 +109,7 @@ void File::Init(const wchar_t* filename, int fileOptions) const auto command = inputPipe ? m_filename.substr(0, m_filename.size() - 1) : m_filename.substr(1); m_file = _wpopen(command.c_str(), options.c_str()); if (!m_file) - RuntimeError("File: error exexuting pipe command '%S': %s", command.c_str(), strerror(errno)); + RuntimeError("File: error exexuting pipe command '%S': %s", command.c_str(), strerror(errno)); m_pcloseNeeded = true; } else attempt([=]() // regular file: use a retry loop diff --git a/Source/Common/Include/BestGpu.h b/Source/Common/Include/BestGpu.h index a972250dfd5b..a14229bc95aa 100644 --- a/Source/Common/Include/BestGpu.h +++ b/Source/Common/Include/BestGpu.h @@ -1,27 +1,27 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#pragma once - -// #define CPUONLY // #define this to build without GPU support nor needing the SDK installed -#include "CommonMatrix.h" - -// define IConfigRecord and ConfigParameters as incomplete types, in order to avoid having to include "ScriptableObjects.h" and "Config.h", as that confuses some .CU code -namespace Microsoft { namespace MSR { namespace ScriptableObjects { - struct IConfigRecord; -}}} - -namespace Microsoft { namespace MSR { namespace CNTK { -#ifndef CPUONLY - class ConfigParameters; - DEVICEID_TYPE DeviceFromConfig(const ConfigParameters & config); - DEVICEID_TYPE DeviceFromConfig(const ScriptableObjects::IConfigRecord & config); -#else - template - static inline DEVICEID_TYPE DeviceFromConfig(const ConfigRecordType & /*config*/) { return -1/*CPUDEVICE*/; } // tells runtime system to not try to use GPUs - // TODO: find a way to use CPUDEVICE without a huge include overhead; OK so far since CPUONLY mode is sorta special... -#endif -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#pragma once + +// #define CPUONLY // #define this to build without GPU support nor needing the SDK installed +#include "CommonMatrix.h" + +// define IConfigRecord and ConfigParameters as incomplete types, in order to avoid having to include "ScriptableObjects.h" and "Config.h", as that confuses some .CU code +namespace Microsoft { namespace MSR { namespace ScriptableObjects { + struct IConfigRecord; +}}} + +namespace Microsoft { namespace MSR { namespace CNTK { +#ifndef CPUONLY + class ConfigParameters; + DEVICEID_TYPE DeviceFromConfig(const ConfigParameters & config); + DEVICEID_TYPE DeviceFromConfig(const ScriptableObjects::IConfigRecord & config); +#else + template + static inline DEVICEID_TYPE DeviceFromConfig(const ConfigRecordType & /*config*/) { return -1/*CPUDEVICE*/; } // tells runtime system to not try to use GPUs + // TODO: find a way to use CPUDEVICE without a huge include overhead; OK so far since CPUONLY mode is sorta special... +#endif +}}} diff --git a/Source/Common/Include/ProgressTracing.h b/Source/Common/Include/ProgressTracing.h index a35942e62702..7e8bc6f34e8a 100644 --- a/Source/Common/Include/ProgressTracing.h +++ b/Source/Common/Include/ProgressTracing.h @@ -1,106 +1,106 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - -#include "TimerUtility.h" - -namespace Microsoft { namespace MSR { namespace CNTK { - - // --------------------------------------------------------------------------- - // ProgressTracing -- static helper class for logging a progress indicator - // - // This is for use by the cluster management tools for indicating global progress to the user. - // - // This logs to stdout (not stderr) in a specific format, e.g. understood by the Philly cluster. The format is: - // PROGRESS xx.xx% - // EVALERR xx.xx% - // - // Specifically, this class handles a two-level progress computation: - // - outer level: loop over multiple training phases, each running multiple steps (epochs) - // - inner level in one training phase: loop over multiple steps, *without* knowledge about the other training phases - // - // In order for the inner level to log correctly in the global context, the outer loop - // must inform this class about the total number of steps and the current offset to apply in the inner level. - // --------------------------------------------------------------------------- - - /*static*/ class ProgressTracing - { - bool m_enabled; - size_t m_totalNumberOfSteps; // total number of epochs in entire training run - size_t m_currentStepOffset; // current offset - Timer m_progressTracingTimer; - - ProgressTracing() - : m_enabled(false), m_totalNumberOfSteps(0), m_currentStepOffset(0) - { - } - - static ProgressTracing & GetStaticInstance() - { - static ProgressTracing us; - return us; - } // wrap static state in an accessor, so we won't need a CPP file - - public: - // call TraceTotalNumberOfSteps() to set the total number of steps - // Calling this with totalNumberOfSteps>0 will enable progress tracing. - static void TraceTotalNumberOfSteps(size_t totalNumberOfSteps) - { - auto & us = GetStaticInstance(); - us.m_enabled = totalNumberOfSteps > 0; - if (us.m_enabled) - { - us.m_totalNumberOfSteps = totalNumberOfSteps; - us.m_progressTracingTimer.Start(); - } - } - - // call SetStepOffset() at start of a multi-epoch training to set the index of the first epoch in that training - // This value is added to the local epoch index in TraceProgress(). - static void SetStepOffset(size_t currentStepOffset) - { - GetStaticInstance().m_currentStepOffset = currentStepOffset; - } - - // emit the trace message for global progress - // 'currentStep' will be offset by m_currentStepOffset. - // This only prints of enough time (10s) has elapsed since last print, and the return value is 'true' if it did print. - static bool TraceProgressPercentage(size_t epochNumber, double mbProg/*0..100*/, bool isTimerPaced) - { - auto & us = GetStaticInstance(); - if (!us.m_enabled) - { - return false; - } - - // compute global progress - bool needToPrint = us.m_progressTracingTimer.ElapsedSeconds() > 10; - if (needToPrint || isTimerPaced) - { - double epochProg = ((100.0f * (double)(us.m_currentStepOffset + epochNumber)) / (double)us.m_totalNumberOfSteps); - mbProg = (mbProg * 100.0f) / (double)us.m_totalNumberOfSteps; - printf("PROGRESS: %.2f%%\n", epochProg + mbProg); - us.m_progressTracingTimer.Restart(); - } - return needToPrint; - } - - // emit a trace message for the train loss value - // The value is printed in percent. - static void TraceTrainLoss(double err) - { - auto & us = GetStaticInstance(); - - if (!us.m_enabled) - { - return; - } - - printf("EVALERR: %.7f%%\n", err); - } - }; - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + +#include "TimerUtility.h" + +namespace Microsoft { namespace MSR { namespace CNTK { + + // --------------------------------------------------------------------------- + // ProgressTracing -- static helper class for logging a progress indicator + // + // This is for use by the cluster management tools for indicating global progress to the user. + // + // This logs to stdout (not stderr) in a specific format, e.g. understood by the Philly cluster. The format is: + // PROGRESS xx.xx% + // EVALERR xx.xx% + // + // Specifically, this class handles a two-level progress computation: + // - outer level: loop over multiple training phases, each running multiple steps (epochs) + // - inner level in one training phase: loop over multiple steps, *without* knowledge about the other training phases + // + // In order for the inner level to log correctly in the global context, the outer loop + // must inform this class about the total number of steps and the current offset to apply in the inner level. + // --------------------------------------------------------------------------- + + /*static*/ class ProgressTracing + { + bool m_enabled; + size_t m_totalNumberOfSteps; // total number of epochs in entire training run + size_t m_currentStepOffset; // current offset + Timer m_progressTracingTimer; + + ProgressTracing() + : m_enabled(false), m_totalNumberOfSteps(0), m_currentStepOffset(0) + { + } + + static ProgressTracing & GetStaticInstance() + { + static ProgressTracing us; + return us; + } // wrap static state in an accessor, so we won't need a CPP file + + public: + // call TraceTotalNumberOfSteps() to set the total number of steps + // Calling this with totalNumberOfSteps>0 will enable progress tracing. + static void TraceTotalNumberOfSteps(size_t totalNumberOfSteps) + { + auto & us = GetStaticInstance(); + us.m_enabled = totalNumberOfSteps > 0; + if (us.m_enabled) + { + us.m_totalNumberOfSteps = totalNumberOfSteps; + us.m_progressTracingTimer.Start(); + } + } + + // call SetStepOffset() at start of a multi-epoch training to set the index of the first epoch in that training + // This value is added to the local epoch index in TraceProgress(). + static void SetStepOffset(size_t currentStepOffset) + { + GetStaticInstance().m_currentStepOffset = currentStepOffset; + } + + // emit the trace message for global progress + // 'currentStep' will be offset by m_currentStepOffset. + // This only prints of enough time (10s) has elapsed since last print, and the return value is 'true' if it did print. + static bool TraceProgressPercentage(size_t epochNumber, double mbProg/*0..100*/, bool isTimerPaced) + { + auto & us = GetStaticInstance(); + if (!us.m_enabled) + { + return false; + } + + // compute global progress + bool needToPrint = us.m_progressTracingTimer.ElapsedSeconds() > 10; + if (needToPrint || isTimerPaced) + { + double epochProg = ((100.0f * (double)(us.m_currentStepOffset + epochNumber)) / (double)us.m_totalNumberOfSteps); + mbProg = (mbProg * 100.0f) / (double)us.m_totalNumberOfSteps; + printf("PROGRESS: %.2f%%\n", epochProg + mbProg); + us.m_progressTracingTimer.Restart(); + } + return needToPrint; + } + + // emit a trace message for the train loss value + // The value is printed in percent. + static void TraceTrainLoss(double err) + { + auto & us = GetStaticInstance(); + + if (!us.m_enabled) + { + return; + } + + printf("EVALERR: %.7f%%\n", err); + } + }; + +}}} diff --git a/Source/Common/Include/ScriptableObjects.h b/Source/Common/Include/ScriptableObjects.h index 38a60d8c61f2..dba3661f9a53 100644 --- a/Source/Common/Include/ScriptableObjects.h +++ b/Source/Common/Include/ScriptableObjects.h @@ -1,682 +1,682 @@ -// BrainScriptObjects.h -- objects that the config parser operates on - -#pragma once - -#include "Basics.h" -#include "Platform.h" // for noexcept workaround on VS2013 - -#include // for shared_ptr<> -#include // for function<> -#include - -namespace Microsoft { namespace MSR { namespace ScriptableObjects { - - using namespace std; - using namespace msra::strfun; // for wstrprintf() - using namespace Microsoft::MSR::CNTK; // for stuff from Basics.h - - // ----------------------------------------------------------------------- - // ScriptingException -- base class for any errors thrown by scripting - // It's a runtime_error with an additional virtual function PrintError(). - // ----------------------------------------------------------------------- - - class ScriptingException : public runtime_error - { - public: - template - ScriptingException(const M & msg) : runtime_error(msg) { } - virtual void PrintError() const = 0; - }; - - // ----------------------------------------------------------------------- - // Object -- common base class for objects that can be used in config files - // ----------------------------------------------------------------------- - - // All values that can be used in config files - // - are heap objects - // - primitives are wrapped - // - object pointers are ref-counted shared_ptr, wrapped in ConfigValuePtr (see BrainScriptEvaluator.h) - // - derive from Object (outside classes get wrapped) - // - // This code supports three kinds of value types: - // - self-defined classes -> derive from Object, e.g. Expression - // - classes defined outside -> wrap in a BoxOf object, e.g. String = BoxOf - // - C++ primitives like 'double' -> wrap in a Wrapper first then in a BoxOf, e.g. Number = BoxOf> - - struct Object { virtual ~Object() { } }; - - // indicates that the object has a name should be set from the expression path - - struct HasName { virtual void SetName(const std::wstring & name) = 0; }; - - // ----------------------------------------------------------------------- - // Wrapped -- wraps non-class primitive C++ type into a class, like 'double'. - // (It can also be used for class types, but better use BoxOf<> below directly.) - // ----------------------------------------------------------------------- - - template class Wrapped - { - T value; // meant to be a primitive type - public: - operator const T&() const { return value; } - operator T&() { return value; } - Wrapped(T value) : value(value) { } - T & operator=(const T & newValue) { value = newValue; return *this; } - }; - typedef Wrapped Double; - typedef Wrapped Bool; - - // ----------------------------------------------------------------------- - // BoxOf -- wraps a pre-defined type, e.g. std::wstring, to derive from Object. - // BoxOf can dynamic_cast to T (e.g. BoxOf is a std::wstring). - // ----------------------------------------------------------------------- - - template - class BoxOf : public Object, public C - { - public: -#if 1 - template BoxOf(_Types&&... _Args) : C(forward<_Types>(_Args)...) { } -#else - // TODO: change this to variadic templates, then we can instantiate everything we need through this - BoxOf(const C & val) : C(val) { } - BoxOf(){} -#endif - }; - - // ----------------------------------------------------------------------- - // String -- a std::string in config files - // Can cast to std::wstring (done in a way that ConfigValuePtr can also cast to std::wstring). - // ----------------------------------------------------------------------- - - typedef BoxOf String; - - // ----------------------------------------------------------------------- - // ComputationNodeObject -- the 'magic' class that our parser understands for infix operations - // TODO: unify with ComputationNodeBase - // ----------------------------------------------------------------------- - - class ComputationNodeObject : public Object { }; // a base class for all nodes (that has no template parameter) - - // ----------------------------------------------------------------------- - // HasToString -- trait to indicate an object can print their content - // Derive from HasToString() and implement ToString() method. - // FormatConfigValue() will then return ToString(). - // ----------------------------------------------------------------------- - - struct HasToString - { - virtual std::wstring ToString() const = 0; - - // some std::string helpers useful for ToString() operations of nested structures - // TODO: move these out from this header into some more general place (I had to move them here because otherwise CNTKEval failed to compile) - static std::wstring IndentString(std::wstring s, size_t indent) - { - const std::wstring prefix(indent, L' '); - size_t pos = 0; - for (;;) - { - s.insert(pos, prefix); - pos = s.find(L'\n', pos + 2); - if (pos == std::wstring::npos) - return s; - pos++; - } - } - static std::wstring NestString(std::wstring s, wchar_t open, bool newline, wchar_t close) - { - std::wstring result = IndentString(s, 2); - if (newline) // have a new line after the open symbol - result = L" \n" + result + L"\n "; - else - result.append(L" "); - result.front() = open; - result.back() = close; - return result; - } - - }; - - // ----------------------------------------------------------------------- - // WithTag -- trait to give an object a tag std::string - // ----------------------------------------------------------------------- - - class WithTag - { - std::wstring m_tag; - public: - WithTag(){} - void SetTag(const std::wstring & tag) { m_tag = tag; } - const std::wstring & GetTag() const { return m_tag; } - }; - - // ======================================================================= - // ConfigValuePtr -- shared pointer to a config value - // ======================================================================= - - // A ConfigValuePtr holds the value of a configuration variable. - // - specifically, it holds a shared_ptr to a strongly typed C++ object - // - ConfigValuePtrs are immutable when consumed. - // - // All configuration values, that is, values that can be held by a ConfigValuePtr, derive from BS::Object. - // To get a shared_ptr of an expected type T, type-cast the ConfigValuePtr to it. - // To get the value of a copyable type like T=double or std::wstring, type-cast to T directly. - // - // ConfigValuePtrs are evaluated on-demand upon first retrieval: - // - initially, a ConfigValuePtr would hold a Thunk; that is, a lambda that computes (resolves) the value - // - upon first use, the Thunk is invoked to compute the value, which will then *replace* the Thunk - // - any consumer of a ConfigValuePtr will only ever see the resolved value, since any access for consumption will force it to be resolved - // - a resolved ConfigValuePtr is immutable - // - // On-demand evaluation is critical to the semantics of this entire configuration system. - // A configuration is but one big expression (of nested records), but some evaluations cause side effects (such as saving a model), and some expressions may not even be in use at all. - // Thus, we must use on-demand evaluation in order to ensure that side effects are only executed when desired. - // - // Further, to ensure a Thunk is executed at most once (otherwise we may get the same side-effect multiple times), - // an unresolved ConfigValuePtr can only live in a single place. This means, - // - an unresolved ConfigValuePtr (i.e. one holding a Thunk) cannot be copied (while resolved ones are immutable and can be copied freely) - // - it can be moved (std::move()) during creation - // - after creation, it should only live in a known location from which it can be retrieved; specifically: - // - ConfigRecord entries - // - ConfigArrays elements - // - ConfigLambdas (default values of named arguments) - - struct IConfigRecord; - class ConfigArray; - - // TODO: separate this out from BrainScript to an interface that still does type casts--possible? - class ConfigValuePtr : public shared_ptr - { - function failfn; // function to call in case of failure due to this value - std::wstring expressionName; // the expression name reflects the path to reach this expression in the (possibly dynamically macro-expanded) expression tree. Used for naming ComputationNodes. - - // Thunk for resolving a value. This Object represents a function that returns a ConfigValuePtr; call to resolve a deferred value - class Thunk : public Object - { - function f; // the function to compute the value - bool currentlyResolving; // set during resolution phase, to detect circular references - function failfn; // function to call in case of failure due to this value - public: - Thunk(function f, const function & failfn) : f(f), failfn(failfn), currentlyResolving(false) { } - ConfigValuePtr ResolveValue() - { - if (currentlyResolving) // detect circular references (infinite recursion) - failfn(L"circular reference (expression to compute identifier's value uses the identifier's value)"); - currentlyResolving = true; // can't run from inside ourselves - return f(); - // no need to reset currentlyResolving because this object gets replaced and thus deleted anyway - } - }; - Thunk * GetThunk() const { return dynamic_cast(get()); } // get Thunk object or nullptr if already resolved - public: - - // --- assignment and copy/move constructors - - ConfigValuePtr() {} // (formally needed somehow) - ConfigValuePtr(const shared_ptr & p, const function & failfn, const std::wstring & expressionName) : shared_ptr(p), failfn(failfn), expressionName(expressionName) { } - //ConfigValuePtr(const function & f, TextLocation location, const std::wstring & expressionName) : shared_ptr(make_shared(f, location)), location(location), expressionName(expressionName) { } - static ConfigValuePtr MakeThunk(const function & f, const function & failfn, const std::wstring & expressionName) - { - return ConfigValuePtr(make_shared(f, failfn), failfn, expressionName); - } - // TODO: somehow the constructor overload from Thunk function fails to compile, so for now use MakeThunk instead - - ConfigValuePtr(const ConfigValuePtr & other) { *this = other; } - ConfigValuePtr(ConfigValuePtr && other) noexcept { *this = move(other); } - void operator=(const ConfigValuePtr & other) - { - if (other.GetThunk()) // unresolved ConfigValuePtrs are not copyable, only movable - Microsoft::MSR::CNTK::LogicError("ConfigValuePtr::operator=() on unresolved object '%ls'; ConfigValuePtr is not assignable until resolved", expressionName.empty() ? L"(unassigned)" : expressionName.c_str()); - (shared_ptr&)*this = other; - failfn = other.failfn; - expressionName = other.expressionName; - } - void operator=(ConfigValuePtr && other) noexcept - { - failfn = move(other.failfn); - expressionName = move(other.expressionName); - (shared_ptr&)*this = move(other); - } - - void Fail(const std::wstring & msg) const { failfn(msg); } - const function & GetFailFn() const { return failfn; } // if you need to pass on the fail function - - // --- retrieving values by type cast - - // access as a reference, that is, as a shared_ptr --use this for Objects - template operator shared_ptr() const { return AsPtr(); } - // access as a (const & to) value --use this for primitive types (also works to get a const std::wstring & from a String) - template operator T() const { return AsRef(); } - // Linux gcc barfs on this ^^ for 'us = (double)((std::wstring)arg).size();' due to some ambiguity error (while it works fine with Visual Studio). - // If you encounter this, instead say 'us = (double)((const std::wstring&)arg).size();' with a &. Don't forget the const (I have seen broken typecasts without). - operator const IConfigRecord &() const { return AsRef(); } - operator const ConfigArray &() const { return AsRef(); } - operator const std::wstring &() const { return AsRef(); } // somehow operator T() does not work here, still giving ambiguous messages. This makes it work. Probably not generic. Need to fix this. - operator double() const { return AsRef(); } - operator float() const { return (float) AsRef(); } - operator bool() const { return AsRef(); } - template INT AsInt() const - { - double val = AsRef(); - INT ival = (INT)val; - const wchar_t * type = L"size_t"; - // TODO: there is some duplication of type checking; can we unify that? - if (ival != val) - Fail(wstrprintf(L"expected expression of type %ls instead of floating-point value %f", type, val)); - return ival; - } - operator size_t() const { return AsInt(); } - operator int() const { return AsInt(); } - - // --- access functions - - template - bool Is() const - { - EnsureIsResolved(); - const auto p = dynamic_cast(get()); - return p != nullptr; - } - template - const C & AsRef() const // returns reference to the 'value' member. Configs are considered immutable, so return a const& - { - // TODO: factor these lines into a separate function - // Note: since this returns a reference into 'this', you must keep the object you call this on around as long as you use the returned reference - EnsureIsResolved(); - //const C * wanted = (C *) nullptr; const auto * got = get(); wanted; got; // allows to see C in the debugger - const auto p = dynamic_cast(get()); - if (p == nullptr) // TODO: can we make this look the same as TypeExpected in BrainScriptEvaluator.cpp? We'd need the type name - Fail(L"config member has wrong type (" + msra::strfun::utf16(typeid(*get()).name()) + L"), expected a " + TypeId()); - return *p; - } - template - shared_ptr AsPtr() const // returns a shared_ptr cast to the 'value' member - { - EnsureIsResolved(); - const auto p = dynamic_pointer_cast(*this); - if (!p) // TODO: can we make this look the same as TypeExpected in BrainScriptEvaluator.cpp? We'd need the type name - Fail(L"config member has wrong type (" + msra::strfun::utf16(typeid(*get()).name()) + L"), expected a " + TypeId()); - return p; - } - - // --- properties - - const char * TypeName() const { return typeid(*get()).name(); } - const std::wstring & GetExpressionName() const{ return expressionName; } - // TODO: ^^ it seems by saving the name in the ConfigValuePtr itself, we don't gain anything; maybe remove again in the future - - // --- methods for resolving the value - - const ConfigValuePtr & ResolveValue() const // (this is const but mutates the value if it resolves) - { - // call this when a a member might be as-of-yet unresolved, to evaluate it on-demand - // get() is a pointer to a Thunk in that case, that is, a function object that yields the value - const auto thunkp = GetThunk(); // is it a Thunk? - if (thunkp) // value is a Thunk: we need to resolve - { - const auto value = thunkp->ResolveValue(); // completely replace ourselves with the actual result. This also releases the Thunk object - const_cast(*this) = value; - ResolveValue(); // allow it to return another Thunk... - } - return *this; // return ourselves so we can access a value as p_resolved = p->ResolveValue() - } - void EnsureIsResolved() const - { - if (GetThunk()) - Microsoft::MSR::CNTK::LogicError("ConfigValuePtr: unexpected access to unresolved object; ConfigValuePtrs can only be accessed after resolution"); - } - }; // ConfigValuePtr - - // use this for primitive values, double and bool - template static inline ConfigValuePtr MakePrimitiveConfigValuePtr(const T & val, const function & failfn, const std::wstring & exprPath) - { - return ConfigValuePtr(make_shared>>(val), failfn, exprPath); - } - - // ----------------------------------------------------------------------- - // IConfigRecord -- config record - // Inside BrainScript, this would be a BS::ConfigRecord, but outside of the - // evaluator, we will only pass it through this interface, to allow for - // extensibility (e.g. Python interfacing). - // Also, Objects themselves can expose this interface to make something accessible. - // ----------------------------------------------------------------------- - - struct IConfigRecord // any class that exposes config can derive from this - { - virtual const ConfigValuePtr & operator[](const std::wstring & id) const = 0; // e.g. confRec[L"message"] - virtual const ConfigValuePtr * Find(const std::wstring & id) const = 0; // returns nullptr if not found - virtual std::vector GetMemberIds() const = 0; // returns the names of all members in this record (but not including parent scopes) - // prettier access if config record is a pointer - const ConfigValuePtr & Get(const std::wstring & id) const { return operator[](id); } // e.g. confRecPtr->Get(L"message") - - // access with default values - template ValueType operator()(const std::wstring & id, const ValueType & defaultValue) const // e.g. confRec("message", "hello) - { - const auto * valp = Find(id); - return valp ? *valp : defaultValue; - } - inline const IConfigRecord & operator()(const std::wstring & id, const IConfigRecord & defaultValue) const // retrieve a nested ConfigRecord - { - const auto * valp = Find(id); - return valp ? valp->AsRef() : defaultValue; - } - std::string operator()(const std::wstring & id, const char * defaultValue) const { return msra::strfun::utf8(operator()(id, (std::wstring)msra::strfun::utf16(defaultValue))); } // special case for narrow strings - std::wstring operator()(const std::wstring & id, const wchar_t * defaultValue) const { return operator()(id, std::wstring(defaultValue)); } - - // ----------------------------------------------------------------------- - // emulation of old CNTK config/NL - // This allows code written for CNTK config to simply turn ConfigParameters into a template parameter and accept an IConfigRecord. - // TODO: change all id args to wide strings, then update the code. - // ----------------------------------------------------------------------- - - const ConfigValuePtr & operator()(const std::wstring & id) const; - template std::vector operator()(const std::wstring & id, const std::vector & defaultValue) const; - bool ExistsCurrent(const std::wstring & id) const; - bool Exists(const std::wstring & id) const { return Find(id) != nullptr; } +// BrainScriptObjects.h -- objects that the config parser operates on + +#pragma once + +#include "Basics.h" +#include "Platform.h" // for noexcept workaround on VS2013 + +#include // for shared_ptr<> +#include // for function<> +#include + +namespace Microsoft { namespace MSR { namespace ScriptableObjects { + + using namespace std; + using namespace msra::strfun; // for wstrprintf() + using namespace Microsoft::MSR::CNTK; // for stuff from Basics.h + + // ----------------------------------------------------------------------- + // ScriptingException -- base class for any errors thrown by scripting + // It's a runtime_error with an additional virtual function PrintError(). + // ----------------------------------------------------------------------- + + class ScriptingException : public runtime_error + { + public: + template + ScriptingException(const M & msg) : runtime_error(msg) { } + virtual void PrintError() const = 0; + }; + + // ----------------------------------------------------------------------- + // Object -- common base class for objects that can be used in config files + // ----------------------------------------------------------------------- + + // All values that can be used in config files + // - are heap objects + // - primitives are wrapped + // - object pointers are ref-counted shared_ptr, wrapped in ConfigValuePtr (see BrainScriptEvaluator.h) + // - derive from Object (outside classes get wrapped) + // + // This code supports three kinds of value types: + // - self-defined classes -> derive from Object, e.g. Expression + // - classes defined outside -> wrap in a BoxOf object, e.g. String = BoxOf + // - C++ primitives like 'double' -> wrap in a Wrapper first then in a BoxOf, e.g. Number = BoxOf> + + struct Object { virtual ~Object() { } }; + + // indicates that the object has a name should be set from the expression path + + struct HasName { virtual void SetName(const std::wstring & name) = 0; }; + + // ----------------------------------------------------------------------- + // Wrapped -- wraps non-class primitive C++ type into a class, like 'double'. + // (It can also be used for class types, but better use BoxOf<> below directly.) + // ----------------------------------------------------------------------- + + template class Wrapped + { + T value; // meant to be a primitive type + public: + operator const T&() const { return value; } + operator T&() { return value; } + Wrapped(T value) : value(value) { } + T & operator=(const T & newValue) { value = newValue; return *this; } + }; + typedef Wrapped Double; + typedef Wrapped Bool; + + // ----------------------------------------------------------------------- + // BoxOf -- wraps a pre-defined type, e.g. std::wstring, to derive from Object. + // BoxOf can dynamic_cast to T (e.g. BoxOf is a std::wstring). + // ----------------------------------------------------------------------- + + template + class BoxOf : public Object, public C + { + public: +#if 1 + template BoxOf(_Types&&... _Args) : C(forward<_Types>(_Args)...) { } +#else + // TODO: change this to variadic templates, then we can instantiate everything we need through this + BoxOf(const C & val) : C(val) { } + BoxOf(){} +#endif + }; + + // ----------------------------------------------------------------------- + // String -- a std::string in config files + // Can cast to std::wstring (done in a way that ConfigValuePtr can also cast to std::wstring). + // ----------------------------------------------------------------------- + + typedef BoxOf String; + + // ----------------------------------------------------------------------- + // ComputationNodeObject -- the 'magic' class that our parser understands for infix operations + // TODO: unify with ComputationNodeBase + // ----------------------------------------------------------------------- + + class ComputationNodeObject : public Object { }; // a base class for all nodes (that has no template parameter) + + // ----------------------------------------------------------------------- + // HasToString -- trait to indicate an object can print their content + // Derive from HasToString() and implement ToString() method. + // FormatConfigValue() will then return ToString(). + // ----------------------------------------------------------------------- + + struct HasToString + { + virtual std::wstring ToString() const = 0; + + // some std::string helpers useful for ToString() operations of nested structures + // TODO: move these out from this header into some more general place (I had to move them here because otherwise CNTKEval failed to compile) + static std::wstring IndentString(std::wstring s, size_t indent) + { + const std::wstring prefix(indent, L' '); + size_t pos = 0; + for (;;) + { + s.insert(pos, prefix); + pos = s.find(L'\n', pos + 2); + if (pos == std::wstring::npos) + return s; + pos++; + } + } + static std::wstring NestString(std::wstring s, wchar_t open, bool newline, wchar_t close) + { + std::wstring result = IndentString(s, 2); + if (newline) // have a new line after the open symbol + result = L" \n" + result + L"\n "; + else + result.append(L" "); + result.front() = open; + result.back() = close; + return result; + } + + }; + + // ----------------------------------------------------------------------- + // WithTag -- trait to give an object a tag std::string + // ----------------------------------------------------------------------- + + class WithTag + { + std::wstring m_tag; + public: + WithTag(){} + void SetTag(const std::wstring & tag) { m_tag = tag; } + const std::wstring & GetTag() const { return m_tag; } + }; + + // ======================================================================= + // ConfigValuePtr -- shared pointer to a config value + // ======================================================================= + + // A ConfigValuePtr holds the value of a configuration variable. + // - specifically, it holds a shared_ptr to a strongly typed C++ object + // - ConfigValuePtrs are immutable when consumed. + // + // All configuration values, that is, values that can be held by a ConfigValuePtr, derive from BS::Object. + // To get a shared_ptr of an expected type T, type-cast the ConfigValuePtr to it. + // To get the value of a copyable type like T=double or std::wstring, type-cast to T directly. + // + // ConfigValuePtrs are evaluated on-demand upon first retrieval: + // - initially, a ConfigValuePtr would hold a Thunk; that is, a lambda that computes (resolves) the value + // - upon first use, the Thunk is invoked to compute the value, which will then *replace* the Thunk + // - any consumer of a ConfigValuePtr will only ever see the resolved value, since any access for consumption will force it to be resolved + // - a resolved ConfigValuePtr is immutable + // + // On-demand evaluation is critical to the semantics of this entire configuration system. + // A configuration is but one big expression (of nested records), but some evaluations cause side effects (such as saving a model), and some expressions may not even be in use at all. + // Thus, we must use on-demand evaluation in order to ensure that side effects are only executed when desired. + // + // Further, to ensure a Thunk is executed at most once (otherwise we may get the same side-effect multiple times), + // an unresolved ConfigValuePtr can only live in a single place. This means, + // - an unresolved ConfigValuePtr (i.e. one holding a Thunk) cannot be copied (while resolved ones are immutable and can be copied freely) + // - it can be moved (std::move()) during creation + // - after creation, it should only live in a known location from which it can be retrieved; specifically: + // - ConfigRecord entries + // - ConfigArrays elements + // - ConfigLambdas (default values of named arguments) + + struct IConfigRecord; + class ConfigArray; + + // TODO: separate this out from BrainScript to an interface that still does type casts--possible? + class ConfigValuePtr : public shared_ptr + { + function failfn; // function to call in case of failure due to this value + std::wstring expressionName; // the expression name reflects the path to reach this expression in the (possibly dynamically macro-expanded) expression tree. Used for naming ComputationNodes. + + // Thunk for resolving a value. This Object represents a function that returns a ConfigValuePtr; call to resolve a deferred value + class Thunk : public Object + { + function f; // the function to compute the value + bool currentlyResolving; // set during resolution phase, to detect circular references + function failfn; // function to call in case of failure due to this value + public: + Thunk(function f, const function & failfn) : f(f), failfn(failfn), currentlyResolving(false) { } + ConfigValuePtr ResolveValue() + { + if (currentlyResolving) // detect circular references (infinite recursion) + failfn(L"circular reference (expression to compute identifier's value uses the identifier's value)"); + currentlyResolving = true; // can't run from inside ourselves + return f(); + // no need to reset currentlyResolving because this object gets replaced and thus deleted anyway + } + }; + Thunk * GetThunk() const { return dynamic_cast(get()); } // get Thunk object or nullptr if already resolved + public: + + // --- assignment and copy/move constructors + + ConfigValuePtr() {} // (formally needed somehow) + ConfigValuePtr(const shared_ptr & p, const function & failfn, const std::wstring & expressionName) : shared_ptr(p), failfn(failfn), expressionName(expressionName) { } + //ConfigValuePtr(const function & f, TextLocation location, const std::wstring & expressionName) : shared_ptr(make_shared(f, location)), location(location), expressionName(expressionName) { } + static ConfigValuePtr MakeThunk(const function & f, const function & failfn, const std::wstring & expressionName) + { + return ConfigValuePtr(make_shared(f, failfn), failfn, expressionName); + } + // TODO: somehow the constructor overload from Thunk function fails to compile, so for now use MakeThunk instead + + ConfigValuePtr(const ConfigValuePtr & other) { *this = other; } + ConfigValuePtr(ConfigValuePtr && other) noexcept { *this = move(other); } + void operator=(const ConfigValuePtr & other) + { + if (other.GetThunk()) // unresolved ConfigValuePtrs are not copyable, only movable + Microsoft::MSR::CNTK::LogicError("ConfigValuePtr::operator=() on unresolved object '%ls'; ConfigValuePtr is not assignable until resolved", expressionName.empty() ? L"(unassigned)" : expressionName.c_str()); + (shared_ptr&)*this = other; + failfn = other.failfn; + expressionName = other.expressionName; + } + void operator=(ConfigValuePtr && other) noexcept + { + failfn = move(other.failfn); + expressionName = move(other.expressionName); + (shared_ptr&)*this = move(other); + } + + void Fail(const std::wstring & msg) const { failfn(msg); } + const function & GetFailFn() const { return failfn; } // if you need to pass on the fail function + + // --- retrieving values by type cast + + // access as a reference, that is, as a shared_ptr --use this for Objects + template operator shared_ptr() const { return AsPtr(); } + // access as a (const & to) value --use this for primitive types (also works to get a const std::wstring & from a String) + template operator T() const { return AsRef(); } + // Linux gcc barfs on this ^^ for 'us = (double)((std::wstring)arg).size();' due to some ambiguity error (while it works fine with Visual Studio). + // If you encounter this, instead say 'us = (double)((const std::wstring&)arg).size();' with a &. Don't forget the const (I have seen broken typecasts without). + operator const IConfigRecord &() const { return AsRef(); } + operator const ConfigArray &() const { return AsRef(); } + operator const std::wstring &() const { return AsRef(); } // somehow operator T() does not work here, still giving ambiguous messages. This makes it work. Probably not generic. Need to fix this. + operator double() const { return AsRef(); } + operator float() const { return (float) AsRef(); } + operator bool() const { return AsRef(); } + template INT AsInt() const + { + double val = AsRef(); + INT ival = (INT)val; + const wchar_t * type = L"size_t"; + // TODO: there is some duplication of type checking; can we unify that? + if (ival != val) + Fail(wstrprintf(L"expected expression of type %ls instead of floating-point value %f", type, val)); + return ival; + } + operator size_t() const { return AsInt(); } + operator int() const { return AsInt(); } + + // --- access functions + + template + bool Is() const + { + EnsureIsResolved(); + const auto p = dynamic_cast(get()); + return p != nullptr; + } + template + const C & AsRef() const // returns reference to the 'value' member. Configs are considered immutable, so return a const& + { + // TODO: factor these lines into a separate function + // Note: since this returns a reference into 'this', you must keep the object you call this on around as long as you use the returned reference + EnsureIsResolved(); + //const C * wanted = (C *) nullptr; const auto * got = get(); wanted; got; // allows to see C in the debugger + const auto p = dynamic_cast(get()); + if (p == nullptr) // TODO: can we make this look the same as TypeExpected in BrainScriptEvaluator.cpp? We'd need the type name + Fail(L"config member has wrong type (" + msra::strfun::utf16(typeid(*get()).name()) + L"), expected a " + TypeId()); + return *p; + } + template + shared_ptr AsPtr() const // returns a shared_ptr cast to the 'value' member + { + EnsureIsResolved(); + const auto p = dynamic_pointer_cast(*this); + if (!p) // TODO: can we make this look the same as TypeExpected in BrainScriptEvaluator.cpp? We'd need the type name + Fail(L"config member has wrong type (" + msra::strfun::utf16(typeid(*get()).name()) + L"), expected a " + TypeId()); + return p; + } + + // --- properties + + const char * TypeName() const { return typeid(*get()).name(); } + const std::wstring & GetExpressionName() const{ return expressionName; } + // TODO: ^^ it seems by saving the name in the ConfigValuePtr itself, we don't gain anything; maybe remove again in the future + + // --- methods for resolving the value + + const ConfigValuePtr & ResolveValue() const // (this is const but mutates the value if it resolves) + { + // call this when a a member might be as-of-yet unresolved, to evaluate it on-demand + // get() is a pointer to a Thunk in that case, that is, a function object that yields the value + const auto thunkp = GetThunk(); // is it a Thunk? + if (thunkp) // value is a Thunk: we need to resolve + { + const auto value = thunkp->ResolveValue(); // completely replace ourselves with the actual result. This also releases the Thunk object + const_cast(*this) = value; + ResolveValue(); // allow it to return another Thunk... + } + return *this; // return ourselves so we can access a value as p_resolved = p->ResolveValue() + } + void EnsureIsResolved() const + { + if (GetThunk()) + Microsoft::MSR::CNTK::LogicError("ConfigValuePtr: unexpected access to unresolved object; ConfigValuePtrs can only be accessed after resolution"); + } + }; // ConfigValuePtr + + // use this for primitive values, double and bool + template static inline ConfigValuePtr MakePrimitiveConfigValuePtr(const T & val, const function & failfn, const std::wstring & exprPath) + { + return ConfigValuePtr(make_shared>>(val), failfn, exprPath); + } + + // ----------------------------------------------------------------------- + // IConfigRecord -- config record + // Inside BrainScript, this would be a BS::ConfigRecord, but outside of the + // evaluator, we will only pass it through this interface, to allow for + // extensibility (e.g. Python interfacing). + // Also, Objects themselves can expose this interface to make something accessible. + // ----------------------------------------------------------------------- + + struct IConfigRecord // any class that exposes config can derive from this + { + virtual const ConfigValuePtr & operator[](const std::wstring & id) const = 0; // e.g. confRec[L"message"] + virtual const ConfigValuePtr * Find(const std::wstring & id) const = 0; // returns nullptr if not found + virtual std::vector GetMemberIds() const = 0; // returns the names of all members in this record (but not including parent scopes) + // prettier access if config record is a pointer + const ConfigValuePtr & Get(const std::wstring & id) const { return operator[](id); } // e.g. confRecPtr->Get(L"message") + + // access with default values + template ValueType operator()(const std::wstring & id, const ValueType & defaultValue) const // e.g. confRec("message", "hello) + { + const auto * valp = Find(id); + return valp ? *valp : defaultValue; + } + inline const IConfigRecord & operator()(const std::wstring & id, const IConfigRecord & defaultValue) const // retrieve a nested ConfigRecord + { + const auto * valp = Find(id); + return valp ? valp->AsRef() : defaultValue; + } + std::string operator()(const std::wstring & id, const char * defaultValue) const { return msra::strfun::utf8(operator()(id, (std::wstring)msra::strfun::utf16(defaultValue))); } // special case for narrow strings + std::wstring operator()(const std::wstring & id, const wchar_t * defaultValue) const { return operator()(id, std::wstring(defaultValue)); } + + // ----------------------------------------------------------------------- + // emulation of old CNTK config/NL + // This allows code written for CNTK config to simply turn ConfigParameters into a template parameter and accept an IConfigRecord. + // TODO: change all id args to wide strings, then update the code. + // ----------------------------------------------------------------------- + + const ConfigValuePtr & operator()(const std::wstring & id) const; + template std::vector operator()(const std::wstring & id, const std::vector & defaultValue) const; + bool ExistsCurrent(const std::wstring & id) const; + bool Exists(const std::wstring & id) const { return Find(id) != nullptr; } bool Match(const std::wstring & id, const std::wstring & compareValue) const; bool CanBeConfigRecord(const std::wstring & id) const { return operator[](id).Is(); } bool CanBeString(const std::wstring & id) const { return operator[](id).Is(); } const std::string ConfigName() const; - static const IConfigRecord & Record(); - template static const std::vector & Array(const V & vec); - }; - typedef shared_ptr IConfigRecordPtr; - - - // ----------------------------------------------------------------------- - // ConfigRecord -- collection of named config values - // ----------------------------------------------------------------------- - - class ConfigRecord : public Object, public IConfigRecord // all configuration arguments to class construction, resolved into ConfigValuePtrs - { - function failfn; // function to call in case of failure due to this value - // change to ContextInsensitiveMap - std::map members; - IConfigRecordPtr parentScope; // we look up the chain - ConfigRecord() { } // forbidden (private) to instantiate without a scope - public: - - // --- creation phase - - ConfigRecord(IConfigRecordPtr parentScope, const function & failfn) : parentScope(parentScope), failfn(failfn) { } - void Add(const std::wstring & id, const function & /*failfn*/, const ConfigValuePtr & value) { members[id] = value; } - void Add(const std::wstring & id, const function & /*failfn*/, ConfigValuePtr && value) { members[id] = move(value); } // use this for unresolved ConfigPtrs - // TODO: Add() does not yet correctly handle the failfn. It is meant to flag the location of the variable identifier - - // --- usage phase - - // regular lookup: just use record[id] or record(id, L"helpful message what 'id' does") - // Any unresolved value is resolved at this time, as it is being consumed. Only after resolving a ConfigValuePtr, it can be copied. - const ConfigValuePtr & /*IConfigRecord::*/operator[](const std::wstring & id) const // e.g. confRec[L"name"] - { - const auto memberIter = members.find(id); - if (memberIter != members.end()) - return memberIter->second.ResolveValue(); // resolve upon access - if (!parentScope) // not found: if at top scope, we fail - failfn(L"required parameter '" + id + L"' not found"); - // The failfn will report the location where the dictionary itself was formed. - // This is because this function is meant to be used by C++ code. - // When we look up a name by a BrainScript ".FIELD" expression, we will use Find() so we can report the error for the offending FIELD itself. - return (*parentScope)[id]; // have parent: look it up there - } - const ConfigValuePtr * /*IConfigRecord::*/Find(const std::wstring & id) const // returns nullptr if not found - { - auto memberIter = members.find(id); - if (memberIter == members.end()) - if (parentScope) - return parentScope->Find(id); - else - return nullptr; - else - return &memberIter->second.ResolveValue(); - } - // get member ids; use this when you intend to consume all record entries and do not know the names - // Note that unlike Find() and operator[], which return parent matches, this only returns entries in this record. - virtual std::vector /*IConfigRecord::*/GetMemberIds() const - { - std::vector ids; - for (auto & member : members) - ids.push_back(member.first); - return ids; - } - }; - typedef shared_ptr ConfigRecordPtr; - // TODO: can ConfigRecordPtr be IConfigRecordPtr? - - // create a runtime object from its type --general case - // There can be specializations of this that instantiate objects that do not take ConfigRecords or involve mapping like ComputationNode. - template - shared_ptr MakeRuntimeObject(const IConfigRecordPtr config) - { - return make_shared(config); - } - - // ----------------------------------------------------------------------- - // ConfigArray -- an array of config values - // ----------------------------------------------------------------------- - - // an array is just a std::vector of config values - class ConfigArray : public Object - { - std::vector values; - int firstIndex; - public: - ConfigArray() : firstIndex(0) { } - ConfigArray(int firstIndex, std::vector && values) : firstIndex(firstIndex), values(move(values)) { } - //ConfigArray(ConfigValuePtr && val) : firstIndex(0), values(std::vector{ move(val) }) { } - pair GetIndexRange() const { return make_pair(firstIndex, firstIndex + (int)values.size() - 1); } - // for use as a plain array: get size and verify that index range starts with 0 - template - size_t GetSize(const FAILFN & Fail) const { if (firstIndex != 0) Fail(L"This array is expected to begin with index 0."); return values.size(); } - // building the array from expressions: append an element or an array - void Append(const ConfigValuePtr & value) { values.push_back(value); } - void Append(ConfigValuePtr && value) { values.push_back(move(value)); } // this appends an unresolved ConfigValuePtr - void Append(const ConfigArray & other) { values.insert(values.end(), other.values.begin(), other.values.end()); } - // get element at index, including bounds check - template - const ConfigValuePtr & At(int index, const FAILFN & Fail/*should report location of the index*/) const - { - if (index < firstIndex || index >= firstIndex + values.size()) - Fail(msra::strfun::wstrprintf(L"Index %d out of bounds [%d..%d].", (int)index, (int)firstIndex, (int)(firstIndex + values.size() - 1))); - return values[(size_t)(index - firstIndex)].ResolveValue(); // resolve upon access - } - // get element when knowing that the bounds are correct, e.g. looping over the item range returned by GetItemRange() - const ConfigValuePtr & At(int index) const { return At(index, [](const std::wstring &){ LogicError("ConfigArray::At(): Index unexpectedly out of bounds."); }); } - // get an entire array into a std::vector. Note that this will force all values to be evaluated. - template - std::vector AsVector(const FAILFN & Fail) const - { - std::vector res; - res.reserve(GetSize(Fail)); - for (const auto & val : values) - res.push_back(val.ResolveValue()); // resolve upon access - return res; - } - }; - typedef shared_ptr ConfigArrayPtr; - - // ----------------------------------------------------------------------- - // ConfigLambda -- a lambda - // ----------------------------------------------------------------------- - - class ConfigLambda : public Object - { - public: - typedef std::map NamedParams; // TODO: maybe even not use a typedef, just use the type - private: - // the function itself is a C++ lambda - function &&, NamedParams &&, const std::wstring & exprName)> f; - // inputs. This defines the interface to the function. Very simple in our case though. - // We pass rvalue references because that allows to pass Thunks. - std::vector paramNames; // #parameters and parameter names (names are used for naming expressions only) - NamedParams namedParams; // lists named parameters with their default values. Named parameters are optional and thus always must have a default. - public: - template - ConfigLambda(std::vector && paramNames, NamedParams && namedParams, const F & f) : paramNames(move(paramNames)), namedParams(move(namedParams)), f(f) { } - size_t GetNumParams() const { return paramNames.size(); } - const std::vector & GetParamNames() const { return paramNames; } // used for expression naming - // what this function does is call f() held in this object with the given arguments except optional arguments are verified and fall back to their defaults if not given - // The arguments are rvalue references, which allows us to pass Thunks, which is important to allow stuff with circular references like CNTK's DelayedNode. - ConfigValuePtr Apply(std::vector && args, NamedParams && namedArgs, const std::wstring & exprName) - { - NamedParams actualNamedArgs; - // actualNamedArgs is a filtered version of namedArgs that contains all optional args listed in namedParams, - // falling back to their default if not given in namedArgs. - // On the other hand, any name in namedArgs that is not found in namedParams should be rejected. - for (const auto & namedParam : namedParams) - { - const auto & id = namedParam.first; // id of expected named parameter - const auto valuei = namedArgs.find(id); // was such parameter passed? - if (valuei == namedArgs.end()) // named parameter not passed - { // if not given then fall back to default - auto f = [&namedParam]() // we pass a lambda that resolves it upon first use, in our original location - { - return namedParam.second.ResolveValue(); - }; - actualNamedArgs[id] = move(ConfigValuePtr::MakeThunk(f, namedParam.second.GetFailFn(), exprName)); - } - else // named parameter was passed - actualNamedArgs[id] = move(valuei->second); // move it, possibly remaining unresolved - // BUGBUG: we should pass in the location of the identifier, not that of the expression - } - for (const auto & namedArg : namedArgs) // make sure there are no extra named args that the macro does not take - if (namedParams.find(namedArg.first) == namedParams.end()) - namedArg.second.Fail(L"function does not have an optional argument '" + namedArg.first + L"'"); - return f(move(args), move(actualNamedArgs), exprName); - } - // TODO: define an overload that takes const & for external users (which will then take a copy and pass it on to Apply &&) - }; - typedef shared_ptr ConfigLambdaPtr; - - // ----------------------------------------------------------------------- - // ConfigurableRuntimeType -- interface to scriptable runtime types - // ----------------------------------------------------------------------- - - // helper for configurableRuntimeTypes initializer below - // This returns a ConfigurableRuntimeType info structure that consists of - // - a lambda that is a constructor for a given runtime type and - // - a bool saying whether T derives from IConfigRecord - struct ConfigurableRuntimeType // TODO: rename to ScriptableObjects::Factory or something like that - { - bool isConfigRecord; // exposes IConfigRecord --in this case the expression name is computed differently, namely relative to this item - // TODO: is this ^^ actually still used anywhere? - function(const IConfigRecordPtr)> construct; // lambda to construct an object of this class - // TODO: we should pass the expression name to construct() as well - }; - - // scriptable runtime types must be exposed by this function - // TODO: should this be a static member of above class? - const ConfigurableRuntimeType * FindExternalRuntimeTypeInfo(const std::wstring & typeId); - - // ----------------------------------------------------------------------- - // ConfigurableRuntimeTypeRegister -- static table of all configurable runtime types - // ----------------------------------------------------------------------- - - class ConfigurableRuntimeTypeRegister - { - // we wrap the static variable in a function so that we don't need a CPP file - static std::map & GetTheRegister() - { - // the one static variable that contains all configurable runtime types - static std::map reg; - return reg; - } - - static void Register(const wchar_t * typeId, ConfigurableRuntimeType && rtInfo) - { - auto & reg = GetTheRegister(); - auto res = reg.insert(std::make_pair((std::wstring)typeId, std::move(rtInfo))); - if (!res.second) - LogicError("RegisterConfigurableRuntimeType: Attempted to register type '%ls' twice.", typeId); - } - public: - - // to instantiate a ConfigurableRuntimeType object, use this function to find its constructor - static const ConfigurableRuntimeType * Find(const std::wstring & typeId) - { - auto & reg = GetTheRegister(); - auto iter = reg.find(typeId); - if (iter == reg.end()) - return nullptr; - else - return &iter->second; - } - - // to register a runtime type, use an instance of this class in each library - // ConfigurableRuntimeTypeRegister::Add registerClassName(L"ClassName")l - template - struct Add - { - Add(const wchar_t * typeId) - { - // create the runtime info - ConfigurableRuntimeType rtInfo; - rtInfo.construct = [](const IConfigRecordPtr config) // lambda to construct--this lambda can construct both the and the variant based on config parameter 'precision' - { - return MakeRuntimeObject(config); - }; - rtInfo.isConfigRecord = is_base_of::value; - // insert it into the static table - Register(typeId, std::move(rtInfo)); - } - }; - // to register a class that exists in dual precisions (Something>, use this one instead - template - struct AddFloatDouble - { - AddFloatDouble(const wchar_t * typeId) - { - // create the runtime info - ConfigurableRuntimeType rtInfo; - rtInfo.construct = [](const IConfigRecordPtr config) // lambda to construct--this lambda can construct both the and the variant based on config parameter 'precision' - { - std::wstring precision = (*config)[L"precision"]; // dispatch on ElemType - if (precision == L"float") - return MakeRuntimeObject(config); - else if (precision == L"double") - return MakeRuntimeObject(config); - else - RuntimeError("invalid value '%ls' for 'precision', must be 'float' or 'double'", precision.c_str()); - }; - rtInfo.isConfigRecord = is_base_of::value; - static_assert(is_base_of::value == is_base_of::value, ""); // we assume that both float and double have the same behavior - // insert it into the static table - Register(typeId, std::move(rtInfo)); - } - }; - }; - - // ----------------------------------------------------------------------- - // IConfigRecord emulation of old CNTK config/NL - // This allows code written for CNTK config to simply turn ConfigParameters into a template parameter and accept an IConfigRecord. - // ----------------------------------------------------------------------- - - inline const ConfigValuePtr & IConfigRecord::operator()(const std::wstring & id) const { return operator[](id); } // e.g. confRec(L"message") instead of confRec[L"message"] - template inline std::vector IConfigRecord::operator()(const std::wstring & id, const std::vector & defaultValue) const // retrieve an argvector (which derives from std::vector) - { - const auto * valp = Find(id); - if (!valp) - return defaultValue; // default value - if (!valp->Is()) - return std::vector(1, (const T &)*valp); // scalar value - const ConfigArray & arr = *valp; // actual array -#if 1 // TODO: test whether this works correctly w.r.t. typecasting - return arr.AsVector([&](const std::wstring & msg){ valp->Fail(msg); }); -#else - const auto size = arr.GetSize([&](const std::wstring & msg){ valp->Fail(msg); }); - std::vector res(size); - for (int i = 0; i < size; i++) - res[i] = (const T &)arr.At(i); - return res; -#endif - } - inline bool IConfigRecord::ExistsCurrent(const std::wstring & id) const // this is inefficient, but we can optimize it if it ever turns out to be a problem. I rather think, this function is misguided. The name is bad, too. - { - for (const auto & idIter : GetMemberIds()) // linear scan. Not using STL algorithm to avoid pulling in a big header at this level - if (idIter == id) - return true; - return false; - } + static const IConfigRecord & Record(); + template static const std::vector & Array(const V & vec); + }; + typedef shared_ptr IConfigRecordPtr; + + + // ----------------------------------------------------------------------- + // ConfigRecord -- collection of named config values + // ----------------------------------------------------------------------- + + class ConfigRecord : public Object, public IConfigRecord // all configuration arguments to class construction, resolved into ConfigValuePtrs + { + function failfn; // function to call in case of failure due to this value + // change to ContextInsensitiveMap + std::map members; + IConfigRecordPtr parentScope; // we look up the chain + ConfigRecord() { } // forbidden (private) to instantiate without a scope + public: + + // --- creation phase + + ConfigRecord(IConfigRecordPtr parentScope, const function & failfn) : parentScope(parentScope), failfn(failfn) { } + void Add(const std::wstring & id, const function & /*failfn*/, const ConfigValuePtr & value) { members[id] = value; } + void Add(const std::wstring & id, const function & /*failfn*/, ConfigValuePtr && value) { members[id] = move(value); } // use this for unresolved ConfigPtrs + // TODO: Add() does not yet correctly handle the failfn. It is meant to flag the location of the variable identifier + + // --- usage phase + + // regular lookup: just use record[id] or record(id, L"helpful message what 'id' does") + // Any unresolved value is resolved at this time, as it is being consumed. Only after resolving a ConfigValuePtr, it can be copied. + const ConfigValuePtr & /*IConfigRecord::*/operator[](const std::wstring & id) const // e.g. confRec[L"name"] + { + const auto memberIter = members.find(id); + if (memberIter != members.end()) + return memberIter->second.ResolveValue(); // resolve upon access + if (!parentScope) // not found: if at top scope, we fail + failfn(L"required parameter '" + id + L"' not found"); + // The failfn will report the location where the dictionary itself was formed. + // This is because this function is meant to be used by C++ code. + // When we look up a name by a BrainScript ".FIELD" expression, we will use Find() so we can report the error for the offending FIELD itself. + return (*parentScope)[id]; // have parent: look it up there + } + const ConfigValuePtr * /*IConfigRecord::*/Find(const std::wstring & id) const // returns nullptr if not found + { + auto memberIter = members.find(id); + if (memberIter == members.end()) + if (parentScope) + return parentScope->Find(id); + else + return nullptr; + else + return &memberIter->second.ResolveValue(); + } + // get member ids; use this when you intend to consume all record entries and do not know the names + // Note that unlike Find() and operator[], which return parent matches, this only returns entries in this record. + virtual std::vector /*IConfigRecord::*/GetMemberIds() const + { + std::vector ids; + for (auto & member : members) + ids.push_back(member.first); + return ids; + } + }; + typedef shared_ptr ConfigRecordPtr; + // TODO: can ConfigRecordPtr be IConfigRecordPtr? + + // create a runtime object from its type --general case + // There can be specializations of this that instantiate objects that do not take ConfigRecords or involve mapping like ComputationNode. + template + shared_ptr MakeRuntimeObject(const IConfigRecordPtr config) + { + return make_shared(config); + } + + // ----------------------------------------------------------------------- + // ConfigArray -- an array of config values + // ----------------------------------------------------------------------- + + // an array is just a std::vector of config values + class ConfigArray : public Object + { + std::vector values; + int firstIndex; + public: + ConfigArray() : firstIndex(0) { } + ConfigArray(int firstIndex, std::vector && values) : firstIndex(firstIndex), values(move(values)) { } + //ConfigArray(ConfigValuePtr && val) : firstIndex(0), values(std::vector{ move(val) }) { } + pair GetIndexRange() const { return make_pair(firstIndex, firstIndex + (int)values.size() - 1); } + // for use as a plain array: get size and verify that index range starts with 0 + template + size_t GetSize(const FAILFN & Fail) const { if (firstIndex != 0) Fail(L"This array is expected to begin with index 0."); return values.size(); } + // building the array from expressions: append an element or an array + void Append(const ConfigValuePtr & value) { values.push_back(value); } + void Append(ConfigValuePtr && value) { values.push_back(move(value)); } // this appends an unresolved ConfigValuePtr + void Append(const ConfigArray & other) { values.insert(values.end(), other.values.begin(), other.values.end()); } + // get element at index, including bounds check + template + const ConfigValuePtr & At(int index, const FAILFN & Fail/*should report location of the index*/) const + { + if (index < firstIndex || index >= firstIndex + values.size()) + Fail(msra::strfun::wstrprintf(L"Index %d out of bounds [%d..%d].", (int)index, (int)firstIndex, (int)(firstIndex + values.size() - 1))); + return values[(size_t)(index - firstIndex)].ResolveValue(); // resolve upon access + } + // get element when knowing that the bounds are correct, e.g. looping over the item range returned by GetItemRange() + const ConfigValuePtr & At(int index) const { return At(index, [](const std::wstring &){ LogicError("ConfigArray::At(): Index unexpectedly out of bounds."); }); } + // get an entire array into a std::vector. Note that this will force all values to be evaluated. + template + std::vector AsVector(const FAILFN & Fail) const + { + std::vector res; + res.reserve(GetSize(Fail)); + for (const auto & val : values) + res.push_back(val.ResolveValue()); // resolve upon access + return res; + } + }; + typedef shared_ptr ConfigArrayPtr; + + // ----------------------------------------------------------------------- + // ConfigLambda -- a lambda + // ----------------------------------------------------------------------- + + class ConfigLambda : public Object + { + public: + typedef std::map NamedParams; // TODO: maybe even not use a typedef, just use the type + private: + // the function itself is a C++ lambda + function &&, NamedParams &&, const std::wstring & exprName)> f; + // inputs. This defines the interface to the function. Very simple in our case though. + // We pass rvalue references because that allows to pass Thunks. + std::vector paramNames; // #parameters and parameter names (names are used for naming expressions only) + NamedParams namedParams; // lists named parameters with their default values. Named parameters are optional and thus always must have a default. + public: + template + ConfigLambda(std::vector && paramNames, NamedParams && namedParams, const F & f) : paramNames(move(paramNames)), namedParams(move(namedParams)), f(f) { } + size_t GetNumParams() const { return paramNames.size(); } + const std::vector & GetParamNames() const { return paramNames; } // used for expression naming + // what this function does is call f() held in this object with the given arguments except optional arguments are verified and fall back to their defaults if not given + // The arguments are rvalue references, which allows us to pass Thunks, which is important to allow stuff with circular references like CNTK's DelayedNode. + ConfigValuePtr Apply(std::vector && args, NamedParams && namedArgs, const std::wstring & exprName) + { + NamedParams actualNamedArgs; + // actualNamedArgs is a filtered version of namedArgs that contains all optional args listed in namedParams, + // falling back to their default if not given in namedArgs. + // On the other hand, any name in namedArgs that is not found in namedParams should be rejected. + for (const auto & namedParam : namedParams) + { + const auto & id = namedParam.first; // id of expected named parameter + const auto valuei = namedArgs.find(id); // was such parameter passed? + if (valuei == namedArgs.end()) // named parameter not passed + { // if not given then fall back to default + auto f = [&namedParam]() // we pass a lambda that resolves it upon first use, in our original location + { + return namedParam.second.ResolveValue(); + }; + actualNamedArgs[id] = move(ConfigValuePtr::MakeThunk(f, namedParam.second.GetFailFn(), exprName)); + } + else // named parameter was passed + actualNamedArgs[id] = move(valuei->second); // move it, possibly remaining unresolved + // BUGBUG: we should pass in the location of the identifier, not that of the expression + } + for (const auto & namedArg : namedArgs) // make sure there are no extra named args that the macro does not take + if (namedParams.find(namedArg.first) == namedParams.end()) + namedArg.second.Fail(L"function does not have an optional argument '" + namedArg.first + L"'"); + return f(move(args), move(actualNamedArgs), exprName); + } + // TODO: define an overload that takes const & for external users (which will then take a copy and pass it on to Apply &&) + }; + typedef shared_ptr ConfigLambdaPtr; + + // ----------------------------------------------------------------------- + // ConfigurableRuntimeType -- interface to scriptable runtime types + // ----------------------------------------------------------------------- + + // helper for configurableRuntimeTypes initializer below + // This returns a ConfigurableRuntimeType info structure that consists of + // - a lambda that is a constructor for a given runtime type and + // - a bool saying whether T derives from IConfigRecord + struct ConfigurableRuntimeType // TODO: rename to ScriptableObjects::Factory or something like that + { + bool isConfigRecord; // exposes IConfigRecord --in this case the expression name is computed differently, namely relative to this item + // TODO: is this ^^ actually still used anywhere? + function(const IConfigRecordPtr)> construct; // lambda to construct an object of this class + // TODO: we should pass the expression name to construct() as well + }; + + // scriptable runtime types must be exposed by this function + // TODO: should this be a static member of above class? + const ConfigurableRuntimeType * FindExternalRuntimeTypeInfo(const std::wstring & typeId); + + // ----------------------------------------------------------------------- + // ConfigurableRuntimeTypeRegister -- static table of all configurable runtime types + // ----------------------------------------------------------------------- + + class ConfigurableRuntimeTypeRegister + { + // we wrap the static variable in a function so that we don't need a CPP file + static std::map & GetTheRegister() + { + // the one static variable that contains all configurable runtime types + static std::map reg; + return reg; + } + + static void Register(const wchar_t * typeId, ConfigurableRuntimeType && rtInfo) + { + auto & reg = GetTheRegister(); + auto res = reg.insert(std::make_pair((std::wstring)typeId, std::move(rtInfo))); + if (!res.second) + LogicError("RegisterConfigurableRuntimeType: Attempted to register type '%ls' twice.", typeId); + } + public: + + // to instantiate a ConfigurableRuntimeType object, use this function to find its constructor + static const ConfigurableRuntimeType * Find(const std::wstring & typeId) + { + auto & reg = GetTheRegister(); + auto iter = reg.find(typeId); + if (iter == reg.end()) + return nullptr; + else + return &iter->second; + } + + // to register a runtime type, use an instance of this class in each library + // ConfigurableRuntimeTypeRegister::Add registerClassName(L"ClassName")l + template + struct Add + { + Add(const wchar_t * typeId) + { + // create the runtime info + ConfigurableRuntimeType rtInfo; + rtInfo.construct = [](const IConfigRecordPtr config) // lambda to construct--this lambda can construct both the and the variant based on config parameter 'precision' + { + return MakeRuntimeObject(config); + }; + rtInfo.isConfigRecord = is_base_of::value; + // insert it into the static table + Register(typeId, std::move(rtInfo)); + } + }; + // to register a class that exists in dual precisions (Something>, use this one instead + template + struct AddFloatDouble + { + AddFloatDouble(const wchar_t * typeId) + { + // create the runtime info + ConfigurableRuntimeType rtInfo; + rtInfo.construct = [](const IConfigRecordPtr config) // lambda to construct--this lambda can construct both the and the variant based on config parameter 'precision' + { + std::wstring precision = (*config)[L"precision"]; // dispatch on ElemType + if (precision == L"float") + return MakeRuntimeObject(config); + else if (precision == L"double") + return MakeRuntimeObject(config); + else + RuntimeError("invalid value '%ls' for 'precision', must be 'float' or 'double'", precision.c_str()); + }; + rtInfo.isConfigRecord = is_base_of::value; + static_assert(is_base_of::value == is_base_of::value, ""); // we assume that both float and double have the same behavior + // insert it into the static table + Register(typeId, std::move(rtInfo)); + } + }; + }; + + // ----------------------------------------------------------------------- + // IConfigRecord emulation of old CNTK config/NL + // This allows code written for CNTK config to simply turn ConfigParameters into a template parameter and accept an IConfigRecord. + // ----------------------------------------------------------------------- + + inline const ConfigValuePtr & IConfigRecord::operator()(const std::wstring & id) const { return operator[](id); } // e.g. confRec(L"message") instead of confRec[L"message"] + template inline std::vector IConfigRecord::operator()(const std::wstring & id, const std::vector & defaultValue) const // retrieve an argvector (which derives from std::vector) + { + const auto * valp = Find(id); + if (!valp) + return defaultValue; // default value + if (!valp->Is()) + return std::vector(1, (const T &)*valp); // scalar value + const ConfigArray & arr = *valp; // actual array +#if 1 // TODO: test whether this works correctly w.r.t. typecasting + return arr.AsVector([&](const std::wstring & msg){ valp->Fail(msg); }); +#else + const auto size = arr.GetSize([&](const std::wstring & msg){ valp->Fail(msg); }); + std::vector res(size); + for (int i = 0; i < size; i++) + res[i] = (const T &)arr.At(i); + return res; +#endif + } + inline bool IConfigRecord::ExistsCurrent(const std::wstring & id) const // this is inefficient, but we can optimize it if it ever turns out to be a problem. I rather think, this function is misguided. The name is bad, too. + { + for (const auto & idIter : GetMemberIds()) // linear scan. Not using STL algorithm to avoid pulling in a big header at this level + if (idIter == id) + return true; + return false; + } inline bool IConfigRecord::Match(const std::wstring & id, const std::wstring & compareValue) const { auto * valp = Find(id); @@ -687,16 +687,16 @@ namespace Microsoft { namespace MSR { namespace ScriptableObjects { { LogicError("ConfigName not supported by BrainScript."); // needed in BinaryWriter } - /*static*/ inline const IConfigRecord & IConfigRecord::Record() // empty record to be passed as a default to operator() when retrieving a nested ConfigRecord - { - static struct EmptyConfigRecord : public IConfigRecord - { - virtual const ScriptableObjects::ConfigValuePtr & operator[](const std::wstring &) const override final { InvalidArgument("EmptyConfigRecord: Attempted to return a value from the empty record."); } - virtual const ScriptableObjects::ConfigValuePtr * Find(const std::wstring &) const override final { return nullptr; } - virtual std::vector GetMemberIds() const { return std::vector(); } - } emptyParameters; - return emptyParameters; - } - template /*static*/ const std::vector & IConfigRecord::Array(const V & vec) { return static_cast &>(vec); } // use this specifically for XXXargvector - -}}} // end namespaces + /*static*/ inline const IConfigRecord & IConfigRecord::Record() // empty record to be passed as a default to operator() when retrieving a nested ConfigRecord + { + static struct EmptyConfigRecord : public IConfigRecord + { + virtual const ScriptableObjects::ConfigValuePtr & operator[](const std::wstring &) const override final { InvalidArgument("EmptyConfigRecord: Attempted to return a value from the empty record."); } + virtual const ScriptableObjects::ConfigValuePtr * Find(const std::wstring &) const override final { return nullptr; } + virtual std::vector GetMemberIds() const { return std::vector(); } + } emptyParameters; + return emptyParameters; + } + template /*static*/ const std::vector & IConfigRecord::Array(const V & vec) { return static_cast &>(vec); } // use this specifically for XXXargvector + +}}} // end namespaces diff --git a/Source/Common/Include/Sequences.h b/Source/Common/Include/Sequences.h index 13f4521dc012..206d79a216d6 100644 --- a/Source/Common/Include/Sequences.h +++ b/Source/Common/Include/Sequences.h @@ -1,928 +1,928 @@ -// Sequences.h -- all about iterating over sequences, that is, MBLayout, FrameRange, and iterators -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#pragma once - -#include "Basics.h" -#include "Matrix.h" -#include -#include // for shared_ptr - -namespace Microsoft { namespace MSR { namespace CNTK { - - // Forward declarations - class FrameRange; - - typedef size_t UniqueSequenceId; -#define GAP_SEQUENCE_ID SIZE_MAX // indicates no data -#define NEW_SEQUENCE_ID (SIZE_MAX-1) // let SetSequence() assign a unique id; for old readers. Don't mix with actual reader-assigned ids. - - // ----------------------------------------------------------------------- - // MBLayout -- layout information of minibatch - // - // Minibatches are collections of one or more sequences, laid out in a way to - // allow to process one time step for multiple sequences in parallel in shared CUDA launches. - // - // This is achieved by interleaving storage. If f(s,t) denotes a frame of sequence s at time t, - // the minibatch matrix would contain this: - // f(0,0) f(1,0) ... f(0,1) f(1,1) ... - // Much of CNTK's efficiency comes from this. - // (Note that some communities, such as language processing, often call sets f(0..s-1,t) a - // "minibatch," where in our definition, a minibatch consists of multiple entire sequences.) - // - // In the special case of frame randomization, every frame is stored as a single-frame sequence. - // - // If we describe this in terms of tensors, a data matrix with sample layout (I,J,K) and - // MBLayout (S,T) can be interpreted as TensorShape(I,J,K,S,T). - // - // Sequences can also be concatenated to fill the space better. For this case, - // this object stores about every frame whether it is at the start or end of a sequence. - // Hence, we distinguish between "sequences" (logical units) and "parallel sequences" - // (where one "parallel sequence" may consist of multiple concatenated "sequences"). - // - // When not all sequences have the same length, some parallel sequences have invalid frames (gaps). - // Gaps are identified by the MBLayouyt as well. Currently, these gaps only occur at the end. - // - // Gaps may also arise due to invalid input data (e.g. a speech utterance for which no alignment could be generated). - // - // An MBLayout provides the following functions: - // - (building:) add a new sequence (or gap range) to the MBLayout - // - inquire the set of sequences (sequence ids) that intersect with this minibatch - // - inquire whether any time step t has a gap or boundary across all sequences - // - inquire for gap or boundary at (s,t) - // - // Truncated BPTT support (partial sequences): - // - in truncated BPTT, minibatches only contain partial sequences, e.g. a range of 20 time steps - // - boundary information is stored for every sequence that intersects with this minibatch, - // including boundaries that fall outside of the time range of the minibatch - // - // An MBLayout object stores: - // - for every sequence in the minibatch the n-tuple (global sequence id, s, first t, last t) - // (where first and last t may sometimes lie outside of the minibatch, e.g. in case of truncated BPTT) - // - number of time steps and parallel sequences (their product is equal to the #columns in the minibatch matrix) - // - lookup tables for looking up gap and boundary information - // ----------------------------------------------------------------------- - - // Contract between ComputationNode, ComputationNetwork, and MBLayout: - // - if a node has no MBLayout, m_{value,gradient} are not samples (they are not activations or input data), but e.g. model parameters - // - ComputationNode::GetNumCols() == MBLayout::GetNumTimeSteps() * MBLayout::GetNumParallelSequences() - // - ComputationNetwork ensures that m_{value,gradient} are allocated correctly before calling ForwardProp() on a node - - struct MBLayout - { - typedef std::shared_ptr MBLayoutPtr; - - // information stored about sequences - struct SequenceInfo - { - UniqueSequenceId seqId; // unique sequence id (or GAP_SEQUENCE_ID--TODO: don't include gaps here) - size_t s; // index of parallel sequence - ptrdiff_t tBegin; // first time index in this minibatch. Note that this may be negative of the sequence started before this MB. - size_t tEnd; // end = first frame index after final frame. May be beyond the minibatch if reql sequence is longer than the MB. - bool operator==(const SequenceInfo & other) const { return seqId == other.seqId && s == other.s && tBegin == other.tBegin && tEnd == other.tEnd; } - size_t GetNumTimeSteps() const { return (size_t)(tEnd - tBegin); } - }; - - // ------------------------------------------------------------------- - // construction - // ------------------------------------------------------------------- - - MBLayout(size_t numParallelSequences, size_t numTimeSteps) : m_distanceToStart(CPUDEVICE), m_distanceToEnd(CPUDEVICE), m_columnsValidityMask(CPUDEVICE) { Init(numParallelSequences, numTimeSteps); } - MBLayout() : MBLayout(1, 0) { } - - // copy the content of another MBLayoutPtr over - // Use this instead of actual assignment to make it super-obvious that this is not copying the pointer but actual content. The pointer is kept fixed. - void CopyFrom(const MBLayoutPtr & other) { *this = *other; } - void MoveFrom(MBLayoutPtr other) { *this = move(*other); other->Init(0, 0); } // destructive copy that steals ownership if the content, like std::move() - private: - MBLayout & operator=(const MBLayout &) = default; // make this private --use CopyFrom() instead, which makes it very clear that it's copying content, not copying the reference - public: - - // resize and reset all frames to None (note: this is an invalid state and must be fixed by caller afterwards) - void Init(size_t numParallelSequences, size_t numTimeSteps) - { - // remember the dimensions - m_numParallelSequences = numParallelSequences; - m_numTimeSteps = numTimeSteps; - // allocate lookup tables (note: except at the start, these don't really allocate new memory most of the time) -#if 0 - if ((m_distanceToStart.GetNumRows() != m_numParallelSequences || m_distanceToStart.GetNumCols() != m_numTimeSteps) && m_numTimeSteps > 0) // sanity check for debugging a regression - fprintf(stderr, "MBLayout::Init: Resizing m_distanceToStart from %d x %d to %d x %d\n", - (int)m_distanceToStart.GetNumRows(), (int)m_distanceToStart.GetNumCols(), (int)m_numParallelSequences, (int)m_numTimeSteps); // (I really want to know about actual allocations, but this is a necessary condition for them) -#endif - m_distanceToStart.Resize(m_numParallelSequences, m_numTimeSteps); - m_distanceToEnd.Resize(m_numParallelSequences, m_numTimeSteps); - m_distanceToNearestStart.assign(m_numTimeSteps, PTRDIFF_MAX); - m_distanceToNearestEnd.assign(m_numTimeSteps, PTRDIFF_MAX); - m_timeStepHasGap.assign(m_numTimeSteps, false); - m_columnsValidityMask.Resize(0, 0); // invalidate - // reset state - m_numFramesDeclared = 0; - m_numGapFrames = 0; - m_sequences.clear(); - m_writable = true; - } - - // ------------------------------------------------------------------- - // accessors - // ------------------------------------------------------------------- - - size_t GetNumTimeSteps() const { return m_numTimeSteps; } - size_t GetNumParallelSequences() const { return m_numParallelSequences; } - - // how many columns the underlying MB matrix has - size_t GetNumCols() const { return GetNumTimeSteps() * GetNumParallelSequences(); } - - // return all sequences stored in this minibatch - const vector & GetAllSequences() const { return m_sequences; } - - // compute the number of actual samples in this layout (not counting gaps) - // This is used by MeanNode and InvStdDevNode, and by statistics reporting. - size_t GetActualNumSamples() const; - - const Matrix & GetColumnsValidityMask(DEVICEID_TYPE deviceId) const; - -#if 0 // in the future we can use the tensor lib to implement this - template const Matrix GetColumnsValidMask() const; - template<> const Matrix GetColumnsValidMask() const { return m_distanceToStart.Reshaped(1, m_distanceToStart.GetNumElements()); } - template<> const Matrix GetColumnsValidMask() const { NOT_IMPLEMENTED; } -#endif - - // compare whether two layouts are the same - bool operator==(const MBLayout & other) const - { - if (this == &other) - return true; - bool res = - m_numTimeSteps == other.m_numTimeSteps && - m_numParallelSequences == other.m_numParallelSequences && - m_sequences == other.m_sequences; - return res; - } - bool operator!=(const MBLayout & other) const { return !(*this == other); } // duh - - // ------------------------------------------------------------------- - // building (adding sequences or gaps) - // ------------------------------------------------------------------- - - // mark a range of frames in a parallel sequence as one sentence - // Note that endTime is the last frame +1. Like begin/end as used in STL containers. - void AddSequence(UniqueSequenceId seqId, size_t s, ptrdiff_t beginTime, size_t endTime) - { - // old readers can just pass this to get an auto-assigned id (which is fine as long as we only have one MBLayout per minibatch) - if (seqId == NEW_SEQUENCE_ID) - { - static UniqueSequenceId makeSeqIdCounter = 0; - seqId = makeSeqIdCounter++; - if (seqId == GAP_SEQUENCE_ID) LogicError("AddSequence: ran out of bits..."); // (will never happen anyway) - } - - AddSequence(SequenceInfo { seqId, s, beginTime, endTime }); - } - - // version that passes a SequenceInfo record directly - void AddSequence(const SequenceInfo & seqDesc) - { - const auto beginTime = seqDesc.tBegin; - const auto endTime = seqDesc.tEnd; - - CheckWritable(); - if ((ptrdiff_t)endTime <= beginTime) - LogicError("AddSequence: Sequences must be a least one frame long."); - if (beginTime >= (ptrdiff_t)m_numTimeSteps) // no need to test endTime since it is always non-negative (size_t) - LogicError("AddSequence: Sequence added to an MBLayout must overlap with minibatch."); - - // remember it -#if 0//def _DEBUG - auto cap = m_sequences.capacity(); // Some sanity check for debugging a speed regression. This should only show up during the first minibatches, and growing only. - m_sequences.push_back(seqDesc); - if (cap != m_sequences.capacity()) - fprintf(stderr, "AddSequence: m_sequences was reallocated from capacity %d to %d\n", (int)cap, (int)m_sequences.capacity()); -#else - m_sequences.push_back(seqDesc); -#endif - - // create all the cached fast-lookup information - const auto seqId = seqDesc.seqId; - const auto s = seqDesc.s; - size_t b = (size_t)(max(beginTime, (ptrdiff_t)0)); - size_t e = min(endTime, m_numTimeSteps); - m_numFramesDeclared += (e - b); - if (seqId == GAP_SEQUENCE_ID) - { - m_numGapFrames += (e - b); - for (size_t t = b; t < e; t++) - { - m_timeStepHasGap[t] = true; - m_distanceToStart(s, t) = -1; // start flags also encode gaps - } - } - else for (size_t t = b; t < e; t++) - { - // update the nearest sentence boundaries, minimum over all parallel sequences - // If 0, then we are on a boundary. If not 0, we can still test in presence of FrameRange.m_timeOffset. - ptrdiff_t distanceToStart = (ptrdiff_t)t - beginTime; - ptrdiff_t distanceToEnd = (ptrdiff_t)(endTime - 1 - t); - m_distanceToStart(s, t) = (float)distanceToStart; - m_distanceToEnd(s, t) = (float)distanceToEnd; - // and the aggregate - if (m_distanceToNearestStart[t] > distanceToStart) - m_distanceToNearestStart[t] = distanceToStart; - if (m_distanceToNearestEnd[t] > distanceToEnd) - m_distanceToNearestEnd[t] = distanceToEnd; - } - } - - // short-hand to initialize an MBLayout for the common case of frame mode - // In frame mode, there is one parallel "sequence" per sample, which is 1 frame long. - // This function provides an efficient short-cut implementation of AddSequence(t, t, 0, 1) for every sample t. - void InitAsFrameMode(size_t numSamples) - { - Init(numSamples, 1); - - // create sequences array - SequenceInfo virginSeqInfo = { 0, 0, 0, 1 }; - m_sequences.resize(numSamples, virginSeqInfo); // pass it here since otherwise STL will initialize everything to 0 unnecessarily - - // update sequence indices - for (size_t s = 0; s < numSamples; s++) - { - // remember it - auto & seqDesc = m_sequences[s]; - seqDesc.seqId = s; - seqDesc.s = s; - } - m_numFramesDeclared = numSamples; - - // create all the cached fast-lookup information - m_distanceToStart.SetValue(0); - m_distanceToEnd.SetValue(0); - m_distanceToNearestStart[0] = 0; - m_distanceToNearestEnd[0] = 0; - - Lock(); - } - - // mark a range of frames in a parallel sequence as invalid - // I'd love to start with all-gaps, but that would require to set flags upfront, and then clearing them. - void AddGap(size_t s, ptrdiff_t beginTime, size_t endTime) { if ((ptrdiff_t)endTime > beginTime) AddSequence(GAP_SEQUENCE_ID, s, beginTime, endTime); } - - // find a sequence by its id - const SequenceInfo & FindSequence(UniqueSequenceId seqId) const - { - for (const auto & seqInfo : m_sequences) - if (seqInfo.seqId == seqId) - return seqInfo; - LogicError("FindSequence: Requested sequence (id %u) not found.", (unsigned int) seqId); - } - - // ------------------------------------------------------------------- - // inquire about gaps or boundaries - // ------------------------------------------------------------------- - - bool HasGaps() const; - bool HasGaps(const FrameRange & fr) const; - - // test boundary flags for a specific condition - bool IsBeyondStartOrEnd(const FrameRange & fr) const; - bool IsGap(const FrameRange & fr) const; - - // test whether at least one sequence crosses the bounds of this minibatch - bool HasSequenceBeyondBegin() const - { - for (const auto & seq : m_sequences) - if (seq.tBegin < 0) - return true; - return false; - } - - bool HasSequenceBeyondEnd() const - { - for (const auto & seq : m_sequences) - if (seq.tEnd > m_numTimeSteps) - return true; - return false; - } - - private: - - // we are trying to access content--this verifies that the structure is consistent - // All frames must now be declared. - void CheckIsValid() const - { - if (m_numFramesDeclared != GetNumCols()) - LogicError("MBLayout: Attempting to read out flags, but only only %d out of %d frames have been defined.", - (int)m_numFramesDeclared, (int)(m_numTimeSteps * m_numParallelSequences)); - } - - // Ensure that the MBLayout allows writes - void CheckWritable() const - { - if (!m_writable) - LogicError("Modification attempted on a MBLayout that is no longer writable."); - } - - // Freeze the MBLayout disallowing further modifications through set operations - void Lock() const - { - CheckIsValid(); - m_writable = false; - } - - private: - - // ------------------------------------------------------------------- - // data members: main information - // ------------------------------------------------------------------- - - // dimensions - size_t m_numTimeSteps; - size_t m_numParallelSequences; - - // all sequences that live inside this minibatch - vector m_sequences; - - private: - - // ------------------------------------------------------------------- - // data members: cached information and inverse lookup tables - // ------------------------------------------------------------------- - - // counters on how much has been declared, for fast access (this can be recomputed from m_sequences as well) - size_t m_numFramesDeclared; - size_t m_numGapFrames; - - // Lookup tables for determining whether any sequence at time t is a boundary or gap. - // An optional time delay can be given, then the test is whether going from t to (t + time delay) crosses a boundary. - // The purpose is for knowing when to reset state of a recurrent node. - // - // For every (s,t), we store the distance to the corresponding sequence begin and end. - // We also store for every [t] an aggregate to know the nearest boundary. - // For example, two sentences used in parallel, one with 5 and one with 3 time steps, in one minibatch, both starting at step 0 - // Would be described by these [2 x 5] matrices: - // m_distanceToStart = [ 0 1 2 3 4 ; - // 0 1 2 -1 -1 ] // (last two time steps have no content) - // m_distanceToEnd = [ 4 3 2 1 0 ; - // 2 1 0 . . ] // (last two time steps undefined) - // m_distanceToNearestStart = [ 0 1 2 3 4 ] - // m_distanceToNearestEnd = [ 2 1 0 1 0 ] - Matrix m_distanceToStart, m_distanceToEnd; // (s,t); value<0 stands for gap - vector m_distanceToNearestStart, m_distanceToNearestEnd; // [t] (does not store info about gaps; consult m_timeStepHasGap[] vector instead) - - vector m_timeStepHasGap; // [t] true if at least one gap in time step t - - // Cached mask indicating the validity of each column in the MBLayout - // TODO: We actually just need a boolean matrix for this. - // A value of 1 indicates that the column has valid content - // and 0 indicates invalid (aka MinibatchPackingFlags::NoInput) - mutable Matrix m_columnsValidityMask; - - // A boolean flag indicating whether the MBLayout can be further modified - // When it's value is false, no set operations are allowed on the MBLayout. - // Meant to guard in lazy creation of m_columnsValidityMask. - mutable bool m_writable; - - public: - - // ------------------------------------------------------------------- - // special deprecated functions that are result of refactoring (to go away) - // ------------------------------------------------------------------- - - // only used in sequence training, must be replaced by a different mechanism - bool IsEnd(size_t s, size_t t) const - { - auto distanceToStart = (ptrdiff_t)m_distanceToStart(s, t); -#if 1 // I don't exactly know what this does, so try assert() fifst - assert(distanceToStart != -1); distanceToStart; -#else - if (distanceToStart == -1) // indicates a gap - return false; -#endif - auto distanceToEnd = (size_t)m_distanceToEnd(s, t); - return distanceToEnd == 0; - } - - }; - typedef MBLayout::MBLayoutPtr MBLayoutPtr; - - // ----------------------------------------------------------------------- - // FrameRange -- identifies a frame or a set of frames to apply computation to - // - // Operations can be applied all at once to all frames (PAR) or sequentially (SEQ). - // - // PAR is typically encountered in feed-forward DNNs, where all frames of a minibatch are independent. - // Thus, operations can be applied to all frames concurrently, using a single CUDA - // launche for all frames at once. In this case, the FrameRange identifies the - // entire minibatch. - // - // SEQ is needed for recurrent networks, where frames must be processed iteratively. - // However, we still process multiple parallel sequences concurrently. In this case, the - // FrameRange would identify frames of the same time step across all sequences. - // - // To access the subset of a minibatch matrix selected by FrameFange, use DataWithMBLayoutFor(). - // - // TODO: This will in the future be able to hold sub-ranges for nested loops as well. - // ----------------------------------------------------------------------- - - // TODO: We should also have a FrameRange that selects all frames of a single sequence. Currently now possible since that would require Matrix::RowSlice() - // - likewise, LSTMNode does its own iteration, hence needs access to GetNumParallelSequences() or NumCols() in the whole-batch iterator - // BUGBUG: These nodes are currently broken and will need to be fixed: - // - CRFNode does not support > 1 parallel sequence - class FrameRange - { - public: // TODO: make private (currently used from masking and DataFor) ; TODO: rename all members with m_ prefix - size_t timeIdxInSeq; // start frame; SIZE_MAX = all frames in MB - ptrdiff_t m_timeOffset; // this is added to timeIdxInSeq wherever it is used - size_t m_timeRange; // use this to describe a custom range > 1 frame - size_t seqIndex; // parallel-sequence index; SIZE_MAX = all sequences in MB (most common case) --TODO: Bad name, 'sequence' and 'parallel sequence' are two different things - MBLayoutPtr m_pMBLayout; // layout associated with this - bool m_broadcastAllowed; // frame range may be broadcast from outer layout (e.g. a matrix with NULL layout and 1 column is acceptable to this frame range). Only applies when iterating over time; otherwise broadcasting is always OK. - const FrameRange *parent; // or NULL: parent range, relative to which this FrameRange is interpreted --TODO: not used yet - - public: - // can construct from a single size_t -> a single-frame range - FrameRange(MBLayoutPtr pMBLayout, size_t timeIdxInSeq) : timeIdxInSeq(timeIdxInSeq), m_timeOffset(0), m_timeRange(1), seqIndex(SIZE_MAX), m_pMBLayout(pMBLayout), m_broadcastAllowed(false), parent(nullptr) {} - - // or without arguments -> entire minibatch / no frame-range - FrameRange(MBLayoutPtr pMBLayout) : FrameRange(pMBLayout, SIZE_MAX) {} - - // no arguments--used if passed as an out parameter - FrameRange() : FrameRange(MBLayoutPtr(), SIZE_MAX) {} - - // return a frame range with broadcast allowed - // This is used, e.g., by PlusNode which can combine minibatch data and single-column vectors. - FrameRange AllowBroadcast() const - { - FrameRange ret = *this; - ret.m_broadcastAllowed = true; - return ret; - } - - // create a FrameRange that accesses a single sequence only - // FrameRange(t).Sequence(seq) - FrameRange Sequence(size_t s) const - { - FrameRange ret = *this; - ret.seqIndex = s; - return ret; - } - - // create a FrameRange with its MBLayout replaced by another - // You must check yourself whether this is correct. - FrameRange WithLayout(MBLayoutPtr pMBLayout) const - { - FrameRange ret = *this; - ret.m_pMBLayout = pMBLayout; - return ret; - } - - // create a FrameRange with a time offset - // If IsAllFrames() then this will cause out-of-bounds slices. - FrameRange WithTimeOffset(ptrdiff_t offset) const - { - FrameRange ret = *this; - ret.m_timeOffset += offset; - return ret; - } - - // create a FrameRange with a time range > 1 - FrameRange WithTimeRange(size_t range) const - { - FrameRange ret = *this; - if (!IsAllFrames()) - ret.m_timeRange = range; - return ret; - } - - // create a FrameRange from another with an updated time index - FrameRange WithTimeStep(size_t begin) const - { - FrameRange ret = *this; - ret.timeIdxInSeq = begin; - return ret; - } - - // dimension we are iterating over; -1 means time dimension; 0 means no layout - int GetIterationDimension() const - { - if (!m_pMBLayout) - return 0; - else - return -1; // TODO: allow user to specify other dimensions - } - - class IndexIteration // range for range-based for over sequences - { - size_t m_beginIndex, m_endIndex; - public: - IndexIteration(size_t beginIndex, size_t endIndex) : m_beginIndex(beginIndex), m_endIndex(endIndex) { } - size_t begin() const { return m_beginIndex; } - size_t end() const { return m_endIndex; } - }; - IndexIteration GetSequenceRange(const shared_ptr & pMBLayout) const { return IndexIteration(seqIndex == SIZE_MAX ? 0 : seqIndex, seqIndex == SIZE_MAX ? pMBLayout->GetNumParallelSequences() : seqIndex + 1); } - - // code that can only handle single-frame ranges will call t() to get the time index, which will throw if numFrames != 1 - // Some functions need just the time index, e.g. for looking up stuff in m_boundaryInfo. That's where an unscaled index is needed. - // Really only used in RecurrentNodes(), where it will be replaced by FrameRange::WithDelay() which allows to access delayed frames through the FrameRange object. - size_t t() const - { - EnsureNotAllFrames(); - ptrdiff_t t = m_timeOffset + (ptrdiff_t)timeIdxInSeq; - if (t < 0 || (size_t)t >= m_pMBLayout->GetNumTimeSteps()) - InvalidArgument("FrameRange::t(): Time offset caused time index to be out of range."); - return (size_t)t; - } - - bool IsAllFrames() const { return timeIdxInSeq == SIZE_MAX; } // if true then above functions may not be called; caller must use entire batch instead (PAR mode) - - private: - void EnsureNotAllFrames() const - { - if (IsAllFrames()) - LogicError("FrameRange::t() called when frame range refers to whole minibatch"); - } - }; - - // ----------------------------------------------------------------------- - // MBLayout functions that require FrameRange - // ----------------------------------------------------------------------- - - inline bool MBLayout::HasGaps() const { return m_numGapFrames > 0; /*HasGaps(FrameRange());*/ } - inline bool MBLayout::HasGaps(const FrameRange & fr) const - { - CheckIsValid(); - if (fr.IsAllFrames()) - return m_numGapFrames > 0; // test entire minibatch - if (fr.seqIndex == SIZE_MAX) - return m_timeStepHasGap[fr.timeIdxInSeq]; // test all seq for one time step - else - return IsGap(fr); // test one sequence - } - - // test whether a given frame is or contains a gap - inline bool MBLayout::IsGap(const FrameRange & fr) const - { - CheckIsValid(); - - if (fr.IsAllFrames()) - LogicError("MBLayout::Get() cannot be applied to FrameRange that specifies more than a single time step."); - - const auto t = fr.timeIdxInSeq; // we test off the frame without offset - const auto s = fr.seqIndex; - if (s == SIZE_MAX) // aggregate requested - return m_timeStepHasGap[t]; - - // determine flags from matrices - return m_distanceToStart(s, t) < 0; // value is -1 for gaps, non-negative otherwise - } - - // test whether frame is exceeding the sentence boundaries - inline bool MBLayout::IsBeyondStartOrEnd(const FrameRange & fr) const - { - CheckIsValid(); - - if (fr.IsAllFrames()) - LogicError("MBLayout::IsBeyondStartOrEnd() cannot be applied to FrameRange that specifies more than a single time step."); - - const auto t = fr.timeIdxInSeq; // we test off the frame without offset - const auto s = fr.seqIndex; - if (s == SIZE_MAX) // aggregate requested - { - // determine flags from aggregate vectors - // Note: We allow that all parallel sequences contain gaps (m_distanceToNearestStart[t] == PTRDIFF_MAX) - // because that makes implementation of the reader easier for truncated BPTT (it knows too late that there are not that many frames left). - auto distanceToStart = (ptrdiff_t)m_distanceToNearestStart[t]; - if (distanceToStart < -fr.m_timeOffset) - return true; - auto distanceToEnd = (ptrdiff_t)m_distanceToNearestEnd[t]; - if (distanceToEnd < fr.m_timeOffset) - return true; - return false; - } - - // determine flags from matrices - auto distanceToStart = (ptrdiff_t)m_distanceToStart(s, t); - if (distanceToStart == -1) // indicates a gap - { - assert(m_timeStepHasGap[t]); - return false; // a gap is not outside, so that we can allow collating - } - else - { - if (distanceToStart < -fr.m_timeOffset) - return true; - auto distanceToEnd = (ptrdiff_t)m_distanceToEnd(s, t); - if (distanceToEnd < fr.m_timeOffset) - return true; - } - return false; - } - - // TODO: Remove this version (with sanity checks) after this has been tested. Then the function can be inlined above. - inline size_t MBLayout::GetActualNumSamples() const - { -#if 0 // sanity check --TODO: delete this after a while - size_t n = GetNumCols(); - if (HasGaps()) - { - for (size_t t = 0; t < GetNumTimeSteps(); t++) - { - FrameRange fr(nullptr, t); - if (IsGap(fr)) - { - for (size_t s = 0; s < GetNumParallelSequences(); s++) - { - if (IsGap(fr.Sequence(s))) - n--; - } - } - } - } - if (m_numGapFrames != GetNumCols() - n) - LogicError("GetActualNumSamples: Gap counting broken, measured %d vs. originally counted %d", (int)(GetNumCols() - n), (int)m_numGapFrames); - assert(m_numFramesDeclared - m_numGapFrames == n); -#endif - return m_numFramesDeclared - m_numGapFrames; - } - - // return m_columnsValidityMask(,), which is lazily created here upon first call - // only called from MaskMissingColumnsTo() - // TODO: Can probably be faster by using the sequence array directly. - // TODO: Or should we just blast m_distanceToStart to GPU, and maks based on that? It is small compared to features. - inline const Matrix & MBLayout::GetColumnsValidityMask(DEVICEID_TYPE deviceId) const - { - CheckIsValid(); - // lazily compute the validity mask - if (m_columnsValidityMask.IsEmpty()) - { - assert(HasGaps()); // must only be called if there are gaps - Lock(); - - // Determine indices of all invalid columns in the minibatch - // TODO: This can be done more efficiently by using m_sequences[]. - size_t nT = GetNumTimeSteps(); - size_t nS = GetNumParallelSequences(); - - std::vector columnsValidityMask(nT * nS, 1); // form the mask in a CPU-side STL vector first - size_t gapsFound = 0; - for (size_t t = 0; t < nT; t++) - { - FrameRange fr(nullptr, t); - if (IsGap(fr)) - { - for (size_t s = 0; s < nS; s++) - { - if (IsGap(fr.Sequence(s))) - { - columnsValidityMask[(t * nS) + s] = 0; - gapsFound++; - } - } - } - } - assert(gapsFound == m_numGapFrames); // sanity check - - if (deviceId != m_columnsValidityMask.GetDeviceId()) - m_columnsValidityMask = Matrix(deviceId); - m_columnsValidityMask.SetValue(1, nS * nT, deviceId, columnsValidityMask.data()); - } - return m_columnsValidityMask; - } - - // class for defining an iteration over a sequence, forward and backward - // One day, we may also have nested structures. For those, FrameRangeIterations will be able to be instantiated from FrameRange objects to loop over their nested dimension. - class FrameRangeIteration - { - MBLayoutPtr m_pMBLayout; - int m_step; - public: - // one-dimensional iteration (time sequences) - // 'Step' specifies the stepping direction of the loop: - // - for left-to-right models -> pass step = +1 - // - for right-to-left models -> pass step = -1 - FrameRangeIteration(MBLayoutPtr pMBLayout, int step) : m_pMBLayout(pMBLayout), m_step(step) { } - // This class is returned by begin() and end(). - // It is a FrameRange with additions ++ and != operators needed in the for loop. - class FrameRangeIterator : public FrameRange - { - ptrdiff_t m_step; - public: - FrameRangeIterator(const FrameRange & begin, ptrdiff_t step) : FrameRange(begin), m_step(step) { } - bool operator!=(const FrameRangeIterator & other) const { return timeIdxInSeq != other.timeIdxInSeq; } - void operator++(int) { timeIdxInSeq = (size_t)(m_step + (ptrdiff_t)timeIdxInSeq); } // going through (int) to avoid undefined behavior - }; - // iterators for iterating forward - FrameRangeIterator begin() const - { - if (m_step > 0) return FrameRangeIterator(FrameRange(m_pMBLayout, 0), +1); - else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps() - 1), -1); - } - FrameRangeIterator end() const - { - if (m_step < 0) return FrameRangeIterator(FrameRange(m_pMBLayout, (size_t)-1), 0/*dummy*/); - else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps()), 0); - } - // iterators for iterating in reverse order (as needed for gradient update) - FrameRangeIterator rbegin() const - { - if (m_step < 0) return FrameRangeIterator(FrameRange(m_pMBLayout, 0), +1); - else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps() - 1), -1); - } - FrameRangeIterator rend() const - { - if (m_step > 0) return FrameRangeIterator(FrameRange(m_pMBLayout, (size_t)-1), 0); - else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps()), 0); - } - }; - - // ----------------------------------------------------------------------- - // ColumnRangeWithMBLayoutFor() -- Return column range for a FrameRange of a Matrix with specified number of columns with a given MBLayout - // ----------------------------------------------------------------------- - - static inline std::pair ColumnRangeWithMBLayoutFor(size_t numCols/*of data matrix to slice*/, - const FrameRange & fr/*select frame or entire batch*/, - const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) - { - // MBLayout of data and of FrameRange must be identical pointers, - // or in case of broadcasting, respective parent pointers. - // MBLayouts that are identical in content but not object identity (pointer) are not admissible. - // For those cases, use a ReconcileMBLayout node. - if (fr.m_pMBLayout != pMBLayout) - { - // if broadcast allowed then it is allowed to broadcast from an outer-loop value - // Currently, the only 'outer' loop we have is to have no layout. - if (fr.m_broadcastAllowed && !pMBLayout && numCols == 1) - return std::pair(0, numCols); - if (fr.m_pMBLayout && pMBLayout && *fr.m_pMBLayout == *pMBLayout) - LogicError("DataFor: fr's MBLayout inconsistent with matrix. They are compatible though--are you missing a ReconcileMBLayout operation?"); - else - LogicError("DataFor: fr's MBLayout inconsistent with matrix"); - } - // if FrameRange refers to whole minibatch (map mode) - // or if we don't even have a layout - // then return the whole matrix - // but as a reference (e.g. it cannot be resized) - if (!pMBLayout || fr.IsAllFrames()) - { - if (fr.m_timeOffset != 0) - LogicError("DataFor: Time offset must not be specified for FrameRanges that reference the entire minibatch."); // (note: the tensor version allows this) - if (fr.seqIndex == SIZE_MAX) - return std::pair(0, numCols); - else - { - if (!pMBLayout) - LogicError("DataFor: Attempting to retrieve a parallel sequence from data without layout."); - else - LogicError("DataFor: Individual parallel sequences cannot be retrieved in Matrix representation. Use TensorView instead."); - } - } - // FrameRange refers to a time slice -> return that - else - { - size_t numParallelSequences = pMBLayout->GetNumParallelSequences(); - size_t startColumn = (fr.timeIdxInSeq + fr.m_timeOffset) * numParallelSequences; - if (startColumn >= numCols) - LogicError("DataFor: FrameRange specifies a time index that is out of range."); - if (fr.seqIndex == SIZE_MAX) - return std::pair(startColumn, numParallelSequences * fr.m_timeRange); - else if (fr.m_timeRange != 1) - LogicError("DataFor: FrameRange only support per-sequence time ranges with tensor slices, not matrix slices."); - else - return std::pair(startColumn + fr.seqIndex, 1); - } - } - - // ----------------------------------------------------------------------- - // DataWithMBLayoutFor() -- create view for a FrameRange of a Matrix with a given MBLayout - // This function binds the above together. - // Any access by FrameRange should only be done through this function. - // ----------------------------------------------------------------------- - - template - static inline Matrix DataWithMBLayoutFor(const Matrix & data, - const FrameRange & fr/*select frame or entire batch*/, - const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) - { - auto columnRange = ColumnRangeWithMBLayoutFor(data.GetNumCols(), fr, pMBLayout); - return data.ColumnSlice(columnRange.first, columnRange.second); - } - - // ----------------------------------------------------------------------- - // TensorSliceWithMBLayoutFor() -- Return tensor slice for a FrameRange with a given MBLayout. - // This implements the logic of interpreting the FrameRange object. - // Unlike the matrix version above, this supports iteration indices other than time. - // If the iteration index is time, then it refers to the minibatch's time, not time of - // individual sequences (the difference is where multiple sequences get concatenated). - // TODO: This ^^. FrameRange still missing is a field to identify the index. - // This function happily returns tensor bounds that are out of bounds, assuming caller will do the right thing. - // ----------------------------------------------------------------------- - - template // e.g. std::vector or SmallVector - static inline std::pair TensorSliceWithMBLayoutFor(const DimensionVector & shape/*actual tensor shape of 'data'*/, - const FrameRange & fr/*select frame or entire batch from 'data'*/, - const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) - { - std::pair result; - typedef decltype(result.first[0]) ElemType; - - // this creates a slice for the entire matrix, which we will then narrow down - result.first.resize(shape.size(), 0); - result.second = shape; - - // get position of time and sequence index - // These are only valid if we have a layout. - // In the future, the 'timeDim' will be identified by the FrameRange. +// Sequences.h -- all about iterating over sequences, that is, MBLayout, FrameRange, and iterators +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#pragma once + +#include "Basics.h" +#include "Matrix.h" +#include +#include // for shared_ptr + +namespace Microsoft { namespace MSR { namespace CNTK { + + // Forward declarations + class FrameRange; + + typedef size_t UniqueSequenceId; +#define GAP_SEQUENCE_ID SIZE_MAX // indicates no data +#define NEW_SEQUENCE_ID (SIZE_MAX-1) // let SetSequence() assign a unique id; for old readers. Don't mix with actual reader-assigned ids. + + // ----------------------------------------------------------------------- + // MBLayout -- layout information of minibatch + // + // Minibatches are collections of one or more sequences, laid out in a way to + // allow to process one time step for multiple sequences in parallel in shared CUDA launches. + // + // This is achieved by interleaving storage. If f(s,t) denotes a frame of sequence s at time t, + // the minibatch matrix would contain this: + // f(0,0) f(1,0) ... f(0,1) f(1,1) ... + // Much of CNTK's efficiency comes from this. + // (Note that some communities, such as language processing, often call sets f(0..s-1,t) a + // "minibatch," where in our definition, a minibatch consists of multiple entire sequences.) + // + // In the special case of frame randomization, every frame is stored as a single-frame sequence. + // + // If we describe this in terms of tensors, a data matrix with sample layout (I,J,K) and + // MBLayout (S,T) can be interpreted as TensorShape(I,J,K,S,T). + // + // Sequences can also be concatenated to fill the space better. For this case, + // this object stores about every frame whether it is at the start or end of a sequence. + // Hence, we distinguish between "sequences" (logical units) and "parallel sequences" + // (where one "parallel sequence" may consist of multiple concatenated "sequences"). + // + // When not all sequences have the same length, some parallel sequences have invalid frames (gaps). + // Gaps are identified by the MBLayouyt as well. Currently, these gaps only occur at the end. + // + // Gaps may also arise due to invalid input data (e.g. a speech utterance for which no alignment could be generated). + // + // An MBLayout provides the following functions: + // - (building:) add a new sequence (or gap range) to the MBLayout + // - inquire the set of sequences (sequence ids) that intersect with this minibatch + // - inquire whether any time step t has a gap or boundary across all sequences + // - inquire for gap or boundary at (s,t) + // + // Truncated BPTT support (partial sequences): + // - in truncated BPTT, minibatches only contain partial sequences, e.g. a range of 20 time steps + // - boundary information is stored for every sequence that intersects with this minibatch, + // including boundaries that fall outside of the time range of the minibatch + // + // An MBLayout object stores: + // - for every sequence in the minibatch the n-tuple (global sequence id, s, first t, last t) + // (where first and last t may sometimes lie outside of the minibatch, e.g. in case of truncated BPTT) + // - number of time steps and parallel sequences (their product is equal to the #columns in the minibatch matrix) + // - lookup tables for looking up gap and boundary information + // ----------------------------------------------------------------------- + + // Contract between ComputationNode, ComputationNetwork, and MBLayout: + // - if a node has no MBLayout, m_{value,gradient} are not samples (they are not activations or input data), but e.g. model parameters + // - ComputationNode::GetNumCols() == MBLayout::GetNumTimeSteps() * MBLayout::GetNumParallelSequences() + // - ComputationNetwork ensures that m_{value,gradient} are allocated correctly before calling ForwardProp() on a node + + struct MBLayout + { + typedef std::shared_ptr MBLayoutPtr; + + // information stored about sequences + struct SequenceInfo + { + UniqueSequenceId seqId; // unique sequence id (or GAP_SEQUENCE_ID--TODO: don't include gaps here) + size_t s; // index of parallel sequence + ptrdiff_t tBegin; // first time index in this minibatch. Note that this may be negative of the sequence started before this MB. + size_t tEnd; // end = first frame index after final frame. May be beyond the minibatch if reql sequence is longer than the MB. + bool operator==(const SequenceInfo & other) const { return seqId == other.seqId && s == other.s && tBegin == other.tBegin && tEnd == other.tEnd; } + size_t GetNumTimeSteps() const { return (size_t)(tEnd - tBegin); } + }; + + // ------------------------------------------------------------------- + // construction + // ------------------------------------------------------------------- + + MBLayout(size_t numParallelSequences, size_t numTimeSteps) : m_distanceToStart(CPUDEVICE), m_distanceToEnd(CPUDEVICE), m_columnsValidityMask(CPUDEVICE) { Init(numParallelSequences, numTimeSteps); } + MBLayout() : MBLayout(1, 0) { } + + // copy the content of another MBLayoutPtr over + // Use this instead of actual assignment to make it super-obvious that this is not copying the pointer but actual content. The pointer is kept fixed. + void CopyFrom(const MBLayoutPtr & other) { *this = *other; } + void MoveFrom(MBLayoutPtr other) { *this = move(*other); other->Init(0, 0); } // destructive copy that steals ownership if the content, like std::move() + private: + MBLayout & operator=(const MBLayout &) = default; // make this private --use CopyFrom() instead, which makes it very clear that it's copying content, not copying the reference + public: + + // resize and reset all frames to None (note: this is an invalid state and must be fixed by caller afterwards) + void Init(size_t numParallelSequences, size_t numTimeSteps) + { + // remember the dimensions + m_numParallelSequences = numParallelSequences; + m_numTimeSteps = numTimeSteps; + // allocate lookup tables (note: except at the start, these don't really allocate new memory most of the time) +#if 0 + if ((m_distanceToStart.GetNumRows() != m_numParallelSequences || m_distanceToStart.GetNumCols() != m_numTimeSteps) && m_numTimeSteps > 0) // sanity check for debugging a regression + fprintf(stderr, "MBLayout::Init: Resizing m_distanceToStart from %d x %d to %d x %d\n", + (int)m_distanceToStart.GetNumRows(), (int)m_distanceToStart.GetNumCols(), (int)m_numParallelSequences, (int)m_numTimeSteps); // (I really want to know about actual allocations, but this is a necessary condition for them) +#endif + m_distanceToStart.Resize(m_numParallelSequences, m_numTimeSteps); + m_distanceToEnd.Resize(m_numParallelSequences, m_numTimeSteps); + m_distanceToNearestStart.assign(m_numTimeSteps, PTRDIFF_MAX); + m_distanceToNearestEnd.assign(m_numTimeSteps, PTRDIFF_MAX); + m_timeStepHasGap.assign(m_numTimeSteps, false); + m_columnsValidityMask.Resize(0, 0); // invalidate + // reset state + m_numFramesDeclared = 0; + m_numGapFrames = 0; + m_sequences.clear(); + m_writable = true; + } + + // ------------------------------------------------------------------- + // accessors + // ------------------------------------------------------------------- + + size_t GetNumTimeSteps() const { return m_numTimeSteps; } + size_t GetNumParallelSequences() const { return m_numParallelSequences; } + + // how many columns the underlying MB matrix has + size_t GetNumCols() const { return GetNumTimeSteps() * GetNumParallelSequences(); } + + // return all sequences stored in this minibatch + const vector & GetAllSequences() const { return m_sequences; } + + // compute the number of actual samples in this layout (not counting gaps) + // This is used by MeanNode and InvStdDevNode, and by statistics reporting. + size_t GetActualNumSamples() const; + + const Matrix & GetColumnsValidityMask(DEVICEID_TYPE deviceId) const; + +#if 0 // in the future we can use the tensor lib to implement this + template const Matrix GetColumnsValidMask() const; + template<> const Matrix GetColumnsValidMask() const { return m_distanceToStart.Reshaped(1, m_distanceToStart.GetNumElements()); } + template<> const Matrix GetColumnsValidMask() const { NOT_IMPLEMENTED; } +#endif + + // compare whether two layouts are the same + bool operator==(const MBLayout & other) const + { + if (this == &other) + return true; + bool res = + m_numTimeSteps == other.m_numTimeSteps && + m_numParallelSequences == other.m_numParallelSequences && + m_sequences == other.m_sequences; + return res; + } + bool operator!=(const MBLayout & other) const { return !(*this == other); } // duh + + // ------------------------------------------------------------------- + // building (adding sequences or gaps) + // ------------------------------------------------------------------- + + // mark a range of frames in a parallel sequence as one sentence + // Note that endTime is the last frame +1. Like begin/end as used in STL containers. + void AddSequence(UniqueSequenceId seqId, size_t s, ptrdiff_t beginTime, size_t endTime) + { + // old readers can just pass this to get an auto-assigned id (which is fine as long as we only have one MBLayout per minibatch) + if (seqId == NEW_SEQUENCE_ID) + { + static UniqueSequenceId makeSeqIdCounter = 0; + seqId = makeSeqIdCounter++; + if (seqId == GAP_SEQUENCE_ID) LogicError("AddSequence: ran out of bits..."); // (will never happen anyway) + } + + AddSequence(SequenceInfo { seqId, s, beginTime, endTime }); + } + + // version that passes a SequenceInfo record directly + void AddSequence(const SequenceInfo & seqDesc) + { + const auto beginTime = seqDesc.tBegin; + const auto endTime = seqDesc.tEnd; + + CheckWritable(); + if ((ptrdiff_t)endTime <= beginTime) + LogicError("AddSequence: Sequences must be a least one frame long."); + if (beginTime >= (ptrdiff_t)m_numTimeSteps) // no need to test endTime since it is always non-negative (size_t) + LogicError("AddSequence: Sequence added to an MBLayout must overlap with minibatch."); + + // remember it +#if 0//def _DEBUG + auto cap = m_sequences.capacity(); // Some sanity check for debugging a speed regression. This should only show up during the first minibatches, and growing only. + m_sequences.push_back(seqDesc); + if (cap != m_sequences.capacity()) + fprintf(stderr, "AddSequence: m_sequences was reallocated from capacity %d to %d\n", (int)cap, (int)m_sequences.capacity()); +#else + m_sequences.push_back(seqDesc); +#endif + + // create all the cached fast-lookup information + const auto seqId = seqDesc.seqId; + const auto s = seqDesc.s; + size_t b = (size_t)(max(beginTime, (ptrdiff_t)0)); + size_t e = min(endTime, m_numTimeSteps); + m_numFramesDeclared += (e - b); + if (seqId == GAP_SEQUENCE_ID) + { + m_numGapFrames += (e - b); + for (size_t t = b; t < e; t++) + { + m_timeStepHasGap[t] = true; + m_distanceToStart(s, t) = -1; // start flags also encode gaps + } + } + else for (size_t t = b; t < e; t++) + { + // update the nearest sentence boundaries, minimum over all parallel sequences + // If 0, then we are on a boundary. If not 0, we can still test in presence of FrameRange.m_timeOffset. + ptrdiff_t distanceToStart = (ptrdiff_t)t - beginTime; + ptrdiff_t distanceToEnd = (ptrdiff_t)(endTime - 1 - t); + m_distanceToStart(s, t) = (float)distanceToStart; + m_distanceToEnd(s, t) = (float)distanceToEnd; + // and the aggregate + if (m_distanceToNearestStart[t] > distanceToStart) + m_distanceToNearestStart[t] = distanceToStart; + if (m_distanceToNearestEnd[t] > distanceToEnd) + m_distanceToNearestEnd[t] = distanceToEnd; + } + } + + // short-hand to initialize an MBLayout for the common case of frame mode + // In frame mode, there is one parallel "sequence" per sample, which is 1 frame long. + // This function provides an efficient short-cut implementation of AddSequence(t, t, 0, 1) for every sample t. + void InitAsFrameMode(size_t numSamples) + { + Init(numSamples, 1); + + // create sequences array + SequenceInfo virginSeqInfo = { 0, 0, 0, 1 }; + m_sequences.resize(numSamples, virginSeqInfo); // pass it here since otherwise STL will initialize everything to 0 unnecessarily + + // update sequence indices + for (size_t s = 0; s < numSamples; s++) + { + // remember it + auto & seqDesc = m_sequences[s]; + seqDesc.seqId = s; + seqDesc.s = s; + } + m_numFramesDeclared = numSamples; + + // create all the cached fast-lookup information + m_distanceToStart.SetValue(0); + m_distanceToEnd.SetValue(0); + m_distanceToNearestStart[0] = 0; + m_distanceToNearestEnd[0] = 0; + + Lock(); + } + + // mark a range of frames in a parallel sequence as invalid + // I'd love to start with all-gaps, but that would require to set flags upfront, and then clearing them. + void AddGap(size_t s, ptrdiff_t beginTime, size_t endTime) { if ((ptrdiff_t)endTime > beginTime) AddSequence(GAP_SEQUENCE_ID, s, beginTime, endTime); } + + // find a sequence by its id + const SequenceInfo & FindSequence(UniqueSequenceId seqId) const + { + for (const auto & seqInfo : m_sequences) + if (seqInfo.seqId == seqId) + return seqInfo; + LogicError("FindSequence: Requested sequence (id %u) not found.", (unsigned int) seqId); + } + + // ------------------------------------------------------------------- + // inquire about gaps or boundaries + // ------------------------------------------------------------------- + + bool HasGaps() const; + bool HasGaps(const FrameRange & fr) const; + + // test boundary flags for a specific condition + bool IsBeyondStartOrEnd(const FrameRange & fr) const; + bool IsGap(const FrameRange & fr) const; + + // test whether at least one sequence crosses the bounds of this minibatch + bool HasSequenceBeyondBegin() const + { + for (const auto & seq : m_sequences) + if (seq.tBegin < 0) + return true; + return false; + } + + bool HasSequenceBeyondEnd() const + { + for (const auto & seq : m_sequences) + if (seq.tEnd > m_numTimeSteps) + return true; + return false; + } + + private: + + // we are trying to access content--this verifies that the structure is consistent + // All frames must now be declared. + void CheckIsValid() const + { + if (m_numFramesDeclared != GetNumCols()) + LogicError("MBLayout: Attempting to read out flags, but only only %d out of %d frames have been defined.", + (int)m_numFramesDeclared, (int)(m_numTimeSteps * m_numParallelSequences)); + } + + // Ensure that the MBLayout allows writes + void CheckWritable() const + { + if (!m_writable) + LogicError("Modification attempted on a MBLayout that is no longer writable."); + } + + // Freeze the MBLayout disallowing further modifications through set operations + void Lock() const + { + CheckIsValid(); + m_writable = false; + } + + private: + + // ------------------------------------------------------------------- + // data members: main information + // ------------------------------------------------------------------- + + // dimensions + size_t m_numTimeSteps; + size_t m_numParallelSequences; + + // all sequences that live inside this minibatch + vector m_sequences; + + private: + + // ------------------------------------------------------------------- + // data members: cached information and inverse lookup tables + // ------------------------------------------------------------------- + + // counters on how much has been declared, for fast access (this can be recomputed from m_sequences as well) + size_t m_numFramesDeclared; + size_t m_numGapFrames; + + // Lookup tables for determining whether any sequence at time t is a boundary or gap. + // An optional time delay can be given, then the test is whether going from t to (t + time delay) crosses a boundary. + // The purpose is for knowing when to reset state of a recurrent node. + // + // For every (s,t), we store the distance to the corresponding sequence begin and end. + // We also store for every [t] an aggregate to know the nearest boundary. + // For example, two sentences used in parallel, one with 5 and one with 3 time steps, in one minibatch, both starting at step 0 + // Would be described by these [2 x 5] matrices: + // m_distanceToStart = [ 0 1 2 3 4 ; + // 0 1 2 -1 -1 ] // (last two time steps have no content) + // m_distanceToEnd = [ 4 3 2 1 0 ; + // 2 1 0 . . ] // (last two time steps undefined) + // m_distanceToNearestStart = [ 0 1 2 3 4 ] + // m_distanceToNearestEnd = [ 2 1 0 1 0 ] + Matrix m_distanceToStart, m_distanceToEnd; // (s,t); value<0 stands for gap + vector m_distanceToNearestStart, m_distanceToNearestEnd; // [t] (does not store info about gaps; consult m_timeStepHasGap[] vector instead) + + vector m_timeStepHasGap; // [t] true if at least one gap in time step t + + // Cached mask indicating the validity of each column in the MBLayout + // TODO: We actually just need a boolean matrix for this. + // A value of 1 indicates that the column has valid content + // and 0 indicates invalid (aka MinibatchPackingFlags::NoInput) + mutable Matrix m_columnsValidityMask; + + // A boolean flag indicating whether the MBLayout can be further modified + // When it's value is false, no set operations are allowed on the MBLayout. + // Meant to guard in lazy creation of m_columnsValidityMask. + mutable bool m_writable; + + public: + + // ------------------------------------------------------------------- + // special deprecated functions that are result of refactoring (to go away) + // ------------------------------------------------------------------- + + // only used in sequence training, must be replaced by a different mechanism + bool IsEnd(size_t s, size_t t) const + { + auto distanceToStart = (ptrdiff_t)m_distanceToStart(s, t); +#if 1 // I don't exactly know what this does, so try assert() fifst + assert(distanceToStart != -1); distanceToStart; +#else + if (distanceToStart == -1) // indicates a gap + return false; +#endif + auto distanceToEnd = (size_t)m_distanceToEnd(s, t); + return distanceToEnd == 0; + } + + }; + typedef MBLayout::MBLayoutPtr MBLayoutPtr; + + // ----------------------------------------------------------------------- + // FrameRange -- identifies a frame or a set of frames to apply computation to + // + // Operations can be applied all at once to all frames (PAR) or sequentially (SEQ). + // + // PAR is typically encountered in feed-forward DNNs, where all frames of a minibatch are independent. + // Thus, operations can be applied to all frames concurrently, using a single CUDA + // launche for all frames at once. In this case, the FrameRange identifies the + // entire minibatch. + // + // SEQ is needed for recurrent networks, where frames must be processed iteratively. + // However, we still process multiple parallel sequences concurrently. In this case, the + // FrameRange would identify frames of the same time step across all sequences. + // + // To access the subset of a minibatch matrix selected by FrameFange, use DataWithMBLayoutFor(). + // + // TODO: This will in the future be able to hold sub-ranges for nested loops as well. + // ----------------------------------------------------------------------- + + // TODO: We should also have a FrameRange that selects all frames of a single sequence. Currently now possible since that would require Matrix::RowSlice() + // - likewise, LSTMNode does its own iteration, hence needs access to GetNumParallelSequences() or NumCols() in the whole-batch iterator + // BUGBUG: These nodes are currently broken and will need to be fixed: + // - CRFNode does not support > 1 parallel sequence + class FrameRange + { + public: // TODO: make private (currently used from masking and DataFor) ; TODO: rename all members with m_ prefix + size_t timeIdxInSeq; // start frame; SIZE_MAX = all frames in MB + ptrdiff_t m_timeOffset; // this is added to timeIdxInSeq wherever it is used + size_t m_timeRange; // use this to describe a custom range > 1 frame + size_t seqIndex; // parallel-sequence index; SIZE_MAX = all sequences in MB (most common case) --TODO: Bad name, 'sequence' and 'parallel sequence' are two different things + MBLayoutPtr m_pMBLayout; // layout associated with this + bool m_broadcastAllowed; // frame range may be broadcast from outer layout (e.g. a matrix with NULL layout and 1 column is acceptable to this frame range). Only applies when iterating over time; otherwise broadcasting is always OK. + const FrameRange *parent; // or NULL: parent range, relative to which this FrameRange is interpreted --TODO: not used yet + + public: + // can construct from a single size_t -> a single-frame range + FrameRange(MBLayoutPtr pMBLayout, size_t timeIdxInSeq) : timeIdxInSeq(timeIdxInSeq), m_timeOffset(0), m_timeRange(1), seqIndex(SIZE_MAX), m_pMBLayout(pMBLayout), m_broadcastAllowed(false), parent(nullptr) {} + + // or without arguments -> entire minibatch / no frame-range + FrameRange(MBLayoutPtr pMBLayout) : FrameRange(pMBLayout, SIZE_MAX) {} + + // no arguments--used if passed as an out parameter + FrameRange() : FrameRange(MBLayoutPtr(), SIZE_MAX) {} + + // return a frame range with broadcast allowed + // This is used, e.g., by PlusNode which can combine minibatch data and single-column vectors. + FrameRange AllowBroadcast() const + { + FrameRange ret = *this; + ret.m_broadcastAllowed = true; + return ret; + } + + // create a FrameRange that accesses a single sequence only + // FrameRange(t).Sequence(seq) + FrameRange Sequence(size_t s) const + { + FrameRange ret = *this; + ret.seqIndex = s; + return ret; + } + + // create a FrameRange with its MBLayout replaced by another + // You must check yourself whether this is correct. + FrameRange WithLayout(MBLayoutPtr pMBLayout) const + { + FrameRange ret = *this; + ret.m_pMBLayout = pMBLayout; + return ret; + } + + // create a FrameRange with a time offset + // If IsAllFrames() then this will cause out-of-bounds slices. + FrameRange WithTimeOffset(ptrdiff_t offset) const + { + FrameRange ret = *this; + ret.m_timeOffset += offset; + return ret; + } + + // create a FrameRange with a time range > 1 + FrameRange WithTimeRange(size_t range) const + { + FrameRange ret = *this; + if (!IsAllFrames()) + ret.m_timeRange = range; + return ret; + } + + // create a FrameRange from another with an updated time index + FrameRange WithTimeStep(size_t begin) const + { + FrameRange ret = *this; + ret.timeIdxInSeq = begin; + return ret; + } + + // dimension we are iterating over; -1 means time dimension; 0 means no layout + int GetIterationDimension() const + { + if (!m_pMBLayout) + return 0; + else + return -1; // TODO: allow user to specify other dimensions + } + + class IndexIteration // range for range-based for over sequences + { + size_t m_beginIndex, m_endIndex; + public: + IndexIteration(size_t beginIndex, size_t endIndex) : m_beginIndex(beginIndex), m_endIndex(endIndex) { } + size_t begin() const { return m_beginIndex; } + size_t end() const { return m_endIndex; } + }; + IndexIteration GetSequenceRange(const shared_ptr & pMBLayout) const { return IndexIteration(seqIndex == SIZE_MAX ? 0 : seqIndex, seqIndex == SIZE_MAX ? pMBLayout->GetNumParallelSequences() : seqIndex + 1); } + + // code that can only handle single-frame ranges will call t() to get the time index, which will throw if numFrames != 1 + // Some functions need just the time index, e.g. for looking up stuff in m_boundaryInfo. That's where an unscaled index is needed. + // Really only used in RecurrentNodes(), where it will be replaced by FrameRange::WithDelay() which allows to access delayed frames through the FrameRange object. + size_t t() const + { + EnsureNotAllFrames(); + ptrdiff_t t = m_timeOffset + (ptrdiff_t)timeIdxInSeq; + if (t < 0 || (size_t)t >= m_pMBLayout->GetNumTimeSteps()) + InvalidArgument("FrameRange::t(): Time offset caused time index to be out of range."); + return (size_t)t; + } + + bool IsAllFrames() const { return timeIdxInSeq == SIZE_MAX; } // if true then above functions may not be called; caller must use entire batch instead (PAR mode) + + private: + void EnsureNotAllFrames() const + { + if (IsAllFrames()) + LogicError("FrameRange::t() called when frame range refers to whole minibatch"); + } + }; + + // ----------------------------------------------------------------------- + // MBLayout functions that require FrameRange + // ----------------------------------------------------------------------- + + inline bool MBLayout::HasGaps() const { return m_numGapFrames > 0; /*HasGaps(FrameRange());*/ } + inline bool MBLayout::HasGaps(const FrameRange & fr) const + { + CheckIsValid(); + if (fr.IsAllFrames()) + return m_numGapFrames > 0; // test entire minibatch + if (fr.seqIndex == SIZE_MAX) + return m_timeStepHasGap[fr.timeIdxInSeq]; // test all seq for one time step + else + return IsGap(fr); // test one sequence + } + + // test whether a given frame is or contains a gap + inline bool MBLayout::IsGap(const FrameRange & fr) const + { + CheckIsValid(); + + if (fr.IsAllFrames()) + LogicError("MBLayout::Get() cannot be applied to FrameRange that specifies more than a single time step."); + + const auto t = fr.timeIdxInSeq; // we test off the frame without offset + const auto s = fr.seqIndex; + if (s == SIZE_MAX) // aggregate requested + return m_timeStepHasGap[t]; + + // determine flags from matrices + return m_distanceToStart(s, t) < 0; // value is -1 for gaps, non-negative otherwise + } + + // test whether frame is exceeding the sentence boundaries + inline bool MBLayout::IsBeyondStartOrEnd(const FrameRange & fr) const + { + CheckIsValid(); + + if (fr.IsAllFrames()) + LogicError("MBLayout::IsBeyondStartOrEnd() cannot be applied to FrameRange that specifies more than a single time step."); + + const auto t = fr.timeIdxInSeq; // we test off the frame without offset + const auto s = fr.seqIndex; + if (s == SIZE_MAX) // aggregate requested + { + // determine flags from aggregate vectors + // Note: We allow that all parallel sequences contain gaps (m_distanceToNearestStart[t] == PTRDIFF_MAX) + // because that makes implementation of the reader easier for truncated BPTT (it knows too late that there are not that many frames left). + auto distanceToStart = (ptrdiff_t)m_distanceToNearestStart[t]; + if (distanceToStart < -fr.m_timeOffset) + return true; + auto distanceToEnd = (ptrdiff_t)m_distanceToNearestEnd[t]; + if (distanceToEnd < fr.m_timeOffset) + return true; + return false; + } + + // determine flags from matrices + auto distanceToStart = (ptrdiff_t)m_distanceToStart(s, t); + if (distanceToStart == -1) // indicates a gap + { + assert(m_timeStepHasGap[t]); + return false; // a gap is not outside, so that we can allow collating + } + else + { + if (distanceToStart < -fr.m_timeOffset) + return true; + auto distanceToEnd = (ptrdiff_t)m_distanceToEnd(s, t); + if (distanceToEnd < fr.m_timeOffset) + return true; + } + return false; + } + + // TODO: Remove this version (with sanity checks) after this has been tested. Then the function can be inlined above. + inline size_t MBLayout::GetActualNumSamples() const + { +#if 0 // sanity check --TODO: delete this after a while + size_t n = GetNumCols(); + if (HasGaps()) + { + for (size_t t = 0; t < GetNumTimeSteps(); t++) + { + FrameRange fr(nullptr, t); + if (IsGap(fr)) + { + for (size_t s = 0; s < GetNumParallelSequences(); s++) + { + if (IsGap(fr.Sequence(s))) + n--; + } + } + } + } + if (m_numGapFrames != GetNumCols() - n) + LogicError("GetActualNumSamples: Gap counting broken, measured %d vs. originally counted %d", (int)(GetNumCols() - n), (int)m_numGapFrames); + assert(m_numFramesDeclared - m_numGapFrames == n); +#endif + return m_numFramesDeclared - m_numGapFrames; + } + + // return m_columnsValidityMask(,), which is lazily created here upon first call + // only called from MaskMissingColumnsTo() + // TODO: Can probably be faster by using the sequence array directly. + // TODO: Or should we just blast m_distanceToStart to GPU, and maks based on that? It is small compared to features. + inline const Matrix & MBLayout::GetColumnsValidityMask(DEVICEID_TYPE deviceId) const + { + CheckIsValid(); + // lazily compute the validity mask + if (m_columnsValidityMask.IsEmpty()) + { + assert(HasGaps()); // must only be called if there are gaps + Lock(); + + // Determine indices of all invalid columns in the minibatch + // TODO: This can be done more efficiently by using m_sequences[]. + size_t nT = GetNumTimeSteps(); + size_t nS = GetNumParallelSequences(); + + std::vector columnsValidityMask(nT * nS, 1); // form the mask in a CPU-side STL vector first + size_t gapsFound = 0; + for (size_t t = 0; t < nT; t++) + { + FrameRange fr(nullptr, t); + if (IsGap(fr)) + { + for (size_t s = 0; s < nS; s++) + { + if (IsGap(fr.Sequence(s))) + { + columnsValidityMask[(t * nS) + s] = 0; + gapsFound++; + } + } + } + } + assert(gapsFound == m_numGapFrames); // sanity check + + if (deviceId != m_columnsValidityMask.GetDeviceId()) + m_columnsValidityMask = Matrix(deviceId); + m_columnsValidityMask.SetValue(1, nS * nT, deviceId, columnsValidityMask.data()); + } + return m_columnsValidityMask; + } + + // class for defining an iteration over a sequence, forward and backward + // One day, we may also have nested structures. For those, FrameRangeIterations will be able to be instantiated from FrameRange objects to loop over their nested dimension. + class FrameRangeIteration + { + MBLayoutPtr m_pMBLayout; + int m_step; + public: + // one-dimensional iteration (time sequences) + // 'Step' specifies the stepping direction of the loop: + // - for left-to-right models -> pass step = +1 + // - for right-to-left models -> pass step = -1 + FrameRangeIteration(MBLayoutPtr pMBLayout, int step) : m_pMBLayout(pMBLayout), m_step(step) { } + // This class is returned by begin() and end(). + // It is a FrameRange with additions ++ and != operators needed in the for loop. + class FrameRangeIterator : public FrameRange + { + ptrdiff_t m_step; + public: + FrameRangeIterator(const FrameRange & begin, ptrdiff_t step) : FrameRange(begin), m_step(step) { } + bool operator!=(const FrameRangeIterator & other) const { return timeIdxInSeq != other.timeIdxInSeq; } + void operator++(int) { timeIdxInSeq = (size_t)(m_step + (ptrdiff_t)timeIdxInSeq); } // going through (int) to avoid undefined behavior + }; + // iterators for iterating forward + FrameRangeIterator begin() const + { + if (m_step > 0) return FrameRangeIterator(FrameRange(m_pMBLayout, 0), +1); + else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps() - 1), -1); + } + FrameRangeIterator end() const + { + if (m_step < 0) return FrameRangeIterator(FrameRange(m_pMBLayout, (size_t)-1), 0/*dummy*/); + else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps()), 0); + } + // iterators for iterating in reverse order (as needed for gradient update) + FrameRangeIterator rbegin() const + { + if (m_step < 0) return FrameRangeIterator(FrameRange(m_pMBLayout, 0), +1); + else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps() - 1), -1); + } + FrameRangeIterator rend() const + { + if (m_step > 0) return FrameRangeIterator(FrameRange(m_pMBLayout, (size_t)-1), 0); + else return FrameRangeIterator(FrameRange(m_pMBLayout, m_pMBLayout->GetNumTimeSteps()), 0); + } + }; + + // ----------------------------------------------------------------------- + // ColumnRangeWithMBLayoutFor() -- Return column range for a FrameRange of a Matrix with specified number of columns with a given MBLayout + // ----------------------------------------------------------------------- + + static inline std::pair ColumnRangeWithMBLayoutFor(size_t numCols/*of data matrix to slice*/, + const FrameRange & fr/*select frame or entire batch*/, + const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) + { + // MBLayout of data and of FrameRange must be identical pointers, + // or in case of broadcasting, respective parent pointers. + // MBLayouts that are identical in content but not object identity (pointer) are not admissible. + // For those cases, use a ReconcileMBLayout node. + if (fr.m_pMBLayout != pMBLayout) + { + // if broadcast allowed then it is allowed to broadcast from an outer-loop value + // Currently, the only 'outer' loop we have is to have no layout. + if (fr.m_broadcastAllowed && !pMBLayout && numCols == 1) + return std::pair(0, numCols); + if (fr.m_pMBLayout && pMBLayout && *fr.m_pMBLayout == *pMBLayout) + LogicError("DataFor: fr's MBLayout inconsistent with matrix. They are compatible though--are you missing a ReconcileMBLayout operation?"); + else + LogicError("DataFor: fr's MBLayout inconsistent with matrix"); + } + // if FrameRange refers to whole minibatch (map mode) + // or if we don't even have a layout + // then return the whole matrix + // but as a reference (e.g. it cannot be resized) + if (!pMBLayout || fr.IsAllFrames()) + { + if (fr.m_timeOffset != 0) + LogicError("DataFor: Time offset must not be specified for FrameRanges that reference the entire minibatch."); // (note: the tensor version allows this) + if (fr.seqIndex == SIZE_MAX) + return std::pair(0, numCols); + else + { + if (!pMBLayout) + LogicError("DataFor: Attempting to retrieve a parallel sequence from data without layout."); + else + LogicError("DataFor: Individual parallel sequences cannot be retrieved in Matrix representation. Use TensorView instead."); + } + } + // FrameRange refers to a time slice -> return that + else + { + size_t numParallelSequences = pMBLayout->GetNumParallelSequences(); + size_t startColumn = (fr.timeIdxInSeq + fr.m_timeOffset) * numParallelSequences; + if (startColumn >= numCols) + LogicError("DataFor: FrameRange specifies a time index that is out of range."); + if (fr.seqIndex == SIZE_MAX) + return std::pair(startColumn, numParallelSequences * fr.m_timeRange); + else if (fr.m_timeRange != 1) + LogicError("DataFor: FrameRange only support per-sequence time ranges with tensor slices, not matrix slices."); + else + return std::pair(startColumn + fr.seqIndex, 1); + } + } + + // ----------------------------------------------------------------------- + // DataWithMBLayoutFor() -- create view for a FrameRange of a Matrix with a given MBLayout + // This function binds the above together. + // Any access by FrameRange should only be done through this function. + // ----------------------------------------------------------------------- + + template + static inline Matrix DataWithMBLayoutFor(const Matrix & data, + const FrameRange & fr/*select frame or entire batch*/, + const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) + { + auto columnRange = ColumnRangeWithMBLayoutFor(data.GetNumCols(), fr, pMBLayout); + return data.ColumnSlice(columnRange.first, columnRange.second); + } + + // ----------------------------------------------------------------------- + // TensorSliceWithMBLayoutFor() -- Return tensor slice for a FrameRange with a given MBLayout. + // This implements the logic of interpreting the FrameRange object. + // Unlike the matrix version above, this supports iteration indices other than time. + // If the iteration index is time, then it refers to the minibatch's time, not time of + // individual sequences (the difference is where multiple sequences get concatenated). + // TODO: This ^^. FrameRange still missing is a field to identify the index. + // This function happily returns tensor bounds that are out of bounds, assuming caller will do the right thing. + // ----------------------------------------------------------------------- + + template // e.g. std::vector or SmallVector + static inline std::pair TensorSliceWithMBLayoutFor(const DimensionVector & shape/*actual tensor shape of 'data'*/, + const FrameRange & fr/*select frame or entire batch from 'data'*/, + const MBLayoutPtr & pMBLayout/*the MB layout of 'data'*/) + { + std::pair result; + typedef decltype(result.first[0]) ElemType; + + // this creates a slice for the entire matrix, which we will then narrow down + result.first.resize(shape.size(), 0); + result.second = shape; + + // get position of time and sequence index + // These are only valid if we have a layout. + // In the future, the 'timeDim' will be identified by the FrameRange. int iterDimParam = fr.GetIterationDimension(); - size_t iterDim = iterDimParam > 0 ? iterDimParam - 1/*regular dimensions are specified as 1-based*/ : shape.size() + iterDimParam/*-1 for time dimension*/; - size_t sequenceDim = shape.size() - 2; // TODO: In case of multiple time dims, this must be adjusted. - bool isTimeIteration = iterDim >= sequenceDim; - - // MBLayout of data and of FrameRange must be identical pointers, - // or in case of broadcasting, respective parent pointers. - // MBLayouts that are identical in content but not object identity (pointer) are not admissible. - // For those cases, use a ReconcileMBLayout node. - if (isTimeIteration && fr.m_pMBLayout != pMBLayout) - { - // if broadcast allowed then it is allowed to broadcast from an outer-loop value - // Currently, the only 'outer' loop we have is to have no layout. - if (fr.m_pMBLayout/*get data for a loop*/ && !pMBLayout/*'data' is not samples*/ && fr.m_broadcastAllowed/*we're OK with that*/) - ; // the time dimension is broadcasting--leave it as is - else if (fr.m_pMBLayout && pMBLayout && *fr.m_pMBLayout == *pMBLayout) - LogicError("DataFor: fr's MBLayout inconsistent with matrix. They are compatible though--are you missing a ReconcileMBLayout operation?"); - else - LogicError("DataFor: fr's MBLayout inconsistent with matrix"); - } - // if FrameRange refers to whole minibatch (map mode) - // or if we don't even have a layout - // then return the whole matrix - // but as a reference (e.g. it cannot be resized) - else if ((isTimeIteration && !pMBLayout) || fr.IsAllFrames()) - { - if (fr.m_timeOffset != 0) - { - if (iterDim >= result.first.size()) - LogicError("DataFor: Time offset cannot be applied to tensors that have no time dimension."); - result.first[iterDim] += (ElemType)fr.m_timeOffset; // Note: If we have an offset, this is guaranteed to yield a slice that is out of bounds. - result.second[iterDim] += (ElemType)fr.m_timeOffset; - if (result.first[iterDim] > result.second[iterDim]) - LogicError("DataFor: Numeric wraparound. You used a size_t vector where an int vector would be needed."); - } - } - // FrameRange refers to a time slice -> return that - else if (result.second[iterDim] > 1) // (if iter dim is broadcasting then always return that one independent of requested index) - { - size_t ts = fr.timeIdxInSeq + fr.m_timeOffset; - size_t te = ts + fr.m_timeRange; - result.first[iterDim] = (ElemType)ts; - result.second[iterDim] = (ElemType)te; - } - - // sequence index - if (fr.seqIndex != SIZE_MAX/*sequence requested*/ && pMBLayout/*have sequences*/ && result.second[sequenceDim] > 1/*>1 sequence (not broadcasting)*/) - { - size_t s = fr.seqIndex; - if (s >= result.second[sequenceDim]) - LogicError("DataFor: FrameRange specifies a paralllel-sequence index that is out of range."); - result.first[sequenceDim] = (ElemType)s; - result.second[sequenceDim] = (ElemType)s + 1; - } - - return result; - } - - // ----------------------------------------------------------------------- - // MaskMissingColumnsTo() -- function to set gaps to zero or NaN - // ----------------------------------------------------------------------- - - // This sets MB columns to 0 (or any 'val') that have the NoLabel or NoFeature flag set. - // Such situation happens when packing multiple sequences for parallel processing--there will be some gaps, which are flagged by these flags. - // Nodes that operate in 'map' style (input(j) -> output(j) independently) can ignore this; it will be garbage-in-garbage-out. - // However, nodes that 'reduce' minibatches (e.g. computing the sum of all frames across all sequences) must deal with the garbage. - // This function sets those to 0, assuming that now they can be reduced without affecting the result. - // This function can operate on the whole range or on a selected single frame and/or a single sequence. - // 'Reduce' style operations--the criterion nodes and gradient computation--call this. - // Warning: The layout used here must match the matrix. E.g. don't pass a child's matrix from a criterion node (use Input(x)->MaskMissing{Values,Gradient}ColumnsToZero() instead. - template - static inline void MaskMissingColumnsTo(Matrix& matrixToMask, const MBLayoutPtr & pMBLayout, const FrameRange & fr, ElemType val) - { - if (pMBLayout && pMBLayout->HasGaps(fr)) - { -#if 0 // in the future we can use the tensor lib to implement this - const auto & maskMatrix = pMBLayout->GetColumnsValidMask(); - auto maskSlice = DataWithMBLayoutFor(maskMatrix, fr, pMBLayout); - auto matrixSliceToMask = DataWithMBLayoutFor(matrixToMask, fr, pMBLayout); - TensorView(matrixSliceToMask).DoMaskNegativeOf(0, TensorView(matrixSliceToMask), TensorView(maskSlice), 1); val; -#else - const auto & maskMatrix = pMBLayout->GetColumnsValidityMask(matrixToMask.GetDeviceId()); - auto maskSlice = DataWithMBLayoutFor(maskMatrix, fr, pMBLayout); - auto matrixSliceToMask = DataWithMBLayoutFor(matrixToMask, fr, pMBLayout); - matrixSliceToMask.MaskColumnsValue(maskSlice, val); -#endif - } - } - -}}} + size_t iterDim = iterDimParam > 0 ? iterDimParam - 1/*regular dimensions are specified as 1-based*/ : shape.size() + iterDimParam/*-1 for time dimension*/; + size_t sequenceDim = shape.size() - 2; // TODO: In case of multiple time dims, this must be adjusted. + bool isTimeIteration = iterDim >= sequenceDim; + + // MBLayout of data and of FrameRange must be identical pointers, + // or in case of broadcasting, respective parent pointers. + // MBLayouts that are identical in content but not object identity (pointer) are not admissible. + // For those cases, use a ReconcileMBLayout node. + if (isTimeIteration && fr.m_pMBLayout != pMBLayout) + { + // if broadcast allowed then it is allowed to broadcast from an outer-loop value + // Currently, the only 'outer' loop we have is to have no layout. + if (fr.m_pMBLayout/*get data for a loop*/ && !pMBLayout/*'data' is not samples*/ && fr.m_broadcastAllowed/*we're OK with that*/) + ; // the time dimension is broadcasting--leave it as is + else if (fr.m_pMBLayout && pMBLayout && *fr.m_pMBLayout == *pMBLayout) + LogicError("DataFor: fr's MBLayout inconsistent with matrix. They are compatible though--are you missing a ReconcileMBLayout operation?"); + else + LogicError("DataFor: fr's MBLayout inconsistent with matrix"); + } + // if FrameRange refers to whole minibatch (map mode) + // or if we don't even have a layout + // then return the whole matrix + // but as a reference (e.g. it cannot be resized) + else if ((isTimeIteration && !pMBLayout) || fr.IsAllFrames()) + { + if (fr.m_timeOffset != 0) + { + if (iterDim >= result.first.size()) + LogicError("DataFor: Time offset cannot be applied to tensors that have no time dimension."); + result.first[iterDim] += (ElemType)fr.m_timeOffset; // Note: If we have an offset, this is guaranteed to yield a slice that is out of bounds. + result.second[iterDim] += (ElemType)fr.m_timeOffset; + if (result.first[iterDim] > result.second[iterDim]) + LogicError("DataFor: Numeric wraparound. You used a size_t vector where an int vector would be needed."); + } + } + // FrameRange refers to a time slice -> return that + else if (result.second[iterDim] > 1) // (if iter dim is broadcasting then always return that one independent of requested index) + { + size_t ts = fr.timeIdxInSeq + fr.m_timeOffset; + size_t te = ts + fr.m_timeRange; + result.first[iterDim] = (ElemType)ts; + result.second[iterDim] = (ElemType)te; + } + + // sequence index + if (fr.seqIndex != SIZE_MAX/*sequence requested*/ && pMBLayout/*have sequences*/ && result.second[sequenceDim] > 1/*>1 sequence (not broadcasting)*/) + { + size_t s = fr.seqIndex; + if (s >= result.second[sequenceDim]) + LogicError("DataFor: FrameRange specifies a paralllel-sequence index that is out of range."); + result.first[sequenceDim] = (ElemType)s; + result.second[sequenceDim] = (ElemType)s + 1; + } + + return result; + } + + // ----------------------------------------------------------------------- + // MaskMissingColumnsTo() -- function to set gaps to zero or NaN + // ----------------------------------------------------------------------- + + // This sets MB columns to 0 (or any 'val') that have the NoLabel or NoFeature flag set. + // Such situation happens when packing multiple sequences for parallel processing--there will be some gaps, which are flagged by these flags. + // Nodes that operate in 'map' style (input(j) -> output(j) independently) can ignore this; it will be garbage-in-garbage-out. + // However, nodes that 'reduce' minibatches (e.g. computing the sum of all frames across all sequences) must deal with the garbage. + // This function sets those to 0, assuming that now they can be reduced without affecting the result. + // This function can operate on the whole range or on a selected single frame and/or a single sequence. + // 'Reduce' style operations--the criterion nodes and gradient computation--call this. + // Warning: The layout used here must match the matrix. E.g. don't pass a child's matrix from a criterion node (use Input(x)->MaskMissing{Values,Gradient}ColumnsToZero() instead. + template + static inline void MaskMissingColumnsTo(Matrix& matrixToMask, const MBLayoutPtr & pMBLayout, const FrameRange & fr, ElemType val) + { + if (pMBLayout && pMBLayout->HasGaps(fr)) + { +#if 0 // in the future we can use the tensor lib to implement this + const auto & maskMatrix = pMBLayout->GetColumnsValidMask(); + auto maskSlice = DataWithMBLayoutFor(maskMatrix, fr, pMBLayout); + auto matrixSliceToMask = DataWithMBLayoutFor(matrixToMask, fr, pMBLayout); + TensorView(matrixSliceToMask).DoMaskNegativeOf(0, TensorView(matrixSliceToMask), TensorView(maskSlice), 1); val; +#else + const auto & maskMatrix = pMBLayout->GetColumnsValidityMask(matrixToMask.GetDeviceId()); + auto maskSlice = DataWithMBLayoutFor(maskMatrix, fr, pMBLayout); + auto matrixSliceToMask = DataWithMBLayoutFor(matrixToMask, fr, pMBLayout); + matrixSliceToMask.MaskColumnsValue(maskSlice, val); +#endif + } + } + +}}} diff --git a/Source/Common/Include/TimerUtility.h b/Source/Common/Include/TimerUtility.h index 1fee699b626e..50a19138876e 100644 --- a/Source/Common/Include/TimerUtility.h +++ b/Source/Common/Include/TimerUtility.h @@ -1,33 +1,33 @@ -#pragma once - -#define MILLI_PER_SEC 1000 -#define MICRO_PER_SEC 1000000 -#define NANO_PER_SEC 1000000000 -#define MILLI_PER_NANO 1000000 -#define MICRO_PER_NANO 1000 - -namespace Microsoft{namespace MSR {namespace CNTK { - // TODO: Replace with std::chrono once VS2015 ships (current std::chrono implementation in VS2013 uses low precision clock) - class Timer - { - public: - Timer() : m_start(0), m_end(0) {} - - void Start(); - void Stop(); - void Restart(); - - double ElapsedSeconds() - { - return ElapsedMicroseconds() / static_cast(MICRO_PER_SEC); - } - - private: - - long long ElapsedMicroseconds(); - long long GetStamp(); - - long long m_start; - long long m_end; - }; -}}} +#pragma once + +#define MILLI_PER_SEC 1000 +#define MICRO_PER_SEC 1000000 +#define NANO_PER_SEC 1000000000 +#define MILLI_PER_NANO 1000000 +#define MICRO_PER_NANO 1000 + +namespace Microsoft{namespace MSR {namespace CNTK { + // TODO: Replace with std::chrono once VS2015 ships (current std::chrono implementation in VS2013 uses low precision clock) + class Timer + { + public: + Timer() : m_start(0), m_end(0) {} + + void Start(); + void Stop(); + void Restart(); + + double ElapsedSeconds() + { + return ElapsedMicroseconds() / static_cast(MICRO_PER_SEC); + } + + private: + + long long ElapsedMicroseconds(); + long long GetStamp(); + + long long m_start; + long long m_end; + }; +}}} diff --git a/Source/Common/Include/stdafx.h b/Source/Common/Include/stdafx.h index 135a6951059b..61b82615b0cf 100644 --- a/Source/Common/Include/stdafx.h +++ b/Source/Common/Include/stdafx.h @@ -1,6 +1,6 @@ -// dummy stdafx.h file for Linux version - -// In the Windows build, there are several stdafx.h files which are used for project-specific precompilation of headers. -// In Linux, this is not used. By placing this dummy file ahead of all other stdafx.h in the include search path, -// we make sure it compiles while not getting confused by Windows-specific content of those files. -#include "Platform.h" +// dummy stdafx.h file for Linux version + +// In the Windows build, there are several stdafx.h files which are used for project-specific precompilation of headers. +// In Linux, this is not used. By placing this dummy file ahead of all other stdafx.h in the include search path, +// we make sure it compiles while not getting confused by Windows-specific content of those files. +#include "Platform.h" diff --git a/Source/Common/fileutil.cpp b/Source/Common/fileutil.cpp index 957f84659299..e23c0f2de032 100644 --- a/Source/Common/fileutil.cpp +++ b/Source/Common/fileutil.cpp @@ -1,2178 +1,2178 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#ifndef _CRT_SECURE_NO_WARNINGS -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings -#endif - -#define _CRT_NONSTDC_NO_DEPRECATE // make VS accept POSIX functions without _ -#pragma warning (disable: 4996) // ^^ this does not seem to work--TODO: make it work -#define _FILE_OFFSET_BITS 64 // to force fseeko() and ftello() 64 bit in Linux - -#ifdef _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES // fixed-buffer overloads for strcpy() etc. -#undef _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES -#endif -#define _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES 1 -#include "Basics.h" -#include "fileutil.h" -#ifdef __unix__ -#include -#include -#include -#include -#endif -#include -#include -#include -#include -#include -#ifdef _WIN32 -#include "Windows.h" // for FILETIME -#endif -#include // for std::find -#include -#include -#include -#ifndef UNDER_CE // some headers don't exist under winCE - the appropriate definitions seem to be in stdlib.h -#if defined(_WIN32) || defined(__CYGWIN__) -#include // for _O_BINARY/TEXT - not needed for wince -#include // for _setmode() -#define SET_BINARY_MODE(handle) setmode(handle, _O_BINARY) -#define SET_TEXT_MODE(handle) setmode(handle, _O_TEXT) -#else -#define SET_BINARY_MODE(handle) ((int)0) -#define SET_TEXT_MODE(handle) ((int)0) -#endif -#endif - -#define __out_z_cap(x) // a fake SAL annotation; this may come in handy some day if we try static code analysis, so I don't want to delete it - -#include - -using namespace std; -using namespace Microsoft::MSR::CNTK; - -// ---------------------------------------------------------------------------- -// some mappings for non-Windows builds -// ---------------------------------------------------------------------------- - -template <> const wchar_t* GetScanFormatString(char) {return L" %hc";} -template <> const wchar_t* GetScanFormatString(wchar_t) {return L" %lc";} -template <> const wchar_t* GetScanFormatString(short) {return L" %hi";} -template <> const wchar_t* GetScanFormatString(int) {return L" %i";} -template <> const wchar_t* GetScanFormatString(long) {return L" %li";} -template <> const wchar_t* GetScanFormatString(unsigned short) {return L" %hu";} -template <> const wchar_t* GetScanFormatString(unsigned int) {return L" %u";} -//template <> const wchar_t* GetScanFormatString(unsigned long) {return L" %lu";} -template <> const wchar_t* GetScanFormatString(float) {return L" %g";} -template <> const wchar_t* GetScanFormatString(double) {return L" %lg";} -template <> const wchar_t* GetScanFormatString(size_t) {return L" %llu";} -template <> const wchar_t* GetScanFormatString(long long) {return L" %lli";} - -template <> const wchar_t* GetFormatString(char) {return L" %hc";} -template <> const wchar_t* GetFormatString(wchar_t) {return L" %lc";} -template <> const wchar_t* GetFormatString(short) {return L" %hi";} -template <> const wchar_t* GetFormatString(int) {return L" %i";} -template <> const wchar_t* GetFormatString(long) {return L" %li";} -template <> const wchar_t* GetFormatString(unsigned short) {return L" %hu";} -template <> const wchar_t* GetFormatString(unsigned int) {return L" %u";} -//template <> const wchar_t* GetFormatString(unsigned long) {return L" %lu";} -template <> const wchar_t* GetFormatString(float) {return L" %.9g";} -template <> const wchar_t* GetFormatString(double) {return L" %.17g";} -template <> const wchar_t* GetFormatString(size_t) {return L" %llu";} -template <> const wchar_t* GetFormatString(long long) {return L" %lli";} -template <> const wchar_t* GetFormatString(const char*) {return L" %hs";} -template <> const wchar_t* GetFormatString(const wchar_t*) {return L" %ls";} - -// ---------------------------------------------------------------------------- -// fgetText() specializations for fwscanf differences: get a value from a text file -// ---------------------------------------------------------------------------- -void fgetText(FILE * f, char& v) -{ - const wchar_t* formatString = GetFormatString(v); - int rc = fwscanf(f, formatString, &v); - if (rc == 0) - RuntimeError ("error reading value from file (invalid format): %ls", formatString); - else if (rc == EOF) - RuntimeError ("error reading from file: %s", strerror (errno)); - assert(rc == 1); -} -void fgetText(FILE * f, wchar_t& v) -{ - const wchar_t* formatString = GetFormatString(v); - int rc = fwscanf(f, formatString, &v); - if (rc == 0) - RuntimeError ("error reading value from file (invalid format): %ls", formatString); - else if (rc == EOF) - RuntimeError ("error reading from file: %s", strerror (errno)); - assert(rc == 1); -} - -// ---------------------------------------------------------------------------- -// fopenOrDie(): like fopen() but terminate with err msg in case of error. -// A pathname of "-" returns stdout or stdin, depending on mode, and it will -// change the binary mode if 'b' or 't' are given. If you use this, make sure -// not to fclose() such a handle. -// ---------------------------------------------------------------------------- - -static const wchar_t * strchr (const wchar_t * s, wchar_t v) { return wcschr (s, v); } - -// pathname is "-" -- open stdin or stdout. Changes bin mode if 'b' or 't' given. -template FILE * fopenStdHandle (const _T * mode) -{ - FILE * f = strchr (mode, 'r') ? stdin : stdout; - if (strchr (mode, 'b') || strchr (mode, 't')) // change binary mode - fsetmode (f, strchr(mode, 'b') ? 'b' : 't'); - return f; -} - -FILE * fopenOrDie (const string & pathname, const char * mode) -{ - FILE * f = (pathname[0] == '-') ? fopenStdHandle (mode) : fopen (pathname.c_str(), mode); - if (f == NULL) - { - RuntimeError ("error opening file '%s': %s", pathname.c_str(), strerror (errno)); - } - if (strchr (mode, 'S')) - { // if optimized for sequential access then use large buffer - setvbuf (f, NULL, _IOFBF, 10000000); // OK if it fails - } - return f; -} - -FILE * fopenOrDie (const wstring & pathname, const wchar_t * mode) -{ - FILE * f = (pathname[0] == '-') ? fopenStdHandle (mode) : _wfopen (pathname.c_str(), mode); - if (f == NULL) - { - RuntimeError ("error opening file '%ls': %s", pathname.c_str(), strerror (errno)); - } - if (strchr (mode, 'S')) - { // if optimized for sequential access then use large buffer - setvbuf (f, NULL, _IOFBF, 10000000); // OK if it fails - } - return f; -} - -// ---------------------------------------------------------------------------- -// set mode to binary or text (pass 'b' or 't') -// ---------------------------------------------------------------------------- - -void fsetmode(FILE * f, char type) -{ - if (type != 'b' && type != 't') - { - RuntimeError ("fsetmode: invalid type '%c'", type); - } -#ifdef UNDER_CE // winCE and win32 have different return types for _fileno - FILE *fd = fileno (f); // note: no error check possible -#else - int fd = fileno (f); // note: no error check possible -#endif - int rc = ( type == 'b' ? SET_BINARY_MODE(fd) : SET_TEXT_MODE(fd) ); - if (rc == -1) - { - RuntimeError ("error changing file mode: %s", strerror (errno)); - } -} - -// ---------------------------------------------------------------------------- -// freadOrDie(): like fread() but terminate with err msg in case of error -// ---------------------------------------------------------------------------- - -void freadOrDie (void * ptr, size_t size, size_t count, FILE * f) -{ - // \\XXX\C$ reads are limited, with some randomness (e.g. 48 MB), on Windows 7 32 bit, so we break this into chunks of some MB. Meh. - while (count > 0) - { - size_t chunkn = min (count, (size_t)15*1024*1024); // BUGBUG: I surely meant this limit to be bytes, not units of 'size'... - size_t n = fread (ptr, size, chunkn, f); - if (n != chunkn) - RuntimeError ("error reading from file: %s", strerror (errno)); - count -= n; - ptr = n * size + (char*) ptr; - } -} - -#ifdef _WIN32 -void freadOrDie (void * ptr, size_t size, size_t count, const HANDLE f) -{ - // \\XXX\C$ reads are limited, with some randomness (e.g. 48 MB), on Windows 7 32 bit, so we break this into chunks of some MB. Meh. - while (count > 0) - { - size_t chunkn = min (count * size, (size_t)15*1024*1024); - DWORD n ; - ReadFile(f, ptr, (DWORD) chunkn, &n, NULL); - if (n != chunkn) - RuntimeError ("error number for reading from file: %s", GetLastError()); - count -= (size_t) (n / size); - ptr = n + (char*) ptr; - } -} -#endif - -// ---------------------------------------------------------------------------- -// fwriteOrDie(): like fwrite() but terminate with err msg in case of error; -// Windows C std lib fwrite() has problems writing >100 MB at a time (fails -// with Invalid Argument error), so we break it into chunks (yak!!) -// ---------------------------------------------------------------------------- - -void fwriteOrDie (const void * ptr, size_t size, size_t count, FILE * f) -{ - const char * p1 = (const char *) ptr; - size_t totalBytes = size * count; - while (totalBytes > 0) - { - size_t wantWrite = totalBytes; -#define LIMIT (16*1024*1024) // limit to 16 MB at a time - if (wantWrite > LIMIT) - { - wantWrite = LIMIT; - } - size_t n = fwrite ((const void *) p1, 1, wantWrite, f); - if (n != wantWrite) - { - RuntimeError ("error writing to file (ptr=0x%08lx, size=%d, count=%d, writing %d bytes after %d): %s", - (unsigned long)(size_t)ptr, (int)size, (int)count, (int)wantWrite, - (int)(size * count - totalBytes), - strerror (errno)); - } - totalBytes -= wantWrite; - p1 += wantWrite; - } -} - -#ifdef _WIN32 -void fwriteOrDie (const void * ptr, size_t size, size_t count, const HANDLE f) -{ - const char * p1 = (const char *) ptr; - DWORD totalBytes = (DWORD) (size * count); - while (totalBytes > 0) - { - DWORD wantWrite = totalBytes; -#define LIMIT (16*1024*1024) // limit to 16 MB at a time - if (wantWrite > LIMIT) - { - wantWrite = LIMIT; - } - DWORD byteWritten = 0 ; - if (WriteFile(f, (const void *) p1, wantWrite, &byteWritten, NULL) == false) - { - RuntimeError ("error writing to file (ptr=0x%08lx, size=%d," - " count=%d, writing %d bytes after %d): %s", - ptr, size, count, (int) wantWrite, - (int) (size * count - totalBytes), - strerror (errno)); - } - totalBytes -= wantWrite; - p1 += wantWrite; - } -} -#endif - -long fseekOrDie (FILE * f, long offset, int mode) -{ - long curPos = ftell (f); - if (curPos == -1L) - { - RuntimeError ("error seeking: %s", strerror (errno)); - } - int rc = fseek (f, offset, mode); - if (rc != 0) - { - RuntimeError ("error seeking: %s", strerror (errno)); - } - return curPos; -} - -// ---------------------------------------------------------------------------- -// fprintfOrDie(): like fprintf() but terminate with err msg in case of error -// ---------------------------------------------------------------------------- - -#pragma warning(push) -#pragma warning(disable : 4793) // 'vararg' : causes native code generation -void fprintfOrDie (FILE * f, const char * fmt, ...) -{ - va_list arg_ptr; - va_start (arg_ptr, fmt); - int rc = vfprintf (f, fmt, arg_ptr); - if (rc < 0) - { - RuntimeError ("error writing to file: %s", strerror (errno)); - } -} -#pragma warning(pop) - -// ---------------------------------------------------------------------------- -// fflushOrDie(): like fflush() but terminate with err msg in case of error -// ---------------------------------------------------------------------------- - -void fflushOrDie (FILE * f) -{ - int rc = fflush (f); - if (rc != 0) - { - RuntimeError ("error flushing to file: %s", strerror (errno)); - } -} - -// ---------------------------------------------------------------------------- -// filesize(): determine size of the file in bytes (with open file) -// ---------------------------------------------------------------------------- -size_t filesize (FILE * f) -{ -#ifdef _WIN32 - size_t curPos = _ftelli64(f); - if (curPos == -1L) - { - RuntimeError ("error determining file position: %s", strerror (errno)); - } - int rc = _fseeki64 (f, 0, SEEK_END); - if (rc != 0) - RuntimeError ("error seeking to end of file: %s", strerror (errno)); - size_t len = _ftelli64 (f); - if (len == -1L) - RuntimeError ("error determining file position: %s", strerror (errno)); - rc = _fseeki64 (f, curPos, SEEK_SET); - if (rc != 0) - RuntimeError ("error resetting file position: %s", strerror (errno)); - return len; -#else // TODO: test this - struct stat stat_buf; - int rc = fstat(fileno(f), &stat_buf); - if (rc != 0) - RuntimeError("error determining length of file: %s", strerror(errno)); - static_assert (sizeof(stat_buf.st_size)>=sizeof(uint64_t), "struct stat not compiled for 64-bit mode"); - return stat_buf.st_size; -#endif -} - -// filesize(): determine size of the file in bytes (with pathname) -size_t filesize (const wchar_t * pathname) -{ - FILE * f = fopenOrDie (pathname, L"rb"); - try - { - size_t len = filesize (f); - fclose (f); - return (size_t) len; - } - catch (...) - { - fclose (f); - throw; - } -} - -#ifndef UNDER_CE // no 64-bit under winCE - -// filesize64(): determine size of the file in bytes (with pathname) -int64_t filesize64 (const wchar_t * pathname) -{ -#ifdef _WIN32 - struct _stat64 fileinfo; - if (_wstat64 (pathname,&fileinfo) == -1) - return 0; - else - return fileinfo.st_size; -#else - return filesize (pathname); -#endif -} -#endif - -// ---------------------------------------------------------------------------- -// fget/setpos(): seek functions with error handling -// ---------------------------------------------------------------------------- - -uint64_t fgetpos (FILE * f) -{ -#ifdef _MSC_VER // standard does not allow to cast between fpos_t and integer numbers, and indeed it does not work on Linux (but on Windows and GCC) - fpos_t post; - int rc = ::fgetpos (f, &post); - if (rc != 0) - RuntimeError ("error getting file position: %s", strerror (errno)); -#else - auto pos = ftello (f); - uint64_t post = (uint64_t) pos; - static_assert (sizeof (post) >= sizeof (pos), "64-bit file offsets not enabled"); - if ((decltype (pos)) post != pos) - LogicError("64-bit file offsets not enabled"); -#endif - return post; -} - -void fsetpos (FILE * f, uint64_t reqpos) -{ -#ifdef _MSC_VER // standard does not allow to cast between fpos_t and integer numbers, and indeed it does not work on Linux (but on Windows and GCC) -#ifdef _MSC_VER // special hack for VS CRT - // Visual Studio's ::fsetpos() flushes the read buffer. This conflicts with a situation where - // we generally read linearly but skip a few bytes or KB occasionally, as is - // the case in speech recognition tools. This requires a number of optimizations. - - uint64_t curpos = fgetpos (f); - uint64_t cureob = curpos + f->_cnt; // UGH: we mess with an internal structure here - while (reqpos >= curpos && reqpos < cureob) - { - // if we made it then do not call fsetpos() - if (reqpos == fgetpos (f)) - return; - - // if we seek within the existing buffer, then just move to the position by dummy reads - char buf[65536]; - size_t n = min ((size_t) reqpos - (size_t) curpos, _countof (buf)); - fread (buf, sizeof (buf[0]), n, f); // (this may fail, but really shouldn't) - curpos += n; - - // since we mess with f->_cnt, if something unexpected happened to the buffer then back off - if (curpos != fgetpos (f) || curpos + f->_cnt != cureob) - break; // oops - } -#endif // end special hack for VS CRT - - // actually perform the seek - fpos_t post = reqpos; - int rc = ::fsetpos(f, &post); -#else // assuming __unix__ - off_t post = (off_t) reqpos; - static_assert (sizeof (off_t) >= sizeof (reqpos), "64-bit file offsets not enabled"); - if ((decltype (reqpos)) post != reqpos) - LogicError("64-bit file offsets not enabled"); - int rc = fseeko(f, post, SEEK_SET); -#endif - if (rc != 0) - RuntimeError("error setting file position: %s", strerror(errno)); -} - -// ---------------------------------------------------------------------------- -// unlinkOrDie(): unlink() with error handling -// ---------------------------------------------------------------------------- - -void unlinkOrDie (const std::string & pathname) -{ - if (unlink (pathname.c_str()) != 0 && errno != ENOENT) // if file is missing that's what we want - RuntimeError ("error deleting file '%s': %s", pathname.c_str(), strerror (errno)); -} -void unlinkOrDie (const std::wstring & pathname) -{ - if (_wunlink (pathname.c_str()) != 0 && errno != ENOENT) // if file is missing that's what we want - RuntimeError ("error deleting file '%ls': %s", pathname.c_str(), strerror (errno)); -} - -// ---------------------------------------------------------------------------- -// renameOrDie(): rename() with error handling -// ---------------------------------------------------------------------------- - -void renameOrDie (const std::string & from, const std::string & to) -{ -#ifdef _WIN32 - // deleting destination file if exits (to match Linux semantic) - if (fexists(to.c_str()) && !DeleteFileA(to.c_str())) - RuntimeError("error deleting file: '%s': %d", to.c_str(), GetLastError()); - - if (!MoveFileA (from.c_str(), to.c_str())) - RuntimeError("error renaming file '%s': %d", from.c_str(), GetLastError()); -#else - if (rename (from.c_str(), to.c_str()) != 0) - RuntimeError("error renaming file '%s': %s", from.c_str(), strerror(errno)); -#endif -} - -void renameOrDie (const std::wstring & from, const std::wstring & to) -{ -#ifdef _WIN32 - // deleting destination file if exits (to match Linux semantic) - if (fexists(to.c_str()) && !DeleteFileW(to.c_str())) - RuntimeError("error deleting file '%ls': %d", to.c_str(), GetLastError()); - - if (!MoveFileW(from.c_str(), to.c_str())) - RuntimeError ("error renaming file '%ls': %d", from.c_str(), GetLastError()); -#else - renameOrDie (wtocharpath(from.c_str()).c_str(), wtocharpath(to.c_str()).c_str()); -#endif -} - -// ---------------------------------------------------------------------------- -// fputstring(): write a 0-terminated string -// ---------------------------------------------------------------------------- - -void fputstring (FILE * f, const char * str) -{ - fwriteOrDie ((void *) str, sizeof (*str), strnlen (str, SIZE_MAX)+1, f); // SECURITY NOTE: string use has been reviewed -} - -void fputstring (FILE * f, const std::string & str) -{ - fputstring (f, str.c_str()); -} - -#ifdef _WIN32 -#pragma warning(push) -#pragma warning(disable : 4127) -#endif -void fputstring (FILE * f, const wchar_t * str) -{ - if (sizeof (*str) == 2) - { - fwriteOrDie ((void *) str, sizeof (*str), wcsnlen (str, SIZE_MAX)+1, f); // SECURITY NOTE: string use has been reviewed - } else if (sizeof (*str) == 4) - { - size_t strLen = wcsnlen(str, SIZE_MAX); - std::unique_ptr str16(new char16_t[strLen + 1]); - for (int i = 0; i < strLen; i++) - { - str16[i] = (char16_t) str[i]; - } - str16[strLen] = 0; - fwriteOrDie((void *)str16.get(), sizeof (*str) / 2, strLen + 1, f); // SECURITY NOTE: string use has been reviewed - } else - { - RuntimeError("error: unknown encoding\n"); - } -} -#ifdef _WIN32 -#pragma warning(pop) -#endif - -void fputstring (FILE * f, const std::wstring & str) -{ - fputstring (f, str.c_str()); -} - -// ---------------------------------------------------------------------------- -// fexists(): test if a file exists -// ---------------------------------------------------------------------------- - -bool fexists (const wchar_t * pathname) -{ -#ifdef _MSC_VER - WIN32_FIND_DATAW findFileData; - HANDLE hFind = FindFirstFileW (pathname, &findFileData); - if (hFind != INVALID_HANDLE_VALUE) - { - FindClose (hFind); - return true; - } - else - { - return false; - } -#else - auto_file_ptr f (_wfopen (pathname, L"r")); - return f != nullptr; -#endif -} - -bool fexists (const char * pathname) -{ -#ifdef _MSC_VER - WIN32_FIND_DATAA findFileData; - HANDLE hFind = FindFirstFileA (pathname, &findFileData); - if (hFind != INVALID_HANDLE_VALUE) - { - FindClose (hFind); - return true; - } - else - { - return false; - } -#else - auto_file_ptr f (fopen (pathname, "r")); - return f != nullptr; -#endif -} - -// ---------------------------------------------------------------------------- -// funicode(): test if a file uses unicode by reading its BOM -// ---------------------------------------------------------------------------- - -bool funicode (FILE * f) -{ - unsigned short testCode; - if (fread (&testCode, sizeof(short), 1, f) == 1 && - (int)testCode == 0xFEFF) - return true; - fseek (f,0,SEEK_SET); - //rewind (f); - return false; -} - -// ---------------------------------------------------------------------------- -// fgetline(): like fgets() but terminate with err msg in case of error; -// removes the newline character at the end (like gets()); -// Returns 'buf' (always). buf guaranteed to be 0-terminated. -// ---------------------------------------------------------------------------- - -#ifdef __CYGWIN__ // strnlen() is somehow missing in Cygwin, which we use to quick-check GCC builds under Windows (although it is not a real target platform) -static inline size_t strnlen (const char *s, size_t n) { return std::find (s, s + n, '\0') - s; } -#endif - -#ifdef UNDER_CE // strlen for char * not defined in winCE -static inline size_t strnlen (const char *s, size_t n) { return std::find (s, s + n, '\0') - s; } -#endif - -static inline wchar_t * fgets(wchar_t * buf, int n, FILE * f) { return fgetws(buf, n, f); } -static inline size_t strnlen(wchar_t * s, size_t n) { return wcsnlen(s, n); } - -template -CHAR * fgetline (FILE * f, CHAR * buf, int size) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - CHAR * p = fgets(buf, size, f); - if (p == NULL) // EOF reached: next time feof() = true - { - if (ferror (f)) - RuntimeError ("error reading line: %s", strerror (errno)); - buf[0] = 0; - return buf; - } - size_t n = strnlen (p, size); - - // check for buffer overflow - - if (n >= (size_t) size -1) - { - basic_string example (p, n < 100 ? n : 100); - uint64_t filepos = fgetpos(f); // (for error message only) - RuntimeError("input line too long at file offset %d (max. %d characters allowed) [%s ...]", (int)filepos, (int)size - 1, msra::strfun::utf8(example).c_str()); - } - - // remove newline at end - - if (n > 0 && p[n-1] == '\n') // UNIX and Windows style - { - n--; - p[n] = 0; - if (n > 0 && p[n-1] == '\r') // Windows style - { - n--; - p[n] = 0; - } - } - else if (n > 0 && p[n-1] == '\r') // Mac style - { - n--; - p[n] = 0; - } - - return buf; -} - -#if 0 -const wchar_t * fgetline (FILE * f, wchar_t * buf, int size) -{ - wchar_t * p = fgetws (buf, size, f); - if (p == NULL) // EOF reached: next time feof() = true - { - if (ferror (f)) - RuntimeError ("error reading line: %s", strerror (errno)); - buf[0] = 0; - return buf; - } - size_t n = wcsnlen (p, size); // SECURITY NOTE: string use has been reviewed - - // check for buffer overflow - - if (n >= (size_t) size -1) - { - wstring example (buf, min (n, 100)); - RuntimeError ("input line too long at file offset %U64d (max. %d characters allowed) [%ls ...]", - fgetpos (f), size -1, example.c_str()); - } - - // remove newline at end - - if (n > 0 && p[n-1] == L'\n') // UNIX and Windows style - { - n--; - p[n] = 0; - if (n > 0 && p[n-1] == L'\r') // Windows style - { - n--; - p[n] = 0; - } - } - else if (n > 0 && p[n-1] == L'\r') // Mac style - { - n--; - p[n] = 0; - } - - return buf; -} -#endif - -// STL string version -std::string fgetline (FILE * f) -{ - vector buf (1000000); - return fgetline (f, &buf[0], (int) buf.size()); -} - -// STL string version -std::wstring fgetlinew (FILE * f) -{ - vector buf (1000000); - return fgetline (f, &buf[0], (int) buf.size()); -} - -// STL string version avoiding most memory allocations -void fgetline (FILE * f, std::string & s, std::vector & buf) -{ - buf.resize (1000000); // enough? // KIT: increased to 1M to be safe - const char * p = fgetline (f, &buf[0], (int) buf.size()); - s.assign (p); -} - -void fgetline (FILE * f, std::wstring & s, std::vector & buf) -{ - buf.resize (1000000); // enough? // KIT: increased to 1M to be safe - const wchar_t * p = fgetline (f, &buf[0], (int) buf.size()); - s.assign (p); -} - -// char buffer version -void fgetline (FILE * f, std::vector & buf) -{ - const int BUF_SIZE = 1000000; // enough? // KIT: increased to 1M to be safe - buf.resize (BUF_SIZE); - fgetline (f, &buf[0], (int) buf.size()); - buf.resize (strnlen (&buf[0], BUF_SIZE) +1); // SECURITY NOTE: string use has been reviewed -} - -void fgetline (FILE * f, std::vector & buf) -{ - const int BUF_SIZE = 1000000; // enough? // KIT: increased to 1M to be safe - buf.resize (BUF_SIZE); - fgetline (f, &buf[0], (int) buf.size()); - buf.resize (wcsnlen (&buf[0], BUF_SIZE) +1); // SECURITY NOTE: string use has been reviewed -} - -// read a 0-terminated string -const char * fgetstring (FILE * f, __out_z_cap(size) char * buf, int size) -{ - int i; - for (i = 0; ; i++) - { - int c = fgetc(f); - if (c == EOF) - RuntimeError ("error reading string or missing 0: %s", strerror(errno)); - if (c == 0) break; - if (i >= size - 1) - RuntimeError ("input line too long (max. %d characters allowed)", size - 1); - buf[i] = (char)c; - } - assert (i < size); - buf[i] = 0; - return buf; -} - -// read a 0-terminated wstring -string fgetstring (FILE * f) -{ - string res; - for (;;) - { - char c = (char)fgetc (f); - if (c == EOF) - RuntimeError ("error reading string or missing 0: %s", strerror (errno)); - if (c == 0) break; - res.push_back (c); - } - return res; -} - -// read a 0-terminated string -const wchar_t * fgetstring (FILE * f, __out_z_cap(size) wchar_t * buf, int size) -{ - int i; - for (i = 0; ; i++) - { - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - wint_t c = fgetwc(f); - if (c == WEOF) - RuntimeError ("error reading string or missing 0: %s", strerror(errno)); - if (c == 0) break; - if (i >= size - 1) - { - RuntimeError ("input line too long (max. %d wchar_tacters allowed)", size - 1); - } - buf[i] = (wchar_t)c; - } - assert(i < size); - buf[i] = 0; - return buf; -} - -#if (_MSC_VER < 1800) -// read a 0-terminated wstring -wstring fgetwstring (FILE * f) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - wstring res; - for (;;) - { - // - // there is a known vc++ runtime bug: Microsoft Connect 768113 - // fgetwc can skip a byte in certain condition - // this is already fixed in update release to VS 2012 - // for now the workaround is to use fgetc twice to simulate fgetwc - // - //wint_t c = fgetwc (f); - int c1 = fgetc(f); - int c2 = fgetc(f); - - // synthetic fgetc output to simulate fgetwc - // note the order below works only for little endian - wint_t c = (wint_t)((c2 << 8) | c1); - if (c == WEOF) - RuntimeError ("error reading string or missing 0: %s", strerror (errno)); - if (c == 0) break; - res.push_back ((wchar_t) c); - } - return res; -} - -#else -// read a 0-terminated wstring -wstring fgetwstring (FILE * f) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - wstring res; - for (;;) - { - wint_t c = fgetwc(f); - if (c == WEOF) - RuntimeError ("error reading string or missing 0: %s", strerror(errno)); - if (c == 0) break; - res.push_back((wchar_t)c); - } - return res; -} -#endif - -bool fskipspace (FILE * f) -{ - int count=0; - for (;;count++) - { - int c = fgetc (f); - if (c == EOF) // hit the end - { - if (ferror(f)) - RuntimeError ("error reading from file: %s", strerror(errno)); - break; - } - if (!isspace (c)) // end of space: undo getting that character - { - int rc = ungetc(c, f); - if (rc != c) - RuntimeError ("error in ungetc(): %s", strerror(errno)); - break; - } - } - return count>0; -} - -bool fskipwspace (FILE * f) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - int count = 0; - for (;;count++) - { - wint_t c = fgetwc (f); - if (c == WEOF) // hit the end - { - if (ferror (f)) - RuntimeError ("error reading from file: %s", strerror (errno)); - break; - } - if (!iswspace (c)) // end of space: undo getting that character - { - wint_t rc = ungetwc (c, f); - if (rc != c) - RuntimeError ("error in ungetc(): %s", strerror (errno)); - break; - } - } - return count > 0; -} - -// fskipNewLine(): skip all white space until end of line incl. the newline -// skip - skip the end of line if true, otherwise leave the end of line (but eat any leading space) -int fskipNewline (FILE * f, bool skip) -{ - int c; - bool found = false; - - // skip white space - - do - { - c = fgetc(f); - } while (c == ' ' || c == '\t'); - - if (c == '\r' || c == '\n') // Accept any type of newline - { - found = true; - if (skip) - c = fgetc(f); - } - - if ((found && !skip) || - !(c == '\r' || c == '\n')) - { - // if we found an EOF, return that unless there was a newline before the EOF - if (c == EOF) - return found?(int)true:EOF; - int rc = ungetc (c, f); - if (rc != c) - RuntimeError ("error in ungetc(): %s", strerror (errno)); - return (int)found; - } - // if we get here we saw a newline - return (int)true; -} - -// fskipwNewLine(): skip all white space until end of line incl. the newline -// skip - skip the end of line if true, otherwise leave the end of line (but eat any leading space) -int fskipwNewline (FILE * f, bool skip) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - wint_t c; - bool found = false; - // skip white space - - do - { - c = fgetwc(f); - } while (c == L' ' || c == L'\t'); - - if (c == L'\r' || c == L'\n') // accept any style of newline - { - found = true; - if (skip) - c = fgetwc(f); - } - - if ((found && !skip) - ||!(c == L'\r' || c == L'\n')) - { - if (c == WEOF) - return found?(int)true:EOF; - wint_t rc = ungetwc (c, f); - if (rc != c) - RuntimeError ("error in ungetwc(): %s", strerror (errno)); - return (int)found; - } - // if we get here we saw a double newline - return (int)true; -} - -// read a space-terminated token -// ...TODO: eat trailing space like fscanf() doessurrounding space) -const char * fgettoken (FILE * f, __out_z_cap(size) char * buf, int size) -{ - fskipspace (f); // skip leading space - int c = -1; - int i; - for (i = 0; ; i++) - { - c = fgetc (f); - if (c == EOF) break; - if (isspace (c)) break; - if (i >= size -1) - RuntimeError ("input token too long (max. %d characters allowed)", size -1); - buf[i] = (char) c; - } - // ... TODO: while (IsWhiteSpace (c)) c = fgetc (f); // skip trailing space - if (c != EOF) - { - int rc = ungetc (c, f); - if (rc != c) - RuntimeError ("error in ungetc(): %s", strerror (errno)); - } - assert (i < size); - buf[i] = 0; - return buf; -} - -string fgettoken (FILE * f) -{ - char buf[80]; - return fgettoken (f, buf, sizeof(buf)/sizeof(*buf)); -} - -// read a space-terminated token -const wchar_t * fgettoken (FILE * f, __out_z_cap(size) wchar_t * buf, int size) -{ - // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) - fskipwspace(f); // skip leading space - wint_t c = WEOF; - int i; - for (i = 0; ; i++) - { - c = fgetwc (f); - if (c == WEOF) break; - if (iswspace (c)) break; - if (i >= size -1) - RuntimeError ("input token too long (max. %d wchar_tacters allowed)", size -1); - buf[i] = (wchar_t) c; - } - // ... TODO: while (IsWhiteSpace (c)) c = fgetc (f); // skip trailing space - if (c != WEOF) - { - int rc = ungetwc (c, f); - if (rc != c) - RuntimeError ("error in ungetwc(): %s", strerror (errno)); - } - assert (i < size); - buf[i] = 0; - return buf; -} - -wstring fgetwtoken (FILE * f) -{ - wchar_t buf[80]; - return fgettoken(f, buf, sizeof(buf) / sizeof(*buf)); -} - -template <> -int ftrygetText(FILE * f, bool& v) -{ - wchar_t c; - int rc = ftrygetText(f, c); - v = (c == L'T'); - return rc; -} - -// ---------------------------------------------------------------------------- -// fputText(): write a bool out as character -// ---------------------------------------------------------------------------- -template <> -void fputText(FILE * f, bool v) -{ - fputText(f, v?L'T':L'F'); -} - -// ---------------------------------------------------------------------------- -// fgetTag(): read a 4-byte tag & return as a string -// ---------------------------------------------------------------------------- - -std::string fgetTag (FILE * f) -{ - char tag[5]; - freadOrDie (&tag[0], sizeof (tag[0]), 4, f); - tag[4] = 0; - return std::string (tag); -} - -// ---------------------------------------------------------------------------- -// fcheckTag(): read a 4-byte tag & verify it; terminate if wrong tag -// ---------------------------------------------------------------------------- - -void fcheckTag (FILE * f, const char * expectedTag) -{ - fcompareTag (fgetTag (f), expectedTag); -} - - -void fcheckTag_ascii (FILE * f, const string & expectedTag) -{ - char buf[20]; // long enough for a tag - fskipspace (f); - fgettoken (f, buf, sizeof(buf)/sizeof(*buf)); - if (expectedTag != buf) - { - RuntimeError ("invalid tag '%s' found; expected '%s'", buf, expectedTag.c_str()); - } -} - -// ---------------------------------------------------------------------------- -// fcompareTag(): compare two tags; terminate if wrong tag -// ---------------------------------------------------------------------------- - -void fcompareTag (const string & readTag, const string & expectedTag) -{ - if (readTag != expectedTag) - { - RuntimeError ("invalid tag '%s' found; expected '%s'", - readTag.c_str(), expectedTag.c_str()); - } -} - -// ---------------------------------------------------------------------------- -// fputTag(): write a 4-byte tag -// ---------------------------------------------------------------------------- - -void fputTag (FILE * f, const char * tag) -{ - const int TAG_LEN = 4; - assert (strnlen (tag, TAG_LEN + 1) == TAG_LEN); - fwriteOrDie ((void *) tag, sizeof (*tag), strnlen (tag, TAG_LEN), f); -} - -// ---------------------------------------------------------------------------- -// fskipstring(): skip a 0-terminated string, such as a pad string -// ---------------------------------------------------------------------------- - -void fskipstring (FILE * f) -{ - char c; - do - { - freadOrDie(&c, sizeof (c), 1, f); - } - while (c); -} - -// ---------------------------------------------------------------------------- -// fpad(): write a 0-terminated string to pad file to a n-byte boundary -// (note: file must be opened in binmode to work properly on DOS/Windows!!!) -// ---------------------------------------------------------------------------- -void fpad (FILE * f, int n) -{ - // get current writing position - int pos = ftell (f); - if (pos == -1) - { - RuntimeError ("error in ftell(): %s", strerror (errno)); - } - // determine how many bytes are needed (at least 1 for the 0-terminator) - // and create a dummy string of that length incl. terminator - int len = n - (pos % n); - const char dummyString[] = "MSR-Asia: JL+FS"; - size_t offset = sizeof(dummyString)/sizeof(dummyString[0]) - len; - assert (offset >= 0); - fputstring (f, dummyString + offset); -} - -// ---------------------------------------------------------------------------- -// fgetbyte(): read a byte value -// ---------------------------------------------------------------------------- - -char fgetbyte (FILE * f) -{ - char v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -// ---------------------------------------------------------------------------- -// fgetshort(): read a short value -// ---------------------------------------------------------------------------- - -short fgetshort (FILE * f) -{ - short v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -short fgetshort_bigendian (FILE * f) -{ - unsigned char b[2]; - freadOrDie (&b, sizeof (b), 1, f); - return (short) ((b[0] << 8) + b[1]); -} - -// ---------------------------------------------------------------------------- -// fgetint24(): read a 3-byte (24-bit) int value -// ---------------------------------------------------------------------------- - -int fgetint24 (FILE * f) -{ - int v; - assert (sizeof (v) == 4); - freadOrDie (&v, sizeof (v) -1, 1, f); // only read 3 lower-order bytes - v <<= 8; // shift up (upper 8 bits uninit'ed) - v >>= 8; // shift down 8 bits with sign-extend - return v; -} - -// ---------------------------------------------------------------------------- -// fgetint(): read an int value -// ---------------------------------------------------------------------------- - -int fgetint (FILE * f) -{ - int v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -int fgetint_bigendian (FILE * f) -{ - unsigned char b[4]; - freadOrDie (&b, sizeof (b), 1, f); - return (int) (((((b[0] << 8) + b[1]) << 8) + b[2]) << 8) + b[3]; -} - -int fgetint_ascii (FILE * f) -{ - fskipspace (f); - int res = 0; - char c; - freadOrDie (&c, sizeof (c), 1, f); - while (isdigit ((unsigned char)c)) - { - res = (10 * res) + (c - '0'); - freadOrDie (&c, sizeof (c), 1, f); - } - int rc = ungetc (c, f); - if (rc != c) - { - RuntimeError ("error in ungetc(): %s", strerror (errno)); - } - return res; -} - -// ---------------------------------------------------------------------------- -// fgetlong(): read an long value -// ---------------------------------------------------------------------------- - -long fgetlong (FILE * f) -{ - long v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -// ---------------------------------------------------------------------------- -// fgetfloat(): read a float value -// ---------------------------------------------------------------------------- - -float fgetfloat (FILE * f) -{ - float v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -float fgetfloat_bigendian (FILE * f) -{ - int bitpattern = fgetint_bigendian (f); - return *((float*) &bitpattern); -} - -float fgetfloat_ascii (FILE * f) -{ - float val; - fskipspace (f); - int rc = fscanf (f, "%f", &val); // security hint: safe overloads - if (rc == 0) - RuntimeError("error reading float value from file (invalid format): %s", strerror(errno)); - else if (rc == EOF) - RuntimeError("error reading from file: %s", strerror(errno)); - assert(rc == 1); - return val; -} - -// ---------------------------------------------------------------------------- -// fgetdouble(): read a double value -// ---------------------------------------------------------------------------- - -double fgetdouble (FILE * f) -{ - double v; - freadOrDie (&v, sizeof (v), 1, f); - return v; -} - -#ifdef _WIN32 - -// ---------------------------------------------------------------------------- -// fgetwav(): read an entire .wav file -// ---------------------------------------------------------------------------- - -void WAVEHEADER::prepareRest (int sampleCount) -{ - FmtLength = 16; - - wFormatTag = 1; - nAvgBytesPerSec = nSamplesPerSec * nBlockAlign; - - riffchar[0] = 'R'; - riffchar[1] = 'I'; - riffchar[2] = 'F'; - riffchar[3] = 'F'; - if (sampleCount != -1) - { - DataLength = sampleCount * nBlockAlign; - RiffLength = 36 + DataLength; - } - else - { - DataLength = 0xffffffff; - RiffLength = 0xffffffff; - } - - wavechar[0] = 'W'; - wavechar[1] = 'A'; - wavechar[2] = 'V'; - wavechar[3] = 'E'; - wavechar[4] = 'f'; - wavechar[5] = 'm'; - wavechar[6] = 't'; - wavechar[7] = ' '; - - datachar[0] = 'd'; - datachar[1] = 'a'; - datachar[2] = 't'; - datachar[3] = 'a'; -} - -void WAVEHEADER::prepare (unsigned int Fs, int Bits, int Channels, int SampleCount) -{ - nChannels = (short) Channels; - nSamplesPerSec = Fs; - nBlockAlign = (short) (Channels * (Bits/8)); - nAvgBytesPerSec = Fs * nBlockAlign; - wBitsPerSample = (short) Bits; - - prepareRest (SampleCount); -} - -void WAVEHEADER::prepare (const WAVEFORMATEX & wfx, int sampleCount /* -1 for unknown */) -{ - nChannels = wfx.nChannels; - nSamplesPerSec = wfx.nSamplesPerSec; - nBlockAlign = wfx.nBlockAlign; - wBitsPerSample = wfx.wBitsPerSample; - - prepareRest (sampleCount); -} - -void WAVEHEADER::write (FILE * f) -{ - fputTag (f, "RIFF"); - fputint (f, RiffLength); - fputTag (f, "WAVE"); - fputTag (f, "fmt "); - fputint (f, FmtLength); - fputshort (f, wFormatTag); - fputshort (f, nChannels); - fputint (f, nSamplesPerSec); - fputint (f, nAvgBytesPerSec); - fputshort (f, nBlockAlign); - fputshort (f, wBitsPerSample); - assert (FmtLength == 16); - assert (wFormatTag == 1); - fputTag (f, "data"); - fputint (f, DataLength); - fflushOrDie (f); -} - -/*static*/ void WAVEHEADER::update (FILE * f) -{ - long curPos = ftell (f); - if (curPos == -1L) - { - RuntimeError ("error determining file position: %s", strerror (errno)); - } - unsigned int len = (unsigned int) filesize (f); - unsigned int RiffLength = len - 8; - unsigned int DataLength = RiffLength - 36; - fseekOrDie (f, 4, SEEK_SET); - fputint (f, RiffLength); - fseekOrDie (f, 40, SEEK_SET); - fputint (f, DataLength); - fseekOrDie (f, curPos, SEEK_SET); -} - -#if 0 -unsigned int WAVEHEADER::read (FILE * f, signed short & wRealFormatTag, int & bytesPerSample) -{ - // read header - fcheckTag (f, "RIFF"); - /*unsigned int riffLen = */ fgetint (f); - fcheckTag (f, "WAVE"); - fcheckTag (f, "fmt "); - unsigned int fmtLen = fgetint (f); - wRealFormatTag = fgetshort (f); - if (wRealFormatTag == -2) // MARecorder.exe [Ivan Tashev] puts a -2 for - { // 8-channel recordings (meaning unknown). - wRealFormatTag = 1; // Workaround: pretend it is 1 (seems safe) - } - (wRealFormatTag == 1 || wRealFormatTag == 7) - || RuntimeError ("WAVEHEADER::read: wFormatTag=%d not supported for now", wRealFormatTag); - unsigned short wChannels = fgetshort (f); - unsigned long dwSamplesPerSec = fgetint (f); - unsigned int sampleRate = dwSamplesPerSec; - /*unsigned long dwAvgBytesPerSec = */ fgetint (f); - unsigned short wBlockAlign = fgetshort (f); - unsigned short wBitsPerSample = fgetshort (f); - (wBitsPerSample <= 16) || RuntimeError ("WAVEHEADER::read: invalid wBitsPerSample %d", wBitsPerSample); - bytesPerSample = wBitsPerSample / 8; - (wBlockAlign == wChannels * bytesPerSample) - || RuntimeError ("WAVEHEADER::read: wBlockAlign != wChannels*bytesPerSample not supported"); - while (fmtLen > 16) // unused extra garbage in header - { - fgetbyte (f); - fmtLen--; - } - if (wRealFormatTag == 7) - { - (bytesPerSample == 1) || RuntimeError ("WAVEHEADER::read: invalid wBitsPerSample %d for mulaw", wBitsPerSample); - fcheckTag (f, "fact"); - unsigned int factLen = fgetint (f); - while (factLen > 0) - { - fgetbyte (f); - factLen--; - } - } - fcheckTag (f, "data"); - unsigned int dataLen = fgetint (f); - unsigned int numSamples = dataLen / wBlockAlign; - - // prepare a nice wave header without junk (44 bytes, 16-bit PCM) - prepare (sampleRate, wBitsPerSample, wChannels, numSamples); - - return numSamples; -} - -static short toolULawToLinear(unsigned char p_ucULawByte) -{ - static short anExpLut[8] = { 0, 132, 396, 924, 1980, 4092, 8316, 16764 }; - short nSign, nExponent, nMantissa, nSample; - - p_ucULawByte=~p_ucULawByte; - nSign=(p_ucULawByte & 0x80); - nExponent=(p_ucULawByte >> 4) & 0x07; - nMantissa=p_ucULawByte & 0x0F; - nSample=anExpLut[nExponent]+(nMantissa<<(nExponent+3)); - if(nSign != 0) - nSample = -nSample; - - return nSample; -} - -// fgetwavraw(): only read data of .wav file. For multi-channel data, samples -// are kept interleaved. -static void fgetwavraw(FILE * f, std::vector & wav, const WAVEHEADER & wavhd) -{ - int bytesPerSample = wavhd.wBitsPerSample / 8; // (sample size on one channel) - wav.resize (wavhd.DataLength / bytesPerSample); - if (wavhd.wFormatTag == 7) // mulaw - { - (wavhd.nChannels == 1) || RuntimeError ("fgetwav: wChannels=%d not supported for mulaw", wavhd.nChannels); - std::vector data; - int numSamples = wavhd.DataLength/wavhd.nBlockAlign; - data.resize (numSamples); - freadOrDie (&data[0], sizeof (data[0]), numSamples, f); - for (int i = 0; i < numSamples; i++) - { - wav[i] = toolULawToLinear (data[i]); - } - } - else if (bytesPerSample == 2) - { // note: we may be reading an interleaved multi-channel signal. - freadOrDie (&wav[0], sizeof (wav[0]), wav.size(), f); - } - // ... TODO: support 8 bit linear PCM samples (implement when needed; samples scaled to 'short') - else - { - RuntimeError ("bytesPerSample != 2 is not supported except mulaw format!\n"); - } -} - -// ---------------------------------------------------------------------------- -// fgetwav(): read an entire .wav file. Stereo is mapped to mono. -// ---------------------------------------------------------------------------- - -void fgetwav (FILE * f, std::vector & wav, int & sampleRate) -{ - WAVEHEADER wavhd; // will be filled in for 16-bit PCM!! - signed short wFormatTag; // real format tag as found in data - int bytesPerSample; // bytes per sample as found in data - - unsigned int numSamples = wavhd.read (f, wFormatTag, bytesPerSample); - sampleRate = (int) wavhd.nSamplesPerSec; - - if (wavhd.nChannels == 1) - { - fgetwavraw (f, wav, wavhd); - } - else if (wavhd.nChannels == 2) - { - //read raw data - std::vector buf; - buf.resize(numSamples * 2); - fgetwavraw(f, buf, wavhd); - - //map to mono - wav.resize (numSamples); - const short * p = &buf[0]; - for (int i = 0; i < (int) numSamples; i++) - { - int l = *p++; - int r = *p++; - int mono = ((l + r) + 1) >> 1; - wav[i] = (short) mono; - } - } - else - { - RuntimeError ("bytesPerSample/wChannels != 2 needs to be implemented"); - } -} - -void fgetwav (const wstring & fn, std::vector & wav, int & sampleRate) -{ - auto_file_ptr f = fopenOrDie (fn, L"rbS"); - fgetwav (f, wav, sampleRate); -} - -// ---------------------------------------------------------------------------- -// ... TODO: -// - rename this function!! -// - also change to read header itself and return sample rate and channels -// fgetraw(): read data of multi-channel .wav file, and separate data of multiple channels. -// For example, data[i][j]: i is channel index, 0 means the first -// channel. j is sample index. -// ---------------------------------------------------------------------------- - -void fgetraw (FILE *f, std::vector< std::vector > & data, const WAVEHEADER & wavhd) -{ - std::vector wavraw; - fgetwavraw (f, wavraw, wavhd); - data.resize (wavhd.nChannels); - int numSamples = wavhd.DataLength/wavhd.nBlockAlign; - assert (numSamples == (int) wavraw.size() / wavhd.nChannels); - - for (int i = 0; i < wavhd.nChannels; i++) - { - data[i].resize (numSamples); - - for (int j = 0; j < numSamples; j++) - { - data[i][j] = wavraw[wavhd.nChannels*j + i]; - } - } -} - -// ---------------------------------------------------------------------------- -// fgetwfx(), fputwfx(): direct access to simple WAV headers -// ---------------------------------------------------------------------------- - -// read header and skip to first data byte; return #samples -unsigned int fgetwfx (FILE * f, WAVEFORMATEX & wfx) -{ - // read header - fcheckTag (f, "RIFF"); - /*unsigned int riffLen = */ fgetint (f); - fcheckTag (f, "WAVE"); - fcheckTag (f, "fmt "); - wfx.cbSize = sizeof (wfx); - int fmtLen = fgetint (f); - wfx.wFormatTag = fgetshort (f); - if (wfx.wFormatTag == -2) // MARecorder.exe [Ivan Tashev] puts a -2 for - { // 8-channel recordings (meaning unknown). - wfx.wFormatTag = 1; // Workaround: pretend it is 1 (seems safe) - } - (wfx.wFormatTag == 1 || wfx.wFormatTag == 3 || wfx.wFormatTag == 7) - || RuntimeError ("WAVEHEADER::read: wFormatTag=%d not supported for now", wfx.wFormatTag); - wfx.nChannels = fgetshort (f); - wfx.nSamplesPerSec = fgetint (f); - wfx.nAvgBytesPerSec = fgetint (f); - wfx.nBlockAlign = fgetshort (f); - wfx.wBitsPerSample = fgetshort (f); - // unused extra garbage in header - for ( ; fmtLen > 16; fmtLen--) fgetbyte (f); - fcheckTag (f, "data"); - unsigned int dataLen = fgetint (f); - unsigned int numSamples = dataLen / wfx.nBlockAlign; - return numSamples; -} - -void fputwfx (FILE *f, const WAVEFORMATEX & wfx, unsigned int numSamples) -{ - unsigned int DataLength = numSamples * wfx.nBlockAlign; - (DataLength / wfx.nBlockAlign == numSamples) - || RuntimeError ("fputwfx: data size exceeds WAV header 32-bit range"); - unsigned int RiffLength = 36 + DataLength; - unsigned int FmtLength = 16; - // file header - assert (wfx.cbSize == 0 || wfx.cbSize == FmtLength + 2); - fputTag (f, "RIFF"); - fputint (f, RiffLength); - fputTag (f, "WAVE"); - // 'fmt ' chunk (to hold wfx) - fputTag (f, "fmt "); - fputint (f, FmtLength); - fputshort (f, wfx.wFormatTag); - fputshort (f, wfx.nChannels); - fputint (f, wfx.nSamplesPerSec); - fputint (f, wfx.nAvgBytesPerSec); - fputshort (f, wfx.nBlockAlign); - fputshort (f, wfx.wBitsPerSample); - // data chunk - fputTag (f, "data"); - fputint (f, DataLength); - fflushOrDie (f); -} - -// ---------------------------------------------------------------------------- -// fputwav(): write an entire .wav file (16 bit PCM) -// ---------------------------------------------------------------------------- - -void fputwav (FILE * f, const vector & wav, int sampleRate, int nChannels) -{ - f;wav;sampleRate;nChannels; - // construct WAVEFORMATEX - WAVEFORMATEX wfx; - wfx.cbSize = 16 + 2; //fmt data + extra data - wfx.nAvgBytesPerSec = (DWORD)(sampleRate * nChannels * 2); //short: 2 bytes per sample - wfx.nBlockAlign = (WORD)nChannels * 2; //short: 2bytes per sample - wfx.nChannels = (WORD)nChannels; - wfx.nSamplesPerSec = sampleRate; - wfx.wBitsPerSample = 16; - wfx.wFormatTag = WAVE_FORMAT_PCM; - //putwfx - fputwfx (f, wfx, (unsigned int) wav.size()); - // wrtie the data - fwriteOrDie (&wav[0], sizeof(wav[0]), wav.size(), f); -} - -void fputwav (const wstring & fn, const vector & wav, int sampleRate, int nChannels) -{ - auto_file_ptr f = fopenOrDie (fn, L"wbS"); - fputwav (f, wav, sampleRate, nChannels); - fflushOrDie (f); // after this, fclose() (in destructor of f) cannot fail -} -#endif -#endif - -// ---------------------------------------------------------------------------- -// fputbyte(): write a byte value -// ---------------------------------------------------------------------------- - -void fputbyte (FILE * f, char v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputshort(): write a short value -// ---------------------------------------------------------------------------- - -void fputshort (FILE * f, short v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputint24(): write a 3-byte (24-bit) int value -// ---------------------------------------------------------------------------- - -void fputint24 (FILE * f, int v) -{ - assert (sizeof (v) == 4); - fwriteOrDie (&v, sizeof (v) -1, 1, f); // write low-order 3 bytes -} - -// ---------------------------------------------------------------------------- -// fputint(): write an int value -// ---------------------------------------------------------------------------- - -void fputint (FILE * f, int v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputlong(): write an long value -// ---------------------------------------------------------------------------- - -void fputlong (FILE * f, long v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputfloat(): write a float value -// ---------------------------------------------------------------------------- - -void fputfloat (FILE * f, float v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputdouble(): write a double value -// ---------------------------------------------------------------------------- - -void fputdouble (FILE * f, double v) -{ - fwriteOrDie (&v, sizeof (v), 1, f); -} - -// ---------------------------------------------------------------------------- -// fputfile(): write a binary block or a string as a file -// ---------------------------------------------------------------------------- - -void fputfile (const wstring & pathname, const std::vector & buffer) -{ - FILE * f = fopenOrDie (pathname, L"wb"); - try - { - if (buffer.size() > 0) - { // ^^ otherwise buffer[0] is an illegal expression - fwriteOrDie (&buffer[0], sizeof (buffer[0]), buffer.size(), f); - } - fcloseOrDie (f); - } - catch (...) - { - fclose (f); - throw; - } -} - -void fputfile (const wstring & pathname, const std::wstring & string) -{ - FILE * f = fopenOrDie (pathname, L"wb"); - try - { - if (string.length() > 0) - { // ^^ otherwise buffer[0] is an illegal expression - fwriteOrDie (string.c_str(), sizeof (string[0]), string.length(), f); - } - fcloseOrDie (f); - } - catch (...) - { - fclose (f); - throw; - } -} - -void fputfile (const wstring & pathname, const std::string & string) -{ - FILE * f = fopenOrDie (pathname, L"wb"); - try - { - if (string.length() > 0) - { // ^^ otherwise buffer[0] is an illegal expression - fwriteOrDie (string.c_str(), sizeof (string[0]), string.length(), f); - } - fcloseOrDie (f); - } - catch (...) - { - fclose (f); - throw; - } -} - -// ---------------------------------------------------------------------------- -// fgetfile(): load a file as a binary block -// ---------------------------------------------------------------------------- - -void fgetfile (const wstring & pathname, std::vector & buffer) -{ - FILE * f = fopenOrDie (pathname, L"rb"); - size_t len = filesize (f); - buffer.resize (len); - if (buffer.size() > 0) - { // ^^ otherwise buffer[0] is an illegal expression - freadOrDie (&buffer[0], sizeof (buffer[0]), buffer.size(), f); - } - fclose (f); -} - -void fgetfile (FILE * f, std::vector & buffer) -{ // this version reads until eof - buffer.resize (0); - buffer.reserve (1000000); // avoid too many reallocations - std::vector inbuf; - inbuf.resize (65536); // read in chunks of this size - while (!feof (f)) // read until eof - { - size_t n = fread (&inbuf[0], sizeof (inbuf[0]), inbuf.size(), f); - if (ferror (f)) - { - RuntimeError ("fgetfile: error reading from file: %s", strerror (errno)); - } - buffer.insert (buffer.end(), inbuf.begin(), inbuf.begin() + n); - } - buffer.reserve (buffer.size()); -} - -// load it into RAM in one huge chunk -static size_t fgetfilechars (const std::wstring & path, vector & buffer) -{ - auto_file_ptr f (fopenOrDie (path, L"rb")); - size_t len = filesize (f); - buffer.reserve (len +1); - freadOrDie (buffer, len, f); - buffer.push_back (0); // this makes it a proper C string - return len; -} - -template static void strtoklines (char * s, LINES & lines) -{ - for (char * p = strtok (s, "\r\n"); p; p = strtok (NULL, "\r\n")) - lines.push_back (p); -} - -void msra::files::fgetfilelines (const std::wstring & path, vector & buffer, std::vector & lines) -{ - // load it into RAM in one huge chunk - const size_t len = fgetfilechars (path, buffer); - - // parse into lines - lines.resize (0); - lines.reserve (len / 20); - strtoklines (&buffer[0], lines); -} - -// same as above but returning const char* (avoiding the memory allocation) -vector msra::files::fgetfilelines (const wstring & path, vector & buffer) -{ - // load it into RAM in one huge chunk - const size_t len = fgetfilechars (path, buffer); - - // parse into lines - vector lines; - lines.reserve (len / 20); - strtoklines (&buffer[0], lines); - return lines; -} - -// ---------------------------------------------------------------------------- -// getfiletime(): access modification time -// ---------------------------------------------------------------------------- - -#ifndef _FILETIME_ -//typedef struct _FILETIME { DWORD dwLowDateTime; DWORD dwHighDateTime; }; // from minwindef.h -typedef time_t FILETIME; -#else -bool operator>= (const FILETIME & targettime, const FILETIME & inputtime) // for use in fuptodate() -{ - return (targettime.dwHighDateTime > inputtime.dwHighDateTime) || - (targettime.dwHighDateTime == inputtime.dwHighDateTime && targettime.dwLowDateTime >= inputtime.dwLowDateTime); -} -#endif - -#ifdef _WIN32 -class auto_find_handle -{ - HANDLE h; - auto_find_handle operator= (const auto_find_handle &); - auto_find_handle(const auto_find_handle &); -public: - auto_find_handle(HANDLE p_h) : h(p_h) {} - ~auto_find_handle() { if (h != INVALID_HANDLE_VALUE) ::FindClose(h); } - operator HANDLE () const { return h; } -}; -#endif - -bool getfiletime(const wstring & path, FILETIME & time) -{ // return file modification time, false if cannot be determined -#ifdef _WIN32 - WIN32_FIND_DATAW findFileData; - auto_find_handle hFind (FindFirstFileW (path.c_str(), &findFileData)); - if (hFind != INVALID_HANDLE_VALUE) - { - time = findFileData.ftLastWriteTime; - return true; - } - else - return false; -#else // TODO: test this; e.g. does st_mtime have the desired resolution? - struct stat buf; - int result; - - // Get data associated with "crt_stat.c": - result = stat(wtocharpath(path.c_str()).c_str(), &buf); - // Check if statistics are valid: - if (result != 0) - return false; - - time = buf.st_mtime; - return true; -#endif -} - -#if 0 -void setfiletime (const wstring & path, const FILETIME & time) -{ // update the file modification time of an existing file - auto_find_handle h (CreateFileW (path.c_str(), FILE_WRITE_ATTRIBUTES, - FILE_SHARE_READ|FILE_SHARE_WRITE, NULL, - OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL)); - if (h == INVALID_HANDLE_VALUE) - { - RuntimeError ("setfiletime: error opening file: %d", GetLastError()); - } - BOOL rc = SetFileTime (h, NULL, NULL, &time); - if (!rc) - { - RuntimeError ("setfiletime: error setting file time information: %d", GetLastError()); - } -} -#endif - -// ---------------------------------------------------------------------------- -// expand_wildcards -- wildcard expansion of a path, including directories. -// ---------------------------------------------------------------------------- - -#ifdef _WIN32 -// Win32-style variant of this function (in case we want to use it some day) -// Returns 0 in case of failure. May throw in case of bad_alloc. -static BOOL ExpandWildcards (wstring path, vector & paths) -{ - // convert root to DOS filename convention - for (size_t k = 0; k < path.length(); k++) if (path[k] == '/') path[k] = '\\'; - - // remove terminating backslash - size_t last = path.length() -1; - if (last >= 0 && path[last] == '\\') path.erase (last); - - // convert root to long filename convention - //if (path.find (L"\\\\?\\") != 0) - // path = L"\\\\?\\" + root; - - // split off everything after first wildcard - size_t wpos = path.find_first_of (L"*?"); - if (wpos == 2 && path[0] == '\\' && path[1] == '\\') - wpos = path.find_first_of (L"*?", 4); // 4=skip "\\?\" - if (wpos == wstring::npos) - { // no wildcard: just return it - paths.push_back (path); - return TRUE; - } - - // split off everything afterwards if any - wstring rest; // remaining path after this directory - size_t spos = path.find_first_of (L"\\", wpos +1); - if (spos != wstring::npos) - { - rest = path.substr (spos +1); - path.erase (spos); - } - - // crawl folder - WIN32_FIND_DATAW ffdata; - auto_find_handle hFind (::FindFirstFileW (path.c_str(), &ffdata)); - if (hFind == INVALID_HANDLE_VALUE) - { - DWORD err = ::GetLastError(); - if (rest.empty() && err == 2) return TRUE; // no matching file: empty - return FALSE; // another error - } - size_t pos = path.find_last_of (L"\\"); - if (pos == wstring::npos) LogicError ("unexpected missing \\ in path"); - wstring parent = path.substr (0, pos); - do - { - // skip this and parent directory - bool isDir = ((ffdata.dwFileAttributes & (FILE_ATTRIBUTE_DIRECTORY | FILE_ATTRIBUTE_REPARSE_POINT)) != 0); - if (isDir && ffdata.cFileName[0] == '.') continue; - - wstring filename = parent + L"\\" + ffdata.cFileName; - if (rest.empty()) - { - paths.push_back (filename); - } - else if (isDir) // multi-wildcards: further expand - { - BOOL rc = ExpandWildcards (filename + L"\\" + rest, paths); - rc; // error here means no match, e.g. Access Denied to one subfolder - } - } while (::FindNextFileW(hFind, &ffdata) != 0); - return TRUE; -} -#endif - -void expand_wildcards (const wstring & path, vector & paths) -{ -#ifdef _WIN32 - BOOL rc = ExpandWildcards (path, paths); - if (!rc) - RuntimeError ("error in expanding wild cards '%ls': Win32 error %d", path.c_str(), (int)::GetLastError()); -#else - // On Linux we have just the function for the job: glob - glob_t globResult; - if (glob(wtocharpath(path.c_str()).c_str(), GLOB_TILDE, NULL, &globResult) != 0) - { - RuntimeError("error in expanding wild cards '%ls': %s", path.c_str(), strerror(errno)); - } - - for(unsigned int i = 0; i < globResult.gl_pathc; ++i) - { - paths.push_back(msra::strfun::utf16(globResult.gl_pathv[i])); - } - globfree(&globResult); -#endif - -} - -// ---------------------------------------------------------------------------- -// make_intermediate_dirs() -- make all intermediate dirs on a path -// ---------------------------------------------------------------------------- - -static void mkdir (const wstring & path) -{ - int rc = _wmkdir (path.c_str()); - if (rc >= 0 || errno == EEXIST) - return; // no error or already existing --ok -#ifdef _WIN32 // bug in _wmkdir(): returns access_denied if folder exists but read-only --check existence - if (errno == EACCES) - { - DWORD att = ::GetFileAttributesW (path.c_str()); - if (att != INVALID_FILE_ATTRIBUTES || (att & FILE_ATTRIBUTE_DIRECTORY) != 0) - return; // ok - } -#endif - RuntimeError ("mkdir: error creating intermediate directory %ls", path.c_str()); -} - -// make subdir of a file including parents -void msra::files::make_intermediate_dirs (const wstring & filepath) -{ - vector buf; - buf.resize (filepath.length() +1, 0); - wcscpy (&buf[0], filepath.c_str()); - wstring subpath; - int skip = 0; -#ifdef _WIN32 - // On windows, if share (\\) then the first two levels (machine, share name) cannot be made. - if ((buf[0] == '/' && buf[1] == '/') || (buf[0] == '\\' && buf[1] == '\\')) - { - subpath = L"/"; - skip = 2; // skip two levels (machine, share) - } -#else - // On unix, if the filepath starts with '/' then it is absolute - // path and the created sub-paths should also start with '/' - if (buf[0] == '/') - { - subpath = L"/"; - } -#endif - // make all constituents except the filename (to make a dir, include a trailing slash) - wchar_t * context = nullptr; - for (const wchar_t * p = wcstok_s (&buf[0], L"/\\", &context); p; p = wcstok_s (NULL, L"/\\", &context)) - { - if (subpath != L"" && subpath != L"/" && subpath != L"\\" && skip == 0) - { - mkdir (subpath); - } - else if (skip > 0) skip--; // skip this level - // rebuild the final path - if (subpath != L"") subpath += L"/"; - subpath += p; - } -} - -// ---------------------------------------------------------------------------- -// fuptodate() -- test whether an output file is at least as new as an input file -// ---------------------------------------------------------------------------- - -// test if file 'target' is not older than 'input' --used for make mode -// 'input' must exist if 'inputrequired'; otherweise if 'target' exists, it is considered up to date -// 'target' may or may not exist -bool msra::files::fuptodate (const wstring & target, const wstring & input, bool inputrequired) -{ - FILETIME targettime; - if (!getfiletime (target, targettime)) return false; // target missing: need to update - FILETIME inputtime; - if (!getfiletime (input, inputtime)) return !inputrequired; // input missing: if required, pretend to be out of date as to force caller to fail - // up to date if target has higher time stamp - return targettime >= inputtime; // note: uses an overload for WIN32 FILETIME (in Linux, FILETIME=time_t=size_t) -} - -/// separate string by separator -vector sep_string(const string & istr, const string & sep) -{ - string str = istr; - str = trim(str); - vector vstr; - string csub; - size_t ifound = 0; - size_t ifoundlast = ifound; - ifound = str.find(sep, ifound); - while (ifound != std::string::npos) - { - csub = str.substr(ifoundlast, ifound - ifoundlast); - vstr.push_back(trim(csub)); - - ifoundlast = ifound+1; - ifound = str.find(sep, ifoundlast); - } - csub = str.substr(ifoundlast, str.length() - ifoundlast); - vstr.push_back(trim(csub)); - - return vstr; -} - -/// separate string by separator -vector wsep_string(const wstring & istr, const wstring & sep) -{ - wstring str = istr; - str = wtrim(str); - vector vstr; - wstring csub; - size_t ifound = 0; - size_t ifoundlast = ifound; - ifound = str.find(sep, ifound); - while (ifound != std::wstring::npos) - { - csub = str.substr(ifoundlast, ifound - ifoundlast); - vstr.push_back(wtrim(csub)); - - ifoundlast = ifound + 1; - ifound = str.find(sep, ifoundlast); - } - csub = str.substr(ifoundlast, str.length() - ifoundlast); - vstr.push_back(wtrim(csub)); - - return vstr; -} -static inline std::string wcstombs(const std::wstring & p) // output: MBCS -{ - size_t len = p.length(); - vector buf(2 * len + 1); // max: 1 wchar => 2 mb chars - fill(buf.begin(), buf.end(), 0); - ::wcstombs(&buf[0], p.c_str(), 2 * len + 1); - return std::string(&buf[0]); -} -static inline std::wstring mbstowcs(const std::string & p) // input: MBCS -{ - size_t len = p.length(); - vector buf(len + 1); // max: >1 mb chars => 1 wchar - fill(buf.begin(), buf.end(), (wchar_t)0); - //OACR_WARNING_SUPPRESS(UNSAFE_STRING_FUNCTION, "Reviewed OK. size checked. [rogeryu 2006/03/21]"); - ::mbstowcs(&buf[0], p.c_str(), len + 1); - return std::wstring(&buf[0]); -} - -wstring s2ws(const string& str) -{ -#ifdef __unix__ - return mbstowcs(str); -#else - typedef std::codecvt_utf8 convert_typeX; - std::wstring_convert converterX; - return converterX.from_bytes(str); - -#endif -} - -string ws2s(const wstring& wstr) -{ -#ifdef __unix__ - return wcstombs(wstr); -#else - typedef codecvt_utf8 convert_typeX; - wstring_convert converterX; - return converterX.to_bytes(wstr); -#endif - -} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#ifndef _CRT_SECURE_NO_WARNINGS +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings +#endif + +#define _CRT_NONSTDC_NO_DEPRECATE // make VS accept POSIX functions without _ +#pragma warning (disable: 4996) // ^^ this does not seem to work--TODO: make it work +#define _FILE_OFFSET_BITS 64 // to force fseeko() and ftello() 64 bit in Linux + +#ifdef _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES // fixed-buffer overloads for strcpy() etc. +#undef _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES +#endif +#define _CRT_SECURE_CPP_OVERLOAD_STANDARD_NAMES 1 +#include "Basics.h" +#include "fileutil.h" +#ifdef __unix__ +#include +#include +#include +#include +#endif +#include +#include +#include +#include +#include +#ifdef _WIN32 +#include "Windows.h" // for FILETIME +#endif +#include // for std::find +#include +#include +#include +#ifndef UNDER_CE // some headers don't exist under winCE - the appropriate definitions seem to be in stdlib.h +#if defined(_WIN32) || defined(__CYGWIN__) +#include // for _O_BINARY/TEXT - not needed for wince +#include // for _setmode() +#define SET_BINARY_MODE(handle) setmode(handle, _O_BINARY) +#define SET_TEXT_MODE(handle) setmode(handle, _O_TEXT) +#else +#define SET_BINARY_MODE(handle) ((int)0) +#define SET_TEXT_MODE(handle) ((int)0) +#endif +#endif + +#define __out_z_cap(x) // a fake SAL annotation; this may come in handy some day if we try static code analysis, so I don't want to delete it + +#include + +using namespace std; +using namespace Microsoft::MSR::CNTK; + +// ---------------------------------------------------------------------------- +// some mappings for non-Windows builds +// ---------------------------------------------------------------------------- + +template <> const wchar_t* GetScanFormatString(char) {return L" %hc";} +template <> const wchar_t* GetScanFormatString(wchar_t) {return L" %lc";} +template <> const wchar_t* GetScanFormatString(short) {return L" %hi";} +template <> const wchar_t* GetScanFormatString(int) {return L" %i";} +template <> const wchar_t* GetScanFormatString(long) {return L" %li";} +template <> const wchar_t* GetScanFormatString(unsigned short) {return L" %hu";} +template <> const wchar_t* GetScanFormatString(unsigned int) {return L" %u";} +//template <> const wchar_t* GetScanFormatString(unsigned long) {return L" %lu";} +template <> const wchar_t* GetScanFormatString(float) {return L" %g";} +template <> const wchar_t* GetScanFormatString(double) {return L" %lg";} +template <> const wchar_t* GetScanFormatString(size_t) {return L" %llu";} +template <> const wchar_t* GetScanFormatString(long long) {return L" %lli";} + +template <> const wchar_t* GetFormatString(char) {return L" %hc";} +template <> const wchar_t* GetFormatString(wchar_t) {return L" %lc";} +template <> const wchar_t* GetFormatString(short) {return L" %hi";} +template <> const wchar_t* GetFormatString(int) {return L" %i";} +template <> const wchar_t* GetFormatString(long) {return L" %li";} +template <> const wchar_t* GetFormatString(unsigned short) {return L" %hu";} +template <> const wchar_t* GetFormatString(unsigned int) {return L" %u";} +//template <> const wchar_t* GetFormatString(unsigned long) {return L" %lu";} +template <> const wchar_t* GetFormatString(float) {return L" %.9g";} +template <> const wchar_t* GetFormatString(double) {return L" %.17g";} +template <> const wchar_t* GetFormatString(size_t) {return L" %llu";} +template <> const wchar_t* GetFormatString(long long) {return L" %lli";} +template <> const wchar_t* GetFormatString(const char*) {return L" %hs";} +template <> const wchar_t* GetFormatString(const wchar_t*) {return L" %ls";} + +// ---------------------------------------------------------------------------- +// fgetText() specializations for fwscanf differences: get a value from a text file +// ---------------------------------------------------------------------------- +void fgetText(FILE * f, char& v) +{ + const wchar_t* formatString = GetFormatString(v); + int rc = fwscanf(f, formatString, &v); + if (rc == 0) + RuntimeError ("error reading value from file (invalid format): %ls", formatString); + else if (rc == EOF) + RuntimeError ("error reading from file: %s", strerror (errno)); + assert(rc == 1); +} +void fgetText(FILE * f, wchar_t& v) +{ + const wchar_t* formatString = GetFormatString(v); + int rc = fwscanf(f, formatString, &v); + if (rc == 0) + RuntimeError ("error reading value from file (invalid format): %ls", formatString); + else if (rc == EOF) + RuntimeError ("error reading from file: %s", strerror (errno)); + assert(rc == 1); +} + +// ---------------------------------------------------------------------------- +// fopenOrDie(): like fopen() but terminate with err msg in case of error. +// A pathname of "-" returns stdout or stdin, depending on mode, and it will +// change the binary mode if 'b' or 't' are given. If you use this, make sure +// not to fclose() such a handle. +// ---------------------------------------------------------------------------- + +static const wchar_t * strchr (const wchar_t * s, wchar_t v) { return wcschr (s, v); } + +// pathname is "-" -- open stdin or stdout. Changes bin mode if 'b' or 't' given. +template FILE * fopenStdHandle (const _T * mode) +{ + FILE * f = strchr (mode, 'r') ? stdin : stdout; + if (strchr (mode, 'b') || strchr (mode, 't')) // change binary mode + fsetmode (f, strchr(mode, 'b') ? 'b' : 't'); + return f; +} + +FILE * fopenOrDie (const string & pathname, const char * mode) +{ + FILE * f = (pathname[0] == '-') ? fopenStdHandle (mode) : fopen (pathname.c_str(), mode); + if (f == NULL) + { + RuntimeError ("error opening file '%s': %s", pathname.c_str(), strerror (errno)); + } + if (strchr (mode, 'S')) + { // if optimized for sequential access then use large buffer + setvbuf (f, NULL, _IOFBF, 10000000); // OK if it fails + } + return f; +} + +FILE * fopenOrDie (const wstring & pathname, const wchar_t * mode) +{ + FILE * f = (pathname[0] == '-') ? fopenStdHandle (mode) : _wfopen (pathname.c_str(), mode); + if (f == NULL) + { + RuntimeError ("error opening file '%ls': %s", pathname.c_str(), strerror (errno)); + } + if (strchr (mode, 'S')) + { // if optimized for sequential access then use large buffer + setvbuf (f, NULL, _IOFBF, 10000000); // OK if it fails + } + return f; +} + +// ---------------------------------------------------------------------------- +// set mode to binary or text (pass 'b' or 't') +// ---------------------------------------------------------------------------- + +void fsetmode(FILE * f, char type) +{ + if (type != 'b' && type != 't') + { + RuntimeError ("fsetmode: invalid type '%c'", type); + } +#ifdef UNDER_CE // winCE and win32 have different return types for _fileno + FILE *fd = fileno (f); // note: no error check possible +#else + int fd = fileno (f); // note: no error check possible +#endif + int rc = ( type == 'b' ? SET_BINARY_MODE(fd) : SET_TEXT_MODE(fd) ); + if (rc == -1) + { + RuntimeError ("error changing file mode: %s", strerror (errno)); + } +} + +// ---------------------------------------------------------------------------- +// freadOrDie(): like fread() but terminate with err msg in case of error +// ---------------------------------------------------------------------------- + +void freadOrDie (void * ptr, size_t size, size_t count, FILE * f) +{ + // \\XXX\C$ reads are limited, with some randomness (e.g. 48 MB), on Windows 7 32 bit, so we break this into chunks of some MB. Meh. + while (count > 0) + { + size_t chunkn = min (count, (size_t)15*1024*1024); // BUGBUG: I surely meant this limit to be bytes, not units of 'size'... + size_t n = fread (ptr, size, chunkn, f); + if (n != chunkn) + RuntimeError ("error reading from file: %s", strerror (errno)); + count -= n; + ptr = n * size + (char*) ptr; + } +} + +#ifdef _WIN32 +void freadOrDie (void * ptr, size_t size, size_t count, const HANDLE f) +{ + // \\XXX\C$ reads are limited, with some randomness (e.g. 48 MB), on Windows 7 32 bit, so we break this into chunks of some MB. Meh. + while (count > 0) + { + size_t chunkn = min (count * size, (size_t)15*1024*1024); + DWORD n ; + ReadFile(f, ptr, (DWORD) chunkn, &n, NULL); + if (n != chunkn) + RuntimeError ("error number for reading from file: %s", GetLastError()); + count -= (size_t) (n / size); + ptr = n + (char*) ptr; + } +} +#endif + +// ---------------------------------------------------------------------------- +// fwriteOrDie(): like fwrite() but terminate with err msg in case of error; +// Windows C std lib fwrite() has problems writing >100 MB at a time (fails +// with Invalid Argument error), so we break it into chunks (yak!!) +// ---------------------------------------------------------------------------- + +void fwriteOrDie (const void * ptr, size_t size, size_t count, FILE * f) +{ + const char * p1 = (const char *) ptr; + size_t totalBytes = size * count; + while (totalBytes > 0) + { + size_t wantWrite = totalBytes; +#define LIMIT (16*1024*1024) // limit to 16 MB at a time + if (wantWrite > LIMIT) + { + wantWrite = LIMIT; + } + size_t n = fwrite ((const void *) p1, 1, wantWrite, f); + if (n != wantWrite) + { + RuntimeError ("error writing to file (ptr=0x%08lx, size=%d, count=%d, writing %d bytes after %d): %s", + (unsigned long)(size_t)ptr, (int)size, (int)count, (int)wantWrite, + (int)(size * count - totalBytes), + strerror (errno)); + } + totalBytes -= wantWrite; + p1 += wantWrite; + } +} + +#ifdef _WIN32 +void fwriteOrDie (const void * ptr, size_t size, size_t count, const HANDLE f) +{ + const char * p1 = (const char *) ptr; + DWORD totalBytes = (DWORD) (size * count); + while (totalBytes > 0) + { + DWORD wantWrite = totalBytes; +#define LIMIT (16*1024*1024) // limit to 16 MB at a time + if (wantWrite > LIMIT) + { + wantWrite = LIMIT; + } + DWORD byteWritten = 0 ; + if (WriteFile(f, (const void *) p1, wantWrite, &byteWritten, NULL) == false) + { + RuntimeError ("error writing to file (ptr=0x%08lx, size=%d," + " count=%d, writing %d bytes after %d): %s", + ptr, size, count, (int) wantWrite, + (int) (size * count - totalBytes), + strerror (errno)); + } + totalBytes -= wantWrite; + p1 += wantWrite; + } +} +#endif + +long fseekOrDie (FILE * f, long offset, int mode) +{ + long curPos = ftell (f); + if (curPos == -1L) + { + RuntimeError ("error seeking: %s", strerror (errno)); + } + int rc = fseek (f, offset, mode); + if (rc != 0) + { + RuntimeError ("error seeking: %s", strerror (errno)); + } + return curPos; +} + +// ---------------------------------------------------------------------------- +// fprintfOrDie(): like fprintf() but terminate with err msg in case of error +// ---------------------------------------------------------------------------- + +#pragma warning(push) +#pragma warning(disable : 4793) // 'vararg' : causes native code generation +void fprintfOrDie (FILE * f, const char * fmt, ...) +{ + va_list arg_ptr; + va_start (arg_ptr, fmt); + int rc = vfprintf (f, fmt, arg_ptr); + if (rc < 0) + { + RuntimeError ("error writing to file: %s", strerror (errno)); + } +} +#pragma warning(pop) + +// ---------------------------------------------------------------------------- +// fflushOrDie(): like fflush() but terminate with err msg in case of error +// ---------------------------------------------------------------------------- + +void fflushOrDie (FILE * f) +{ + int rc = fflush (f); + if (rc != 0) + { + RuntimeError ("error flushing to file: %s", strerror (errno)); + } +} + +// ---------------------------------------------------------------------------- +// filesize(): determine size of the file in bytes (with open file) +// ---------------------------------------------------------------------------- +size_t filesize (FILE * f) +{ +#ifdef _WIN32 + size_t curPos = _ftelli64(f); + if (curPos == -1L) + { + RuntimeError ("error determining file position: %s", strerror (errno)); + } + int rc = _fseeki64 (f, 0, SEEK_END); + if (rc != 0) + RuntimeError ("error seeking to end of file: %s", strerror (errno)); + size_t len = _ftelli64 (f); + if (len == -1L) + RuntimeError ("error determining file position: %s", strerror (errno)); + rc = _fseeki64 (f, curPos, SEEK_SET); + if (rc != 0) + RuntimeError ("error resetting file position: %s", strerror (errno)); + return len; +#else // TODO: test this + struct stat stat_buf; + int rc = fstat(fileno(f), &stat_buf); + if (rc != 0) + RuntimeError("error determining length of file: %s", strerror(errno)); + static_assert (sizeof(stat_buf.st_size)>=sizeof(uint64_t), "struct stat not compiled for 64-bit mode"); + return stat_buf.st_size; +#endif +} + +// filesize(): determine size of the file in bytes (with pathname) +size_t filesize (const wchar_t * pathname) +{ + FILE * f = fopenOrDie (pathname, L"rb"); + try + { + size_t len = filesize (f); + fclose (f); + return (size_t) len; + } + catch (...) + { + fclose (f); + throw; + } +} + +#ifndef UNDER_CE // no 64-bit under winCE + +// filesize64(): determine size of the file in bytes (with pathname) +int64_t filesize64 (const wchar_t * pathname) +{ +#ifdef _WIN32 + struct _stat64 fileinfo; + if (_wstat64 (pathname,&fileinfo) == -1) + return 0; + else + return fileinfo.st_size; +#else + return filesize (pathname); +#endif +} +#endif + +// ---------------------------------------------------------------------------- +// fget/setpos(): seek functions with error handling +// ---------------------------------------------------------------------------- + +uint64_t fgetpos (FILE * f) +{ +#ifdef _MSC_VER // standard does not allow to cast between fpos_t and integer numbers, and indeed it does not work on Linux (but on Windows and GCC) + fpos_t post; + int rc = ::fgetpos (f, &post); + if (rc != 0) + RuntimeError ("error getting file position: %s", strerror (errno)); +#else + auto pos = ftello (f); + uint64_t post = (uint64_t) pos; + static_assert (sizeof (post) >= sizeof (pos), "64-bit file offsets not enabled"); + if ((decltype (pos)) post != pos) + LogicError("64-bit file offsets not enabled"); +#endif + return post; +} + +void fsetpos (FILE * f, uint64_t reqpos) +{ +#ifdef _MSC_VER // standard does not allow to cast between fpos_t and integer numbers, and indeed it does not work on Linux (but on Windows and GCC) +#ifdef _MSC_VER // special hack for VS CRT + // Visual Studio's ::fsetpos() flushes the read buffer. This conflicts with a situation where + // we generally read linearly but skip a few bytes or KB occasionally, as is + // the case in speech recognition tools. This requires a number of optimizations. + + uint64_t curpos = fgetpos (f); + uint64_t cureob = curpos + f->_cnt; // UGH: we mess with an internal structure here + while (reqpos >= curpos && reqpos < cureob) + { + // if we made it then do not call fsetpos() + if (reqpos == fgetpos (f)) + return; + + // if we seek within the existing buffer, then just move to the position by dummy reads + char buf[65536]; + size_t n = min ((size_t) reqpos - (size_t) curpos, _countof (buf)); + fread (buf, sizeof (buf[0]), n, f); // (this may fail, but really shouldn't) + curpos += n; + + // since we mess with f->_cnt, if something unexpected happened to the buffer then back off + if (curpos != fgetpos (f) || curpos + f->_cnt != cureob) + break; // oops + } +#endif // end special hack for VS CRT + + // actually perform the seek + fpos_t post = reqpos; + int rc = ::fsetpos(f, &post); +#else // assuming __unix__ + off_t post = (off_t) reqpos; + static_assert (sizeof (off_t) >= sizeof (reqpos), "64-bit file offsets not enabled"); + if ((decltype (reqpos)) post != reqpos) + LogicError("64-bit file offsets not enabled"); + int rc = fseeko(f, post, SEEK_SET); +#endif + if (rc != 0) + RuntimeError("error setting file position: %s", strerror(errno)); +} + +// ---------------------------------------------------------------------------- +// unlinkOrDie(): unlink() with error handling +// ---------------------------------------------------------------------------- + +void unlinkOrDie (const std::string & pathname) +{ + if (unlink (pathname.c_str()) != 0 && errno != ENOENT) // if file is missing that's what we want + RuntimeError ("error deleting file '%s': %s", pathname.c_str(), strerror (errno)); +} +void unlinkOrDie (const std::wstring & pathname) +{ + if (_wunlink (pathname.c_str()) != 0 && errno != ENOENT) // if file is missing that's what we want + RuntimeError ("error deleting file '%ls': %s", pathname.c_str(), strerror (errno)); +} + +// ---------------------------------------------------------------------------- +// renameOrDie(): rename() with error handling +// ---------------------------------------------------------------------------- + +void renameOrDie (const std::string & from, const std::string & to) +{ +#ifdef _WIN32 + // deleting destination file if exits (to match Linux semantic) + if (fexists(to.c_str()) && !DeleteFileA(to.c_str())) + RuntimeError("error deleting file: '%s': %d", to.c_str(), GetLastError()); + + if (!MoveFileA (from.c_str(), to.c_str())) + RuntimeError("error renaming file '%s': %d", from.c_str(), GetLastError()); +#else + if (rename (from.c_str(), to.c_str()) != 0) + RuntimeError("error renaming file '%s': %s", from.c_str(), strerror(errno)); +#endif +} + +void renameOrDie (const std::wstring & from, const std::wstring & to) +{ +#ifdef _WIN32 + // deleting destination file if exits (to match Linux semantic) + if (fexists(to.c_str()) && !DeleteFileW(to.c_str())) + RuntimeError("error deleting file '%ls': %d", to.c_str(), GetLastError()); + + if (!MoveFileW(from.c_str(), to.c_str())) + RuntimeError ("error renaming file '%ls': %d", from.c_str(), GetLastError()); +#else + renameOrDie (wtocharpath(from.c_str()).c_str(), wtocharpath(to.c_str()).c_str()); +#endif +} + +// ---------------------------------------------------------------------------- +// fputstring(): write a 0-terminated string +// ---------------------------------------------------------------------------- + +void fputstring (FILE * f, const char * str) +{ + fwriteOrDie ((void *) str, sizeof (*str), strnlen (str, SIZE_MAX)+1, f); // SECURITY NOTE: string use has been reviewed +} + +void fputstring (FILE * f, const std::string & str) +{ + fputstring (f, str.c_str()); +} + +#ifdef _WIN32 +#pragma warning(push) +#pragma warning(disable : 4127) +#endif +void fputstring (FILE * f, const wchar_t * str) +{ + if (sizeof (*str) == 2) + { + fwriteOrDie ((void *) str, sizeof (*str), wcsnlen (str, SIZE_MAX)+1, f); // SECURITY NOTE: string use has been reviewed + } else if (sizeof (*str) == 4) + { + size_t strLen = wcsnlen(str, SIZE_MAX); + std::unique_ptr str16(new char16_t[strLen + 1]); + for (int i = 0; i < strLen; i++) + { + str16[i] = (char16_t) str[i]; + } + str16[strLen] = 0; + fwriteOrDie((void *)str16.get(), sizeof (*str) / 2, strLen + 1, f); // SECURITY NOTE: string use has been reviewed + } else + { + RuntimeError("error: unknown encoding\n"); + } +} +#ifdef _WIN32 +#pragma warning(pop) +#endif + +void fputstring (FILE * f, const std::wstring & str) +{ + fputstring (f, str.c_str()); +} + +// ---------------------------------------------------------------------------- +// fexists(): test if a file exists +// ---------------------------------------------------------------------------- + +bool fexists (const wchar_t * pathname) +{ +#ifdef _MSC_VER + WIN32_FIND_DATAW findFileData; + HANDLE hFind = FindFirstFileW (pathname, &findFileData); + if (hFind != INVALID_HANDLE_VALUE) + { + FindClose (hFind); + return true; + } + else + { + return false; + } +#else + auto_file_ptr f (_wfopen (pathname, L"r")); + return f != nullptr; +#endif +} + +bool fexists (const char * pathname) +{ +#ifdef _MSC_VER + WIN32_FIND_DATAA findFileData; + HANDLE hFind = FindFirstFileA (pathname, &findFileData); + if (hFind != INVALID_HANDLE_VALUE) + { + FindClose (hFind); + return true; + } + else + { + return false; + } +#else + auto_file_ptr f (fopen (pathname, "r")); + return f != nullptr; +#endif +} + +// ---------------------------------------------------------------------------- +// funicode(): test if a file uses unicode by reading its BOM +// ---------------------------------------------------------------------------- + +bool funicode (FILE * f) +{ + unsigned short testCode; + if (fread (&testCode, sizeof(short), 1, f) == 1 && + (int)testCode == 0xFEFF) + return true; + fseek (f,0,SEEK_SET); + //rewind (f); + return false; +} + +// ---------------------------------------------------------------------------- +// fgetline(): like fgets() but terminate with err msg in case of error; +// removes the newline character at the end (like gets()); +// Returns 'buf' (always). buf guaranteed to be 0-terminated. +// ---------------------------------------------------------------------------- + +#ifdef __CYGWIN__ // strnlen() is somehow missing in Cygwin, which we use to quick-check GCC builds under Windows (although it is not a real target platform) +static inline size_t strnlen (const char *s, size_t n) { return std::find (s, s + n, '\0') - s; } +#endif + +#ifdef UNDER_CE // strlen for char * not defined in winCE +static inline size_t strnlen (const char *s, size_t n) { return std::find (s, s + n, '\0') - s; } +#endif + +static inline wchar_t * fgets(wchar_t * buf, int n, FILE * f) { return fgetws(buf, n, f); } +static inline size_t strnlen(wchar_t * s, size_t n) { return wcsnlen(s, n); } + +template +CHAR * fgetline (FILE * f, CHAR * buf, int size) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + CHAR * p = fgets(buf, size, f); + if (p == NULL) // EOF reached: next time feof() = true + { + if (ferror (f)) + RuntimeError ("error reading line: %s", strerror (errno)); + buf[0] = 0; + return buf; + } + size_t n = strnlen (p, size); + + // check for buffer overflow + + if (n >= (size_t) size -1) + { + basic_string example (p, n < 100 ? n : 100); + uint64_t filepos = fgetpos(f); // (for error message only) + RuntimeError("input line too long at file offset %d (max. %d characters allowed) [%s ...]", (int)filepos, (int)size - 1, msra::strfun::utf8(example).c_str()); + } + + // remove newline at end + + if (n > 0 && p[n-1] == '\n') // UNIX and Windows style + { + n--; + p[n] = 0; + if (n > 0 && p[n-1] == '\r') // Windows style + { + n--; + p[n] = 0; + } + } + else if (n > 0 && p[n-1] == '\r') // Mac style + { + n--; + p[n] = 0; + } + + return buf; +} + +#if 0 +const wchar_t * fgetline (FILE * f, wchar_t * buf, int size) +{ + wchar_t * p = fgetws (buf, size, f); + if (p == NULL) // EOF reached: next time feof() = true + { + if (ferror (f)) + RuntimeError ("error reading line: %s", strerror (errno)); + buf[0] = 0; + return buf; + } + size_t n = wcsnlen (p, size); // SECURITY NOTE: string use has been reviewed + + // check for buffer overflow + + if (n >= (size_t) size -1) + { + wstring example (buf, min (n, 100)); + RuntimeError ("input line too long at file offset %U64d (max. %d characters allowed) [%ls ...]", + fgetpos (f), size -1, example.c_str()); + } + + // remove newline at end + + if (n > 0 && p[n-1] == L'\n') // UNIX and Windows style + { + n--; + p[n] = 0; + if (n > 0 && p[n-1] == L'\r') // Windows style + { + n--; + p[n] = 0; + } + } + else if (n > 0 && p[n-1] == L'\r') // Mac style + { + n--; + p[n] = 0; + } + + return buf; +} +#endif + +// STL string version +std::string fgetline (FILE * f) +{ + vector buf (1000000); + return fgetline (f, &buf[0], (int) buf.size()); +} + +// STL string version +std::wstring fgetlinew (FILE * f) +{ + vector buf (1000000); + return fgetline (f, &buf[0], (int) buf.size()); +} + +// STL string version avoiding most memory allocations +void fgetline (FILE * f, std::string & s, std::vector & buf) +{ + buf.resize (1000000); // enough? // KIT: increased to 1M to be safe + const char * p = fgetline (f, &buf[0], (int) buf.size()); + s.assign (p); +} + +void fgetline (FILE * f, std::wstring & s, std::vector & buf) +{ + buf.resize (1000000); // enough? // KIT: increased to 1M to be safe + const wchar_t * p = fgetline (f, &buf[0], (int) buf.size()); + s.assign (p); +} + +// char buffer version +void fgetline (FILE * f, std::vector & buf) +{ + const int BUF_SIZE = 1000000; // enough? // KIT: increased to 1M to be safe + buf.resize (BUF_SIZE); + fgetline (f, &buf[0], (int) buf.size()); + buf.resize (strnlen (&buf[0], BUF_SIZE) +1); // SECURITY NOTE: string use has been reviewed +} + +void fgetline (FILE * f, std::vector & buf) +{ + const int BUF_SIZE = 1000000; // enough? // KIT: increased to 1M to be safe + buf.resize (BUF_SIZE); + fgetline (f, &buf[0], (int) buf.size()); + buf.resize (wcsnlen (&buf[0], BUF_SIZE) +1); // SECURITY NOTE: string use has been reviewed +} + +// read a 0-terminated string +const char * fgetstring (FILE * f, __out_z_cap(size) char * buf, int size) +{ + int i; + for (i = 0; ; i++) + { + int c = fgetc(f); + if (c == EOF) + RuntimeError ("error reading string or missing 0: %s", strerror(errno)); + if (c == 0) break; + if (i >= size - 1) + RuntimeError ("input line too long (max. %d characters allowed)", size - 1); + buf[i] = (char)c; + } + assert (i < size); + buf[i] = 0; + return buf; +} + +// read a 0-terminated wstring +string fgetstring (FILE * f) +{ + string res; + for (;;) + { + char c = (char)fgetc (f); + if (c == EOF) + RuntimeError ("error reading string or missing 0: %s", strerror (errno)); + if (c == 0) break; + res.push_back (c); + } + return res; +} + +// read a 0-terminated string +const wchar_t * fgetstring (FILE * f, __out_z_cap(size) wchar_t * buf, int size) +{ + int i; + for (i = 0; ; i++) + { + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + wint_t c = fgetwc(f); + if (c == WEOF) + RuntimeError ("error reading string or missing 0: %s", strerror(errno)); + if (c == 0) break; + if (i >= size - 1) + { + RuntimeError ("input line too long (max. %d wchar_tacters allowed)", size - 1); + } + buf[i] = (wchar_t)c; + } + assert(i < size); + buf[i] = 0; + return buf; +} + +#if (_MSC_VER < 1800) +// read a 0-terminated wstring +wstring fgetwstring (FILE * f) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + wstring res; + for (;;) + { + // + // there is a known vc++ runtime bug: Microsoft Connect 768113 + // fgetwc can skip a byte in certain condition + // this is already fixed in update release to VS 2012 + // for now the workaround is to use fgetc twice to simulate fgetwc + // + //wint_t c = fgetwc (f); + int c1 = fgetc(f); + int c2 = fgetc(f); + + // synthetic fgetc output to simulate fgetwc + // note the order below works only for little endian + wint_t c = (wint_t)((c2 << 8) | c1); + if (c == WEOF) + RuntimeError ("error reading string or missing 0: %s", strerror (errno)); + if (c == 0) break; + res.push_back ((wchar_t) c); + } + return res; +} + +#else +// read a 0-terminated wstring +wstring fgetwstring (FILE * f) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + wstring res; + for (;;) + { + wint_t c = fgetwc(f); + if (c == WEOF) + RuntimeError ("error reading string or missing 0: %s", strerror(errno)); + if (c == 0) break; + res.push_back((wchar_t)c); + } + return res; +} +#endif + +bool fskipspace (FILE * f) +{ + int count=0; + for (;;count++) + { + int c = fgetc (f); + if (c == EOF) // hit the end + { + if (ferror(f)) + RuntimeError ("error reading from file: %s", strerror(errno)); + break; + } + if (!isspace (c)) // end of space: undo getting that character + { + int rc = ungetc(c, f); + if (rc != c) + RuntimeError ("error in ungetc(): %s", strerror(errno)); + break; + } + } + return count>0; +} + +bool fskipwspace (FILE * f) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + int count = 0; + for (;;count++) + { + wint_t c = fgetwc (f); + if (c == WEOF) // hit the end + { + if (ferror (f)) + RuntimeError ("error reading from file: %s", strerror (errno)); + break; + } + if (!iswspace (c)) // end of space: undo getting that character + { + wint_t rc = ungetwc (c, f); + if (rc != c) + RuntimeError ("error in ungetc(): %s", strerror (errno)); + break; + } + } + return count > 0; +} + +// fskipNewLine(): skip all white space until end of line incl. the newline +// skip - skip the end of line if true, otherwise leave the end of line (but eat any leading space) +int fskipNewline (FILE * f, bool skip) +{ + int c; + bool found = false; + + // skip white space + + do + { + c = fgetc(f); + } while (c == ' ' || c == '\t'); + + if (c == '\r' || c == '\n') // Accept any type of newline + { + found = true; + if (skip) + c = fgetc(f); + } + + if ((found && !skip) || + !(c == '\r' || c == '\n')) + { + // if we found an EOF, return that unless there was a newline before the EOF + if (c == EOF) + return found?(int)true:EOF; + int rc = ungetc (c, f); + if (rc != c) + RuntimeError ("error in ungetc(): %s", strerror (errno)); + return (int)found; + } + // if we get here we saw a newline + return (int)true; +} + +// fskipwNewLine(): skip all white space until end of line incl. the newline +// skip - skip the end of line if true, otherwise leave the end of line (but eat any leading space) +int fskipwNewline (FILE * f, bool skip) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + wint_t c; + bool found = false; + // skip white space + + do + { + c = fgetwc(f); + } while (c == L' ' || c == L'\t'); + + if (c == L'\r' || c == L'\n') // accept any style of newline + { + found = true; + if (skip) + c = fgetwc(f); + } + + if ((found && !skip) + ||!(c == L'\r' || c == L'\n')) + { + if (c == WEOF) + return found?(int)true:EOF; + wint_t rc = ungetwc (c, f); + if (rc != c) + RuntimeError ("error in ungetwc(): %s", strerror (errno)); + return (int)found; + } + // if we get here we saw a double newline + return (int)true; +} + +// read a space-terminated token +// ...TODO: eat trailing space like fscanf() doessurrounding space) +const char * fgettoken (FILE * f, __out_z_cap(size) char * buf, int size) +{ + fskipspace (f); // skip leading space + int c = -1; + int i; + for (i = 0; ; i++) + { + c = fgetc (f); + if (c == EOF) break; + if (isspace (c)) break; + if (i >= size -1) + RuntimeError ("input token too long (max. %d characters allowed)", size -1); + buf[i] = (char) c; + } + // ... TODO: while (IsWhiteSpace (c)) c = fgetc (f); // skip trailing space + if (c != EOF) + { + int rc = ungetc (c, f); + if (rc != c) + RuntimeError ("error in ungetc(): %s", strerror (errno)); + } + assert (i < size); + buf[i] = 0; + return buf; +} + +string fgettoken (FILE * f) +{ + char buf[80]; + return fgettoken (f, buf, sizeof(buf)/sizeof(*buf)); +} + +// read a space-terminated token +const wchar_t * fgettoken (FILE * f, __out_z_cap(size) wchar_t * buf, int size) +{ + // TODO: we should redefine this to write UTF-16 (which matters on GCC which defines wchar_t as 32 bit) + fskipwspace(f); // skip leading space + wint_t c = WEOF; + int i; + for (i = 0; ; i++) + { + c = fgetwc (f); + if (c == WEOF) break; + if (iswspace (c)) break; + if (i >= size -1) + RuntimeError ("input token too long (max. %d wchar_tacters allowed)", size -1); + buf[i] = (wchar_t) c; + } + // ... TODO: while (IsWhiteSpace (c)) c = fgetc (f); // skip trailing space + if (c != WEOF) + { + int rc = ungetwc (c, f); + if (rc != c) + RuntimeError ("error in ungetwc(): %s", strerror (errno)); + } + assert (i < size); + buf[i] = 0; + return buf; +} + +wstring fgetwtoken (FILE * f) +{ + wchar_t buf[80]; + return fgettoken(f, buf, sizeof(buf) / sizeof(*buf)); +} + +template <> +int ftrygetText(FILE * f, bool& v) +{ + wchar_t c; + int rc = ftrygetText(f, c); + v = (c == L'T'); + return rc; +} + +// ---------------------------------------------------------------------------- +// fputText(): write a bool out as character +// ---------------------------------------------------------------------------- +template <> +void fputText(FILE * f, bool v) +{ + fputText(f, v?L'T':L'F'); +} + +// ---------------------------------------------------------------------------- +// fgetTag(): read a 4-byte tag & return as a string +// ---------------------------------------------------------------------------- + +std::string fgetTag (FILE * f) +{ + char tag[5]; + freadOrDie (&tag[0], sizeof (tag[0]), 4, f); + tag[4] = 0; + return std::string (tag); +} + +// ---------------------------------------------------------------------------- +// fcheckTag(): read a 4-byte tag & verify it; terminate if wrong tag +// ---------------------------------------------------------------------------- + +void fcheckTag (FILE * f, const char * expectedTag) +{ + fcompareTag (fgetTag (f), expectedTag); +} + + +void fcheckTag_ascii (FILE * f, const string & expectedTag) +{ + char buf[20]; // long enough for a tag + fskipspace (f); + fgettoken (f, buf, sizeof(buf)/sizeof(*buf)); + if (expectedTag != buf) + { + RuntimeError ("invalid tag '%s' found; expected '%s'", buf, expectedTag.c_str()); + } +} + +// ---------------------------------------------------------------------------- +// fcompareTag(): compare two tags; terminate if wrong tag +// ---------------------------------------------------------------------------- + +void fcompareTag (const string & readTag, const string & expectedTag) +{ + if (readTag != expectedTag) + { + RuntimeError ("invalid tag '%s' found; expected '%s'", + readTag.c_str(), expectedTag.c_str()); + } +} + +// ---------------------------------------------------------------------------- +// fputTag(): write a 4-byte tag +// ---------------------------------------------------------------------------- + +void fputTag (FILE * f, const char * tag) +{ + const int TAG_LEN = 4; + assert (strnlen (tag, TAG_LEN + 1) == TAG_LEN); + fwriteOrDie ((void *) tag, sizeof (*tag), strnlen (tag, TAG_LEN), f); +} + +// ---------------------------------------------------------------------------- +// fskipstring(): skip a 0-terminated string, such as a pad string +// ---------------------------------------------------------------------------- + +void fskipstring (FILE * f) +{ + char c; + do + { + freadOrDie(&c, sizeof (c), 1, f); + } + while (c); +} + +// ---------------------------------------------------------------------------- +// fpad(): write a 0-terminated string to pad file to a n-byte boundary +// (note: file must be opened in binmode to work properly on DOS/Windows!!!) +// ---------------------------------------------------------------------------- +void fpad (FILE * f, int n) +{ + // get current writing position + int pos = ftell (f); + if (pos == -1) + { + RuntimeError ("error in ftell(): %s", strerror (errno)); + } + // determine how many bytes are needed (at least 1 for the 0-terminator) + // and create a dummy string of that length incl. terminator + int len = n - (pos % n); + const char dummyString[] = "MSR-Asia: JL+FS"; + size_t offset = sizeof(dummyString)/sizeof(dummyString[0]) - len; + assert (offset >= 0); + fputstring (f, dummyString + offset); +} + +// ---------------------------------------------------------------------------- +// fgetbyte(): read a byte value +// ---------------------------------------------------------------------------- + +char fgetbyte (FILE * f) +{ + char v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +// ---------------------------------------------------------------------------- +// fgetshort(): read a short value +// ---------------------------------------------------------------------------- + +short fgetshort (FILE * f) +{ + short v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +short fgetshort_bigendian (FILE * f) +{ + unsigned char b[2]; + freadOrDie (&b, sizeof (b), 1, f); + return (short) ((b[0] << 8) + b[1]); +} + +// ---------------------------------------------------------------------------- +// fgetint24(): read a 3-byte (24-bit) int value +// ---------------------------------------------------------------------------- + +int fgetint24 (FILE * f) +{ + int v; + assert (sizeof (v) == 4); + freadOrDie (&v, sizeof (v) -1, 1, f); // only read 3 lower-order bytes + v <<= 8; // shift up (upper 8 bits uninit'ed) + v >>= 8; // shift down 8 bits with sign-extend + return v; +} + +// ---------------------------------------------------------------------------- +// fgetint(): read an int value +// ---------------------------------------------------------------------------- + +int fgetint (FILE * f) +{ + int v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +int fgetint_bigendian (FILE * f) +{ + unsigned char b[4]; + freadOrDie (&b, sizeof (b), 1, f); + return (int) (((((b[0] << 8) + b[1]) << 8) + b[2]) << 8) + b[3]; +} + +int fgetint_ascii (FILE * f) +{ + fskipspace (f); + int res = 0; + char c; + freadOrDie (&c, sizeof (c), 1, f); + while (isdigit ((unsigned char)c)) + { + res = (10 * res) + (c - '0'); + freadOrDie (&c, sizeof (c), 1, f); + } + int rc = ungetc (c, f); + if (rc != c) + { + RuntimeError ("error in ungetc(): %s", strerror (errno)); + } + return res; +} + +// ---------------------------------------------------------------------------- +// fgetlong(): read an long value +// ---------------------------------------------------------------------------- + +long fgetlong (FILE * f) +{ + long v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +// ---------------------------------------------------------------------------- +// fgetfloat(): read a float value +// ---------------------------------------------------------------------------- + +float fgetfloat (FILE * f) +{ + float v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +float fgetfloat_bigendian (FILE * f) +{ + int bitpattern = fgetint_bigendian (f); + return *((float*) &bitpattern); +} + +float fgetfloat_ascii (FILE * f) +{ + float val; + fskipspace (f); + int rc = fscanf (f, "%f", &val); // security hint: safe overloads + if (rc == 0) + RuntimeError("error reading float value from file (invalid format): %s", strerror(errno)); + else if (rc == EOF) + RuntimeError("error reading from file: %s", strerror(errno)); + assert(rc == 1); + return val; +} + +// ---------------------------------------------------------------------------- +// fgetdouble(): read a double value +// ---------------------------------------------------------------------------- + +double fgetdouble (FILE * f) +{ + double v; + freadOrDie (&v, sizeof (v), 1, f); + return v; +} + +#ifdef _WIN32 + +// ---------------------------------------------------------------------------- +// fgetwav(): read an entire .wav file +// ---------------------------------------------------------------------------- + +void WAVEHEADER::prepareRest (int sampleCount) +{ + FmtLength = 16; + + wFormatTag = 1; + nAvgBytesPerSec = nSamplesPerSec * nBlockAlign; + + riffchar[0] = 'R'; + riffchar[1] = 'I'; + riffchar[2] = 'F'; + riffchar[3] = 'F'; + if (sampleCount != -1) + { + DataLength = sampleCount * nBlockAlign; + RiffLength = 36 + DataLength; + } + else + { + DataLength = 0xffffffff; + RiffLength = 0xffffffff; + } + + wavechar[0] = 'W'; + wavechar[1] = 'A'; + wavechar[2] = 'V'; + wavechar[3] = 'E'; + wavechar[4] = 'f'; + wavechar[5] = 'm'; + wavechar[6] = 't'; + wavechar[7] = ' '; + + datachar[0] = 'd'; + datachar[1] = 'a'; + datachar[2] = 't'; + datachar[3] = 'a'; +} + +void WAVEHEADER::prepare (unsigned int Fs, int Bits, int Channels, int SampleCount) +{ + nChannels = (short) Channels; + nSamplesPerSec = Fs; + nBlockAlign = (short) (Channels * (Bits/8)); + nAvgBytesPerSec = Fs * nBlockAlign; + wBitsPerSample = (short) Bits; + + prepareRest (SampleCount); +} + +void WAVEHEADER::prepare (const WAVEFORMATEX & wfx, int sampleCount /* -1 for unknown */) +{ + nChannels = wfx.nChannels; + nSamplesPerSec = wfx.nSamplesPerSec; + nBlockAlign = wfx.nBlockAlign; + wBitsPerSample = wfx.wBitsPerSample; + + prepareRest (sampleCount); +} + +void WAVEHEADER::write (FILE * f) +{ + fputTag (f, "RIFF"); + fputint (f, RiffLength); + fputTag (f, "WAVE"); + fputTag (f, "fmt "); + fputint (f, FmtLength); + fputshort (f, wFormatTag); + fputshort (f, nChannels); + fputint (f, nSamplesPerSec); + fputint (f, nAvgBytesPerSec); + fputshort (f, nBlockAlign); + fputshort (f, wBitsPerSample); + assert (FmtLength == 16); + assert (wFormatTag == 1); + fputTag (f, "data"); + fputint (f, DataLength); + fflushOrDie (f); +} + +/*static*/ void WAVEHEADER::update (FILE * f) +{ + long curPos = ftell (f); + if (curPos == -1L) + { + RuntimeError ("error determining file position: %s", strerror (errno)); + } + unsigned int len = (unsigned int) filesize (f); + unsigned int RiffLength = len - 8; + unsigned int DataLength = RiffLength - 36; + fseekOrDie (f, 4, SEEK_SET); + fputint (f, RiffLength); + fseekOrDie (f, 40, SEEK_SET); + fputint (f, DataLength); + fseekOrDie (f, curPos, SEEK_SET); +} + +#if 0 +unsigned int WAVEHEADER::read (FILE * f, signed short & wRealFormatTag, int & bytesPerSample) +{ + // read header + fcheckTag (f, "RIFF"); + /*unsigned int riffLen = */ fgetint (f); + fcheckTag (f, "WAVE"); + fcheckTag (f, "fmt "); + unsigned int fmtLen = fgetint (f); + wRealFormatTag = fgetshort (f); + if (wRealFormatTag == -2) // MARecorder.exe [Ivan Tashev] puts a -2 for + { // 8-channel recordings (meaning unknown). + wRealFormatTag = 1; // Workaround: pretend it is 1 (seems safe) + } + (wRealFormatTag == 1 || wRealFormatTag == 7) + || RuntimeError ("WAVEHEADER::read: wFormatTag=%d not supported for now", wRealFormatTag); + unsigned short wChannels = fgetshort (f); + unsigned long dwSamplesPerSec = fgetint (f); + unsigned int sampleRate = dwSamplesPerSec; + /*unsigned long dwAvgBytesPerSec = */ fgetint (f); + unsigned short wBlockAlign = fgetshort (f); + unsigned short wBitsPerSample = fgetshort (f); + (wBitsPerSample <= 16) || RuntimeError ("WAVEHEADER::read: invalid wBitsPerSample %d", wBitsPerSample); + bytesPerSample = wBitsPerSample / 8; + (wBlockAlign == wChannels * bytesPerSample) + || RuntimeError ("WAVEHEADER::read: wBlockAlign != wChannels*bytesPerSample not supported"); + while (fmtLen > 16) // unused extra garbage in header + { + fgetbyte (f); + fmtLen--; + } + if (wRealFormatTag == 7) + { + (bytesPerSample == 1) || RuntimeError ("WAVEHEADER::read: invalid wBitsPerSample %d for mulaw", wBitsPerSample); + fcheckTag (f, "fact"); + unsigned int factLen = fgetint (f); + while (factLen > 0) + { + fgetbyte (f); + factLen--; + } + } + fcheckTag (f, "data"); + unsigned int dataLen = fgetint (f); + unsigned int numSamples = dataLen / wBlockAlign; + + // prepare a nice wave header without junk (44 bytes, 16-bit PCM) + prepare (sampleRate, wBitsPerSample, wChannels, numSamples); + + return numSamples; +} + +static short toolULawToLinear(unsigned char p_ucULawByte) +{ + static short anExpLut[8] = { 0, 132, 396, 924, 1980, 4092, 8316, 16764 }; + short nSign, nExponent, nMantissa, nSample; + + p_ucULawByte=~p_ucULawByte; + nSign=(p_ucULawByte & 0x80); + nExponent=(p_ucULawByte >> 4) & 0x07; + nMantissa=p_ucULawByte & 0x0F; + nSample=anExpLut[nExponent]+(nMantissa<<(nExponent+3)); + if(nSign != 0) + nSample = -nSample; + + return nSample; +} + +// fgetwavraw(): only read data of .wav file. For multi-channel data, samples +// are kept interleaved. +static void fgetwavraw(FILE * f, std::vector & wav, const WAVEHEADER & wavhd) +{ + int bytesPerSample = wavhd.wBitsPerSample / 8; // (sample size on one channel) + wav.resize (wavhd.DataLength / bytesPerSample); + if (wavhd.wFormatTag == 7) // mulaw + { + (wavhd.nChannels == 1) || RuntimeError ("fgetwav: wChannels=%d not supported for mulaw", wavhd.nChannels); + std::vector data; + int numSamples = wavhd.DataLength/wavhd.nBlockAlign; + data.resize (numSamples); + freadOrDie (&data[0], sizeof (data[0]), numSamples, f); + for (int i = 0; i < numSamples; i++) + { + wav[i] = toolULawToLinear (data[i]); + } + } + else if (bytesPerSample == 2) + { // note: we may be reading an interleaved multi-channel signal. + freadOrDie (&wav[0], sizeof (wav[0]), wav.size(), f); + } + // ... TODO: support 8 bit linear PCM samples (implement when needed; samples scaled to 'short') + else + { + RuntimeError ("bytesPerSample != 2 is not supported except mulaw format!\n"); + } +} + +// ---------------------------------------------------------------------------- +// fgetwav(): read an entire .wav file. Stereo is mapped to mono. +// ---------------------------------------------------------------------------- + +void fgetwav (FILE * f, std::vector & wav, int & sampleRate) +{ + WAVEHEADER wavhd; // will be filled in for 16-bit PCM!! + signed short wFormatTag; // real format tag as found in data + int bytesPerSample; // bytes per sample as found in data + + unsigned int numSamples = wavhd.read (f, wFormatTag, bytesPerSample); + sampleRate = (int) wavhd.nSamplesPerSec; + + if (wavhd.nChannels == 1) + { + fgetwavraw (f, wav, wavhd); + } + else if (wavhd.nChannels == 2) + { + //read raw data + std::vector buf; + buf.resize(numSamples * 2); + fgetwavraw(f, buf, wavhd); + + //map to mono + wav.resize (numSamples); + const short * p = &buf[0]; + for (int i = 0; i < (int) numSamples; i++) + { + int l = *p++; + int r = *p++; + int mono = ((l + r) + 1) >> 1; + wav[i] = (short) mono; + } + } + else + { + RuntimeError ("bytesPerSample/wChannels != 2 needs to be implemented"); + } +} + +void fgetwav (const wstring & fn, std::vector & wav, int & sampleRate) +{ + auto_file_ptr f = fopenOrDie (fn, L"rbS"); + fgetwav (f, wav, sampleRate); +} + +// ---------------------------------------------------------------------------- +// ... TODO: +// - rename this function!! +// - also change to read header itself and return sample rate and channels +// fgetraw(): read data of multi-channel .wav file, and separate data of multiple channels. +// For example, data[i][j]: i is channel index, 0 means the first +// channel. j is sample index. +// ---------------------------------------------------------------------------- + +void fgetraw (FILE *f, std::vector< std::vector > & data, const WAVEHEADER & wavhd) +{ + std::vector wavraw; + fgetwavraw (f, wavraw, wavhd); + data.resize (wavhd.nChannels); + int numSamples = wavhd.DataLength/wavhd.nBlockAlign; + assert (numSamples == (int) wavraw.size() / wavhd.nChannels); + + for (int i = 0; i < wavhd.nChannels; i++) + { + data[i].resize (numSamples); + + for (int j = 0; j < numSamples; j++) + { + data[i][j] = wavraw[wavhd.nChannels*j + i]; + } + } +} + +// ---------------------------------------------------------------------------- +// fgetwfx(), fputwfx(): direct access to simple WAV headers +// ---------------------------------------------------------------------------- + +// read header and skip to first data byte; return #samples +unsigned int fgetwfx (FILE * f, WAVEFORMATEX & wfx) +{ + // read header + fcheckTag (f, "RIFF"); + /*unsigned int riffLen = */ fgetint (f); + fcheckTag (f, "WAVE"); + fcheckTag (f, "fmt "); + wfx.cbSize = sizeof (wfx); + int fmtLen = fgetint (f); + wfx.wFormatTag = fgetshort (f); + if (wfx.wFormatTag == -2) // MARecorder.exe [Ivan Tashev] puts a -2 for + { // 8-channel recordings (meaning unknown). + wfx.wFormatTag = 1; // Workaround: pretend it is 1 (seems safe) + } + (wfx.wFormatTag == 1 || wfx.wFormatTag == 3 || wfx.wFormatTag == 7) + || RuntimeError ("WAVEHEADER::read: wFormatTag=%d not supported for now", wfx.wFormatTag); + wfx.nChannels = fgetshort (f); + wfx.nSamplesPerSec = fgetint (f); + wfx.nAvgBytesPerSec = fgetint (f); + wfx.nBlockAlign = fgetshort (f); + wfx.wBitsPerSample = fgetshort (f); + // unused extra garbage in header + for ( ; fmtLen > 16; fmtLen--) fgetbyte (f); + fcheckTag (f, "data"); + unsigned int dataLen = fgetint (f); + unsigned int numSamples = dataLen / wfx.nBlockAlign; + return numSamples; +} + +void fputwfx (FILE *f, const WAVEFORMATEX & wfx, unsigned int numSamples) +{ + unsigned int DataLength = numSamples * wfx.nBlockAlign; + (DataLength / wfx.nBlockAlign == numSamples) + || RuntimeError ("fputwfx: data size exceeds WAV header 32-bit range"); + unsigned int RiffLength = 36 + DataLength; + unsigned int FmtLength = 16; + // file header + assert (wfx.cbSize == 0 || wfx.cbSize == FmtLength + 2); + fputTag (f, "RIFF"); + fputint (f, RiffLength); + fputTag (f, "WAVE"); + // 'fmt ' chunk (to hold wfx) + fputTag (f, "fmt "); + fputint (f, FmtLength); + fputshort (f, wfx.wFormatTag); + fputshort (f, wfx.nChannels); + fputint (f, wfx.nSamplesPerSec); + fputint (f, wfx.nAvgBytesPerSec); + fputshort (f, wfx.nBlockAlign); + fputshort (f, wfx.wBitsPerSample); + // data chunk + fputTag (f, "data"); + fputint (f, DataLength); + fflushOrDie (f); +} + +// ---------------------------------------------------------------------------- +// fputwav(): write an entire .wav file (16 bit PCM) +// ---------------------------------------------------------------------------- + +void fputwav (FILE * f, const vector & wav, int sampleRate, int nChannels) +{ + f;wav;sampleRate;nChannels; + // construct WAVEFORMATEX + WAVEFORMATEX wfx; + wfx.cbSize = 16 + 2; //fmt data + extra data + wfx.nAvgBytesPerSec = (DWORD)(sampleRate * nChannels * 2); //short: 2 bytes per sample + wfx.nBlockAlign = (WORD)nChannels * 2; //short: 2bytes per sample + wfx.nChannels = (WORD)nChannels; + wfx.nSamplesPerSec = sampleRate; + wfx.wBitsPerSample = 16; + wfx.wFormatTag = WAVE_FORMAT_PCM; + //putwfx + fputwfx (f, wfx, (unsigned int) wav.size()); + // wrtie the data + fwriteOrDie (&wav[0], sizeof(wav[0]), wav.size(), f); +} + +void fputwav (const wstring & fn, const vector & wav, int sampleRate, int nChannels) +{ + auto_file_ptr f = fopenOrDie (fn, L"wbS"); + fputwav (f, wav, sampleRate, nChannels); + fflushOrDie (f); // after this, fclose() (in destructor of f) cannot fail +} +#endif +#endif + +// ---------------------------------------------------------------------------- +// fputbyte(): write a byte value +// ---------------------------------------------------------------------------- + +void fputbyte (FILE * f, char v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputshort(): write a short value +// ---------------------------------------------------------------------------- + +void fputshort (FILE * f, short v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputint24(): write a 3-byte (24-bit) int value +// ---------------------------------------------------------------------------- + +void fputint24 (FILE * f, int v) +{ + assert (sizeof (v) == 4); + fwriteOrDie (&v, sizeof (v) -1, 1, f); // write low-order 3 bytes +} + +// ---------------------------------------------------------------------------- +// fputint(): write an int value +// ---------------------------------------------------------------------------- + +void fputint (FILE * f, int v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputlong(): write an long value +// ---------------------------------------------------------------------------- + +void fputlong (FILE * f, long v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputfloat(): write a float value +// ---------------------------------------------------------------------------- + +void fputfloat (FILE * f, float v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputdouble(): write a double value +// ---------------------------------------------------------------------------- + +void fputdouble (FILE * f, double v) +{ + fwriteOrDie (&v, sizeof (v), 1, f); +} + +// ---------------------------------------------------------------------------- +// fputfile(): write a binary block or a string as a file +// ---------------------------------------------------------------------------- + +void fputfile (const wstring & pathname, const std::vector & buffer) +{ + FILE * f = fopenOrDie (pathname, L"wb"); + try + { + if (buffer.size() > 0) + { // ^^ otherwise buffer[0] is an illegal expression + fwriteOrDie (&buffer[0], sizeof (buffer[0]), buffer.size(), f); + } + fcloseOrDie (f); + } + catch (...) + { + fclose (f); + throw; + } +} + +void fputfile (const wstring & pathname, const std::wstring & string) +{ + FILE * f = fopenOrDie (pathname, L"wb"); + try + { + if (string.length() > 0) + { // ^^ otherwise buffer[0] is an illegal expression + fwriteOrDie (string.c_str(), sizeof (string[0]), string.length(), f); + } + fcloseOrDie (f); + } + catch (...) + { + fclose (f); + throw; + } +} + +void fputfile (const wstring & pathname, const std::string & string) +{ + FILE * f = fopenOrDie (pathname, L"wb"); + try + { + if (string.length() > 0) + { // ^^ otherwise buffer[0] is an illegal expression + fwriteOrDie (string.c_str(), sizeof (string[0]), string.length(), f); + } + fcloseOrDie (f); + } + catch (...) + { + fclose (f); + throw; + } +} + +// ---------------------------------------------------------------------------- +// fgetfile(): load a file as a binary block +// ---------------------------------------------------------------------------- + +void fgetfile (const wstring & pathname, std::vector & buffer) +{ + FILE * f = fopenOrDie (pathname, L"rb"); + size_t len = filesize (f); + buffer.resize (len); + if (buffer.size() > 0) + { // ^^ otherwise buffer[0] is an illegal expression + freadOrDie (&buffer[0], sizeof (buffer[0]), buffer.size(), f); + } + fclose (f); +} + +void fgetfile (FILE * f, std::vector & buffer) +{ // this version reads until eof + buffer.resize (0); + buffer.reserve (1000000); // avoid too many reallocations + std::vector inbuf; + inbuf.resize (65536); // read in chunks of this size + while (!feof (f)) // read until eof + { + size_t n = fread (&inbuf[0], sizeof (inbuf[0]), inbuf.size(), f); + if (ferror (f)) + { + RuntimeError ("fgetfile: error reading from file: %s", strerror (errno)); + } + buffer.insert (buffer.end(), inbuf.begin(), inbuf.begin() + n); + } + buffer.reserve (buffer.size()); +} + +// load it into RAM in one huge chunk +static size_t fgetfilechars (const std::wstring & path, vector & buffer) +{ + auto_file_ptr f (fopenOrDie (path, L"rb")); + size_t len = filesize (f); + buffer.reserve (len +1); + freadOrDie (buffer, len, f); + buffer.push_back (0); // this makes it a proper C string + return len; +} + +template static void strtoklines (char * s, LINES & lines) +{ + for (char * p = strtok (s, "\r\n"); p; p = strtok (NULL, "\r\n")) + lines.push_back (p); +} + +void msra::files::fgetfilelines (const std::wstring & path, vector & buffer, std::vector & lines) +{ + // load it into RAM in one huge chunk + const size_t len = fgetfilechars (path, buffer); + + // parse into lines + lines.resize (0); + lines.reserve (len / 20); + strtoklines (&buffer[0], lines); +} + +// same as above but returning const char* (avoiding the memory allocation) +vector msra::files::fgetfilelines (const wstring & path, vector & buffer) +{ + // load it into RAM in one huge chunk + const size_t len = fgetfilechars (path, buffer); + + // parse into lines + vector lines; + lines.reserve (len / 20); + strtoklines (&buffer[0], lines); + return lines; +} + +// ---------------------------------------------------------------------------- +// getfiletime(): access modification time +// ---------------------------------------------------------------------------- + +#ifndef _FILETIME_ +//typedef struct _FILETIME { DWORD dwLowDateTime; DWORD dwHighDateTime; }; // from minwindef.h +typedef time_t FILETIME; +#else +bool operator>= (const FILETIME & targettime, const FILETIME & inputtime) // for use in fuptodate() +{ + return (targettime.dwHighDateTime > inputtime.dwHighDateTime) || + (targettime.dwHighDateTime == inputtime.dwHighDateTime && targettime.dwLowDateTime >= inputtime.dwLowDateTime); +} +#endif + +#ifdef _WIN32 +class auto_find_handle +{ + HANDLE h; + auto_find_handle operator= (const auto_find_handle &); + auto_find_handle(const auto_find_handle &); +public: + auto_find_handle(HANDLE p_h) : h(p_h) {} + ~auto_find_handle() { if (h != INVALID_HANDLE_VALUE) ::FindClose(h); } + operator HANDLE () const { return h; } +}; +#endif + +bool getfiletime(const wstring & path, FILETIME & time) +{ // return file modification time, false if cannot be determined +#ifdef _WIN32 + WIN32_FIND_DATAW findFileData; + auto_find_handle hFind (FindFirstFileW (path.c_str(), &findFileData)); + if (hFind != INVALID_HANDLE_VALUE) + { + time = findFileData.ftLastWriteTime; + return true; + } + else + return false; +#else // TODO: test this; e.g. does st_mtime have the desired resolution? + struct stat buf; + int result; + + // Get data associated with "crt_stat.c": + result = stat(wtocharpath(path.c_str()).c_str(), &buf); + // Check if statistics are valid: + if (result != 0) + return false; + + time = buf.st_mtime; + return true; +#endif +} + +#if 0 +void setfiletime (const wstring & path, const FILETIME & time) +{ // update the file modification time of an existing file + auto_find_handle h (CreateFileW (path.c_str(), FILE_WRITE_ATTRIBUTES, + FILE_SHARE_READ|FILE_SHARE_WRITE, NULL, + OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL)); + if (h == INVALID_HANDLE_VALUE) + { + RuntimeError ("setfiletime: error opening file: %d", GetLastError()); + } + BOOL rc = SetFileTime (h, NULL, NULL, &time); + if (!rc) + { + RuntimeError ("setfiletime: error setting file time information: %d", GetLastError()); + } +} +#endif + +// ---------------------------------------------------------------------------- +// expand_wildcards -- wildcard expansion of a path, including directories. +// ---------------------------------------------------------------------------- + +#ifdef _WIN32 +// Win32-style variant of this function (in case we want to use it some day) +// Returns 0 in case of failure. May throw in case of bad_alloc. +static BOOL ExpandWildcards (wstring path, vector & paths) +{ + // convert root to DOS filename convention + for (size_t k = 0; k < path.length(); k++) if (path[k] == '/') path[k] = '\\'; + + // remove terminating backslash + size_t last = path.length() -1; + if (last >= 0 && path[last] == '\\') path.erase (last); + + // convert root to long filename convention + //if (path.find (L"\\\\?\\") != 0) + // path = L"\\\\?\\" + root; + + // split off everything after first wildcard + size_t wpos = path.find_first_of (L"*?"); + if (wpos == 2 && path[0] == '\\' && path[1] == '\\') + wpos = path.find_first_of (L"*?", 4); // 4=skip "\\?\" + if (wpos == wstring::npos) + { // no wildcard: just return it + paths.push_back (path); + return TRUE; + } + + // split off everything afterwards if any + wstring rest; // remaining path after this directory + size_t spos = path.find_first_of (L"\\", wpos +1); + if (spos != wstring::npos) + { + rest = path.substr (spos +1); + path.erase (spos); + } + + // crawl folder + WIN32_FIND_DATAW ffdata; + auto_find_handle hFind (::FindFirstFileW (path.c_str(), &ffdata)); + if (hFind == INVALID_HANDLE_VALUE) + { + DWORD err = ::GetLastError(); + if (rest.empty() && err == 2) return TRUE; // no matching file: empty + return FALSE; // another error + } + size_t pos = path.find_last_of (L"\\"); + if (pos == wstring::npos) LogicError ("unexpected missing \\ in path"); + wstring parent = path.substr (0, pos); + do + { + // skip this and parent directory + bool isDir = ((ffdata.dwFileAttributes & (FILE_ATTRIBUTE_DIRECTORY | FILE_ATTRIBUTE_REPARSE_POINT)) != 0); + if (isDir && ffdata.cFileName[0] == '.') continue; + + wstring filename = parent + L"\\" + ffdata.cFileName; + if (rest.empty()) + { + paths.push_back (filename); + } + else if (isDir) // multi-wildcards: further expand + { + BOOL rc = ExpandWildcards (filename + L"\\" + rest, paths); + rc; // error here means no match, e.g. Access Denied to one subfolder + } + } while (::FindNextFileW(hFind, &ffdata) != 0); + return TRUE; +} +#endif + +void expand_wildcards (const wstring & path, vector & paths) +{ +#ifdef _WIN32 + BOOL rc = ExpandWildcards (path, paths); + if (!rc) + RuntimeError ("error in expanding wild cards '%ls': Win32 error %d", path.c_str(), (int)::GetLastError()); +#else + // On Linux we have just the function for the job: glob + glob_t globResult; + if (glob(wtocharpath(path.c_str()).c_str(), GLOB_TILDE, NULL, &globResult) != 0) + { + RuntimeError("error in expanding wild cards '%ls': %s", path.c_str(), strerror(errno)); + } + + for(unsigned int i = 0; i < globResult.gl_pathc; ++i) + { + paths.push_back(msra::strfun::utf16(globResult.gl_pathv[i])); + } + globfree(&globResult); +#endif + +} + +// ---------------------------------------------------------------------------- +// make_intermediate_dirs() -- make all intermediate dirs on a path +// ---------------------------------------------------------------------------- + +static void mkdir (const wstring & path) +{ + int rc = _wmkdir (path.c_str()); + if (rc >= 0 || errno == EEXIST) + return; // no error or already existing --ok +#ifdef _WIN32 // bug in _wmkdir(): returns access_denied if folder exists but read-only --check existence + if (errno == EACCES) + { + DWORD att = ::GetFileAttributesW (path.c_str()); + if (att != INVALID_FILE_ATTRIBUTES || (att & FILE_ATTRIBUTE_DIRECTORY) != 0) + return; // ok + } +#endif + RuntimeError ("mkdir: error creating intermediate directory %ls", path.c_str()); +} + +// make subdir of a file including parents +void msra::files::make_intermediate_dirs (const wstring & filepath) +{ + vector buf; + buf.resize (filepath.length() +1, 0); + wcscpy (&buf[0], filepath.c_str()); + wstring subpath; + int skip = 0; +#ifdef _WIN32 + // On windows, if share (\\) then the first two levels (machine, share name) cannot be made. + if ((buf[0] == '/' && buf[1] == '/') || (buf[0] == '\\' && buf[1] == '\\')) + { + subpath = L"/"; + skip = 2; // skip two levels (machine, share) + } +#else + // On unix, if the filepath starts with '/' then it is absolute + // path and the created sub-paths should also start with '/' + if (buf[0] == '/') + { + subpath = L"/"; + } +#endif + // make all constituents except the filename (to make a dir, include a trailing slash) + wchar_t * context = nullptr; + for (const wchar_t * p = wcstok_s (&buf[0], L"/\\", &context); p; p = wcstok_s (NULL, L"/\\", &context)) + { + if (subpath != L"" && subpath != L"/" && subpath != L"\\" && skip == 0) + { + mkdir (subpath); + } + else if (skip > 0) skip--; // skip this level + // rebuild the final path + if (subpath != L"") subpath += L"/"; + subpath += p; + } +} + +// ---------------------------------------------------------------------------- +// fuptodate() -- test whether an output file is at least as new as an input file +// ---------------------------------------------------------------------------- + +// test if file 'target' is not older than 'input' --used for make mode +// 'input' must exist if 'inputrequired'; otherweise if 'target' exists, it is considered up to date +// 'target' may or may not exist +bool msra::files::fuptodate (const wstring & target, const wstring & input, bool inputrequired) +{ + FILETIME targettime; + if (!getfiletime (target, targettime)) return false; // target missing: need to update + FILETIME inputtime; + if (!getfiletime (input, inputtime)) return !inputrequired; // input missing: if required, pretend to be out of date as to force caller to fail + // up to date if target has higher time stamp + return targettime >= inputtime; // note: uses an overload for WIN32 FILETIME (in Linux, FILETIME=time_t=size_t) +} + +/// separate string by separator +vector sep_string(const string & istr, const string & sep) +{ + string str = istr; + str = trim(str); + vector vstr; + string csub; + size_t ifound = 0; + size_t ifoundlast = ifound; + ifound = str.find(sep, ifound); + while (ifound != std::string::npos) + { + csub = str.substr(ifoundlast, ifound - ifoundlast); + vstr.push_back(trim(csub)); + + ifoundlast = ifound+1; + ifound = str.find(sep, ifoundlast); + } + csub = str.substr(ifoundlast, str.length() - ifoundlast); + vstr.push_back(trim(csub)); + + return vstr; +} + +/// separate string by separator +vector wsep_string(const wstring & istr, const wstring & sep) +{ + wstring str = istr; + str = wtrim(str); + vector vstr; + wstring csub; + size_t ifound = 0; + size_t ifoundlast = ifound; + ifound = str.find(sep, ifound); + while (ifound != std::wstring::npos) + { + csub = str.substr(ifoundlast, ifound - ifoundlast); + vstr.push_back(wtrim(csub)); + + ifoundlast = ifound + 1; + ifound = str.find(sep, ifoundlast); + } + csub = str.substr(ifoundlast, str.length() - ifoundlast); + vstr.push_back(wtrim(csub)); + + return vstr; +} +static inline std::string wcstombs(const std::wstring & p) // output: MBCS +{ + size_t len = p.length(); + vector buf(2 * len + 1); // max: 1 wchar => 2 mb chars + fill(buf.begin(), buf.end(), 0); + ::wcstombs(&buf[0], p.c_str(), 2 * len + 1); + return std::string(&buf[0]); +} +static inline std::wstring mbstowcs(const std::string & p) // input: MBCS +{ + size_t len = p.length(); + vector buf(len + 1); // max: >1 mb chars => 1 wchar + fill(buf.begin(), buf.end(), (wchar_t)0); + //OACR_WARNING_SUPPRESS(UNSAFE_STRING_FUNCTION, "Reviewed OK. size checked. [rogeryu 2006/03/21]"); + ::mbstowcs(&buf[0], p.c_str(), len + 1); + return std::wstring(&buf[0]); +} + +wstring s2ws(const string& str) +{ +#ifdef __unix__ + return mbstowcs(str); +#else + typedef std::codecvt_utf8 convert_typeX; + std::wstring_convert converterX; + return converterX.from_bytes(str); + +#endif +} + +string ws2s(const wstring& wstr) +{ +#ifdef __unix__ + return wcstombs(wstr); +#else + typedef codecvt_utf8 convert_typeX; + wstring_convert converterX; + return converterX.to_bytes(wstr); +#endif + +} diff --git a/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj b/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj index 8669940d77cd..3661e261df30 100644 --- a/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj +++ b/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj @@ -1,203 +1,203 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} - - - - - - - - - Win32Proj - CNTK - ComputationNetworkLib - - - - StaticLibrary - true - v120 - Unicode - - - StaticLibrary - false - v120 - true - Unicode - - - - - - - - - - - - true - ..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - false - ..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(ExecutablePath) - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - - - - Level4 - Disabled - _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - true - /bigobj %(AdditionalOptions) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - Math.dll; nvml.dll; cudart64_70.dll - 100000000 - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt - - - true - Copy content files to target directory - - - - - - - - - Level4 - - - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - Speed - /d2Zi+ %(AdditionalOptions) - true - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - true - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - true - Math.dll; nvml.dll; cudart64_70.dll - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - - - - - - - true - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - NotUsing - - - NotUsing - - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {928ABD1B-4D3B-4017-AEF1-0FA1B4467513} + + + + + + + + + Win32Proj + CNTK + ComputationNetworkLib + + + + StaticLibrary + true + v120 + Unicode + + + StaticLibrary + false + v120 + true + Unicode + + + + + + + + + + + + true + ..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + false + ..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;..\..\DataReader\HTKMLFReader;$(WindowsSDK_IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(ExecutablePath) + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + + + + Level4 + Disabled + _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + true + /bigobj %(AdditionalOptions) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + Math.dll; nvml.dll; cudart64_70.dll + 100000000 + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt + + + true + Copy content files to target directory + + + + + + + + + Level4 + + + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + Speed + /d2Zi+ %(AdditionalOptions) + true + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + true + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + true + Math.dll; nvml.dll; cudart64_70.dll + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + + + + + + + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + + + + + + + + + + + \ No newline at end of file diff --git a/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj.filters b/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj.filters index d65affb2139d..00174c13cabd 100644 --- a/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj.filters +++ b/Source/ComputationNetworkLib/ComputationNetworkLib.vcxproj.filters @@ -1,150 +1,150 @@ - - - - - Common - - - Common - - - Nodes - - - Misc - - - Common - - - GPU Interfacing - - - Network - - - Network - - - Network - - - Network - - - Network - - - Network - - - - - Common\Include - - - Common\Include - - - Network - - - Nodes - - - Misc - - - Misc - - - Common\Include - - - Common\Include - - - Common\Include - - - Nodes - - - Nodes - - - Nodes - - - Nodes - - - Nodes - - - Nodes - - - Nodes - - - Nodes - - - Common\Include - - - Common\Include - - - Network - - - Network - - - Common\Include - - - from Math - - - Nodes - - - Common\Include - - - Nodes - - - Common\Include - - - Common\Include - - - - - {b3d05c7b-7bcf-4b12-bcb5-dced86717202} - - - {85226dda-87ba-4da6-af04-563d0ce23b94} - - - {0b366814-48b2-4619-bf92-85ee24e3cbc1} - - - {3c119a92-ffb2-4850-adae-01778324974d} - - - {8d99b2cc-5209-40e4-8b4b-a7616973ae3b} - - - {498bb2e9-53de-4955-970e-813e3f21025b} - - - {7d838fa4-b5a1-4b8a-b37d-823fb026055b} - - - + + + + + Common + + + Common + + + Nodes + + + Misc + + + Common + + + GPU Interfacing + + + Network + + + Network + + + Network + + + Network + + + Network + + + Network + + + + + Common\Include + + + Common\Include + + + Network + + + Nodes + + + Misc + + + Misc + + + Common\Include + + + Common\Include + + + Common\Include + + + Nodes + + + Nodes + + + Nodes + + + Nodes + + + Nodes + + + Nodes + + + Nodes + + + Nodes + + + Common\Include + + + Common\Include + + + Network + + + Network + + + Common\Include + + + from Math + + + Nodes + + + Common\Include + + + Nodes + + + Common\Include + + + Common\Include + + + + + {b3d05c7b-7bcf-4b12-bcb5-dced86717202} + + + {85226dda-87ba-4da6-af04-563d0ce23b94} + + + {0b366814-48b2-4619-bf92-85ee24e3cbc1} + + + {3c119a92-ffb2-4850-adae-01778324974d} + + + {8d99b2cc-5209-40e4-8b4b-a7616973ae3b} + + + {498bb2e9-53de-4955-970e-813e3f21025b} + + + {7d838fa4-b5a1-4b8a-b37d-823fb026055b} + + + diff --git a/Source/ComputationNetworkLib/ComputationNode.cpp b/Source/ComputationNetworkLib/ComputationNode.cpp index 0dd479b5fe65..65eb066b0edd 100644 --- a/Source/ComputationNetworkLib/ComputationNode.cpp +++ b/Source/ComputationNetworkLib/ComputationNode.cpp @@ -1,185 +1,185 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings - -#include "ComputationNode.h" -#include "InputAndParamNodes.h" -#include "ComputationNetworkBuilder.h" // TODO: We should only pull in NewComputationNodeFromConfig(). Nodes should not know about network at large. -#include "TensorShape.h" - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings + +#include "ComputationNode.h" +#include "InputAndParamNodes.h" +#include "ComputationNetworkBuilder.h" // TODO: We should only pull in NewComputationNodeFromConfig(). Nodes should not know about network at large. +#include "TensorShape.h" + #ifndef let #define let const auto #endif -namespace Microsoft { - namespace MSR { - namespace CNTK { - - using namespace std; - - // ----------------------------------------------------------------------- - // subroutines for Validate() implementations - // ----------------------------------------------------------------------- - - // helper function to infer the MBLayout for this node from inputs, for the *standard case* - // the standard case is: - // - all inputs must share the same layout (e.g. adding two minibatches) - // - with the exception of NULL layouts (e.g. TimesNode) - // - all layouts may be NULL (e.g. W' = W * Exp(Stabilizer)) - // - if there are more than one different layouts involved, this function will fail - void ComputationNodeBase::InferMBLayoutFromInputsForStandardCase() - { - MBLayoutPtr pMBLayout; // start with NULL layout - for (auto child : m_inputs) - { - if (!child) // node not set yet (DelayedValueNodeBase seems to allow this)--BUGBUG: Then this function won't operate correctly. - ; - else if (!child->m_pMBLayout) // NULL layout (typical for parameter nodes) - ; - else if (!pMBLayout) // first non-NULL layout: just copy it - pMBLayout = child->m_pMBLayout; - else if (pMBLayout != child->m_pMBLayout) // got a layout--compare whether it is the same - RuntimeError("InferMBLayoutFromInputsForStandardCase: Found inconsistent layout in %ls %ls operation, mismatch detected for child %ls %ls.", - NodeName().c_str(), OperationName().c_str(), child->NodeName().c_str(), child->OperationName().c_str()); - } - // all are consistent: install it - LinkToMBLayout(pMBLayout); - } - - // single input that maps its input element-wise (e.g. Sigmoid) - void ComputationNodeBase::ValidateUnaryMap(bool isFinalValidationPass) - { - assert(m_inputs.size() == 1); - ComputationNodeBase::Validate(isFinalValidationPass); - InferMBLayoutFromInputsForStandardCase(); - SetDims(Input(0)); - } - // binary zip operation, e.g. Plus - // If allowScaling then one can be a sub-dimension of the other (if layout then only for rows, otherwise for cols, too). - // This also helpfully resizes the children if not yet sized. - void ComputationNodeBase::ValidateBinaryZip(bool isFinalValidationPass, bool allowMultiples) - { - assert(m_inputs.size() == 2); - ComputationNodeBase::Validate(isFinalValidationPass); - InferMBLayoutFromInputsForStandardCase(); - - ValidateInferBinaryInputDims(); - - size_t rows0 = Input(0)->GetNumRows(), cols0 = Input(0)->GetNumCols(); - size_t rows1 = Input(1)->GetNumRows(), cols1 = Input(1)->GetNumCols(); - -#if 1//ndef ENABLE_TENSORVIEW - // TODO: This test will go away once we switch to full tensor lib. - if (isFinalValidationPass && !( - (rows0 == rows1 && (Input(0)->GetMBLayout() == Input(1)->GetMBLayout() || cols0 == cols1)) || // matching size (obvious case) - (allowMultiples && (rows0 == 1 || rows1 == 1) && (Input(0)->GetMBLayout() == Input(1)->GetMBLayout() || cols0 == cols1)) || // one is row vec - (allowMultiples && ((!HasMBLayout() && cols0 > cols1 && cols0 % cols1 == 0) || (cols0 == 1 && rows1 % rows0 == 0) || (cols1 == 1 && rows0 % rows1 == 0))) - )) // TODO: ^^ I don't understand the asymmetry of this last one - { - LogicError("The Matrix dimensions in the %ls %ls operation do not match.", NodeName().c_str(), OperationName().c_str()); - } -#else - rows0; rows1; -#endif - - // result has tensor shape with dimensions being the max over both - let shape0 = GetInputSampleLayout(0); - let shape1 = GetInputSampleLayout(1); - SmallVector dims = shape0.GetDims(); - if (shape1.GetRank() > dims.size()) - dims.resize(shape1.GetRank(), 1); // pad with ones - - // If rank of [0] is higher than we only need to take max over rank [1]. - // If rank of [1] is higher then we have padded to equal lentgh. - for (size_t k = 0; k < shape1.GetRank(); k++) - { - size_t dim1 = shape1[k]; - if (dims[k] == 1) // is [0] broadcasting? - dims[k] = dim1; // then use dimension we broadcast to - else if (dim1 == 1) // if [1] is broadcasting - ; // dims is already correct - else if (isFinalValidationPass && dim1 != dims[k]) // no broadcasting: they must match - InvalidArgument("%ls %ls operation: Input dimensions [%s] and [%s] are not compatible.", - NodeName().c_str(), OperationName().c_str(), string(shape0).c_str(), string(shape1).c_str()); - } - - SetDims(TensorShape(dims), GetMBLayout() ? GetMBLayout()->GetNumCols() : max(cols0, cols1)); - } - // unary reduce-to-(1,1) operation, e.g. MatrixL1RegNode - void ComputationNodeBase::ValidateUnaryReduce(bool isFinalValidationPass) - { - assert(m_inputs.size() == 1); - ComputationNodeBase::Validate(isFinalValidationPass); - m_pMBLayout = nullptr; // this node does not hold mini-batch data - SetDims(TensorShape(1), 1); - } - // binary reduce-to-(1,1) operation, e.g. CrossEntropyWithSoftmaxNode - // Currently only called by criterion nodes. - // This function also infers child LearnableParameters. In case you wonder why this is needed for criterion nodes, there are edge cases, e.g. a - // learnable parameter being regularized by a criterion node, where the learnable parameter is fed both into that criterion node and other places. - void ComputationNodeBase::ValidateBinaryReduce(bool isFinalValidationPass) - { - ComputationNodeBase::Validate(isFinalValidationPass); - m_pMBLayout = nullptr; // this node does not hold mini-batch data - ValidateInferBinaryInputDims(); - if (isFinalValidationPass && - !(Input(0)->GetNumRows() == Input(1)->GetNumRows() && - (Input(0)->HasMBLayout() || (Input(0)->GetNumCols() == Input(1)->GetNumCols())))) - LogicError("The Matrix dimensions in the %ls %ls operation do not match.", NodeName().c_str(), OperationName().c_str()); - SetDims(TensorShape(1), 1); - } - // helper function for validation - // In bad cases of convolution, dimensions are quite complex to know. - // This is a feature that allows a node to help resizing its input node to the expected value. - // TODO: This is shaky by design. - void ComputationNodeBase::ValidateInferBinaryInputDims() - { - // limited inference of children dimensions - // if dimension not specified we assume two operands' dimensions should be the same - // NOTE: The assert is set to check if >= 2 since this is called from nodes which have more than two children. - // The number of children is formally verified elsewhere, so this will not break consistency. - assert(m_inputs.size() >= 2); - for (size_t index = 0; index < 2; index++) - { - auto in = Input(index); - auto other = Input(1 - index); - // borrow any unset dimension on one input from the other input - size_t rows = in->GetNumRows() == 0 ? other->GetNumRows()/*borrow from peer*/ : in->GetNumRows()/*keep as is*/; - size_t cols = (!in->HasMBLayout() && in->GetNumCols() == 0) ? other->GetNumCols()/*borrow from peer*/ : in->GetNumCols()/*keep as is*/; - ValidateInferInputDims(index, rows, cols); - } - } - // BUGBUG: Change this to take a TensorShape. - template - void ComputationNode::ValidateInferInputDims(size_t i, size_t rows, size_t cols) //override final - { - if (Input(i)->OperationName() == OperationNameOf(LearnableParameter) && Input(i)->GetNumRows() == 0) - { - if (rows == 0 || cols == 0) - LogicError("ValidateInferInputDims: Inferred matrix must not be empty."); - Input(i)->SetDims(rows == Input(i)->GetNumRows() ? Input(i)->GetSampleLayout() : TensorShape(rows), cols); - // BUGBUG: This will loose tensor shape. - Input(i)->Validate(true); // validate it properly - // BUGBUG: ^^ Validate() calls are under the control of ValidateSubNetwork(). E.g. it checks whether something has changed & re-validates until there is no change. If we validate here, the change goes unnoticed. - // big BUGBUG: This should do random initialization as requested by user in the first place. - Input(i)->Value().SetValue(0); - fprintf(stderr, "ValidateInferInputDims: %ls %ls operation inferred, resized to (%d x %d), and (incorrectly) initialized to 0.\n", Input(i)->NodeName().c_str(), Input(i)->OperationName().c_str(), (int)rows, (int)cols); - } - } - - // ----------------------------------------------------------------------- - // tensor helpers - // ----------------------------------------------------------------------- - - // determine the sample tensor dimension to use for operations based on output and all inputs - // 'Sample tensor' means we only consider single samples. If we have an MBLayout, that is the sample layout of a single matrix column. +namespace Microsoft { + namespace MSR { + namespace CNTK { + + using namespace std; + + // ----------------------------------------------------------------------- + // subroutines for Validate() implementations + // ----------------------------------------------------------------------- + + // helper function to infer the MBLayout for this node from inputs, for the *standard case* + // the standard case is: + // - all inputs must share the same layout (e.g. adding two minibatches) + // - with the exception of NULL layouts (e.g. TimesNode) + // - all layouts may be NULL (e.g. W' = W * Exp(Stabilizer)) + // - if there are more than one different layouts involved, this function will fail + void ComputationNodeBase::InferMBLayoutFromInputsForStandardCase() + { + MBLayoutPtr pMBLayout; // start with NULL layout + for (auto child : m_inputs) + { + if (!child) // node not set yet (DelayedValueNodeBase seems to allow this)--BUGBUG: Then this function won't operate correctly. + ; + else if (!child->m_pMBLayout) // NULL layout (typical for parameter nodes) + ; + else if (!pMBLayout) // first non-NULL layout: just copy it + pMBLayout = child->m_pMBLayout; + else if (pMBLayout != child->m_pMBLayout) // got a layout--compare whether it is the same + RuntimeError("InferMBLayoutFromInputsForStandardCase: Found inconsistent layout in %ls %ls operation, mismatch detected for child %ls %ls.", + NodeName().c_str(), OperationName().c_str(), child->NodeName().c_str(), child->OperationName().c_str()); + } + // all are consistent: install it + LinkToMBLayout(pMBLayout); + } + + // single input that maps its input element-wise (e.g. Sigmoid) + void ComputationNodeBase::ValidateUnaryMap(bool isFinalValidationPass) + { + assert(m_inputs.size() == 1); + ComputationNodeBase::Validate(isFinalValidationPass); + InferMBLayoutFromInputsForStandardCase(); + SetDims(Input(0)); + } + // binary zip operation, e.g. Plus + // If allowScaling then one can be a sub-dimension of the other (if layout then only for rows, otherwise for cols, too). + // This also helpfully resizes the children if not yet sized. + void ComputationNodeBase::ValidateBinaryZip(bool isFinalValidationPass, bool allowMultiples) + { + assert(m_inputs.size() == 2); + ComputationNodeBase::Validate(isFinalValidationPass); + InferMBLayoutFromInputsForStandardCase(); + + ValidateInferBinaryInputDims(); + + size_t rows0 = Input(0)->GetNumRows(), cols0 = Input(0)->GetNumCols(); + size_t rows1 = Input(1)->GetNumRows(), cols1 = Input(1)->GetNumCols(); + +#if 1//ndef ENABLE_TENSORVIEW + // TODO: This test will go away once we switch to full tensor lib. + if (isFinalValidationPass && !( + (rows0 == rows1 && (Input(0)->GetMBLayout() == Input(1)->GetMBLayout() || cols0 == cols1)) || // matching size (obvious case) + (allowMultiples && (rows0 == 1 || rows1 == 1) && (Input(0)->GetMBLayout() == Input(1)->GetMBLayout() || cols0 == cols1)) || // one is row vec + (allowMultiples && ((!HasMBLayout() && cols0 > cols1 && cols0 % cols1 == 0) || (cols0 == 1 && rows1 % rows0 == 0) || (cols1 == 1 && rows0 % rows1 == 0))) + )) // TODO: ^^ I don't understand the asymmetry of this last one + { + LogicError("The Matrix dimensions in the %ls %ls operation do not match.", NodeName().c_str(), OperationName().c_str()); + } +#else + rows0; rows1; +#endif + + // result has tensor shape with dimensions being the max over both + let shape0 = GetInputSampleLayout(0); + let shape1 = GetInputSampleLayout(1); + SmallVector dims = shape0.GetDims(); + if (shape1.GetRank() > dims.size()) + dims.resize(shape1.GetRank(), 1); // pad with ones + + // If rank of [0] is higher than we only need to take max over rank [1]. + // If rank of [1] is higher then we have padded to equal lentgh. + for (size_t k = 0; k < shape1.GetRank(); k++) + { + size_t dim1 = shape1[k]; + if (dims[k] == 1) // is [0] broadcasting? + dims[k] = dim1; // then use dimension we broadcast to + else if (dim1 == 1) // if [1] is broadcasting + ; // dims is already correct + else if (isFinalValidationPass && dim1 != dims[k]) // no broadcasting: they must match + InvalidArgument("%ls %ls operation: Input dimensions [%s] and [%s] are not compatible.", + NodeName().c_str(), OperationName().c_str(), string(shape0).c_str(), string(shape1).c_str()); + } + + SetDims(TensorShape(dims), GetMBLayout() ? GetMBLayout()->GetNumCols() : max(cols0, cols1)); + } + // unary reduce-to-(1,1) operation, e.g. MatrixL1RegNode + void ComputationNodeBase::ValidateUnaryReduce(bool isFinalValidationPass) + { + assert(m_inputs.size() == 1); + ComputationNodeBase::Validate(isFinalValidationPass); + m_pMBLayout = nullptr; // this node does not hold mini-batch data + SetDims(TensorShape(1), 1); + } + // binary reduce-to-(1,1) operation, e.g. CrossEntropyWithSoftmaxNode + // Currently only called by criterion nodes. + // This function also infers child LearnableParameters. In case you wonder why this is needed for criterion nodes, there are edge cases, e.g. a + // learnable parameter being regularized by a criterion node, where the learnable parameter is fed both into that criterion node and other places. + void ComputationNodeBase::ValidateBinaryReduce(bool isFinalValidationPass) + { + ComputationNodeBase::Validate(isFinalValidationPass); + m_pMBLayout = nullptr; // this node does not hold mini-batch data + ValidateInferBinaryInputDims(); + if (isFinalValidationPass && + !(Input(0)->GetNumRows() == Input(1)->GetNumRows() && + (Input(0)->HasMBLayout() || (Input(0)->GetNumCols() == Input(1)->GetNumCols())))) + LogicError("The Matrix dimensions in the %ls %ls operation do not match.", NodeName().c_str(), OperationName().c_str()); + SetDims(TensorShape(1), 1); + } + // helper function for validation + // In bad cases of convolution, dimensions are quite complex to know. + // This is a feature that allows a node to help resizing its input node to the expected value. + // TODO: This is shaky by design. + void ComputationNodeBase::ValidateInferBinaryInputDims() + { + // limited inference of children dimensions + // if dimension not specified we assume two operands' dimensions should be the same + // NOTE: The assert is set to check if >= 2 since this is called from nodes which have more than two children. + // The number of children is formally verified elsewhere, so this will not break consistency. + assert(m_inputs.size() >= 2); + for (size_t index = 0; index < 2; index++) + { + auto in = Input(index); + auto other = Input(1 - index); + // borrow any unset dimension on one input from the other input + size_t rows = in->GetNumRows() == 0 ? other->GetNumRows()/*borrow from peer*/ : in->GetNumRows()/*keep as is*/; + size_t cols = (!in->HasMBLayout() && in->GetNumCols() == 0) ? other->GetNumCols()/*borrow from peer*/ : in->GetNumCols()/*keep as is*/; + ValidateInferInputDims(index, rows, cols); + } + } + // BUGBUG: Change this to take a TensorShape. + template + void ComputationNode::ValidateInferInputDims(size_t i, size_t rows, size_t cols) //override final + { + if (Input(i)->OperationName() == OperationNameOf(LearnableParameter) && Input(i)->GetNumRows() == 0) + { + if (rows == 0 || cols == 0) + LogicError("ValidateInferInputDims: Inferred matrix must not be empty."); + Input(i)->SetDims(rows == Input(i)->GetNumRows() ? Input(i)->GetSampleLayout() : TensorShape(rows), cols); + // BUGBUG: This will loose tensor shape. + Input(i)->Validate(true); // validate it properly + // BUGBUG: ^^ Validate() calls are under the control of ValidateSubNetwork(). E.g. it checks whether something has changed & re-validates until there is no change. If we validate here, the change goes unnoticed. + // big BUGBUG: This should do random initialization as requested by user in the first place. + Input(i)->Value().SetValue(0); + fprintf(stderr, "ValidateInferInputDims: %ls %ls operation inferred, resized to (%d x %d), and (incorrectly) initialized to 0.\n", Input(i)->NodeName().c_str(), Input(i)->OperationName().c_str(), (int)rows, (int)cols); + } + } + + // ----------------------------------------------------------------------- + // tensor helpers + // ----------------------------------------------------------------------- + + // determine the sample tensor dimension to use for operations based on output and all inputs + // 'Sample tensor' means we only consider single samples. If we have an MBLayout, that is the sample layout of a single matrix column. size_t ComputationNodeBase::DetermineElementwiseTensorRank() const - { - // determine largest tensor dimension amongst the sample shapes of output and the selected inputs + { + // determine largest tensor dimension amongst the sample shapes of output and the selected inputs size_t maxRank = GetSampleLayout().GetRank(); for (size_t i = 0; i < GetNumInputs(); i++) { @@ -190,7 +190,7 @@ namespace Microsoft { maxRank = rank; } return maxRank; - } + } // form the actual tensor that describes the full object TensorShape ComputationNodeBase::GetTensorShape(size_t rank) const @@ -221,106 +221,106 @@ namespace Microsoft { tensorShape.NarrowTo(slice); return tensorShape; } - - // ----------------------------------------------------------------------- - // others - // ----------------------------------------------------------------------- - - template - /*virtual*/ void ComputationNode::DumpNodeInfo(const bool /*printValues*/, File& fstream) const - { - fstream << L"\n" + NodeName() + L"=" + OperationName(); - - if (!IsLeaf()) - { - fstream << wstring(L"("); - for (size_t i = 0; i 0) - fstream << wstring(L","); - fstream << (Input(i) ? Input(i)->NodeName() : L"NULL"); - } - fstream << wstring(L")"); - } - } - - // ----------------------------------------------------------------------- - // instantiate the core class templates - // ----------------------------------------------------------------------- - - typedef Matrix FloatMatrix; - typedef Matrix DoubleMatrix; - - atomic_ullong TimeStamp::s_timeStampCounter = ATOMIC_VAR_INIT(0); - - template<> std::map> ComputationNode::s_constOnes{}; - template<> std::map> ComputationNode::s_constOnes{}; - - template class ComputationNode; - template class ComputationNode; - - template class LearnableParameter; - template class LearnableParameter; -}}} - -namespace Microsoft { namespace MSR { namespace ScriptableObjects { - - using namespace Microsoft::MSR::CNTK; - - // ----------------------------------------------------------------------- - // register ComputationNode with the ScriptableObject system - // ----------------------------------------------------------------------- - - template<> shared_ptr MakeRuntimeObject(const IConfigRecordPtr configp) - { - return NewComputationNodeFromConfig(configp); - } - - ScriptableObjects::ConfigurableRuntimeTypeRegister::Add registerComputationNode(L"ComputationNode"); - - // ----------------------------------------------------------------------- - // register a boxed version of TensorShape with the ScriptableObject system - // ----------------------------------------------------------------------- - - // e.g. - // new TensorShape [ dims = 13:42 ] - class BoxedTensorShape : public BoxOf - { - // create a TensorShape from config - static TensorShape TensorShapeFromConfig(const IConfigRecord & config) - { - const auto & valp = config[L"dims"]; - if (valp.Is()) - return valp.AsRef(); // UNTESTED - else if (valp.Is()) - return TensorShape(valp.AsRef().AsVector([&](const wstring & msg){ valp.Fail(msg); })); - else - return TensorShape(std::vector(1, (size_t)valp)); // single element - } - public: - BoxedTensorShape(const IConfigRecordPtr configp) : BoxOf(TensorShapeFromConfig(*configp)) { } - }; - - template - class BoxedVector : public BoxOf> - { - // create a vector from config - static vector VectorFromConfig(const IConfigRecord & config) - { - const auto & valp = config[L"items"]; - if (valp.Is>()) - return valp.AsRef>(); // UNTESTED - else if (valp.Is()) - return valp.AsRef().AsVector([&](const wstring & msg){ valp.Fail(msg); }); - else - return std::vector(1, (E)valp); // single element - } - public: - BoxedVector(const IConfigRecordPtr configp) : BoxOf>(VectorFromConfig(*configp)) { } - }; - - ScriptableObjects::ConfigurableRuntimeTypeRegister::Add registerTensorShape(L"TensorShape"); - ScriptableObjects::ConfigurableRuntimeTypeRegister::Add> registerIntVector(L"IntVector"); - ScriptableObjects::ConfigurableRuntimeTypeRegister::Add> registerSizeVector(L"SizeVector"); - -}}} + + // ----------------------------------------------------------------------- + // others + // ----------------------------------------------------------------------- + + template + /*virtual*/ void ComputationNode::DumpNodeInfo(const bool /*printValues*/, File& fstream) const + { + fstream << L"\n" + NodeName() + L"=" + OperationName(); + + if (!IsLeaf()) + { + fstream << wstring(L"("); + for (size_t i = 0; i 0) + fstream << wstring(L","); + fstream << (Input(i) ? Input(i)->NodeName() : L"NULL"); + } + fstream << wstring(L")"); + } + } + + // ----------------------------------------------------------------------- + // instantiate the core class templates + // ----------------------------------------------------------------------- + + typedef Matrix FloatMatrix; + typedef Matrix DoubleMatrix; + + atomic_ullong TimeStamp::s_timeStampCounter = ATOMIC_VAR_INIT(0); + + template<> std::map> ComputationNode::s_constOnes{}; + template<> std::map> ComputationNode::s_constOnes{}; + + template class ComputationNode; + template class ComputationNode; + + template class LearnableParameter; + template class LearnableParameter; +}}} + +namespace Microsoft { namespace MSR { namespace ScriptableObjects { + + using namespace Microsoft::MSR::CNTK; + + // ----------------------------------------------------------------------- + // register ComputationNode with the ScriptableObject system + // ----------------------------------------------------------------------- + + template<> shared_ptr MakeRuntimeObject(const IConfigRecordPtr configp) + { + return NewComputationNodeFromConfig(configp); + } + + ScriptableObjects::ConfigurableRuntimeTypeRegister::Add registerComputationNode(L"ComputationNode"); + + // ----------------------------------------------------------------------- + // register a boxed version of TensorShape with the ScriptableObject system + // ----------------------------------------------------------------------- + + // e.g. + // new TensorShape [ dims = 13:42 ] + class BoxedTensorShape : public BoxOf + { + // create a TensorShape from config + static TensorShape TensorShapeFromConfig(const IConfigRecord & config) + { + const auto & valp = config[L"dims"]; + if (valp.Is()) + return valp.AsRef(); // UNTESTED + else if (valp.Is()) + return TensorShape(valp.AsRef().AsVector([&](const wstring & msg){ valp.Fail(msg); })); + else + return TensorShape(std::vector(1, (size_t)valp)); // single element + } + public: + BoxedTensorShape(const IConfigRecordPtr configp) : BoxOf(TensorShapeFromConfig(*configp)) { } + }; + + template + class BoxedVector : public BoxOf> + { + // create a vector from config + static vector VectorFromConfig(const IConfigRecord & config) + { + const auto & valp = config[L"items"]; + if (valp.Is>()) + return valp.AsRef>(); // UNTESTED + else if (valp.Is()) + return valp.AsRef().AsVector([&](const wstring & msg){ valp.Fail(msg); }); + else + return std::vector(1, (E)valp); // single element + } + public: + BoxedVector(const IConfigRecordPtr configp) : BoxOf>(VectorFromConfig(*configp)) { } + }; + + ScriptableObjects::ConfigurableRuntimeTypeRegister::Add registerTensorShape(L"TensorShape"); + ScriptableObjects::ConfigurableRuntimeTypeRegister::Add> registerIntVector(L"IntVector"); + ScriptableObjects::ConfigurableRuntimeTypeRegister::Add> registerSizeVector(L"SizeVector"); + +}}} diff --git a/Source/ComputationNetworkLib/InputAndParamNodes.h b/Source/ComputationNetworkLib/InputAndParamNodes.h index f53ef7a38d80..bb2d6354d2b5 100644 --- a/Source/ComputationNetworkLib/InputAndParamNodes.h +++ b/Source/ComputationNetworkLib/InputAndParamNodes.h @@ -1,574 +1,574 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - -#include "Basics.h" -#include "ComputationNode.h" -#include "ScriptableObjects.h" -#include "Matrix.h" -#include "File.h" // for LoadMatrixFromTextFile() -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace Microsoft { namespace MSR { namespace CNTK { - - // ----------------------------------------------------------------------- - // LearnableParameter (/*no input*/) - // represents weight matrices and biases - // TODO: add -Node to the class name - // ----------------------------------------------------------------------- - - template - class LearnableParameter : public ComputationNode, public NumInputs<0> - { - typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; - static const std::wstring TypeName() { return L"LearnableParameter"; } - public: - LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name) : - Base(deviceId, name) - { - m_parameterUpdateRequired = true; - this->m_valueSharable = false; - SetDims(TensorShape(), 0); - } - LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & shape) : - Base(deviceId, name) - { - m_parameterUpdateRequired = true; - CreateMatrixIfNull(m_value); - this->m_valueSharable = false; - // for now we split off the trailing dimension into the matrix column dimension - // TODO: This is for compat, but is is inconsistent. Decide what a sample layout means for a node without MBLayout w.r.t. non-tensor ops. - auto dims = shape.GetDims(); - size_t cols = 1; - if (dims.size() > 1) - { - cols = dims.back(); - dims.resize(dims.size()-1); - } - SetDims(TensorShape(dims), cols); - UpdateFunctionValuesSize(); // this allocates the matrix - Value().SetValue(0); - } - LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, size_t cols) : - LearnableParameter(deviceId, name, TensorShape(rows, cols)) - { } - LearnableParameter(const ScriptableObjects::IConfigRecordPtr configp) : - LearnableParameter(configp->Get(L"deviceId"), L"", configp->Get(L"shape")) - { - // TODO: Change dimensions to take a generic tensor instead. That will be a (minor) breaking change that will require fix-ups when converting from NDL to BrainScript. - AttachInputs(configp, this->GetExpectedNumInputs()); - // parameters[rows, [cols=1]] plus other optional parameters (needGradient=[true|false], init=[uniform|gaussian|fixedvalue], initValueScale=[1|float], value=[0|float]) - // TODO: "needGradient" should be renamed to better match m_parameterUpdateRequired - SetParameterUpdateRequired(configp->Get(L"needGradient")); - wstring initString = configp->Get(L"init"); - if (initString == L"fixedValue") - Value().SetValue((ElemType)configp->Get(L"value")); - else if (initString == L"uniform" || initString == L"gaussian") - { - // TODO: add these options also to old NDL - static unsigned long randomSeed = 1; - int forcedRandomSeed = configp->Get(L"randomSeed"); // forcing a specific random seed is useful for testing to get repeatable initialization independent of evaluation order - InitRandom((initString == L"uniform"), forcedRandomSeed < 0 ? randomSeed++ : (unsigned long)forcedRandomSeed, configp->Get(L"initValueScale"), configp->Get(L"initOnCPUOnly")); - } - else if (initString == L"fromFile") - { - wstring initFromFilePath = configp->Get(L"initFromFilePath"); - if (initFromFilePath.empty()) - RuntimeError("initFromFilePath must be set when using \"fromFile\" initialization method"); - InitFromFile(initFromFilePath); - } - else - RuntimeError("init must be one of the values of [ uniform | gaussian | fixedValue | fromFile ]"); - } - - virtual void Save(File& fstream) const override - { - Base::Save(fstream); - fstream << m_parameterUpdateRequired; - fstream << (size_t)0/*#rows in a legacy file format*/ << GetNumCols(); - m_sampleLayout.Save(fstream); - fstream << Value(); - } - - virtual void Load(File& fstream, size_t modelVersion) override - { - Base::Load(fstream, modelVersion); - - size_t rows, cols; - fstream >> m_parameterUpdateRequired; - fstream >> rows >> cols; - - TensorShape sampleLayout; - if (rows != 0) // legacy file format - sampleLayout = TensorShape(rows); - else - sampleLayout.Load(fstream, /*acceptLegacyFormat=*/true); - LoadValue(fstream); - SetDims(sampleLayout, cols); // note: call this after LoadValue() since LoadValue() overwrites m_sampleLayout - } - - // initialize with random numbers - void InitRandom(const bool uniformInit, - const unsigned long randomSeed, - const ElemType initValueScale, - bool initOnCPUOnly) // if true then always init on CPU, making initialization consistent across both (for testing) - { - size_t inputSize = GetNumCols(); - //fprintf(stderr, "%d x %d: %d %ls\n", (int)GetNumRows(), (int)GetNumCols(), (int)randomSeed, NodeName().c_str()); - - // the random seed offset is set via the "randomSeedOffset" parameter in config - if (initOnCPUOnly) - m_value->TransferToDeviceIfNotThereAndNotAutoPlace(CPUDEVICE, true); - if (uniformInit) - { - // TODO: move these crazy extra factors out from here and into NDL, and make them visible in BS - ElemType randRange = 0.05f * initValueScale; - Value().SetUniformRandomValue(-randRange, randRange, randomSeed); - } - else - { - ElemType randInitstd = 0.2f * initValueScale / sqrt(ElemType(inputSize)); - Value().SetGaussianRandomValue(0, randInitstd, randomSeed); - } - if (initOnCPUOnly) - m_value->TransferToDeviceIfNotThereAndNotAutoPlace(m_deviceId, true); - } - - // initialize by reading a matrix from a text file - void InitFromFile(const std::wstring & initFromFilePath) - { - size_t numRows = 0; - size_t numCols = 0; - auto array = File::LoadMatrixFromTextFile(msra::strfun::utf8(initFromFilePath), numRows, numCols); // TODO: change pathname to wstring - Value().SetValue(numRows, numCols, m_deviceId, array.data(), matrixFlagNormal); - } - - void ReviseFromFile(const std::wstring & reviseFromFilePath) - { - size_t numRows = 0; - size_t numCols = 0; - auto array = File::LoadMatrixFromTextFile(msra::strfun::utf8(reviseFromFilePath), numRows, numCols); // TODO: change pathname to wstring - size_t nRows = m_value->GetNumRows(); - size_t nCols = m_value->GetNumCols(); - - if (numRows != nRows || numCols != nCols) - { - RuntimeError("Error in ReviseFromFile for node %ls using file %ls: original size (%d x %d) vs current size (%d x %d)", - m_nodeName.c_str(), reviseFromFilePath.c_str(), (int)nRows, (int)nCols, (int)numRows, (int)numCols); - } - - Value().SetValue(numRows, numCols, m_deviceId, array.data(), matrixFlagNormal); - - } - - // computation functions don't do anything for parameter nodes - virtual void UpdateFunctionMBSize() override { } - - virtual void /*ComputationNode::*/BackpropTo(const size_t /*inputIndex*/, const FrameRange &) override { } - virtual void /*ComputationNode::*/ForwardProp(const FrameRange &) override { } - - virtual void /*ComputationNodeBase::*/Validate(bool isFinalValidationPass) override - { +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + +#include "Basics.h" +#include "ComputationNode.h" +#include "ScriptableObjects.h" +#include "Matrix.h" +#include "File.h" // for LoadMatrixFromTextFile() +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace Microsoft { namespace MSR { namespace CNTK { + + // ----------------------------------------------------------------------- + // LearnableParameter (/*no input*/) + // represents weight matrices and biases + // TODO: add -Node to the class name + // ----------------------------------------------------------------------- + + template + class LearnableParameter : public ComputationNode, public NumInputs<0> + { + typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; + static const std::wstring TypeName() { return L"LearnableParameter"; } + public: + LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name) : + Base(deviceId, name) + { + m_parameterUpdateRequired = true; + this->m_valueSharable = false; + SetDims(TensorShape(), 0); + } + LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & shape) : + Base(deviceId, name) + { + m_parameterUpdateRequired = true; + CreateMatrixIfNull(m_value); + this->m_valueSharable = false; + // for now we split off the trailing dimension into the matrix column dimension + // TODO: This is for compat, but is is inconsistent. Decide what a sample layout means for a node without MBLayout w.r.t. non-tensor ops. + auto dims = shape.GetDims(); + size_t cols = 1; + if (dims.size() > 1) + { + cols = dims.back(); + dims.resize(dims.size()-1); + } + SetDims(TensorShape(dims), cols); + UpdateFunctionValuesSize(); // this allocates the matrix + Value().SetValue(0); + } + LearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, size_t cols) : + LearnableParameter(deviceId, name, TensorShape(rows, cols)) + { } + LearnableParameter(const ScriptableObjects::IConfigRecordPtr configp) : + LearnableParameter(configp->Get(L"deviceId"), L"", configp->Get(L"shape")) + { + // TODO: Change dimensions to take a generic tensor instead. That will be a (minor) breaking change that will require fix-ups when converting from NDL to BrainScript. + AttachInputs(configp, this->GetExpectedNumInputs()); + // parameters[rows, [cols=1]] plus other optional parameters (needGradient=[true|false], init=[uniform|gaussian|fixedvalue], initValueScale=[1|float], value=[0|float]) + // TODO: "needGradient" should be renamed to better match m_parameterUpdateRequired + SetParameterUpdateRequired(configp->Get(L"needGradient")); + wstring initString = configp->Get(L"init"); + if (initString == L"fixedValue") + Value().SetValue((ElemType)configp->Get(L"value")); + else if (initString == L"uniform" || initString == L"gaussian") + { + // TODO: add these options also to old NDL + static unsigned long randomSeed = 1; + int forcedRandomSeed = configp->Get(L"randomSeed"); // forcing a specific random seed is useful for testing to get repeatable initialization independent of evaluation order + InitRandom((initString == L"uniform"), forcedRandomSeed < 0 ? randomSeed++ : (unsigned long)forcedRandomSeed, configp->Get(L"initValueScale"), configp->Get(L"initOnCPUOnly")); + } + else if (initString == L"fromFile") + { + wstring initFromFilePath = configp->Get(L"initFromFilePath"); + if (initFromFilePath.empty()) + RuntimeError("initFromFilePath must be set when using \"fromFile\" initialization method"); + InitFromFile(initFromFilePath); + } + else + RuntimeError("init must be one of the values of [ uniform | gaussian | fixedValue | fromFile ]"); + } + + virtual void Save(File& fstream) const override + { + Base::Save(fstream); + fstream << m_parameterUpdateRequired; + fstream << (size_t)0/*#rows in a legacy file format*/ << GetNumCols(); + m_sampleLayout.Save(fstream); + fstream << Value(); + } + + virtual void Load(File& fstream, size_t modelVersion) override + { + Base::Load(fstream, modelVersion); + + size_t rows, cols; + fstream >> m_parameterUpdateRequired; + fstream >> rows >> cols; + + TensorShape sampleLayout; + if (rows != 0) // legacy file format + sampleLayout = TensorShape(rows); + else + sampleLayout.Load(fstream, /*acceptLegacyFormat=*/true); + LoadValue(fstream); + SetDims(sampleLayout, cols); // note: call this after LoadValue() since LoadValue() overwrites m_sampleLayout + } + + // initialize with random numbers + void InitRandom(const bool uniformInit, + const unsigned long randomSeed, + const ElemType initValueScale, + bool initOnCPUOnly) // if true then always init on CPU, making initialization consistent across both (for testing) + { + size_t inputSize = GetNumCols(); + //fprintf(stderr, "%d x %d: %d %ls\n", (int)GetNumRows(), (int)GetNumCols(), (int)randomSeed, NodeName().c_str()); + + // the random seed offset is set via the "randomSeedOffset" parameter in config + if (initOnCPUOnly) + m_value->TransferToDeviceIfNotThereAndNotAutoPlace(CPUDEVICE, true); + if (uniformInit) + { + // TODO: move these crazy extra factors out from here and into NDL, and make them visible in BS + ElemType randRange = 0.05f * initValueScale; + Value().SetUniformRandomValue(-randRange, randRange, randomSeed); + } + else + { + ElemType randInitstd = 0.2f * initValueScale / sqrt(ElemType(inputSize)); + Value().SetGaussianRandomValue(0, randInitstd, randomSeed); + } + if (initOnCPUOnly) + m_value->TransferToDeviceIfNotThereAndNotAutoPlace(m_deviceId, true); + } + + // initialize by reading a matrix from a text file + void InitFromFile(const std::wstring & initFromFilePath) + { + size_t numRows = 0; + size_t numCols = 0; + auto array = File::LoadMatrixFromTextFile(msra::strfun::utf8(initFromFilePath), numRows, numCols); // TODO: change pathname to wstring + Value().SetValue(numRows, numCols, m_deviceId, array.data(), matrixFlagNormal); + } + + void ReviseFromFile(const std::wstring & reviseFromFilePath) + { + size_t numRows = 0; + size_t numCols = 0; + auto array = File::LoadMatrixFromTextFile(msra::strfun::utf8(reviseFromFilePath), numRows, numCols); // TODO: change pathname to wstring + size_t nRows = m_value->GetNumRows(); + size_t nCols = m_value->GetNumCols(); + + if (numRows != nRows || numCols != nCols) + { + RuntimeError("Error in ReviseFromFile for node %ls using file %ls: original size (%d x %d) vs current size (%d x %d)", + m_nodeName.c_str(), reviseFromFilePath.c_str(), (int)nRows, (int)nCols, (int)numRows, (int)numCols); + } + + Value().SetValue(numRows, numCols, m_deviceId, array.data(), matrixFlagNormal); + + } + + // computation functions don't do anything for parameter nodes + virtual void UpdateFunctionMBSize() override { } + + virtual void /*ComputationNode::*/BackpropTo(const size_t /*inputIndex*/, const FrameRange &) override { } + virtual void /*ComputationNode::*/ForwardProp(const FrameRange &) override { } + + virtual void /*ComputationNodeBase::*/Validate(bool isFinalValidationPass) override + { Base::Validate(isFinalValidationPass); - m_pMBLayout = nullptr; // this node does not hold mini-batch data - } - - virtual void DumpNodeInfo(const bool printValues, File& fstream) const override - { - Base::DumpNodeInfo(printValues, fstream); - - char str[4096]; - sprintf(str, "[%lu,%lu] ", GetNumRows(), GetNumCols()); - fstream << string(str); - sprintf(str, "NeedGradient=%s", m_parameterUpdateRequired ? "true" : "false"); // TODO: update NDL to accept a better matching name as well - fstream << string(str); - - PrintNodeValuesToFile(printValues, fstream); - } - - }; - -#if 0 - // ----------------------------------------------------------------------- - // SparseLearnableParameter (/*no input*/) - // ----------------------------------------------------------------------- - - // WARNING: Don't use SparseLearnableParameter yet since the current version assumes the parameter is dense instead of sparse - // WARNING: After the right implementation is put here we need to turn it on in NetworkDescriptionLangauge.cpp - template - class SparseLearnableParameter : public LearnableParameter - { - typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; - static const std::wstring TypeName() { return L"SparseLearnableParameter"; } - public: - DeclareConstructorFromConfigWithNumInputs(SparseLearnableParameter); - SparseLearnableParameter(DEVICEID_TYPE deviceId, const wstring & name) : - LearnableParameter(deviceId, name) - { - CreateMatrixIfNull(m_gradient); - m_gradient->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseBlockCol, false); - } - SparseLearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, size_t cols, size_t size) : - LearnableParameter(deviceId, name, rows, cols) - { - CreateMatrixIfNull(m_gradient); - m_gradient->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseBlockCol, false); - m_gradient->Resize(rows, cols, size); - } - - virtual void Load(File& fstream, size_t modelVersion) override - { - LearnableParameter::Load(fstream, modelVersion); - CreateMatrixIfNull(m_gradient); - m_gradient->Resize(GetNumRows(), GetNumCols()); - } - }; - - template class SparseLearnableParameter; - template class SparseLearnableParameter; -#endif - - // ----------------------------------------------------------------------- - // InputValueBase (/*no input*/) - // Base class for InputValue and SparseInputValue (typically fed by a DataReader) - // this covers four types: (regular vs. image) x (non-sparse vs. sparse) - // TODO: add -Node to the class names - // ----------------------------------------------------------------------- - - template - class InputValueBase : public ComputationNode, public NumInputs<0> - { - typedef ComputationNode Base; UsingComputationNodeMembers; - - void Init(const TensorShape & sampleLayout, bool isSparse) - { - m_isSparse = isSparse; - CreateMatrixIfNull(m_value); - if (isSparse) - ConvertToSparseMatrix(); - - SetDims(sampleLayout, 0); - UpdateFunctionValuesSize(); // we must allocate the matrix so that the readers get objects with valid row dimensions (some readers expect that) - m_parameterUpdateRequired = false; - this->m_valueSharable = false; - } - protected: - InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & sampleLayout, bool isSparse) : - Base(deviceId, name) - { - Init(sampleLayout, isSparse); - } - InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, bool isSparse) : - InputValueBase(deviceId, name, TensorShape(rows), isSparse) - { } - InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, bool isSparse) : - InputValueBase(deviceId, name, TensorShape(), isSparse) - { } - InputValueBase(const ScriptableObjects::IConfigRecordPtr configp, bool isSparse) : - Base(configp->Get(L"deviceId"), L"") - { - AttachInputs(configp, this->GetExpectedNumInputs()); - bool isImage = configp->Get(L"isImage"); - if (!isImage) - Init(configp->Get(L"shape"), isSparse); - else - Init(ImageDimensions::AsTensorShape(configp->Get(L"imageWidth"), configp->Get(L"imageHeight"), configp->Get(L"imageChannels"), ImageLayoutKindFrom(configp->Get(L"imageLayout"))), isSparse); - } - public: - - virtual void Save(File& fstream) const override - { - Base::Save(fstream); - size_t rows = GetNumRows(); // using explicitly typed variables to be 100% symmetrical to Load() - size_t colsDummy = 0; // This should not be saved. InputValues always are minibatches. - fstream << rows << colsDummy; - m_sampleLayout.Save(fstream); - } - - virtual void Load(File& fstream, size_t modelVersion) override - { - Base::Load(fstream, modelVersion); - - size_t rows, colsDummy; - fstream >> rows >> colsDummy; - TensorShape sampleLayout; - sampleLayout.Load(fstream, /*acceptLegacyFormat=*/true); - // some older files may have inconsistent tensor information - if (rows != sampleLayout.GetNumElements()) - { - fprintf(stderr, "WARNING: %ls InputValue has inconsistent serialized sample layout %s vs. number of rows %d. Resetting sample layout to vector.\n", - NodeName().c_str(), string(sampleLayout).c_str(), (int)rows); - sampleLayout = TensorShape(rows); - } - Init(sampleLayout, m_isSparse); - } - - // InputValue must not resize its inputs because that might destroy it. It should already have the correct size. - virtual void UpdateFunctionMBSize() override - { - if (!m_pMBLayout) // if no layout, this node contains parameters independent of MB size, don't resize - VerifyDims(GetNumRows(), m_pMBLayout->GetNumCols()); - } - - virtual void /*ComputationNode::*/ForwardProp(const FrameRange &) override { } - virtual void /*ComputationNode::*/BackpropTo(const size_t /*inputIndex*/, const FrameRange &) { } - - virtual void DumpNodeInfo(const bool printValues, File& fstream) const override - { - Base::DumpNodeInfo(printValues, fstream); - - char str[4096]; - sprintf(str, "[%lu,%lu]", GetNumRows(), GetNumCols()); - fstream << string(str); // TODO: string(.) necessary? - } - private: - bool m_isSparse = false; - void ConvertToSparseMatrix() - { - m_value->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseCSC, false); - } - }; - - // ----------------------------------------------------------------------- - // InputValue (/*no input*/) - // an input value (typically fed by a DataReader) - // this covers two types: (regular vs. image) - // TODO: There is still debate whether an InputValue without layout makes sense. - // ----------------------------------------------------------------------- - - template - class InputValue : public InputValueBase - { - typedef InputValueBase Base; UsingComputationNodeMembersBoilerplate; - static const std::wstring TypeName() { return L"InputValue"; } - public: - InputValue(DEVICEID_TYPE deviceId, const wstring & name) : - Base(deviceId, name, false) - { } - InputValue(DEVICEID_TYPE deviceId, const wstring & name, size_t rows) : - Base(deviceId, name, rows, false) - { } - InputValue(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & sampleLayout) : - Base(deviceId, name, sampleLayout, false) - { } - InputValue(const ScriptableObjects::IConfigRecordPtr configp) : - Base(configp, false) - { } - }; - - template class InputValue; - template class InputValue; - - // ----------------------------------------------------------------------- - // SparseInputValue (/*no input*/) - // a sparse input value (typically fed by a DataReader) - // this covers two types: (regular vs. image) - // ----------------------------------------------------------------------- - - template - class SparseInputValue : public InputValueBase - { - typedef InputValueBase Base; UsingComputationNodeMembersBoilerplate; - static const std::wstring TypeName() { return L"SparseInputValue"; } - public: - SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name) : - Base(deviceId, name, true) - { } - SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name, size_t rows) : - Base(deviceId, name, rows, true) - { } - SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & imageLayout) : - Base(deviceId, name, imageLayout, true) - { } - SparseInputValue(const ScriptableObjects::IConfigRecordPtr configp) : - Base(configp, true) - { } - }; - - template class SparseInputValue; - template class SparseInputValue; - - // ----------------------------------------------------------------------- - // LookupTableNode (embedding matrix, bag-of-word representation of the inputs) - // implements an embedding, assuming a specific representation of the input data - // ----------------------------------------------------------------------- - - template - class LookupTableNode : public ComputationNode, public NumInputs<2> - { - typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; - static const std::wstring TypeName() { return L"LookupTable"; } - public: - DeclareConstructorFromConfigWithNumInputs(LookupTableNode); - LookupTableNode(DEVICEID_TYPE deviceId, const wstring & name) : - Base(deviceId, name) - { } - - virtual void /*ComputationNode::*/BackpropTo(const size_t inputIndex, const FrameRange & t) override - { - if (inputIndex == 0) // left derivative (embedding matrix) - { - // This is a reduction operation, hence we need to mask out gaps. - Matrix sliceInput1Value = Input(1)->MaskedValueFor(t); - Matrix sliceOutputGrad = MaskedGradientFor(t); - - BackpropToLeft(sliceInput1Value, Input(0)->Gradient(), sliceOutputGrad); - } - else if (inputIndex == 1) // right derivative (input) - { - Matrix sliceInput1Grad = Input(1)->GradientFor(t); - Matrix sliceOutputGrad = GradientFor(t); - - BackpropToRight(Input(0)->Value(), sliceInput1Grad, sliceOutputGrad); - } - } - - /*TODO: merge with call site*/void BackpropToLeft(Matrix& inputFunctionValues, Matrix& inputGradientValues, Matrix& gradientValues) - { - size_t rows1 = inputFunctionValues.GetNumRows(), cols1 = inputFunctionValues.GetNumCols(); - size_t rowsp = gradientValues.GetNumRows(), colsp = gradientValues.GetNumCols(); - int wordsInEachSample = rows1 / inputGradientValues.GetNumCols(); - - inputFunctionValues.Reshape(rows1 / wordsInEachSample, cols1 * wordsInEachSample); - gradientValues.Reshape(rowsp / wordsInEachSample, colsp * wordsInEachSample); - - Matrix::MultiplyAndAdd(gradientValues, false, inputFunctionValues, true, inputGradientValues); - - inputFunctionValues.Reshape(rows1, cols1); - gradientValues.Reshape(rowsp, colsp); - } - - /*TODO: merge with call site*/void BackpropToRight(Matrix& inputFunctionValues, Matrix& inputGradientValues, Matrix& gradientValues) - { - size_t rows1 =inputGradientValues.GetNumRows(), cols1 = inputGradientValues.GetNumCols(); - size_t rowsp = gradientValues.GetNumRows(), colsp = gradientValues.GetNumCols(); - int wordsInEachSample = rows1 / inputFunctionValues.GetNumCols(); - - inputGradientValues.Reshape(rows1 / wordsInEachSample, cols1 * wordsInEachSample); - gradientValues.Reshape(rowsp / wordsInEachSample, colsp * wordsInEachSample); - - Matrix::MultiplyAndAdd(inputFunctionValues, true, gradientValues, false, inputGradientValues); - - inputGradientValues.Reshape(rows1, cols1); - gradientValues.Reshape(rowsp, colsp); - } - - virtual void /*ComputationNode::*/ForwardProp(const FrameRange & t) override - { - // input0 is the weight (each column is an embedding of one word), input 1 contains m_bnrLooked words in each column (sample) - Matrix functionValues = ValueFor(t); - const Matrix& input0 = Input(0)->Value(); - Matrix input1 = Input(1)->ValueFor(t); - - size_t rows1 = input1.GetNumRows(), cols1 = input1.GetNumCols(); - size_t cols0 = input0.GetNumCols(); - - if (rows1 % cols0 != 0) - LogicError("LookupTableNode: rows of input 1 and cols of input 0 are not modular. e.g., rows1 = 0.9 cols and this is not allowed. Check feature reader and network definition. This usually happens when the feature dimension is not specified as that in the network definition of look-up-table dimension size."); - - int wordsInEachSample = rows1 / cols0; - - auto input1Reshaped = input1.Reshaped(rows1 / wordsInEachSample, cols1 * wordsInEachSample); - - auto functionValuesReshaped = functionValues.Reshaped(input0.GetNumRows(), input1Reshaped.GetNumCols()); - functionValuesReshaped.AssignProductOf(input0, false, input1Reshaped, false); - } - - virtual void /*ComputationNodeBase::*/Validate(bool isFinalValidationPass) override - { + m_pMBLayout = nullptr; // this node does not hold mini-batch data + } + + virtual void DumpNodeInfo(const bool printValues, File& fstream) const override + { + Base::DumpNodeInfo(printValues, fstream); + + char str[4096]; + sprintf(str, "[%lu,%lu] ", GetNumRows(), GetNumCols()); + fstream << string(str); + sprintf(str, "NeedGradient=%s", m_parameterUpdateRequired ? "true" : "false"); // TODO: update NDL to accept a better matching name as well + fstream << string(str); + + PrintNodeValuesToFile(printValues, fstream); + } + + }; + +#if 0 + // ----------------------------------------------------------------------- + // SparseLearnableParameter (/*no input*/) + // ----------------------------------------------------------------------- + + // WARNING: Don't use SparseLearnableParameter yet since the current version assumes the parameter is dense instead of sparse + // WARNING: After the right implementation is put here we need to turn it on in NetworkDescriptionLangauge.cpp + template + class SparseLearnableParameter : public LearnableParameter + { + typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; + static const std::wstring TypeName() { return L"SparseLearnableParameter"; } + public: + DeclareConstructorFromConfigWithNumInputs(SparseLearnableParameter); + SparseLearnableParameter(DEVICEID_TYPE deviceId, const wstring & name) : + LearnableParameter(deviceId, name) + { + CreateMatrixIfNull(m_gradient); + m_gradient->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseBlockCol, false); + } + SparseLearnableParameter(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, size_t cols, size_t size) : + LearnableParameter(deviceId, name, rows, cols) + { + CreateMatrixIfNull(m_gradient); + m_gradient->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseBlockCol, false); + m_gradient->Resize(rows, cols, size); + } + + virtual void Load(File& fstream, size_t modelVersion) override + { + LearnableParameter::Load(fstream, modelVersion); + CreateMatrixIfNull(m_gradient); + m_gradient->Resize(GetNumRows(), GetNumCols()); + } + }; + + template class SparseLearnableParameter; + template class SparseLearnableParameter; +#endif + + // ----------------------------------------------------------------------- + // InputValueBase (/*no input*/) + // Base class for InputValue and SparseInputValue (typically fed by a DataReader) + // this covers four types: (regular vs. image) x (non-sparse vs. sparse) + // TODO: add -Node to the class names + // ----------------------------------------------------------------------- + + template + class InputValueBase : public ComputationNode, public NumInputs<0> + { + typedef ComputationNode Base; UsingComputationNodeMembers; + + void Init(const TensorShape & sampleLayout, bool isSparse) + { + m_isSparse = isSparse; + CreateMatrixIfNull(m_value); + if (isSparse) + ConvertToSparseMatrix(); + + SetDims(sampleLayout, 0); + UpdateFunctionValuesSize(); // we must allocate the matrix so that the readers get objects with valid row dimensions (some readers expect that) + m_parameterUpdateRequired = false; + this->m_valueSharable = false; + } + protected: + InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & sampleLayout, bool isSparse) : + Base(deviceId, name) + { + Init(sampleLayout, isSparse); + } + InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, size_t rows, bool isSparse) : + InputValueBase(deviceId, name, TensorShape(rows), isSparse) + { } + InputValueBase(DEVICEID_TYPE deviceId, const wstring & name, bool isSparse) : + InputValueBase(deviceId, name, TensorShape(), isSparse) + { } + InputValueBase(const ScriptableObjects::IConfigRecordPtr configp, bool isSparse) : + Base(configp->Get(L"deviceId"), L"") + { + AttachInputs(configp, this->GetExpectedNumInputs()); + bool isImage = configp->Get(L"isImage"); + if (!isImage) + Init(configp->Get(L"shape"), isSparse); + else + Init(ImageDimensions::AsTensorShape(configp->Get(L"imageWidth"), configp->Get(L"imageHeight"), configp->Get(L"imageChannels"), ImageLayoutKindFrom(configp->Get(L"imageLayout"))), isSparse); + } + public: + + virtual void Save(File& fstream) const override + { + Base::Save(fstream); + size_t rows = GetNumRows(); // using explicitly typed variables to be 100% symmetrical to Load() + size_t colsDummy = 0; // This should not be saved. InputValues always are minibatches. + fstream << rows << colsDummy; + m_sampleLayout.Save(fstream); + } + + virtual void Load(File& fstream, size_t modelVersion) override + { + Base::Load(fstream, modelVersion); + + size_t rows, colsDummy; + fstream >> rows >> colsDummy; + TensorShape sampleLayout; + sampleLayout.Load(fstream, /*acceptLegacyFormat=*/true); + // some older files may have inconsistent tensor information + if (rows != sampleLayout.GetNumElements()) + { + fprintf(stderr, "WARNING: %ls InputValue has inconsistent serialized sample layout %s vs. number of rows %d. Resetting sample layout to vector.\n", + NodeName().c_str(), string(sampleLayout).c_str(), (int)rows); + sampleLayout = TensorShape(rows); + } + Init(sampleLayout, m_isSparse); + } + + // InputValue must not resize its inputs because that might destroy it. It should already have the correct size. + virtual void UpdateFunctionMBSize() override + { + if (!m_pMBLayout) // if no layout, this node contains parameters independent of MB size, don't resize + VerifyDims(GetNumRows(), m_pMBLayout->GetNumCols()); + } + + virtual void /*ComputationNode::*/ForwardProp(const FrameRange &) override { } + virtual void /*ComputationNode::*/BackpropTo(const size_t /*inputIndex*/, const FrameRange &) { } + + virtual void DumpNodeInfo(const bool printValues, File& fstream) const override + { + Base::DumpNodeInfo(printValues, fstream); + + char str[4096]; + sprintf(str, "[%lu,%lu]", GetNumRows(), GetNumCols()); + fstream << string(str); // TODO: string(.) necessary? + } + private: + bool m_isSparse = false; + void ConvertToSparseMatrix() + { + m_value->SwitchToMatrixType(MatrixType::SPARSE, matrixFormatSparseCSC, false); + } + }; + + // ----------------------------------------------------------------------- + // InputValue (/*no input*/) + // an input value (typically fed by a DataReader) + // this covers two types: (regular vs. image) + // TODO: There is still debate whether an InputValue without layout makes sense. + // ----------------------------------------------------------------------- + + template + class InputValue : public InputValueBase + { + typedef InputValueBase Base; UsingComputationNodeMembersBoilerplate; + static const std::wstring TypeName() { return L"InputValue"; } + public: + InputValue(DEVICEID_TYPE deviceId, const wstring & name) : + Base(deviceId, name, false) + { } + InputValue(DEVICEID_TYPE deviceId, const wstring & name, size_t rows) : + Base(deviceId, name, rows, false) + { } + InputValue(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & sampleLayout) : + Base(deviceId, name, sampleLayout, false) + { } + InputValue(const ScriptableObjects::IConfigRecordPtr configp) : + Base(configp, false) + { } + }; + + template class InputValue; + template class InputValue; + + // ----------------------------------------------------------------------- + // SparseInputValue (/*no input*/) + // a sparse input value (typically fed by a DataReader) + // this covers two types: (regular vs. image) + // ----------------------------------------------------------------------- + + template + class SparseInputValue : public InputValueBase + { + typedef InputValueBase Base; UsingComputationNodeMembersBoilerplate; + static const std::wstring TypeName() { return L"SparseInputValue"; } + public: + SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name) : + Base(deviceId, name, true) + { } + SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name, size_t rows) : + Base(deviceId, name, rows, true) + { } + SparseInputValue(DEVICEID_TYPE deviceId, const wstring & name, const TensorShape & imageLayout) : + Base(deviceId, name, imageLayout, true) + { } + SparseInputValue(const ScriptableObjects::IConfigRecordPtr configp) : + Base(configp, true) + { } + }; + + template class SparseInputValue; + template class SparseInputValue; + + // ----------------------------------------------------------------------- + // LookupTableNode (embedding matrix, bag-of-word representation of the inputs) + // implements an embedding, assuming a specific representation of the input data + // ----------------------------------------------------------------------- + + template + class LookupTableNode : public ComputationNode, public NumInputs<2> + { + typedef ComputationNode Base; UsingComputationNodeMembersBoilerplate; + static const std::wstring TypeName() { return L"LookupTable"; } + public: + DeclareConstructorFromConfigWithNumInputs(LookupTableNode); + LookupTableNode(DEVICEID_TYPE deviceId, const wstring & name) : + Base(deviceId, name) + { } + + virtual void /*ComputationNode::*/BackpropTo(const size_t inputIndex, const FrameRange & t) override + { + if (inputIndex == 0) // left derivative (embedding matrix) + { + // This is a reduction operation, hence we need to mask out gaps. + Matrix sliceInput1Value = Input(1)->MaskedValueFor(t); + Matrix sliceOutputGrad = MaskedGradientFor(t); + + BackpropToLeft(sliceInput1Value, Input(0)->Gradient(), sliceOutputGrad); + } + else if (inputIndex == 1) // right derivative (input) + { + Matrix sliceInput1Grad = Input(1)->GradientFor(t); + Matrix sliceOutputGrad = GradientFor(t); + + BackpropToRight(Input(0)->Value(), sliceInput1Grad, sliceOutputGrad); + } + } + + /*TODO: merge with call site*/void BackpropToLeft(Matrix& inputFunctionValues, Matrix& inputGradientValues, Matrix& gradientValues) + { + size_t rows1 = inputFunctionValues.GetNumRows(), cols1 = inputFunctionValues.GetNumCols(); + size_t rowsp = gradientValues.GetNumRows(), colsp = gradientValues.GetNumCols(); + int wordsInEachSample = rows1 / inputGradientValues.GetNumCols(); + + inputFunctionValues.Reshape(rows1 / wordsInEachSample, cols1 * wordsInEachSample); + gradientValues.Reshape(rowsp / wordsInEachSample, colsp * wordsInEachSample); + + Matrix::MultiplyAndAdd(gradientValues, false, inputFunctionValues, true, inputGradientValues); + + inputFunctionValues.Reshape(rows1, cols1); + gradientValues.Reshape(rowsp, colsp); + } + + /*TODO: merge with call site*/void BackpropToRight(Matrix& inputFunctionValues, Matrix& inputGradientValues, Matrix& gradientValues) + { + size_t rows1 =inputGradientValues.GetNumRows(), cols1 = inputGradientValues.GetNumCols(); + size_t rowsp = gradientValues.GetNumRows(), colsp = gradientValues.GetNumCols(); + int wordsInEachSample = rows1 / inputFunctionValues.GetNumCols(); + + inputGradientValues.Reshape(rows1 / wordsInEachSample, cols1 * wordsInEachSample); + gradientValues.Reshape(rowsp / wordsInEachSample, colsp * wordsInEachSample); + + Matrix::MultiplyAndAdd(inputFunctionValues, true, gradientValues, false, inputGradientValues); + + inputGradientValues.Reshape(rows1, cols1); + gradientValues.Reshape(rowsp, colsp); + } + + virtual void /*ComputationNode::*/ForwardProp(const FrameRange & t) override + { + // input0 is the weight (each column is an embedding of one word), input 1 contains m_bnrLooked words in each column (sample) + Matrix functionValues = ValueFor(t); + const Matrix& input0 = Input(0)->Value(); + Matrix input1 = Input(1)->ValueFor(t); + + size_t rows1 = input1.GetNumRows(), cols1 = input1.GetNumCols(); + size_t cols0 = input0.GetNumCols(); + + if (rows1 % cols0 != 0) + LogicError("LookupTableNode: rows of input 1 and cols of input 0 are not modular. e.g., rows1 = 0.9 cols and this is not allowed. Check feature reader and network definition. This usually happens when the feature dimension is not specified as that in the network definition of look-up-table dimension size."); + + int wordsInEachSample = rows1 / cols0; + + auto input1Reshaped = input1.Reshaped(rows1 / wordsInEachSample, cols1 * wordsInEachSample); + + auto functionValuesReshaped = functionValues.Reshaped(input0.GetNumRows(), input1Reshaped.GetNumCols()); + functionValuesReshaped.AssignProductOf(input0, false, input1Reshaped, false); + } + + virtual void /*ComputationNodeBase::*/Validate(bool isFinalValidationPass) override + { Base::Validate(isFinalValidationPass); - InferMBLayoutFromInputsForStandardCase(); - - if (isFinalValidationPass && Input(1)->GetNumRows() % Input(0)->GetNumCols() != 0) - InvalidArgument("Mismatched dimension. Rows in input1 must be multiples of cols in input0."); - - int wordsInEachSample = Input(1)->GetNumRows() / Input(0)->GetNumCols(); - - // TODO: Should this add a tensor dimension? - SetDims(TensorShape(Input(0)->GetNumRows() * wordsInEachSample), Input(1)->GetNumCols()); - } - - bool UnitTest() - { - try - { - size_t nInput = 2; - size_t nHidden = 3; - size_t nOutput = 3; - - Input(0)->SetDims1(nInput, nHidden); - Input(0)->UpdateFunctionValuesSize(); - Input(0)->Value().SetValue(1.0); - Input(1)->Value().TransferFromDeviceToDevice(m_deviceId, CPUDEVICE, true); - Input(1)->Value().SwitchToMatrixType(DENSE, matrixFormatDense, false); - Input(1)->SetDims1(nHidden, nOutput); - Input(1)->UpdateFunctionValuesSize(); - Input(1)->Value().SetValue(0.0); - Input(1)->Value().SetValue(0, 0, 1.0); - Input(1)->Value().SetValue(1, 1, 2.0); - Input(1)->Value().TransferFromDeviceToDevice(CPUDEVICE, m_deviceId, true); - Input(1)->Value().SwitchToMatrixType(SPARSE, matrixFormatSparseCSC, true); - SetDims1(nInput, nOutput); - UpdateFunctionValuesSize(); - - ForwardProp(FrameRange(m_pMBLayout)); - - /// check with expected values - Value().TransferFromDeviceToDevice(m_deviceId, CPUDEVICE, true); - if (!ISCLOSE(Value()(0, 0), 1.0, EPSILON) || - !ISCLOSE(Value()(0, 1), 2.0, EPSILON) || - !ISCLOSE(Value()(1, 1), 2.0, EPSILON) ) - throw("LSTMNode forward computation error"); - - Value().TransferToDeviceIfNotThere( m_deviceId, true); - - Gradient().Resize(nInput, nOutput); - Gradient().SetValue(1.0); - for (size_t i = 0; i < 2; i++) - { - Input(i)->Gradient().Resize(Input(i)->GetNumRows(), Input(i)->GetNumCols()); - Input(i)->Gradient().SetValue(0); - } - for (size_t i = 0; i < 2; i++) - BackpropTo(i, FrameRange(m_pMBLayout)); - - // check with expected values - if (!ISCLOSE(Input(1)->Gradient()(0, 0), 2, EPSILON) /// bi - || !ISCLOSE(Input(1)->Gradient()(0, 1), 2, EPSILON) // Wxi - || !ISCLOSE(Input(1)->Gradient()(1, 0), 2, EPSILON) // Whi - || !ISCLOSE(Input(1)->Gradient()(2, 1), 2, EPSILON) // Wci - ) - throw("LSTMNode gradient error on input gates"); - - for (size_t i = 0; i < 2; i++) - Input(i)->Gradient().TransferToDeviceIfNotThere(m_deviceId, true); - } - catch (...) - { - fprintf(stderr, "LookupTableNode unit test is not passed!"); - return false; - } - - fprintf(stderr, "LookupTableNode unit test passed!\n"); - return true; - } - }; - - template class LookupTableNode; - template class LookupTableNode; - -}}} + InferMBLayoutFromInputsForStandardCase(); + + if (isFinalValidationPass && Input(1)->GetNumRows() % Input(0)->GetNumCols() != 0) + InvalidArgument("Mismatched dimension. Rows in input1 must be multiples of cols in input0."); + + int wordsInEachSample = Input(1)->GetNumRows() / Input(0)->GetNumCols(); + + // TODO: Should this add a tensor dimension? + SetDims(TensorShape(Input(0)->GetNumRows() * wordsInEachSample), Input(1)->GetNumCols()); + } + + bool UnitTest() + { + try + { + size_t nInput = 2; + size_t nHidden = 3; + size_t nOutput = 3; + + Input(0)->SetDims1(nInput, nHidden); + Input(0)->UpdateFunctionValuesSize(); + Input(0)->Value().SetValue(1.0); + Input(1)->Value().TransferFromDeviceToDevice(m_deviceId, CPUDEVICE, true); + Input(1)->Value().SwitchToMatrixType(DENSE, matrixFormatDense, false); + Input(1)->SetDims1(nHidden, nOutput); + Input(1)->UpdateFunctionValuesSize(); + Input(1)->Value().SetValue(0.0); + Input(1)->Value().SetValue(0, 0, 1.0); + Input(1)->Value().SetValue(1, 1, 2.0); + Input(1)->Value().TransferFromDeviceToDevice(CPUDEVICE, m_deviceId, true); + Input(1)->Value().SwitchToMatrixType(SPARSE, matrixFormatSparseCSC, true); + SetDims1(nInput, nOutput); + UpdateFunctionValuesSize(); + + ForwardProp(FrameRange(m_pMBLayout)); + + /// check with expected values + Value().TransferFromDeviceToDevice(m_deviceId, CPUDEVICE, true); + if (!ISCLOSE(Value()(0, 0), 1.0, EPSILON) || + !ISCLOSE(Value()(0, 1), 2.0, EPSILON) || + !ISCLOSE(Value()(1, 1), 2.0, EPSILON) ) + throw("LSTMNode forward computation error"); + + Value().TransferToDeviceIfNotThere( m_deviceId, true); + + Gradient().Resize(nInput, nOutput); + Gradient().SetValue(1.0); + for (size_t i = 0; i < 2; i++) + { + Input(i)->Gradient().Resize(Input(i)->GetNumRows(), Input(i)->GetNumCols()); + Input(i)->Gradient().SetValue(0); + } + for (size_t i = 0; i < 2; i++) + BackpropTo(i, FrameRange(m_pMBLayout)); + + // check with expected values + if (!ISCLOSE(Input(1)->Gradient()(0, 0), 2, EPSILON) /// bi + || !ISCLOSE(Input(1)->Gradient()(0, 1), 2, EPSILON) // Wxi + || !ISCLOSE(Input(1)->Gradient()(1, 0), 2, EPSILON) // Whi + || !ISCLOSE(Input(1)->Gradient()(2, 1), 2, EPSILON) // Wci + ) + throw("LSTMNode gradient error on input gates"); + + for (size_t i = 0; i < 2; i++) + Input(i)->Gradient().TransferToDeviceIfNotThere(m_deviceId, true); + } + catch (...) + { + fprintf(stderr, "LookupTableNode unit test is not passed!"); + return false; + } + + fprintf(stderr, "LookupTableNode unit test passed!\n"); + return true; + } + }; + + template class LookupTableNode; + template class LookupTableNode; + +}}} diff --git a/Source/EvalDll/EvalDll.vcxproj b/Source/EvalDll/EvalDll.vcxproj index a535ca3ff1c2..19740f22d0f1 100644 --- a/Source/EvalDll/EvalDll.vcxproj +++ b/Source/EvalDll/EvalDll.vcxproj @@ -1,159 +1,159 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {482999D1-B7E2-466E-9F8D-2119F93EAFD9} - - - - - - - - - Win32Proj - EvalDll - EvalDll - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\SGDLib;..\ComputationNetworkLib;..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) - ..\ComputationNetworkLib;..\Math;C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(CUDA_PATH)\lib\$(Platform);$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(Platform) - $(Platform)\$(Configuration)\$(ProjectName)\ - EvalDll - - - false - ..\SGDLib;..\ComputationNetworkLib;..\SequenceTrainingLib;..\Math;..\Common\Include;..\CNTK\BrainScript;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) - ..\ComputationNetworkLib;..\Math;C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(CUDA_PATH)\lib\$(Platform);$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(Platform) - $(Platform)\$(Configuration)\$(ProjectName)\ - EvalDll - - - - NotUsing - Level4 - Disabled - EVALDLL;WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\Common\include;..\Math\Math;"c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - true - /bigobj %(AdditionalOptions) - - - Console - true - ComputationNetworkLib.lib; 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nvml.dll; cudart64_70.dll + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + + + + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + NotUsing + + + + false + + + + + Create + + + + + + \ No newline at end of file diff --git a/Source/EvalDll/EvalDll.vcxproj.filters b/Source/EvalDll/EvalDll.vcxproj.filters index 972bb977ea2a..180a1e338de9 100644 --- a/Source/EvalDll/EvalDll.vcxproj.filters +++ b/Source/EvalDll/EvalDll.vcxproj.filters @@ -1,73 +1,73 @@ - - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - Misc - - - Misc - - - Common - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Misc - - - Misc - - - Common\Include - - - - - {bed53b47-70b1-494c-824d-0748362003b2} - - - {f3bf0104-8a08-40c9-a4d9-af8411c49669} - - - {3660ead9-4e83-4246-8f76-dd1fda8e2590} - - + + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + Misc + + + Misc + + + Common + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Misc + + + Misc + + + Common\Include + + + + + {bed53b47-70b1-494c-824d-0748362003b2} + + + {f3bf0104-8a08-40c9-a4d9-af8411c49669} + + + {3660ead9-4e83-4246-8f76-dd1fda8e2590} + + \ No newline at end of file diff --git a/Source/Math/CPUMatrix.cpp b/Source/Math/CPUMatrix.cpp index 6e57bb1d377c..4275f8c2c9d1 100644 --- a/Source/Math/CPUMatrix.cpp +++ b/Source/Math/CPUMatrix.cpp @@ -1,5816 +1,5816 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// CPUMatrix.cpp : full implementation of all matrix functions on the CPU side -// - -#include "stdafx.h" -#include "Basics.h" -#include "File.h" - -#include "CPUMatrix.h" -#include "TensorOps.h" -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#ifdef _WIN32 -#include -#else -#ifndef max -#define max(a,b) (((a) > (b)) ? (a) : (b)) -#endif -#include -#endif - -#ifdef LEAKDETECT -#include -#endif - -#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this -#pragma warning (disable: 4702) // unreachable code; triggered for unknown reasons - -#ifndef USE_MKL -// use ACML as default. -// Download ACML 5.3.1 (e.g., acml5.3.1-ifort64.exe) or above -// from http://developer.amd.com/tools/cpu-development/amd-core-math-library-acml/acml-downloads-resources/ -// Install the ifort64_mp variant (compiled with intel compiler) of the library -// Set Environment variable ACML_PATH to C:\AMD\acml5.3.1\ifort64_mp or the folder you installed acml -// to point to your folder for the include file and link library -#include // requires ACML 5.3.1 and above -#else -// requires MKL 10.0 and above -#include -#endif - -#ifndef USE_MKL //MKL has one additional parameter for different matrix order -#define BLAS_COLMAJOR -#else -#define BLAS_COLMAJOR (int)MatrixOrder::ColMajor, -#endif - -#define SWAP(a,b) {(a) ^= (b); (b) ^= (a); (a) ^= (b);} -#define IDX2C(i,j,ld) (((j)*(ld))+(i)) // 0 based indexing -namespace Microsoft { namespace MSR { namespace CNTK { - -#pragma region Helpful Enum Definitions - enum class MatrixOrder - { - RowMajor = 101, // row-major arrays - ColMajor = 102 // column-major arrays - }; - - enum class MatrixTranspose : char - { - NoTrans = 'N', // trans='N' - Trans = 'T', // trans='T' - ConjTrans = 'C' // trans='C' - }; - - enum class SymMatrixType : char - { - Up = 'U', // symmetric matrix is stored in the upper part - Low = 'L', // symmetric matrix is stored in thelower part - Full = 'F', //full populated - NotSymmetric = 'N' //not a symmetric matrix - }; - - enum class MatrixOpSide : char - { - Left = 'L', // left multiply - Right = 'R', // right multiply - }; -#pragma endregion Helpful Enum Definitions - -#pragma region Constructors and Destructor - - //should only be used by constructors. - template - void CPUMatrix::ZeroInit() - { - m_computeDevice = CPUDEVICE; - m_pArray = nullptr; - m_numRows = 0; - m_numCols = 0; - m_elemSizeAllocated = 0; - m_matrixName=NULL; - m_format = matrixFormatDense; - m_externalBuffer = false; - } - - template - CPUMatrix::CPUMatrix() - { - ZeroInit(); - } - - //matrixName is used to verify that correct matrix is read. - template - CPUMatrix::CPUMatrix(FILE* f, const char * matrixName) - { - ZeroInit(); - ReadFromFile(f, matrixName); - } - - // helper to allocate an array of ElemType - // Use this instead of new[] to get NaN initialization for debugging. - template - static ElemType * NewArray(size_t n) - { - ElemType * p = new ElemType[n](); -#if 0//_DEBUG - ElemType nan = Matrix::MakeNan(__LINE__); - for (size_t i = 0; i < n; i++) - p[i] = nan; -#endif - return p; - } - - template - CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols) - { - ZeroInit(); - - m_numRows = numRows; - m_numCols = numCols; - m_elemSizeAllocated = GetNumElements(); - - if (m_elemSizeAllocated != 0) - m_pArray = NewArray(m_elemSizeAllocated); - } - - template - CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags) - { - ZeroInit(); - SetValue(numRows, numCols, pArray, matrixFlags); - } - - //copy constructor, deep copy - template - CPUMatrix::CPUMatrix(const CPUMatrix& deepCopyFrom) - { - ZeroInit(); - if (!deepCopyFrom.IsEmpty()) - SetValue(deepCopyFrom); - SetMatrixName(deepCopyFrom.m_matrixName); - } - - //assignment operator, deep copy - template - CPUMatrix& CPUMatrix::operator=(const CPUMatrix& deepCopyFrom) - { - Clear(); - if (!deepCopyFrom.IsEmpty()) - SetValue(deepCopyFrom); - SetMatrixName(deepCopyFrom.m_matrixName); - return *this; - } - - - //move constructor, shallow copy - template - CPUMatrix::CPUMatrix(CPUMatrix&& moveFrom) - { - m_computeDevice = moveFrom.m_computeDevice; - m_numRows = moveFrom.m_numRows; - m_numCols = moveFrom.m_numCols; - m_elemSizeAllocated = moveFrom.m_elemSizeAllocated; - m_pArray = moveFrom.m_pArray; //shallow copy the pointer - m_matrixName = moveFrom.m_matrixName; - m_format = moveFrom.m_format; - m_externalBuffer = moveFrom.m_externalBuffer; - //release the pointer from the source object so that the destructor won't release it twice - moveFrom.ZeroInit(); - } - - //move assignment operator, shallow copy - template - CPUMatrix& CPUMatrix::operator=(CPUMatrix&& moveFrom) - { - if (this != &moveFrom) - { - if (OwnBuffer() && m_pArray != nullptr) - delete[] m_pArray; //always delete the data pointer since we will use the pointer from moveFrom - - m_computeDevice = moveFrom.m_computeDevice; - m_numRows = moveFrom.m_numRows; - m_numCols = moveFrom.m_numCols; - m_elemSizeAllocated = moveFrom.m_elemSizeAllocated; - m_pArray = moveFrom.m_pArray; - m_format = moveFrom.m_format; - m_externalBuffer = moveFrom.m_externalBuffer; - - //release the pointer from the source object so that the destructor won't release it twice - moveFrom.ZeroInit(); - } - return *this; - } - - template - CPUMatrix::~CPUMatrix() - { - Clear(); - } - - template - void CPUMatrix::Clear() - { - if (m_pArray!=nullptr && OwnBuffer()) - { - delete [] m_pArray; - m_pArray = nullptr; - m_elemSizeAllocated = 0; - } - BaseMatrix::Clear(); - - ZeroInit(); - } - -#pragma endregion Constructors and Destructor - -#pragma region Basic Operators - - template - CPUMatrix CPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const - { - //if (numCols == 0) - // LogicError("The slice cannot have 0 columns."); - - if (startColumn + numCols > m_numCols) - InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int)startColumn, (int)numCols, (int)m_numCols); - - CPUMatrix slice; - - slice.m_externalBuffer = true; //memory of a slice is managed externally. - slice.m_numRows = m_numRows; - slice.m_numCols = numCols; - slice.m_elemSizeAllocated = slice.GetNumElements(); - slice.m_pArray = m_pArray + startColumn * m_numRows; - slice.m_format = m_format; - - return slice; - } - - // set this(:, 0:numCols-1) = fromMatrix(:, startColumn : startColumn+numCols-1) - // TODO: why not say *this = ColumnSlice()? - template - CPUMatrix& CPUMatrix::AssignColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols) - { - //if (numCols == 0) - // LogicError("The slice cannot have 0 columns."); - - if (startColumn + numCols > fromMatrix.m_numCols) - InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int)startColumn, (int)numCols, (int)fromMatrix.m_numCols); - - Clear(); - - SetOwnBuffer(false); //memory of a slice is managed externally. - m_numRows = fromMatrix.m_numRows; - m_numCols = numCols; - m_elemSizeAllocated = GetNumElements(); - m_pArray = fromMatrix.m_pArray + startColumn *m_numRows; - - return *this; - } - - // set this(: , startColumn:startColumn+numCols-1)= fromMatrix; - template - CPUMatrix& CPUMatrix::SetColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols) - { - //if (numCols == 0) - // LogicError("The slice cannot have 0 columns."); - if (startColumn + numCols > m_numCols) - LogicError("The slice is out of range of the destination matrix."); - if (numCols > fromMatrix.GetNumCols()) - InvalidArgument("The slice (%d) is out of range of the source matrix (%d).", (int)numCols, (int)fromMatrix.GetNumCols()); - if (m_numRows != fromMatrix.m_numRows) - LogicError("The number of rows in source and destination matrices do not match"); - - //SetOwnBuffer(false); - memcpy(m_pArray + startColumn*m_numRows, fromMatrix.m_pArray, numCols*m_numRows*sizeof(ElemType)); - - return *this; - } - - template - void CPUMatrix::CopyColumnsStrided(const CPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) - { - if ((((numCols - 1) * srcNumColsStride) + 1) > fromMatrix.m_numCols) - LogicError("The numCols to copy and srcNumColsStride specified is out of range of the source matrix."); - if ((((numCols - 1) * destNumColsStride) + 1) > m_numCols) - LogicError("The numCols to copy and srcNumColsStride specified is out of range of the destination matrix."); - if (m_numRows != fromMatrix.m_numRows) - LogicError("The number of rows in source and destination matrices do not match"); - - long n = (long)numCols, m = (long)m_numRows; - - auto& us = *this; - -#pragma omp parallel for - for (long j = 0; j - CPUMatrix& CPUMatrix::AssignToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) - { - if (a.GetNumRows() != numRows) - LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows."); - - if (startIndex + numRows > GetNumRows()) - LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows()."); - - if (a.GetNumCols() != GetNumCols()) - LogicError("AddToRowSliceValuesOf: columns does not match."); - - long n = (long)a.GetNumCols(), m = (long)numRows; - - auto& us = *this; - -#pragma omp parallel for - for (long j = 0; j - CPUMatrix& CPUMatrix::AssignRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) - { - if (startIndex + numRows > a.GetNumRows()) - LogicError("AssignRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows()."); - - Resize(numRows, a.GetNumCols()); - - long n = (long)a.GetNumCols(); // note: OpenMP requires loop indices to be long, not size_t - long k = (long)a.GetNumRows(); - -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::AddToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) - { - if (a.IsEmpty()) - LogicError("AddToRowSliceValuesOf: input matrix a is empty."); - - if (a.GetNumRows() != numRows) - LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows."); - - if (startIndex + numRows > GetNumRows()) - LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows()."); - - if (a.GetNumCols() != GetNumCols()) - LogicError("AddToRowSliceValuesOf: columns does not match."); - - long n=(long)a.GetNumCols(), m=(long)numRows; - - auto& us = *this; - -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::AddWithRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) - { - if (a.IsEmpty()) - LogicError("AddWithRowSliceValuesOf: input matrix a is empty."); - - if (GetNumRows() != numRows) - LogicError("AddWithRowSliceValuesOf: GetNumRows() != numRows."); - - if (startIndex + numRows > a.GetNumRows()) - LogicError("AddWithRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows()."); - - if (a.GetNumCols() != GetNumCols()) - LogicError("AddWithRowSliceValuesOf: columns does not match."); - - long n = (long)a.GetNumCols(), m = (long)numRows; - - auto& us = *this; - -#pragma omp parallel for - for (long j = 0; j - CPUMatrix CPUMatrix::Diagonal() const - { - if (m_numRows != m_numCols) - LogicError("Diagonal can be called only for square matrix. (rows=%d, cols=%d)", (int)m_numRows, (int)m_numCols); - - CPUMatrix diag(1, m_numCols); - - auto& us = *this; - -#pragma omp parallel for - for (long i = 0; i < m_numRows; i++) - { - diag(0, (size_t)i) = us(i, i); - } - - return diag; - } - -#if 0 - //stack the columns in inputMatrices (starting from sliceStartCol for sliceNumCols columns) and assign it to [this] object. - template - CPUMatrix& CPUMatrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) - { - if (sliceNumCols == 0) - LogicError("AssignRowStackValuesOf: sliceNumCols should > 0."); - - size_t totalRows = 0; - size_t* startRowIndeces = new size_t[inputMatrices.size()]; - startRowIndeces[0] = 0; - for (int i = 0; i < inputMatrices.size(); i++) - { - const CPUMatrix& a = *inputMatrices[i]; - if (a.IsEmpty()) - LogicError("AssignRowStackValuesOf: input matrix (%d) is empty.", i); - - if (a.GetNumCols() < sliceStartCol + sliceNumCols) - LogicError("AssignRowStackValuesOf: input matrix (%d) GetNumCols() < sliceStartCol + sliceNumCols.", i); - - totalRows += a.GetNumRows(); - if (iGetNumRows() * sizeof(ElemType)); - } - } - - delete [] startRowIndeces; - - return *this; - } -#endif - - template - void CPUMatrix::MinusOneAt(CPUMatrix& c, const size_t position) - { - if (position < c.GetNumElements()) - c.m_pArray[position] -= 1.0; - else - RuntimeError("MinusOneAt: position is out of CPU matrix size"); - } - - template - CPUMatrix& CPUMatrix::AssignRepeatOf(const CPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats) - { - if (this == &a) - LogicError("AssignRepeatOf: a is the same as [this]. Does not support inplace repeat."); - - if (a.IsEmpty()) - LogicError("AssignRepeatOf: Matrix a is empty."); - - Resize(a.GetNumRows() * numRowRepeats, a.GetNumCols() * numColRepeats); - long n = (long)a.GetNumCols(), m = (long)a.GetNumRows(); - auto& us = *this; - -#pragma omp parallel for - for (long q = 0; q < numColRepeats; q++) - { - for (long p = 0; p < numRowRepeats; p++) - { - long colOffset = q*n; - - for (long j = 0; j < n; j++, colOffset++) - { - long rowOffset = p*m; - - //four-way unrolling - for (long i = 0; i < (m & ~3); i += 4, rowOffset += 4) - { - us(rowOffset, colOffset) = a(i, j); - us(rowOffset + 1, colOffset) = a(i + 1, j); - us(rowOffset + 2, colOffset) = a(i + 2, j); - us(rowOffset + 3, colOffset) = a(i + 3, j); - } - //handle remaining stuffs - for (long i = m & ~3; i < m; i++, rowOffset++) - { - us(rowOffset, colOffset) = a(i, j); - } - } - } - } - - return *this; - } - - template - CPUMatrix& CPUMatrix::AddToRowRepeatValuesOf(const CPUMatrix& a, const size_t numRepeats) - { - if (a.IsEmpty()) - LogicError("AddToRowRepeatValuesOf: input matrix a is empty."); - - if (a.GetNumRows() != GetNumRows() * numRepeats) - LogicError("AddToRowRepeatValuesOf: a.GetNumRows() != GetNumRows() * numRepeats."); - - long n = (long)a.GetNumCols(), m = (long)GetNumRows(); - - auto& us = *this; - -#pragma omp parallel for - for (long j = 0; j - CPUMatrix& CPUMatrix::AssignPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) - { - a; posNumber; negNumber; shiftNumber; - NOT_IMPLEMENTED; - } - - template - CPUMatrix& CPUMatrix::AddFoldedPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) - { - a; posNumber; negNumber; shiftNumber; - NOT_IMPLEMENTED; - } - - template - CPUMatrix CPUMatrix::Transpose() - { - if (IsEmpty()) - LogicError("Transpose: Matrix is empty."); - - CPUMatrix c; - c.AssignTransposeOf(*this); - return c; - } - - template - CPUMatrix& CPUMatrix::AssignTransposeOf (const CPUMatrix& a) - { - if (this == &a) - LogicError("AssignTransposeOf: a is the same as [this]. Does not support inplace transpose."); - - if (a.IsEmpty()) - LogicError("AssignTransposeOf: Matrix a is empty."); - - Resize(a.GetNumCols(), a.GetNumRows()); - long n=(long)a.GetNumCols(), m=(long)a.GetNumRows(); - - auto& us = *this; - -#pragma omp parallel for - for (long j=0; j - void CPUMatrix::SetValue(const ElemType v) - { - if (IsEmpty()) - LogicError("SetValue: Matrix is empty."); - bool isFinite = std::numeric_limits::is_integer || std::isfinite((double)v); - if (isFinite && v == 0) - { - memset(m_pArray, 0, sizeof(ElemType) * GetNumElements()); - } - else - { - long m=(long)GetNumElements(); - // 2-way thread parallelism is sufficient for the memory bound - // operation of just setting the values of an array. - const unsigned SETVALUE_NUM_THREADS = 2; -#pragma omp parallel for num_threads(SETVALUE_NUM_THREADS) - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - m_pArray[i] = v; - m_pArray[i+1] = v; - m_pArray[i+2] = v; - m_pArray[i+3] = v; - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::MaskColumnsValue(const CPUMatrix& columnsMask, ElemType val) - { - if (GetNumCols() != columnsMask.GetNumCols()) - RuntimeError("Matrix and column mask must have equal number of columns"); - - auto& us = *this; - long n = (long)GetNumCols(), m = (long)GetNumRows(); -#pragma omp parallel for - for (long j = 0; j - void CPUMatrix::SetColumn(const ElemType* colPointer, size_t j) - { - if (IsEmpty()) - LogicError("SetColumn: Matrix is empty."); - if (colPointer==NULL) - return; - - auto& us = *this; - long m=(long)GetNumRows(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - us(i,j) = colPointer[i]; - us(i+1,j) = colPointer[i+1]; - us(i+2,j) = colPointer[i+2]; - us(i+3,j) = colPointer[i+3]; - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetColumn(const ElemType val, size_t j) - { - if (IsEmpty()) - LogicError("SetColumn: Matrix is empty."); - - auto& us = *this; - long m=(long)GetNumRows(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - us(i,j) = val; - us(i+1,j) = val; - us(i+2,j) = val; - us(i+3,j) = val; - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetColumn(const CPUMatrix& valMat, size_t j) - { - if (IsEmpty()) - LogicError("SetColumn: Matrix is empty."); - assert(valMat.GetNumRows() == GetNumRows() && valMat.GetNumCols() == 1) ; - - auto& us = *this; - long m=(long)GetNumRows(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - us(i,j) = valMat(i,0); - us(i+1,j) = valMat(i+1,0); - us(i+2,j) = valMat(i+2,0); - us(i+3,j) = valMat(i+3,0); - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetValue(const CPUMatrix& deepCopyFrom) - { - if (this == &deepCopyFrom) - return; - - Resize(deepCopyFrom.GetNumRows(), deepCopyFrom.GetNumCols()); - memcpy(m_pArray, deepCopyFrom.m_pArray, deepCopyFrom.GetNumElements() * sizeof(ElemType)); - } - - template - void CPUMatrix::SetValue(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags) - { - if (pArray == nullptr) - InvalidArgument("Invalid pArray."); - - m_format = matrixFormatDense; - m_computeDevice = CPUDEVICE; - - // if it's externally managed, then populate the structure - if (matrixFlags&matrixFlagDontOwnBuffer) - { - // free previous array allocation if any before overwriting - if (m_pArray != nullptr) - delete [] m_pArray; - - m_pArray = pArray; - m_numRows = numRows; - m_numCols = numCols; - m_elemSizeAllocated = GetNumElements(); - m_externalBuffer = true; - } - else - { - Resize(numRows, numCols); - - if (IsEmpty()) - { - InvalidArgument("NumRows or NumCols is 0. Nothing to copy"); - } - else - { - if (!(matrixFlags&matrixFormatRowMajor)) //compatible to internal structure - { - memcpy(m_pArray, pArray, GetNumElements()*sizeof(ElemType)); - } - else //need to transpose - { - auto& us = *this; - if (sizeof(ElemType) == sizeof(double)) - { - #pragma omp parallel for - foreach_column(j, us) - { - #ifndef USE_MKL - dcopy((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); - #else - cblas_dcopy ((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); - #endif - } - } - else - { - #pragma omp parallel for - foreach_column(j, us) - { - { - #pragma warning (suppress: 4244) - #ifndef USE_MKL - scopy((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); - #else - cblas_scopy ((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); - #endif - } - } - } - } - } - } - } - - template - void CPUMatrix::SetDiagonalValue(const ElemType v) - { - if (IsEmpty()) - LogicError("SetDiagonalValue: Matrix is empty."); - - if (GetNumRows() != GetNumCols()) - LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); - - auto& us = *this; - long m=(long)GetNumRows(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - us(i,i) = v; - us(i+1,i+1) = v; - us(i+2,i+2) = v; - us(i+3,i+3) = v; - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetDiagonalValue(const CPUMatrix& vector) - { - if (IsEmpty() || vector.IsEmpty()) - LogicError("SetDiagonalValue: Matrix is empty."); - - if (GetNumRows() != GetNumCols()) - LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); - - if (vector.GetNumRows() != 1 && vector.GetNumCols() != 1) - LogicError("SetDiagonalValue: input vector must be a vector."); - - if (vector.GetNumElements() == 1) //reduce to simple form - SetDiagonalValue(vector(0,0)); - else if (vector.GetNumRows() != GetNumRows()) - LogicError("SetDiagonalValue: input vector's dimension does not agree with [this]."); - else - { - auto& us = *this; - - long m=(long)GetNumRows(); - if (vector.GetNumRows() == 1) //row vector - { -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - us(i,i) = vector(0, i); - us(i+1,i+1) = vector(0, i+1); - us(i+2,i+2) = vector(0, i+2); - us(i+3,i+3) = vector(0, i+3); - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) - { - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - -#ifdef _MSC_VER // TODO: check if available under GCC/Linux - std::ranlux64_base_01 generator; - generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); -#else - std::default_random_engine generator (seed); -#endif - std::uniform_real_distribution r(low, high); - - long m=(long)GetNumElements(); - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - m_pArray[i] = r(generator); - m_pArray[i+1] = r(generator); - m_pArray[i+2] = r(generator); - m_pArray[i+3] = r(generator); - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) - { - if (sigma <= 0) - InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); - - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - auto& us = *this; -#ifdef _MSC_VER // TODO: check if available under GCC/Linux - std::ranlux64_base_01 generator; - generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); -#else - std::default_random_engine generator (seed); -#endif - std::normal_distribution r(mean, sigma); - //#pragma omp parallel for //is it thread safe? - foreach_coord(i,j,us) - { - us(i,j) = r(generator); - } - } - - template - void CPUMatrix::AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) - { - if (sigma <= 0) - InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); - - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - auto& us = *this; -#ifdef _MSC_VER // TODO: check if available under GCC/Linux - std::ranlux64_base_01 generator; - generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); -#else - std::default_random_engine generator (seed); -#endif - std::normal_distribution r(mean, sigma); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); - for (long j=0; j - void CPUMatrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) - { - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - auto& us = *this; -#ifdef _MSC_VER // TODO: check if available under GCC/Linux - std::ranlux64_base_01 generator; - generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); -#else - std::default_random_engine generator (seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); -#endif - std::uniform_real_distribution r(0, 1); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); - ElemType v; - for (long j=0; j - ElemType CPUMatrix::Adagrad(CPUMatrix& gradients, const bool needAveMultiplier) - { - ElemType aveMultiplier = 0; - - if (IsEmpty() || gradients.GetNumCols() != GetNumCols() || gradients.GetNumRows() != GetNumRows()) - { - Resize(gradients.GetNumRows(), gradients.GetNumCols()); - SetValue(0.0); - } - - assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == gradients.GetNumCols()); - - ElemType *a=m_pArray, *d_v=gradients.m_pArray; - size_t n = GetNumElements(); - - const ElemType floor = 1e-16f; - ElemType a0, a1, a2, a3; - - //disable omp here because aveMultiper needs to be added atomically. however, it seems the result is incorrect even if rmp atomic and amp critical are used. -//#pragma omp parallel for - for (long i = 0; i<(n & ~3); i += 4) //four-way unrolling - { - a[i] += d_v[i] * d_v[i]; - a[i+1] += d_v[i+1] * d_v[i+1]; - a[i+2] += d_v[i+2] * d_v[i+2]; - a[i+3] += d_v[i+3] * d_v[i+3]; - - a0 = sqrt(a[i] + floor); - a1 = sqrt(a[i + 1] + floor); - a2 = sqrt(a[i + 2] + floor); - a3 = sqrt(a[i + 3] + floor); - - d_v[i] /= a0; - d_v[i+1] /= a1; - d_v[i+2] /= a2; - d_v[i+3] /= a3; - - if (needAveMultiplier) - { - aveMultiplier += 1 / a0 + 1 / a1 + 1 / a2 + 1 / a3; - } - } - - // get the last few elements if any - for (long i = n & ~3; i 0) - return aveMultiplier / n; - else - return 1; - } - - template - void CPUMatrix::FSAdagrad(CPUMatrix& gradients, - CPUMatrix& functionValues, - ElemType learnRatePerSample, - ElemType momentum, - ElemType adaWeight, - ElemType adaMul) - { - size_t numColsNeeded = 2 * gradients.GetNumCols(); - - if (IsEmpty() || (GetNumCols() < numColsNeeded)) - { - Resize(gradients.GetNumRows(), numColsNeeded); - SetValue(0.0); - } - - assert((GetNumRows() == gradients.GetNumRows()) && (GetNumCols() == numColsNeeded)); - - size_t n = gradients.GetNumElements(); - ElemType* grad = gradients.m_pArray; - ElemType* smoothAda = m_pArray; - ElemType* smoothMom = m_pArray + n; - ElemType* val = functionValues.m_pArray; -#pragma omp parallel for - // TODO: Unroll 4-times for better performance leveraging vectorization - for (long i = 0; i < n; i++) - { - ElemType g = grad[i]; - ElemType adaSqr = adaWeight * smoothAda[i] + (1.0f - adaWeight) * g * g; - smoothAda[i] = adaSqr; - if (adaSqr != 0.0f) - { - ElemType ada = sqrt(adaSqr); - ElemType w = adaMul * ((ElemType)1.0 / ada); - - if (w > 10.0f) - w = 10.0f; - g *= w; - } - - if (momentum > 0.0f) - { - g = momentum * smoothMom[i] + (1.0f - momentum) * g; - smoothMom[i] = g; - } - - g *= learnRatePerSample; - val[i] -= g; - } - } - - template - ElemType CPUMatrix::RmsProp(CPUMatrix& gradients, - ElemType RMS_GAMMA, - ElemType RMS_WGT_INC, - ElemType RMS_WGT_MAX, - ElemType RMS_WGT_DEC, - ElemType RMS_WGT_MIN, - const bool needAveMultiplier - ) - { - const ElemType floor = 1e-6f; - - size_t n = gradients.GetNumElements(); - ElemType *curr_grad=gradients.m_pArray; - - if (IsEmpty() || GetNumCols() < gradients.GetNumCols() * 3) - { - Resize(gradients.GetNumRows(), gradients.GetNumCols() * 3); - SetValue(0.0); - - ElemType *avars=m_pArray; // accumulated variances for RMS scaling - ElemType *steps=m_pArray+2*n; // current step size - - // initialize moving average of gradient-squared - for( long i = 0; i < n; i++ ) - avars[i] = curr_grad[i]*curr_grad[i]; - - // initialize starting step size - for( long i = 0; i < n; i++ ) - steps[i] = ElemType(0.02); - } - - ElemType *avars=m_pArray; // accumulated variances for RMS scaling - ElemType *signs=m_pArray+n; // sign of previous gradient - ElemType *steps=m_pArray+2*n; // current step size - - assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == gradients.GetNumCols() * 3); - - ElemType ONE_MINUS_GAMMA = ElemType(1.0) - RMS_GAMMA; - //int upd[] = { - // 2,2,0, - // 2,2,0, - // 1,1,1, - // 2,2,0, - // 1,2,1, - // 0,2,2, - // 1,1,1, - // 0,2,2, - // 0,2,2, - //}; - - // for (long i=0; ineg, 1->zero, 2->pos - // const int grad_sign = 1 + (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); - - // // signs[i] contains three consecutive grad_sign - // signs[i] = 3*(int(signs[i]) % 9) + grad_sign; - - // switch(upd[int(signs[i])]) - // { - // case 0: - // steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); - // break; - // case 2: - // steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); - // break; - // } - // curr_grad[i] *= steps[i] / sqrt(avars[i] + floor); - // } - - ElemType aveMultiplier = 0, a; - for (long i=0; i 0 ) - steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); - else - steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); - - a = steps[i] / sqrt(avars[i] + floor); - curr_grad[i] *= a; - signs[i] = (ElemType)grad_sign; - - if (needAveMultiplier) - aveMultiplier += a; - } - - if (needAveMultiplier) - return aveMultiplier / n; - else - return 1; - } - - template - void CPUMatrix::Reshape(const size_t numRows, const size_t numCols) - { - assert (numRows*numCols == GetNumElements()); - if (numRows*numCols != GetNumElements()) - InvalidArgument("Reshape: Total number of elements does not match."); - - m_numRows = numRows; - m_numCols = numCols; - } - - // Resize() -- change matrix size - // This function is cheap if the matrix size does not change. - // Current content is not preserved. - // BUGBUG: There is code that relies on zero initialization (without, we get subtle variations of output). That is wrong--we should initialize to QNaN and see where it fails. - // If growOnly is true, resize will not reallocate memory if the current memory is large enough (i.e., will not shrink). - // If this object does not own its memory then new memory cannot be allocated (one can still shrink and/or reshape). - template - void CPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly /*=true*/) - { - if (m_numRows == numRows && m_numCols == numCols) - return; - - size_t numElements = numRows * numCols; - if (numElements > m_elemSizeAllocated || // grow allocation - (!growOnly && (numElements != m_elemSizeAllocated))) // shrink allocation (not if 'growOnly') - { - // reallocate buffer - ElemType * pArray = nullptr; - if (numElements > 0) - { - if (!OwnBuffer()) - LogicError("Resize: Resizing an matrix you don't own is not supported."); - pArray = NewArray(numElements); - } - // success: update the object - if (OwnBuffer()) - delete[] m_pArray; - else - assert(pArray == nullptr); // (if !OwnBuffer we can still resize to 0) - m_pArray = pArray; - m_elemSizeAllocated = numElements; - } - - // success - m_numRows = numRows; - m_numCols = numCols; - } - - // allocated by the callee but should be deleted by the caller - // TODO: change to use STL vector instead - template - ElemType* CPUMatrix::CopyToArray() const - { - size_t numElements = GetNumElements(); - if (numElements != 0) - { - ElemType* arrayCopyTo = NewArray(numElements); - memcpy(arrayCopyTo, m_pArray, sizeof(ElemType)*numElements); - return arrayCopyTo; - } - else - { - return nullptr; - } - } - - //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done - //return number of elements copied - template - size_t CPUMatrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const - { - size_t numElements = GetNumElements(); - - if (numElements > currentArraySize) - { - delete arrayCopyTo; - arrayCopyTo = NewArray(numElements); - currentArraySize = numElements; - } - - if (numElements != 0) - { - memcpy(arrayCopyTo, m_pArray, sizeof(ElemType)*numElements); - } - - return numElements; - } - - template - void CPUMatrix::CopySection(size_t /*numRows*/, size_t /*numCols*/, ElemType* /*dst*/, size_t /*colStride*/) const - { - // REVIEW alexeyk: currently not used by CPU, but implement when possible. - RuntimeError("Not implemented."); - } - - template - inline size_t CPUMatrix::LocateColumn(const size_t col) const - { - assert(col < m_numCols); - return col * m_numRows; // matrix in column-wise storage - } - - template - inline size_t CPUMatrix::LocateElement (const size_t row, const size_t col) const - { - assert (row < m_numRows); - return LocateColumn(col) + row; // matrix in column-wise storage - } - -#pragma endregion Basic Operators - -#pragma region Member BLAS Functions - - template - CPUMatrix& CPUMatrix::operator+= (ElemType alpha) - { - return AssignSumOf(alpha, *this); - } - - template - CPUMatrix CPUMatrix::operator+ (ElemType alpha) const - { - CPUMatrix c(GetNumRows(), GetNumCols()); - c.AssignSumOf(alpha, *this); - return c; - } - - template - CPUMatrix& CPUMatrix::AssignSumOf(const ElemType alpha, const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSumOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::operator+= (const CPUMatrix& a) - { - //if (a.GetNumElements() == 1) - // *this += a(0,0); - //else - ScaleAndAdd(1, a, *this); - - return *this; - } - - //if [this] and a have same dimension then OUTPUT=[this]+a - //if a is a column vector, add to all columns of [this] - //if a is a row vector, add to all rows of [this] - template - CPUMatrix CPUMatrix::operator+ (const CPUMatrix& a) const - { - if (GetNumElements() == 1) - { - CPUMatrix c(a); - c += (*this)(0,0); - return c; - } - else if (a.GetNumElements() == 1) - { - CPUMatrix c(*this); - c += a(0,0); - return c; - } - else - { - CPUMatrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code - c += a; - return c; - } - } - - template - CPUMatrix& CPUMatrix::AssignSumOf(const CPUMatrix& a, const CPUMatrix& b) - { - if (a.GetNumElements() == 1) - { - SetValue(b); - (*this) += a; - } - else - { - SetValue(a); - (*this) += b; - } - return *this; - } - - template - CPUMatrix& CPUMatrix::operator-= (ElemType alpha) - { - return AssignDifferenceOf(*this, alpha); - } - - template - CPUMatrix CPUMatrix::operator- (ElemType alpha) const - { - CPUMatrix c(GetNumRows(), GetNumCols()); - c.AssignDifferenceOf(*this, alpha); - return c; - } - - template - CPUMatrix& CPUMatrix::AssignDifferenceOf(const ElemType alpha, const CPUMatrix& a) - { - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::AssignDifferenceOf(const CPUMatrix& a, const ElemType alpha) - { - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::operator-= (const CPUMatrix& a) - { - ScaleAndAdd(-1, a, *this); - - return *this; - } - - //if [this] and a have same dimension then output=[this]-a - //if a is a column vector, minus it from all columns of [this] - //if a is a row vector, minus it from all rows of [this] - template - CPUMatrix CPUMatrix::operator- (const CPUMatrix& a) const - { - CPUMatrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code - c -= a; - return c; - } - - template - CPUMatrix& CPUMatrix::AssignDifferenceOf(const CPUMatrix& a, const CPUMatrix& b) - { - if (this != &a) - { - Resize(a.GetNumRows(), a.GetNumCols()); - SetValue(a); - } - (*this) -= b; - return *this; - } - - template - CPUMatrix& CPUMatrix::operator*= (ElemType alpha) - { - Scale(alpha, *this); - return *this; - } - - template - CPUMatrix CPUMatrix::operator* (ElemType alpha) const - { - CPUMatrix c(GetNumRows(), GetNumCols()); - Scale(alpha, *this, c); - return c; - } - - template - CPUMatrix& CPUMatrix::AssignProductOf(const ElemType alpha, const CPUMatrix& a) - { - Scale(alpha, a, *this); - return *this; - } - - // [this]=a*b - template - CPUMatrix& CPUMatrix::AssignProductOf (const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB) - { - if (a.GetNumElements() == 1) - { - if (transposeB) - AssignTransposeOf(b); - (*this) *= a(0,0); - } - else if (b.GetNumElements() == 1) - { - if (transposeA) - AssignTransposeOf(a); - (*this) *= b(0,0); - } - else - Multiply(a, transposeA, b, transposeB, *this); - - return *this; - } - - template - CPUMatrix CPUMatrix::operator* (const CPUMatrix& a) const - { - auto& us = *this; - if (GetNumElements() == 1) - { - CPUMatrix c; - c.AssignProductOf(us(0,0), a); - return c; - } - else if (a.GetNumElements() == 1) - { - CPUMatrix c; - c.AssignProductOf(a(0,0), us); - return c; - } - else - { - CPUMatrix c; - Multiply(*this, a, c); - return c; - } - } - - template - CPUMatrix& CPUMatrix::operator/= (ElemType alpha) - { - (*this) *= 1/alpha; - return (*this); - } - - template - CPUMatrix CPUMatrix::operator/ (ElemType alpha) const - { - return ((*this) * (1/alpha)); - } - - //element-wise power - template - CPUMatrix& CPUMatrix::operator^= (ElemType alpha) - { - auto& us = *this; - ElementWisePower(alpha, us, us); - return us; - } - - //element-wise power - template - CPUMatrix CPUMatrix::operator^ (ElemType alpha) const - { - CPUMatrix c(GetNumRows(), GetNumCols()); - ElementWisePower(alpha, *this, c); - return c; - } - - - template - CPUMatrix& CPUMatrix::AssignElementPowerOf(const CPUMatrix& a, const ElemType power) - { - ElementWisePower(power, a, *this); - return *this; - } - - //[this]=[this] .* a (we cannot override operator .* in c++) - template - CPUMatrix& CPUMatrix::ElementMultiplyWith (const CPUMatrix& a) - { - return AssignElementProductOf(*this, a); - } - - //[this]=[this] .* a (we cannot override operator .* in c++) - template - CPUMatrix& CPUMatrix::ElementDivideBy(const CPUMatrix& a) - { - return AssignElementDivisionOf(*this, a); - } - - //[this]=a .* b - template - CPUMatrix& CPUMatrix::AssignElementProductOf (const CPUMatrix& a, const CPUMatrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AssignElementProductOf: The input matrix dimensions do not match."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::AddElementProductOf (const CPUMatrix& a, const CPUMatrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AddElementProductOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AddElementProductOf : The input matrix dimensions do not match."); - - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == GetNumCols())) - InvalidArgument("AddElementProductOf : The input matrix dimensions do not match [this]."); - - auto& us=*this; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::AssignElementDivisionOf (const CPUMatrix& a, const CPUMatrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementDivisionOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AssignElementDivisionOf : The input matrix dimensions do not match."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - ElemType smallValue = EPS_IN_INVERSE; - -#pragma omp parallel for - foreach_coord(i,j,us) - { - ElemType v = b(i,j); - if (v >= 0 && v < smallValue) - us(i,j) = a(i,j) / smallValue; - else if (v < 0 && v > -smallValue) - us(i,j) = a(i,j) / (-smallValue); - else - us(i,j) = a(i,j) / v; - } - - return *this; - } - - template - CPUMatrix& CPUMatrix::ColumnElementMultiplyWith(const CPUMatrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("ColumnElementMultiplyWith: Matrix is empty."); - - assert (a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1); - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) - InvalidArgument("ColumnElementMultiplyWith: The input matrix should be a col vector and match [this]'s rows."); - - auto& us=*this; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::RowElementMultiplyWith(const CPUMatrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("RowElementMultiplyWith: Matrix is empty."); - - assert (a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()); - if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols())) - InvalidArgument("RowElementMultiplyWith: The input matrix should be a row vector and match [this]'s columns."); - - auto& us=*this; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::RowElementDivideBy(const CPUMatrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("RowElementDivideBy: Matrix is empty."); - - assert(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()); - if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols())) - InvalidArgument("RowElementDivideBy: The input matrix should be a row vector and match [this]'s columns."); - - auto& us = *this; - - long m = (long)GetNumRows(), n = (long)GetNumCols(); -#pragma omp parallel for - for (long j = 0; j= 0 && v < EPS_IN_INVERSE) - v = EPS_IN_INVERSE; - else if (v < 0 && v > -EPS_IN_INVERSE) - v = (-EPS_IN_INVERSE); - - //four-way unrolling - for (long i = 0; i<(m & ~3); i += 4) - { - us(i, j) /= v; - us(i + 1, j) /= v; - us(i + 2, j) /= v; - us(i + 3, j) /= v; - } - //handle remaining stuffs - for (long i = m & ~3; i - CPUMatrix& CPUMatrix::ColumnElementDivideBy(const CPUMatrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("ColumnElementDivideBy: Matrix is empty."); - - assert (a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1); - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) - InvalidArgument("ColumnElementDivideBy: The input matrix should be a col vector and match [this]'s rows."); - - auto& us=*this; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); - - ElemType smallValue = EPS_IN_INVERSE; -#pragma omp parallel for - for (long j=0; j= 0 && v < smallValue) - us(i,j) /= smallValue; - else if (v < 0 && v > -smallValue) - us(i,j) /= (-smallValue); - else - us(i,j) /= v; - } - } - - return *this; - } - - - //[this]=1 ./ a - template - CPUMatrix& CPUMatrix::ElementInverse () - { - return AssignElementInverseOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignElementInverseOf (const CPUMatrix& a) - { - ElemType smallValue = EPS_IN_INVERSE; - - if (a.IsEmpty()) - LogicError("AssignElementInverseOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,us) - { - if (a(i,j) <0 && a(i,j) > -smallValue) - us(i,j) = 1/(-smallValue); - else if (a(i,j) >=0 && a(i,j) < smallValue) - us(i,j) = 1/smallValue; - else - us(i,j) = 1 / a(i,j); - } - - return *this; - } - - //[this]=sigmoid([this]) element wise - template - CPUMatrix& CPUMatrix::InplaceSigmoid () - { - return AssignSigmoidOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignSigmoidOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSigmoidOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,us) - { - if (a(i,j) >= 0) - us(i,j) = 1 / (1+exp(-a(i,j))); - else - { - ElemType v = exp(a(i,j)); - us(i,j) = v / (1+v); - } - } - - return *this; - } - - template - CPUMatrix& CPUMatrix::InplaceLinearRectifierDerivative () - { - return AssignLinearRectifierDerivativeOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignLinearRectifierDerivativeOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignLinearRectifierDerivativeOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j 0.0f ? 1.0f : 0.0f; - us(i+1,j) = a(i+1,j) > 0.0f ? 1.0f : 0.0f; - us(i+2,j) = a(i+2,j) > 0.0f ? 1.0f : 0.0f; - us(i+3,j) = a(i+3,j) > 0.0f ? 1.0f : 0.0f; - } - //handle remaining stuffs - for (long i=m & ~3; i 0.0f ? 1.0f : 0.0f; - } - } - - - return *this; - } - - template - CPUMatrix& CPUMatrix::InplaceSigmoidDerivative () - { - return AssignSigmoidDerivativeOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignSigmoidDerivativeOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSigmoidDerivativeOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceTanh () - { - return AssignTanhOf(*this); - } - - - template - CPUMatrix& CPUMatrix::AssignTanhOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignTanhOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceLogSoftmax (const bool isColWise) - { - return AssignLogSoftmaxOf(*this, isColWise); - } - - template - CPUMatrix& CPUMatrix::AssignLogSoftmaxOf (const CPUMatrix& a, const bool isColWise) - { - if (a.IsEmpty()) - LogicError("AssignLogSoftmaxOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - if (isColWise) - { -#pragma omp parallel for - foreach_column(j,a) - { - //we need to extract max before applying exp to avoid overflow - ElemType maxV = a(0,j); - foreach_row(i, a) - maxV = max(maxV, a(i,j)); - - ElemType sum = 0; - foreach_row(i, a) - sum += exp(us(i,j) = a(i,j) - maxV); - sum = log(sum); - foreach_row(i, us) - us(i,j) -= sum; - } - - } - else - { -#pragma omp parallel for - foreach_row(i, a) - { - //we need to extract max before applying exp to avoid overflow - ElemType maxV = a(i,0); - foreach_column(j,a) - maxV = max(maxV, a(i,j)); - - ElemType sum = 0; - foreach_column(j,a) - sum += exp(us(i,j) = a(i,j) - maxV); - sum = log(sum); - foreach_column(j,us) - us(i,j) -= sum; - } - - } - - return *this; - } - - //[this]=hardmax([this]) - //the max element is 1 else is 0 - template - CPUMatrix& CPUMatrix::InplaceHardmax(const bool isColWise) - { - return AssignHardmaxOf(*this, isColWise); - } - - template - CPUMatrix& CPUMatrix::AssignHardmaxOf(const CPUMatrix& a, const bool isColWise) - { - if (a.IsEmpty()) - LogicError("AssignHardmaxOf: Matrix a is empty."); - - auto& us = *this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - if (isColWise) - { -#pragma omp parallel for - foreach_column(j, a) - { - //we need to extract max - ElemType maxV = a(0, j); - long maxI = 0; - foreach_row(i, a) - { - if (maxV < a(i, j)) - { - maxV = a(i, j); - maxI = i; - } - } - - foreach_row(i, us) - us(i, j) = (i == maxI) ? 1.0f : 0.0f; - } - - } - else - { -#pragma omp parallel for - foreach_row(i, a) - { - //we need to extract max - ElemType maxV = a(i, 0); - long maxJ = 0; - foreach_column(j, a) - { - if (maxV < a(i, j)) - { - maxV = a(i, j); - maxJ = j; - } - } - - foreach_column(j, us) - us(i, j) = (j == maxJ)? 1.0f : 0.0f; - } - - } - - return *this; - } - - //[this]=sqrt([this]) element wise - template - CPUMatrix& CPUMatrix::InplaceSqrt () - { - return AssignSqrtOf(*this); - } - - //to prevent negative values caused by floating operations, we force inputs to be >=0 - //this may, however, hide problems in the caller. - template - CPUMatrix& CPUMatrix::AssignSqrtOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSqrtOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceExp () - { - return AssignExpOf(*this); - } - - - template - CPUMatrix& CPUMatrix::AssignExpOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignExpOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceAbs () - { - return AssignAbsOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignAbsOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignAbsOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceLog () - { - return AssignLogOf(*this); - } - - //[this]=log([this]) element wise - template - CPUMatrix& CPUMatrix::InplaceLog10 () - { - return AssignLog10Of(*this); - } - - template - CPUMatrix& CPUMatrix::AssignLogOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignLogOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - const ElemType v = a(i,j); - if (v < EPS_IN_LOG) - { - us(i,j) = LOG_OF_EPS_IN_LOG; - } - else - us(i,j) = log(v); - } - - return *this; - } - - template - CPUMatrix& CPUMatrix::AssignLog10Of (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignLogOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - const ElemType v = a(i,j); - if (v <= 0) - LogicError("AssignLogOf: Log can only applied to numbers larger than 0."); - else if (v < EPS_IN_LOG) - { - us(i,j) = LOG10_OF_EPS_IN_LOG; - } - else - us(i,j) = log10(v); - } - - - return *this; - } - - //[this]=cos([this]) element wise - template - CPUMatrix& CPUMatrix::InplaceCosine () - { - return AssignCosineOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignCosineOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignCosineOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - const ElemType v = a(i,j); - us(i,j) = cos(v); - } - - return *this; - } - - //[this]=-sin([this]) element wise - template - CPUMatrix& CPUMatrix::InplaceNegativeSine () - { - return AssignNegativeSineOf(*this); - } - - template - CPUMatrix& CPUMatrix::AssignNegativeSineOf (const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignCosineOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - const ElemType v = a(i,j); - us(i,j) = -sin(v); - } - - return *this; - } - - - //Threshold truncating: this[i] = max( this[i], threshold ) - template - CPUMatrix& CPUMatrix::InplaceTruncateBottom (const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncateBottom: Matrix is empty."); - - auto& us=*this; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j - CPUMatrix& CPUMatrix::InplaceTruncate (const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncate: Matrix is empty."); - - auto& us=*this; - ElemType locThresholdPos = abs(threshold); - ElemType locTHresholdNeg = -locThresholdPos; - - long m=(long)GetNumRows(), n=(long)GetNumCols(); -#pragma omp parallel for - for (long j=0; j locThresholdPos ) - us(i,j) = locThresholdPos ; - else if (us(i,j) < locTHresholdNeg ) - us(i,j) = locTHresholdNeg ; - - if (us(i+1,j) > locThresholdPos ) - us(i+1,j) = locThresholdPos ; - else if (us(i+1,j) < locTHresholdNeg ) - us(i+1,j) = locTHresholdNeg ; - - if (us(i+2,j) > locThresholdPos ) - us(i+2,j) = locThresholdPos ; - else if (us(i+2,j) < locTHresholdNeg ) - us(i+2,j) = locTHresholdNeg ; - - if (us(i+3,j) > locThresholdPos ) - us(i+3,j) = locThresholdPos ; - else if (us(i+3,j) < locTHresholdNeg ) - us(i+3,j) = locTHresholdNeg ; - } - //handle remaining stuffs - for (long i=m & ~3; i locThresholdPos ) - us(i,j) = locThresholdPos ; - else if (us(i,j) < locTHresholdNeg ) - us(i,j) = locTHresholdNeg ; - } - } - - return *this; - } - - //x= x-threshold if x>threshold, x+threshold if x<-threshold, 0 otherwise - template - CPUMatrix& CPUMatrix::InplaceSoftThreshold(const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncate: Matrix is empty."); - - long m = (long)GetNumElements(); - -#pragma omp parallel for - for (long i = 0; i<(m & ~3); i += 4) //four-way unrolling - { - if (m_pArray[i] > threshold) - m_pArray[i] -= threshold; - else if (m_pArray[i] < -threshold) - m_pArray[i] += threshold; - else - m_pArray[i] = 0; - - if (m_pArray[i+1] > threshold) - m_pArray[i+1] -= threshold; - else if (m_pArray[i+1] < -threshold) - m_pArray[i+1] += threshold; - else - m_pArray[i+1] = 0; - - if (m_pArray[i+2] > threshold) - m_pArray[i+2] -= threshold; - else if (m_pArray[i+2] < -threshold) - m_pArray[i+2] += threshold; - else - m_pArray[i+2] = 0; - - if (m_pArray[i+3] > threshold) - m_pArray[i+3] -= threshold; - else if (m_pArray[i+3] < -threshold) - m_pArray[i+3] += threshold; - else - m_pArray[i+3] = 0; - } - //handle remaining stuffs - for (long i = m & ~3; i threshold) - m_pArray[i] -= threshold; - else if (m_pArray[i] < -threshold) - m_pArray[i] += threshold; - else - m_pArray[i] = 0; - } - - return *this; - } - - //Threshold truncating: this[i] = max( a[i], threshold ) - template - CPUMatrix& CPUMatrix::AssignTruncateBottomOf (const CPUMatrix& a, const ElemType threshold) - { - if (a.IsEmpty()) - LogicError("AssignTruncateBottomOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - if (a(i,j) < threshold) - us(i,j) = threshold; - else - us(i,j) = a(i,j); - } - - return *this; - } - - //Threshold truncating: this[i] = min( this[i], threshold ) - template - CPUMatrix& CPUMatrix::InplaceTruncateTop (const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncateTop: Matrix is empty."); - - auto& us=*this; - -#pragma omp parallel for - foreach_coord(i,j,us) - { - if (us(i,j) > threshold) - us(i,j) = threshold; - } - - return *this; - } - - //Threshold truncating: this[i] = min( a[i], threshold ) - template - CPUMatrix& CPUMatrix::AssignTruncateTopOf (const CPUMatrix& a, const ElemType threshold) - { - if (a.IsEmpty()) - LogicError("AssignTruncateTopOf: Matrix a is empty."); - - auto& us=*this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_coord(i,j,a) - { - if (a(i,j) > threshold) - us(i,j) = threshold; - else - us(i,j) = a(i,j); - } - - return *this; - } - //Threshold truncating: this[i] = 0 if abs(this[i] - CPUMatrix& CPUMatrix::SetToZeroIfAbsLessThan (const ElemType threshold) - { - if (IsEmpty()) - LogicError("SetToZeroIfAbsLessThan: Matrix is empty."); - - auto& us=*this; - -#pragma omp parallel for - foreach_coord(i,j,us) - { - if (abs(us(i,j)) < threshold) - us(i,j) = 0; - } - - return *this; - } - - //sum of all abs(elements) - template - ElemType CPUMatrix::SumOfAbsElements () const - { - if (IsEmpty()) - LogicError("SumOfAbsElements: Matrix is empty."); - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - return (ElemType)dasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); -#else - return (ElemType)cblas_dasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); -#endif - } - else - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - return sasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); -#else - return cblas_sasum ((int)GetNumElements(), reinterpret_cast (m_pArray), 1); -#endif - } - } - - //sum of all elements - template - ElemType CPUMatrix::SumOfElements () const - { - if (IsEmpty()) - LogicError("SumOfElements: Matrix is empty."); - - ElemType sum=0; - long m=(long)GetNumElements(); // note: OpenMP requires loop indices to be long, not size_t - - //four-way unrolling -#pragma omp parallel for reduction(+:sum) - for (long i=0; i<(m & ~3); i+=4) - { - sum += m_pArray[i] + m_pArray[i+1] + m_pArray[i+2] + m_pArray[i+3] ; - } - //handle remaining stuffs - for (long i=m & ~3; i - CPUMatrix& CPUMatrix::AssignSumOfElements(const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSumOfElements: Matrix a is empty."); - - auto& us=*this; - us.Resize(1,1); - us(0,0) =a.SumOfElements(); - - return *this; - } - - template - bool CPUMatrix::IsEqualTo(const CPUMatrix& a, const ElemType threshold /*= 1e-8*/) const - { - return AreEqual(*this, a, threshold); - } - - - template - void CPUMatrix::VectorSum(const CPUMatrix& a, CPUMatrix& c, const bool isColWise) - { - if (a.IsEmpty()) - LogicError("VectorSum: Input matrix a is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - - assert(m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - c.Resize(1, n); - -#pragma omp parallel for - foreach_column(j, a) - { - ElemType v = 0; - foreach_row(i, a) - { -#pragma omp atomic - v += a(i, j); - } - c(0, j) = v; - } - } - else - { - c.Resize(m, 1); - -#pragma omp parallel for - foreach_row(i, a) - { - ElemType v = 0; - foreach_column(j, a) - { -#pragma omp atomic - v += a(i, j); - } - c(i, 0) = v; - } - } - } - - template - void CPUMatrix::VectorNorm1(CPUMatrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNorm1: Matrix is empty."); - - auto& us=*this; - - const int m = (int)us.GetNumRows(); - const int n = (int)us.GetNumCols(); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - c.Resize(1, n); - -#pragma omp parallel for - foreach_column(j,us) - { - ElemType v = 0; - foreach_row(i,us) - { -#pragma omp atomic - v += abs(us(i,j)); - } - c(0,j) = v; - } - - - } - else - { - c.Resize(m, 1); - -#pragma omp parallel for - foreach_row(i,us) - { - ElemType v = 0; - foreach_column(j,us) - { -#pragma omp atomic - v += abs(us(i,j)); - } - c(i,0) = v; - } - - - } - } - - template - CPUMatrix& CPUMatrix::AssignVectorNorm1Of(CPUMatrix& a, const bool isColWise) - { - a.VectorNorm1(*this, isColWise); - return *this; - } - - template - void CPUMatrix::VectorNorm2(CPUMatrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNorm2: Matrix is empty."); - - auto& us=*this; - - const int m = (int)us.GetNumRows(); - const int n = (int)us.GetNumCols(); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - c.Resize(1, n); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_column(j,c) - { -#ifndef USE_MKL - c(0,j) = (ElemType) dnrm2(m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); -#else - c(0,j) = (ElemType) cblas_dnrm2 (m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); -#endif - } - } - else - { -#pragma omp parallel for - foreach_column(j,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - c(0,j) = snrm2(m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); -#else - c(0,j) = cblas_snrm2 (m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); -#endif - } - } - } - else - { - c.Resize(m, 1); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_row(i,c) - { -#ifndef USE_MKL - c(i,0) = dnrm2(n, reinterpret_cast (us.m_pArray+i), m); -#else - c(i,0) = cblas_dnrm2 (n, reinterpret_cast (us.m_pArray+i), m); -#endif - } - } - else - { -#pragma omp parallel for - foreach_row(i,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - c(i,0) = snrm2(n, reinterpret_cast (us.m_pArray+i), m); -#else - c(i,0) = cblas_snrm2 (n, reinterpret_cast (us.m_pArray+i), m); -#endif - } - - } - } - } - - template - CPUMatrix& CPUMatrix::AssignVectorNorm2Of(CPUMatrix& a, const bool isColWise) - { - a.VectorNorm2(*this, isColWise); - return *this; - } - - template - void CPUMatrix::VectorNormInf(CPUMatrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNormInf: Matrix is empty."); - - auto& us=*this; - - const int m = (int)us.GetNumRows(); - const int n = (int)us.GetNumCols(); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - c.Resize(1, n); - - //#pragma omp parallel for - foreach_column(j,us) - { - ElemType v = 0; - foreach_row(i,us) - { - v = max( v, abs(us(i,j))); - } - c(0,j) = v; - } - - - } - else - { - c.Resize(m, 1); - - //#pragma omp parallel for - foreach_row(i,us) - { - ElemType v = 0; - foreach_column(j,us) - { - v = max( v, abs(us(i,j))); - } - c(i,0) = v; - } - - } - } - - template - CPUMatrix& CPUMatrix::AssignVectorNormInfOf(CPUMatrix& a, const bool isColWise) - { - a.VectorNormInf(*this, isColWise); - return *this; - } - - template - CPUMatrix& CPUMatrix::AssignInnerProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool isColWise) - { - InnerProduct (a, b, *this,isColWise); - return *this; - } - - //column-wise crossproduct - template - CPUMatrix& CPUMatrix::AssignKhatriRaoProductOf(const CPUMatrix& a, const CPUMatrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignKhatriRaoProductOf: Matrix is empty."); - - long cols = (long) a.GetNumCols(); - assert(cols == b.GetNumCols()); - if (cols != b.GetNumCols()) - InvalidArgument("a.GetNumCols() != b.GetNumCols()"); - - long rowsA = (long) a.GetNumRows(); - long rowsB = (long) b.GetNumRows(); - Resize(rowsA * rowsB, cols); - -#ifdef __INTEL_COMPILER // TODO: check this -#pragma simd statement -#endif -#pragma omp parallel for - for (long k=0; k - CPUMatrix& CPUMatrix::AddColumnReshapeProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool transposeAColumn) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AddColumnReshapeProductOf: Matrix is empty."); - - long cols = (long) a.GetNumCols(); - assert(cols == b.GetNumCols()); - if (cols != b.GetNumCols()) - InvalidArgument("AddColumnReshapeProductOf: a.GetNumCols() != b.GetNumCols()"); - - long rowsA = (long) a.GetNumRows(); - long rowsB = (long) b.GetNumRows(); - - if (rowsA % rowsB != 0) - InvalidArgument("AddColumnReshapeProductOf: number of rows in a should be multiples of that in b."); - - long rowsC = rowsA / rowsB; - if (rowsC != GetNumRows() || cols != GetNumCols()) - InvalidArgument("AddColumnReshapeProductOf: This matrix does not have the right size."); - - auto & us = *this; - - if (transposeAColumn) - { - //find nrows and ncols of tbe reshaped a - long nrows = rowsB; - long ncols = rowsC; - -#ifdef __INTEL_COMPILER // TODO: check this -#pragma simd statement -#endif -#pragma omp parallel for - foreach_column(t, a) - { - size_t k=0; - for (size_t j=0; j - CPUMatrix& CPUMatrix::AddWithScaleOf(ElemType alpha, const CPUMatrix& a) - { - ScaleAndAdd(alpha, a, *this); - return *this; - } - - template - ElemType CPUMatrix::FrobeniusNorm() const - { - if (IsEmpty()) - LogicError("FrobeniusNorm: Matrix is empty."); - - ElemType v = 0; - - long m=(long)GetNumElements(); - - //four-way unrolling -#pragma omp parallel for reduction(+:v) - for (long i=0; i<(m & ~3); i+=4) - { - v += m_pArray[i] * m_pArray[i] + m_pArray[i+1] * m_pArray[i+1] + m_pArray[i+2] * m_pArray[i+2] + m_pArray[i+3] * m_pArray[i+3]; - } - //handle remaining stuffs - for (long i=m & ~3; i - CPUMatrix& CPUMatrix::AssignFrobeniusNormOf(const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignFrobeniusNormOf: Matrix a is empty."); - - auto& us=*this; - us.Resize(1,1); - us(0,0) = a.FrobeniusNorm(); - - return us; - } - - template - ElemType CPUMatrix::MatrixNormInf() const - { - if (IsEmpty()) - LogicError("MatrixNormInf: Matrix is empty."); - - auto& us=*this; - - ElemType v = 0; -#pragma omp parallel for - foreach_coord(i,j,us) - { -#pragma omp critical - { - v = max( v, abs(us(i,j))); - } - } - return v; - } - - template - ElemType CPUMatrix::MatrixNorm0() const - { - if (IsEmpty()) - LogicError("MatrixNorm0: Matrix is empty."); - - auto& us=*this; - - ElemType v = 0; -#pragma omp parallel for - foreach_coord(i,j,us) - { - if (us(i,j)!=0) - { -#pragma omp critical - { - ++v; - } - } - } - return v; - } - - template - ElemType CPUMatrix::MatrixNorm1() const - { - if (IsEmpty()) - LogicError("MatrixNorm1: Matrix is empty."); - - auto& us=*this; - - ElemType sum = 0; -#pragma omp parallel for reduction(+:sum) - foreach_coord(i,j,us) - { - sum += abs(us(i,j)); - } - return sum; - } - - template - CPUMatrix& CPUMatrix::AssignSignOf(const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSignOf: Matrix a is empty."); - - auto& us = *this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_column(j, us) - { - foreach_row(i, us) - { - ElemType v = a(i, j); - if (!std::isnan(v)) - us(i, j) = (v == (ElemType)0 ? (ElemType)0 : (v > 0 ? (ElemType)1 : (ElemType)(-1))); - else - us(i, j) = v; - } - } - - return us; - } - - template - CPUMatrix& CPUMatrix::AddSignOf(const CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("AddSignOf: Matrix a is empty."); - - auto& us = *this; - if (this != &a) - Resize(a.GetNumRows(), a.GetNumCols()); - -#pragma omp parallel for - foreach_column(j, us) - { - foreach_row(i, us) - { - ElemType v = a(i, j); - if (!std::isnan(v)) - us(i, j) += (v == (ElemType)0 ? (ElemType)0 : (v > 0 ? (ElemType)1 : (ElemType)(-1))); - else - us(i, j) = v; - } - } - - return us; - } - //I decided to use CPUMatrix& maxIndexes instead of integer vector because the result may be used to do additional calculation - template - void CPUMatrix::VectorMax(CPUMatrix& maxIndexes, CPUMatrix& maxValues, const bool isColWise, int topK) const - { - if (IsEmpty()) - LogicError("VectorMax: Matrix is empty."); - - auto& us=*this; - const int m = (int)GetNumRows(); - const int n = (int)GetNumCols(); - assert(topK <= m); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - maxValues.Resize(topK, n); - maxIndexes.Resize(topK, n); - - if (topK == 1) - { -#pragma omp parallel for - for (int j = 0; j < n; j++) - { - ElemType v = us(0, j); - size_t index = 0; - foreach_row(i, us) - { - if (v < us(i, j)) - { - index = i; - v = us(i, j); - } - } - maxValues(0, j) = v; - maxIndexes(0, j) = (ElemType)index; - } - } - else - { - std::vector indices(m); - int i = 0; - std::generate(indices.begin(), indices.end(), [&i] { return i++; }); - - const ElemType* curVal = m_pArray; - ElemType* curIdx = maxIndexes.m_pArray; - ElemType* curMax = maxValues.m_pArray; - for (int icol = 0; icol < n; icol++, curVal += m, curIdx += topK, curMax += topK) - { - // Partial sort, descending order. - std::nth_element(indices.begin(), indices.begin() + topK, indices.end(), - [curVal](const int& a, const int& b) { return curVal[a] > curVal[b]; }); - // REVIEW alexeyk: the following produces warning (see SCL_SECURE_NO_WARNINGS) so use loop instead. - //std::transform(indices.begin(), indices.begin() + topK, curIdx, [](const int& a) { return static_cast(a); }); - for (int i = 0; i < topK; i++) - { - curIdx[i] = static_cast(indices[i]); - curMax[i] = curVal[indices[i]]; - } - } - } - } - else - { - if (topK > 1) - RuntimeError("Row-wise TopK max is not supported."); - - maxValues.Resize(m,1); - maxIndexes.Resize(m, 1); - -#pragma omp parallel for - for (int i=0; i - void CPUMatrix::VectorMin(CPUMatrix& minIndexes, CPUMatrix& minValues, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorMin: Matrix is empty."); - - auto& us=*this; - const int m = (int)GetNumRows(); - const int n = (int)GetNumCols(); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - - if (isColWise) //col-wise - { - minValues.Resize(1, n); - minIndexes.Resize(1, n); - -#pragma omp parallel for - for (int j=0; j us(i,j)) - { - index = i; - v = us(i,j); - } - - } - minValues(0,j) = v; - minIndexes(0,j) = (ElemType)index; - } - } - else - { - minValues.Resize(m,1); - minIndexes.Resize(m, 1); - -#pragma omp parallel for - for (int i=0; i us(i,j)) - { - index = j; - v = us(i,j); - } - - } - minValues(i,0) = v; - minIndexes(i,0) = (ElemType)index; - } - } - } - - template - CPUMatrix& CPUMatrix::AssignNumOfDiff(const CPUMatrix& a, const CPUMatrix& b, bool searchInCol) - { - if (a.GetNumCols() != b.GetNumCols()) - throw std::invalid_argument("AssignNumOfDiff: a and b must have the same number of columns."); - if (!searchInCol && a.GetNumRows() != b.GetNumRows()) - throw std::invalid_argument("AssignNumOfDiff: a and b must have the same number of rows."); - - ElemType n = 0; - if (!searchInCol) - { - foreach_coord(i, j, a) - { - n += (a(i, j) != b(i, j)); - } - } - else - { - size_t crow = b.GetNumRows(); - const ElemType* curCol = b.m_pArray; - for (size_t icol = 0; icol < a.GetNumCols(); icol++, curCol += crow) - { - auto res = std::find(curCol, curCol + crow, a(0, icol)); - if (res == curCol + crow) - n++; - } - } - - Resize(1, 1); //result should be one element - (*this)(0, 0) = n; - - return *this; - } - - -#pragma endregion Member BLAS Functions - -#pragma region Other helper Functions - - template - void CPUMatrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const - { - if (IsEmpty()) - LogicError("Print: Matrix is empty."); - - if (rowEnd >= GetNumRows() || colEnd >= GetNumCols()) - InvalidArgument("Index out of range."); - - if (matrixName != nullptr) - fprintf (stderr, "\n###### %s (%lu, %lu) ######\n", matrixName, GetNumRows(), GetNumCols()); - else - fprintf (stderr, "\n###### Unnamed Matrix (%lu, %lu) ######\n", GetNumRows(), GetNumCols()); - - fprintf (stderr, "\n------ Print Range (%lu:%lu, %lu:%lu) ------\n", rowStart, rowEnd, colStart, colEnd); - - const auto& us = *this; - for (size_t i = rowStart; i <= rowEnd; i++) - { - for (size_t j = colStart; j <= colEnd; j++) - fprintf(stderr, "%.10f\t", us(i, j)); - fprintf (stderr, "\n"); - } - } - - template - void CPUMatrix::Print(const char* matrixName /*=nullptr*/) const - { - Print(matrixName, 0, GetNumRows()-1, 0, GetNumCols()-1); - } - - // file I/O - //matrixName is used to verify that correct matrix is read. - template - void CPUMatrix::ReadFromFile(FILE*, const char * /*matrixName*/) - { - RuntimeError("not implemented."); - } - - //matrixName is used to verify that correct matrix is read. - template - void CPUMatrix::WriteToFile(FILE*, const char * /*matrixName*/) - { - RuntimeError("not implemented."); - } - - //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) - template - CPUMatrix& CPUMatrix::AssignPackedConvolutionInput(const CPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t /*outputChannels*/, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) - { - assert (verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth); - - const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; - const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel - const size_t inputDim = inputWidth*inputHeight*inputChannels; - const size_t smallBatchSize = inputSubBatch.GetNumCols(); - const long inputHeightTimesChannel = (long) (inputHeight * inputChannels); - Resize(packedInputRows, packedInputColsPerSample * smallBatchSize); - if (zeroPadding) - SetValue((ElemType)0); - - const long halfKernelWidth = (long) kernelWidth/2; - const long halfKernelHeight = (long) kernelHeight/2; - - -#pragma omp parallel for //each input element is copied to many places - for (long sample = 0; sample =0 && x1=0 && y1=0; wcol++, posyInKernel -= (long) horizontalSubsample) - { - long packRowBase = (long) (c * kernelWidth * kernelHeight + posyInKernel * kernelHeight); - for (long wrow = x0, posxInKernel = x1; wrow < (long) outputHeight && posxInKernel>=0; wrow++, posxInKernel -= (long) verticalSubsample) - { - const long packRow = packRowBase + posxInKernel; - const long packCol = packColBase + wrow; - (*this)(packRow, packCol) = currentInputValue; - } - packColBase += (long) outputHeight; - } - } - } - - return *this; - } - //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) - template - CPUMatrix& CPUMatrix::UnpackConvolutionInput(CPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t /*outputChannels*/, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) const - { - assert (verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth); - - const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel - const size_t inputDim = inputWidth*inputHeight*inputChannels; - const size_t smallBatchSize = inputSubBatch.GetNumCols(); - const long inputHeightTimesChannel = (long) (inputHeight * inputChannels); - - const long halfKernelWidth = (long) kernelWidth/2; - const long halfKernelHeight = (long) kernelHeight/2; - -#pragma omp parallel for //each input element is copied to many places - for (long sample = 0; sample =0 && x1=0 && y1=0; wcol++, posyInKernel -= (long) horizontalSubsample) - { - long packRowBase = (long) (c * kernelWidth * kernelHeight + posyInKernel * kernelHeight); - for (long wrow = x0, posxInKernel = x1; wrow < (long) outputHeight && posxInKernel>=0; wrow++, posxInKernel -= (long) verticalSubsample) - { - const long packRow = packRowBase + posxInKernel; - const long packCol = packColBase + wrow; - currentInputValue += (*this)(packRow, packCol); - } - packColBase += (long) outputHeight; - } - inputSubBatch(id, sample) = currentInputValue; - } - } - - return inputSubBatch; - } - - //assume each column is an input sample. Each sample is stored in (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) - template - CPUMatrix& CPUMatrix::AssignMaxPoolingResult(const CPUMatrix& inputBatch, const size_t channels, - const size_t /*inputWidth*/, const size_t inputHeight, const size_t /*inputSizePerSample*/, - const size_t /*outputWidth*/, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - const long inputHeightTimesChannel = (long) (inputHeight * channels); - const long outputHeightTimesChannel = (long) (outputHeight * channels); - const size_t batchSize = inputBatch.GetNumCols(); - Resize(outputSizePerSample, batchSize); - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - -#pragma omp parallel for - for (long sample = 0; sample < (long) batchSize; sample ++) - { - for (long outputIndexWithinSample=0; outputIndexWithinSample - CPUMatrix& CPUMatrix::AddMaxPoolingGradient(const CPUMatrix& outputGradientBatch, const CPUMatrix& inputBatch, const CPUMatrix& outputBatch, - const size_t channels, - const size_t /*inputWidth*/, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t /*outputSizePerSample*/, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - size_t batchSize = inputBatch.GetNumCols(); - const long inputHeightTimesChannel = (long) (inputHeight * channels); - const long outputHeightTimesChannel = (long) (outputHeight * channels); - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - -#pragma omp parallel for - for (long sample = 0; sample < batchSize; sample ++) - { - for (long inputIndexWithinSample=0; inputIndexWithinSample - CPUMatrix& CPUMatrix::AssignAveragePoolingResult(const CPUMatrix& inputBatch, const size_t channels, - const size_t /*inputWidth*/, const size_t inputHeight, const size_t /*inputSizePerSample*/, - const size_t /*outputWidth*/, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - const long inputHeightTimesChannel = (long) (inputHeight * channels); - const long outputHeightTimesChannel = (long) (outputHeight * channels); - const size_t batchSize = inputBatch.GetNumCols(); - const size_t windowSize = windowWidth * windowHeight; - Resize(outputSizePerSample, batchSize); - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - -#pragma omp parallel for - for (long sample = 0; sample < batchSize; sample ++) - { - for (long outputIndexWithinSample=0; outputIndexWithinSample - CPUMatrix& CPUMatrix::AddAveragePoolingGradient(const CPUMatrix& outputGradientBatch, - const size_t channels, - const size_t /*inputWidth*/, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t /*outputSizePerSample*/, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - size_t batchSize = outputGradientBatch.GetNumCols(); - const long inputHeightTimesChannel = (long)(inputHeight * channels); - const long outputHeightTimesChannel = (long)(outputHeight * channels); - const long windowSize = (long) (windowWidth * windowHeight); - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - -#pragma omp parallel for - for (long sample = 0; sample < batchSize; sample ++) - { - for (long inputIndexWithinSample=0; inputIndexWithinSampleMatrix-matrix multiply with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c - /// Scalar - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Scalar - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, - ElemType beta, CPUMatrix& c) - { - if (a.IsEmpty() || b.IsEmpty()) - return; - - int m, n, k, l; - int lda, ldb, ldc; -#ifndef USE_MKL - char transA, transB; -#else - CBLAS_TRANSPOSE mklTransA; - CBLAS_TRANSPOSE mklTransB; -#endif - - if (transposeA) - { - m = (int)a.GetNumCols(); - k = (int)a.GetNumRows(); - lda = k; -#ifndef USE_MKL - transA = (char)MatrixTranspose::Trans; -#else - mklTransA = CBLAS_TRANSPOSE::CblasTrans; -#endif - } - else - { - m = (int)a.GetNumRows(); - k = (int)a.GetNumCols(); - lda = m; -#ifndef USE_MKL - transA = (char)MatrixTranspose::NoTrans; -#else - mklTransA = CBLAS_TRANSPOSE::CblasNoTrans; -#endif - } - - if (transposeB) - { - l = (int)b.GetNumCols(); - n = (int)b.GetNumRows(); - ldb = n; -#ifndef USE_MKL - transB = (char)MatrixTranspose::Trans; -#else - mklTransB = CBLAS_TRANSPOSE::CblasTrans; -#endif - } - else - { - l = (int)b.GetNumRows(); - n = (int)b.GetNumCols(); - ldb = l; -#ifndef USE_MKL - transB = (char)MatrixTranspose::NoTrans; -#else - mklTransB = CBLAS_TRANSPOSE::CblasNoTrans; -#endif - } - - assert (m>0 && k>0 && l>0 && n>0); //converting from size_t to int may cause overflow - assert (k == l); - if (k != l) - InvalidArgument("CPUMatrix::MultiplyAndWeightedAdd : The inner dimensions of a and b must match."); - - if (beta == 0) - c.Resize(m, n); - else - c.VerifySize(m, n); // Can't resize if beta != 0 - - ldc = (int)c.GetNumRows(); - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - dgemm(transA, transB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); -#else - cblas_dgemm ((CBLAS_ORDER) BLAS_COLMAJOR mklTransA, mklTransB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); -#endif - } - else - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - sgemm(BLAS_COLMAJOR transA, transB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); -#else - cblas_sgemm ((CBLAS_ORDER) BLAS_COLMAJOR mklTransA, mklTransB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); -#endif - } - } - - template - void CPUMatrix::Multiply1x1AndWeightedAdd(ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, - ElemType beta, CPUMatrix& c) - { - assert(a.GetNumElements() == 1); // a is a scalar - - ElemType f = alpha * a.Get00Element(); - if (beta == 0) // don't even read the memory if beta is 0 -#pragma omp parallel for - foreach_coord(i, j, c) - c(i, j) = b(i, j) * f; - else -#pragma omp parallel for - foreach_coord(i, j, c) - c(i, j) = b(i, j) * f + c(i, j) * beta; - } - - /* compute singular value decomposition as - A = U*SIGMA*VT - W is used as temp working memory - */ - template - void CPUMatrix::SVD(const CPUMatrix& A, CPUMatrix& SIGMA, CPUMatrix& U, CPUMatrix& VT, CPUMatrix& W) - { - if (A.IsEmpty()) - LogicError("SVD: input matrix is empty."); - - int info; - int m, n, lda, ldu, ldvt; - m = (int)A.GetNumRows(); - n = (int)A.GetNumCols(); - W.GetNumRows(); //W is used as temp working memory - lda = m; - ldu = m; - ldvt = n; - U.Resize(m, m); - SIGMA.Resize(min(m, n), 1); - VT.Resize(n, n); - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - dgesvd('A', 'A', (int)m, (int)n, reinterpret_cast (A.m_pArray), (int)lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), (int)ldu, reinterpret_cast (VT.m_pArray), (int)ldvt, &info); -#else - double wkopt; - int lwork = -1; - dgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, &wkopt, &lwork, &info); - lwork = (int)wkopt; - W.Resize(lwork, 1); - dgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, reinterpret_cast (W.m_pArray), &lwork, &info); -#endif - } - else - { -#ifndef USE_MKL -#pragma warning (suppress: 4244) - sgesvd('A', 'A', (int)m, (int)n, reinterpret_cast (A.m_pArray), (int)lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), (int)ldu, reinterpret_cast (VT.m_pArray), (int)ldvt, &info); -#else - float wkopt; - int lwork = -1; - sgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, &wkopt, &lwork, &info); - lwork = (int)wkopt; - W.Resize(lwork, 1); - sgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, reinterpret_cast (W.m_pArray), &lwork, &info); -#endif - } - - if (info > 0) - { - RuntimeError("The algorithm computing SVD failed to converge.\n"); - } - } - - /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + c - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::MultiplyAndAdd(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, - CPUMatrix& c) - { - return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 1.0, c); - } - template - void CPUMatrix::AssignSoftmaxSum(const CPUMatrix& softmax, CPUMatrix& c) - { - ElemType log_likelihood = 0.0; - size_t batch_size = this->GetNumCols(); -#pragma omp parallel for reduction(+:log_likelihood) - for (int instance_id = 0; instance_id < batch_size; instance_id++) - { - int sample = (int)(*this)(0, instance_id); - log_likelihood += softmax(instance_id, sample); - } - c(0, 0) = -log_likelihood; - } - - template - void CPUMatrix::AssignNCEUnnormalizedEval(const CPUMatrix& a, - const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& c) - //this: samples+probs - // a: hidden - // b: embedding - // tmp: softmax - // c: loglikelihood - { - ElemType log_likelihood = 0.0; - size_t batch_size = this->GetNumCols(); -#pragma omp parallel for reduction(+:log_likelihood) - for (int instance_id = 0; instance_id < batch_size; instance_id++) - { - int sample = -(int)(*this)(0, instance_id); - ElemType score = bias(sample, 0); - for (int dim = 0; dim < b.GetNumRows(); dim++) - score += b(dim, sample)* a(dim, instance_id); - log_likelihood += score; - } - c(0, 0) = -log_likelihood; - } - - //samples+prob gradient hidden embedding embedding/hidden - //a.m_CPUMatrix->AssignNCEDerivative(*tmp.m_CPUMatrix, *a.m_CPUMatrix, *b.m_CPUMatrix, inputIndex, *c.m_CPUMatrix); - template - CPUMatrix& CPUMatrix::AssignNCEDerivative(const CPUMatrix& tmp, const CPUMatrix& a, const CPUMatrix& b, size_t inputIndex, CPUMatrix& c) - { - size_t sample_size = this->GetNumRows() / 2; - size_t batch_size = this->GetNumCols(); - if (inputIndex == 1) - { -#pragma omp parallel for - for (int instance_id = 0; instance_id < batch_size; instance_id++) - for (int sample_id = 0; sample_id < sample_size; sample_id++) - { - int sample = (int)(*this)(2 * sample_id, instance_id); - for (int dim = 0; dim < b.GetNumRows(); dim++) - c(dim, instance_id) -= b(dim, sample)* tmp(sample_id, instance_id); - } - } - else if (inputIndex == 2) - { - int i_blocks = omp_get_num_threads() * 16; - // Assume only one block in k direction. - // We don't need to explicitly block in the j direction. -#pragma omp parallel for - for (int ib = 0; ib < i_blocks; ib++) - for (int instance_id = 0; instance_id < batch_size; instance_id++) - for (int sample_id = 0; sample_id < sample_size; sample_id++) - { - int sample =(int) (*this)(2 * sample_id, instance_id); - if (sample % i_blocks == ib) - for (int dim = 0; dim < b.GetNumRows(); dim++) - c(dim, sample) -= a(dim, instance_id)* tmp(sample_id, instance_id); - } - } - else - { - assert(inputIndex == 3); - // Assume only one block in k direction. - // We don't need to explicitly block in the j direction. - for (int instance_id = 0; instance_id < batch_size; instance_id++) - for (int sample_id = 0; sample_id < sample_size; sample_id++) - { - int sample =(int) (*this)(2 * sample_id, instance_id); - c(0, sample) -= tmp(sample_id, instance_id); - } - } - return *this; - } - - template - void CPUMatrix::AssignNoiseContrastiveEstimation(const CPUMatrix& a, - const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& tmp, CPUMatrix& c) - //this: samples+probs - // a: hidden - // b: embedding - // tmp: softmax - // c: loglikelihood - { - double log_likelihood = 0.0; - size_t sample_size = this->GetNumRows() / 2; - size_t batch_size = this->GetNumCols(); - size_t num_noise_samples = sample_size - 1; - double log_num_noise_samples = std::log(num_noise_samples); -#pragma omp parallel for reduction(+:log_likelihood) - for (int instance_id = 0; instance_id < batch_size; instance_id++) - for (int sample_id = 0; sample_id < sample_size; sample_id++) - { - int sample = (int)(*this)(2 * sample_id, instance_id); - double score = bias(0, sample); - for (int dim = 0; dim < b.GetNumRows(); dim++) - score += a(dim, instance_id)* b(dim, sample); - double sample_prob = -(*this)(2 * sample_id + 1, instance_id); - if (sample_id == 0) - sample_prob = -sample_prob; - double score_noise = log_num_noise_samples + sample_prob; - double z = LogAdd(score, score_noise); - double logprob = score - z; - double logprob_noise = score_noise - z; - tmp(sample_id, instance_id) = (ElemType)-std::exp(logprob); - if (sample_id == 0) - tmp(sample_id, instance_id) += 1; - log_likelihood += sample_id == 0 ? logprob : logprob_noise; - } - c(0, 0) = (ElemType)-log_likelihood; - } - - /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::Multiply(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, - CPUMatrix& c) - { - return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 0.0, c); - } - - /// Matrix-matrix multiply with col-major matrices (a and b are not transposed): c = a * b - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::Multiply(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) - { - return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, false, b, false, 0.0, c); - } - - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c - /// if a is a column vector, add to all columns of c - /// if a is a row vector, add to all rows of c - /// if a is a scalar, add to all rows of c - /// Scalar - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::ScaleAndAdd(ElemType alpha, const CPUMatrix& a, CPUMatrix& c) - { - if (a.IsEmpty() || c.IsEmpty()) - LogicError("ScaleAndAdd: one of the input matrices is empty."); - - if (a.GetNumRows() != 1 && a.GetNumCols() != 1) // a is not a col or row vector - { - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int len = m * n; - const int incx = 1; - const int incy = 1; - - assert (m>0 && n>0 && len>0); //converting from size_t to int may cause overflow - assert ((int)c.GetNumRows() == m && (int)c.GetNumCols() == n); - if ((int)c.GetNumRows() != m || (int)c.GetNumCols() != n) - InvalidArgument("Dimension of matrix c does not match dimension of matrix a."); - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - daxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); -#else - cblas_daxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); -#endif - } - else - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - saxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); -#else - cblas_saxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); -#endif - } - } - else if (a.GetNumElements() == 1) //scalar, add to all elements - { - ElemType v = alpha*a(0,0); - long m=(long)c.GetNumRows(), n=(long)c.GetNumCols(); -#pragma omp parallel for - for (long j=0; j(a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); -#else - cblas_daxpy (m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); -#endif - } - } - else - { -#pragma omp parallel for - foreach_column(j,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - saxpy(m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); -#else - cblas_saxpy (m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); -#endif - } - } - } - else //row vector, add it to all rows - { - int m = (int)c.GetNumRows(); - int n = (int)c.GetNumCols(); - assert (n == (int)a.GetNumCols()); - if (n != (int)a.GetNumCols()) - InvalidArgument("To add row vector, cols should match."); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_row(i,c) - { -#ifndef USE_MKL - daxpy(n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); -#else - cblas_daxpy (n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); -#endif - } - } - else - { -#pragma omp parallel for - foreach_row(i,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - saxpy(n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); -#else - cblas_saxpy (n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); -#endif - - } - } - } - } - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::AddScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) - { - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() && - a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols()); - - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() && - a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols())) - { - InvalidArgument("AddScaledDifference: a, b, and c must have same dimension."); - } - - if (a.IsEmpty()) - LogicError("AddScaledDifference: Input matrix a is empty."); - - long m=(long)c.GetNumElements(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - c.m_pArray[i] += alpha * (a.m_pArray[i]-b.m_pArray[i]); - c.m_pArray[i+1] += alpha * (a.m_pArray[i+1]-b.m_pArray[i+1]); - c.m_pArray[i+2] += alpha * (a.m_pArray[i+2]-b.m_pArray[i+2]); - c.m_pArray[i+3] += alpha * (a.m_pArray[i+3]-b.m_pArray[i+3]); - } - //handle remaining stuffs - for (long i=m & ~3; i c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::AssignScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) - { - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols() ); - - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - { - InvalidArgument("AssignScaledDifference: a, b must have same dimension."); - } - - if (a.IsEmpty()) - LogicError("AssignScaledDifference: Input matrix a is empty."); - - if (&c != &a && &c != &b) - c.Resize(a.GetNumRows(), a.GetNumCols()); - - long m=(long)c.GetNumElements(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(m & ~3); i+=4) - { - c.m_pArray[i] = alpha * (a.m_pArray[i]-b.m_pArray[i]); - c.m_pArray[i+1] = alpha * (a.m_pArray[i+1]-b.m_pArray[i+1]); - c.m_pArray[i+2] = alpha * (a.m_pArray[i+2]-b.m_pArray[i+2]); - c.m_pArray[i+3] = alpha * (a.m_pArray[i+3]-b.m_pArray[i+3]); - } - //handle remaining stuffs - for (long i=m & ~3; i - void CPUMatrix::AddElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) - { - if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || - ci >= c.GetNumRows() || cj >=c.GetNumCols()) - InvalidArgument("AddElementToElement: index out of range."); - - c(ci, cj) += a(ai, aj); - } - - ////c[ci,cj] += a[ai,aj] - //template - //void CPUMatrix::AddLogElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) - //{ - // if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || - // ci >= c.GetNumRows() || cj >=c.GetNumCols()) - // InvalidArgument("AddElementToElement: index out of range."); - // - // ElemType v = a(ai,aj); - // c(ci, cj) += ((v < EPS_IN_LOG) ? LOG_OF_EPS_IN_LOG : log(v)); - //} - - //c[ci,cj] = a[ai,aj] - template - void CPUMatrix::AssignElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) - { - if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || - ci >= c.GetNumRows() || cj >=c.GetNumCols()) - InvalidArgument("AssignElementToElement: index out of range."); - - c(ci, cj) = a(ai, aj); - } - - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// 1X1 matrix - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::AddScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) - { - assert(alpha.GetNumElements() == 1); - if (!(alpha.GetNumElements() == 1)) - InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix."); - - AddScaledDifference(alpha(0,0), a, b, c); - } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// 1X1 matrix - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::AssignScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) - { - assert(alpha.GetNumElements() == 1); - if (!(alpha.GetNumElements() == 1)) - InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix."); - - AssignScaledDifference(alpha(0,0), a, b, c); - } - /// Matrix-scalar multiply with col-major matrices: c = alpha * a - /// Scalar - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void CPUMatrix::Scale(ElemType alpha, const CPUMatrix& a, CPUMatrix& c) - { - if (a.IsEmpty()) - LogicError("Scale: Input matrix a is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - - assert (m>0 && n>0); //converting from size_t to int may cause overflow - c.Resize(m,n); - - long size=(long)c.GetNumElements(); -#pragma omp parallel for - //four-way unrolling - for (long i=0; i<(size & ~3); i+=4) - { - c.m_pArray[i] = alpha * a.m_pArray[i]; - c.m_pArray[i+1] = alpha * a.m_pArray[i+1]; - c.m_pArray[i+2] = alpha * a.m_pArray[i+2]; - c.m_pArray[i+3] = alpha * a.m_pArray[i+3]; - } - //handle remaining stuffs - for (long i=size & ~3; iMatrix-scalar multiply with col-major matrices: a = alpha * a - /// Scalar - /// Input matrix - template - void CPUMatrix::Scale(ElemType alpha, CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("Scale: Input matrix a is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int len = m * n; - const int incx = 1; - - assert (m>0 && n>0 && len>0); //converting from size_t to int may cause overflow - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - dscal(len, alpha, reinterpret_cast (a.m_pArray), incx); -#else - cblas_dscal(len, alpha, reinterpret_cast (a.m_pArray), incx); -#endif - } - else - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - sscal(len, alpha, reinterpret_cast (a.m_pArray), incx); -#else - cblas_sscal (len, alpha, reinterpret_cast (a.m_pArray), incx); -#endif - } - } - - /// Matrix multiply with col-major matrices: a = alpha[1,1] * a - /// 1x1 matrix - /// Input matrix - template - void CPUMatrix::Scale(CPUMatrix alpha, CPUMatrix& a) - { - if (a.IsEmpty()) - LogicError("Scale: Input matrix a is empty."); - if (alpha.GetNumElements()!=1) - LogicError("Matrix alpha must be 1x1"); - CPUMatrix::Scale(alpha(0,0),a); - } - - template - void CPUMatrix::InnerProduct (const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrices is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int k = (int)b.GetNumRows(); - const int l = (int)b.GetNumCols(); - - assert (m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow - assert (m==k && n==l); //converting from size_t to int may cause overflow - if (m!=k || n!=l) - InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); - - if ((isColWise && m == 1) || !isColWise && n == 1) //in this case it's equivalent to element-wise product - { - c.AssignElementProductOf(a, b); - } - else if (isColWise) //col-wise - { - c.Resize(1,n); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_column(j,c) - { -#ifndef USE_MKL - c(0,j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); -#else - c(0,j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); -#endif - } - } - else - { -#pragma omp parallel for - foreach_column(j,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - c(0,j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); -#else - c(0,j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); -#endif - } - } - } - else - { - c.Resize(m, 1); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_row(i,c) - { -#ifndef USE_MKL - c(i,0) = ddot(n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); -#else - c(i,0) = cblas_ddot (n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); -#endif - } - } - else - { -#pragma omp parallel for - foreach_row(i,c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - c(i,0) = sdot(n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); -#else - c(i,0) = cblas_sdot (n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); -#endif - } - } - } - } - - // treat matrices as vectors. do vec(a)^T vec(b) - template - ElemType CPUMatrix::InnerProductOfMatrices(const CPUMatrix& a, const CPUMatrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProductOfMatrices: one of the input matrices is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int k = (int)b.GetNumRows(); - const int l = (int)b.GetNumCols(); - - assert (m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow - assert (m==k && n==l); //converting from size_t to int may cause overflow - if (m!=k || n!=l) - InvalidArgument("InnerProductOfMatrices: Matrices a and b should have same dimension."); - - if (sizeof(ElemType) == sizeof(double)) - { -#ifndef USE_MKL - return (ElemType)ddot((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); -#else - return (ElemType)cblas_ddot ((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); -#endif - } - else - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - return (ElemType)sdot((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); -#else - return (ElemType)cblas_sdot ((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); -#endif - } - } - - template - void CPUMatrix::ElementWisePower (ElemType alpha, const CPUMatrix& a, CPUMatrix& c) - { - if (a.IsEmpty()) - LogicError("Scale: The input matrix a is empty."); - - c.Resize(a.GetNumRows(), a.GetNumCols()); - - if (alpha == 2) - { -#pragma omp parallel for - foreach_coord(i,j,c) - { - c(i,j) = a(i,j) * a(i,j); - } - } - else if (alpha == 3) - { -#pragma omp parallel for - foreach_coord(i,j,c) - { - c(i,j) = a(i,j) * a(i,j) * a(i,j); - } - } - else - { -#pragma omp parallel for - foreach_coord(i,j,c) - { - c(i,j) = pow(a(i,j), alpha); - } - } - } - - template - bool CPUMatrix::AreEqual(const CPUMatrix& a, const CPUMatrix& b, const ElemType threshold /*= 1e-8*/) - { - if (a.GetNumRows() != b.GetNumRows() || a.GetNumCols() != b.GetNumCols()) - return false; - - bool result=true; -#pragma omp parallel for - foreach_coord(i, j, a) - { - if (abs(a(i,j)-b(i,j)) > threshold) - { - result = false; - break; - } - } - - return result; - } - - // see Matrix::TensorShuffleScaleAndAdd() for comments - template - void CPUMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const CPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const CPUMatrix& b, CPUMatrix& c) - { - size_t N = D * S * M * K * T; - const ElemType * pa = a.m_pArray; - const ElemType * pb = b.m_pArray; - ElemType * pc = c.m_pArray; - // Note: This code is written to match a GPU implementation. It is not super-efficient on the CPU. - for (size_t na = 0; na < N; na++) // loop over all elements - { - // recover the 5 indices from the loop counter - size_t d = na % D; - size_t s = (na / D ) % S; - size_t m = (na / D / S ) % M; - size_t k = (na / D / S / M ) % K; - size_t t = (na / D / S / M / K) % T; - // compute index for the a and b/c tensors - assert(na== (((t * K + k) * M + m) * S + s) * D + d); // input tensor of dimension (D x S x M x K x T) - size_t nb = (((t * S + s) * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S x T): k/K and s/S swapped - assert(nb < N); - // perform the computation - ElemType cval = keepWeight ? keepWeight * pb[nb] : 0; // if weight is 0 then don't bother to read memory (efficiency) or to multiply (NaN-safe) - cval += scaleFactor * pa[na]; - pc[nb] = cval; - } - } - - template - CPUMatrix CPUMatrix::Ones(const size_t rows, const size_t cols) - { - CPUMatrix c(rows, cols); //will initialize to 0 - c.SetValue(1); - return c; - } - - template - CPUMatrix CPUMatrix::Zeros(const size_t rows, const size_t cols) - { - CPUMatrix c(rows, cols); //will initialize to 0 - c.SetValue(0); - return c; - } - - template - CPUMatrix CPUMatrix::Eye(const size_t rows) - { - CPUMatrix c(rows, rows); //will initialize to 0 - c.SetDiagonalValue(1); - return c; - } - - template - CPUMatrix CPUMatrix::RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed) - { - CPUMatrix c(rows, cols); //will initialize to 0 - c.SetUniformRandomValue(low, high, seed); - return c; - } - - template - CPUMatrix CPUMatrix::RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed) - { - CPUMatrix c(rows, cols); //will initialize to 0 - c.SetGaussianRandomValue(mean, sigma, seed); - return c; - } - - template - bool CPUMatrix::HasElement(const CPUMatrix& mat, const ElemType v) - { - bool bHas = false; - - bool isvFinite = std::isfinite(v); -#pragma omp parallel for - for (long j = 0; j < mat.GetNumElements(); j++) - { -#pragma omp flush(bHas) - if (!bHas) - { - ElemType cur = mat.m_pArray[j]; - if (isvFinite && std::isfinite(cur)) - { - if (cur == v) - bHas = true; - } - else if (std::isnan(v) && std::isnan(cur)) - bHas = true; - else if (std::isinf(v) && std::isinf(cur) && std::signbit(v) == std::signbit(cur)) - bHas = true; - } - } - - return bHas; - } - - // CPUMatrix& AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber); - //[this]=a .* b - // here, a and b must be two row vectors of the same size, i.e. [1,m] - // the inputs are two rwo vectors - // the output is a matrix of size(neg+1, col) - template - CPUMatrix& CPUMatrix::AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); - - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix dimensions do not match."); - - if (a.GetNumRows() != 1) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); - - auto& us = *this; - if (this != &a) - { - Resize(negnumber + 1, a.GetNumCols()); - // Resize(a.GetNumRows(), a.GetNumCols()); - } - - long m = (long)GetNumRows(), n = (long)GetNumCols(); // a and b are of size (1,n) - //#pragma omp parallel for - - for (long j = 0; j < n; j++) - { - us(0, j) = a(0, j) * b(0, j); - } - for (long j = 0; j - void CPUMatrix::InnerProductWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise, size_t shift, size_t negnumber) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrices is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int k = (int)b.GetNumRows(); - const int l = (int)b.GetNumCols(); - - assert(m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow - assert(m == k && n == l); //converting from size_t to int may cause overflow - if (m != k || n != l) - InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); - - if ((isColWise && m == 1) || !isColWise && n == 1) //in this case it's equivalent to element-wise product - { - InvalidArgument("InnerProduct: Both matrices should be normal ones, not vectors"); - // c.AssignElementProductOf(a, b); - } - else if (isColWise) //col-wise - { - c.Resize(negnumber + 1, n); // this line ischanged - - if (sizeof(ElemType) == sizeof(double)) - { - for (long j = 0; j < n; j++) - { -#ifndef USE_MKL - c(0, j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); -#else - c(0, j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); -#endif - } - for (long j = 0; j < n; j++) - { - for (long i = 1; i < negnumber + 1; i++) - { -#ifndef USE_MKL - c(i, j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); -#else - c(i, j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); -#endif - } - } - - } - else - { - for (long j = 0; j < n; j++) - { -#ifndef USE_MKL - c(0, j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); -#else - c(0, j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); -#endif - } - for (long j = 0; j < n; j++) - { - for (long i = 1; i < negnumber + 1; i++) - { -#ifndef USE_MKL - c(i, j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); -#else - c(i, j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); -#endif - } - } - } - } - else - { - InvalidArgument("InnerProduct: Rowwise is not supported yet"); - - c.Resize(m, 1); - - if (sizeof(ElemType) == sizeof(double)) - { -#pragma omp parallel for - foreach_row(i, c) - { -#ifndef USE_MKL - c(i, 0) = (ElemType)ddot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); -#else - c(i, 0) = (ElemType)cblas_ddot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); -#endif - } - } - else - { -#pragma omp parallel for - foreach_row(i, c) - { -#pragma warning (suppress: 4244) -#ifndef USE_MKL - c(i, 0) = sdot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); -#else - c(i, 0) = cblas_sdot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); -#endif - } - } - } - } - - - template - CPUMatrix& CPUMatrix::GetARowByIndex(const CPUMatrix& a, size_t index) - { - if (a.IsEmpty()) - LogicError("GetARowByIndex: the input matrices is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - - if (index <0 || index >= m) - LogicError("GetARowByIndex: the row index is out of range."); - - assert(m>0 && n>0); //converting from size_t to int may cause overflow - - auto& us = *this; - this->Resize(1, n); - for (long j = 0; j < n; j++) - { - us(0, j) = a(index, j); - } - - return *this; - - } - - - // input: a, a row vector - // input: b, a matrix. b.col == a.col - // input firstmatrixfixed: If true, keep a's order. Otherwise, keep b's order - // output: c, a matrix. c.size == b.size - /* - Example, a = [a1 a2 a3] - b = [b11 b12 b13; - b21 b22 b23 ] - - if true: - shift = 1 - - then c = [a1*b12 a2*b13 a3*b11 - a1*b22 a2*b23 a3*b21] - - if shift = 2 - then c = [ a1*b13 a2*b11 a3*b12 - a1*b23 a2*b21 a3*b22] - i.e. we do column-wise shift - - if false: - shift = 1 - - then c = [a2*b11 a3*b12 a1*b13 - a2*b21 a3*b22 a1*b23] - - shift = 2 - - then c = [ a3*b11 a1*b12 a2*b13 - a3*b21 a1*b22 a2*b23] - - - */ - template - void CPUMatrix::ConductRowElementMultiplyWithShift(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, size_t shift, bool bFirstmatrixfixed) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrices is empty."); - - const int m = (int)a.GetNumRows(); - const int n = (int)a.GetNumCols(); - const int k = (int)b.GetNumRows(); - const int l = (int)b.GetNumCols(); - - assert(m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow - assert(m == 1 && n == l); //converting from size_t to int may cause overflow - if (m != 1 || n != l) - InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); - - c.Resize(k, l); // c must the the same size of b - - if (bFirstmatrixfixed) - { - for (long j = 0; j < l; j++) - { - for (long i = 0; i < k; i++) - { - c(i, j) = a(0, j) * b(i, (j + shift) % l); - } - } - } - else - { - for (long j = 0; j < l; j++) - { - for (long i = 0; i < k; i++) - { - c(i, j) = a(0, (j + shift) % l) * b(i, j); - } - } - } - - - - } - - - // CPUMatrix& AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, size_t shift); - //[this]=a .* b - // here, a and b must be two row vectors of the same size, i.e. [1,m]. We will do element product with shift. - // inputs are 2 row vectors - // output is a row vector - template - CPUMatrix& CPUMatrix::AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, size_t shift) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); - - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix dimensions do not match."); - - if (a.GetNumRows() != 1) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); - - auto& us = *this; - if (this != &a) - { - Resize(1, a.GetNumCols()); - // Resize(a.GetNumRows(), a.GetNumCols()); - } - - //long m = (long)GetNumRows(), n = (long)GetNumCols(); // a and b are of size (1,n) - long n = (long)GetNumCols(); // a and b are of size (1,n) -#pragma omp parallel for - for (long j = 0; j - ElemType CPUMatrix::LogAddSumOfElements() const - { - ElemType fAlpha = (ElemType)LZERO; - for (int k = 0; k < GetNumElements(); k++) - fAlpha = (ElemType) LogAddD(fAlpha, m_pArray[k]); - return fAlpha; - } - - template - void CPUMatrix::RCRFBackwardCompute(const CPUMatrix& alpha, CPUMatrix& beta, - const CPUMatrix& lbls, - const CPUMatrix& pair_scores) - { - int iNumPos = (int)lbls.GetNumCols(); - int iNumLab = (int)lbls.GetNumRows(); - - int lastLbl = -1; - for (int ik = 0; ik < lbls.GetNumRows(); ik++) - if (lbls(ik, iNumPos - 1) != 0){ - lastLbl = ik; break; - } - - beta.Resize(iNumLab, iNumPos); - - for (int t = iNumPos - 1; t >= 0; t--) - { -#pragma omp parallel for - for (int k = 0; k < iNumLab; k++) - { - _rcrfBackwardCompute(t, k, alpha, beta, pair_scores); - } - } - - }; - - /// the kernel function for RCRF backward computation - template - void CPUMatrix::_rcrfBackwardCompute(size_t t, size_t k, const CPUMatrix& alpha, - CPUMatrix& beta, - const CPUMatrix& pair_scores) - { - size_t iNumLab = alpha.GetNumRows(); - size_t iNumPos = alpha.GetNumCols(); - - ElemType fSum; - ElemType fTmp = (ElemType)LZERO; - if (t == iNumPos - 1) - { - fSum = (ElemType)LZERO; - for (int j = 0; j < iNumLab; j++) - { - fSum = (ElemType)LogAddD(fSum, alpha(j, t)); - } - - fTmp = alpha(k, t) - fSum; - beta(k, t) = fTmp; - } - else - { - for (int j = 0; j < iNumLab; j++) - { - fSum = (ElemType)LZERO; - for (int m = 0; m < iNumLab; m++) - { - fSum = (ElemType)LogAddD(fSum, alpha(m, t) + pair_scores(j, m)); - } - - fTmp = (ElemType)LogAddD(fTmp, beta(j, t + 1) + alpha(k, t) + pair_scores(j, k) - fSum); - } - beta(k, t) = fTmp; - } - } - - template - void CPUMatrix::RCRFTransGrdCompute(const CPUMatrix& lbls, - const CPUMatrix& alpha, - const CPUMatrix& beta, - const CPUMatrix& pair_scores, - CPUMatrix& grd) - { - int iNumPos = (int)alpha.GetNumCols(); - int iNumLab = (int)alpha.GetNumRows(); - - int firstLbl = -1; - for (int ik = 0; ik < lbls.GetNumRows(); ik++) - if (lbls(ik, 0) != 0){ - firstLbl = ik; break; - } - - for (size_t tPos = 0; tPos < iNumPos; tPos++) - { - CPUMatrix b = beta.ColumnSlice(tPos, 1); - CPUMatrix a; - if (tPos > 0) - a = alpha.ColumnSlice(tPos - 1, 1); - -#pragma omp parallel for - for (int i = 0; i < iNumLab; i++){ - _rcrfTransGrdCompute(i, lbls, alpha, beta, pair_scores, grd, tPos); - } - - /// transition score - int i = -1; - if (tPos == 0) i = firstLbl; - else { - for (int ik = 0; ik < lbls.GetNumRows(); ik++) - if (lbls(ik, tPos - 1) != 0){ - i = ik; break; - } - } - - int j = -1; - for (int ik = 0; ik < lbls.GetNumRows(); ik++){ - if (lbls(ik, tPos) != 0){ - j = ik; break; - } - } - - grd(j, i) -= 1.0; - } - }; - - template - void CPUMatrix::_rcrfTransGrdCompute(size_t i, - const CPUMatrix& lbls, - const CPUMatrix& alpha, - const CPUMatrix& beta, - const CPUMatrix& pair_scores, - CPUMatrix& grd, - const size_t tPos /// position - ) - { - int iNumLab = (int)alpha.GetNumRows(); - - int firstLbl = -1; - for (int ik = 0; ik < lbls.GetNumRows(); ik++) - if (lbls(ik, 0) != 0){ - firstLbl = ik; break; - } - - CPUMatrix b = beta.ColumnSlice(tPos, 1); - CPUMatrix a; - if (tPos > 0) - a = alpha.ColumnSlice(tPos - 1, 1); - - { - ElemType fTmp = (ElemType)LZERO; - for (int j = 0; j < iNumLab; j++){ - if (tPos == 0){ - if (i == firstLbl){ - fTmp = 0; - } - else{ - fTmp = (ElemType)LZERO; - } - } - else{ - fTmp = a(i, 0); - } - fTmp += pair_scores(j, i); - - - ElemType fSum = (ElemType)LZERO; - for (int k = 0; k < iNumLab; k++){ - ElemType fTmp2; - if (tPos == 0){ - if (k == firstLbl){ - fTmp2 = 0; - } - else{ - fTmp2 = (ElemType)LZERO; - } - } - else{ - fTmp2 = a(k, 0); - } - fSum = (ElemType)LogAddD(fSum, fTmp2 + pair_scores(j, k)); - } - - fTmp -= fSum; - fTmp += b(j, 0); - - grd(j, i) += exp(fTmp); - } - } - }; - template - CPUMatrix& CPUMatrix::DropFrame(const CPUMatrix& label, const CPUMatrix& gamma, const ElemType & threshhold) - { - auto& us = *this; - if (us.GetNumCols() != gamma.GetNumCols() || us.GetNumRows() != gamma.GetNumRows()) - LogicError("DropFrame: target matrix is not in the same size as gamm matrix."); - -#pragma omp parallel for - foreach_column(j, label) - { - - bool dropframe = false; - foreach_row(i, label) - { - if (fabs(label(i, j) - 1.0f) < 0.1) - { - if (gamma(i, j) < threshhold) - dropframe = true; - break; - } - } - - foreach_row(i, label) - { - us(i, j) = 0.0f; - } - } - - return *this; - } - - template - CPUMatrix& CPUMatrix::AssignSequenceError(const ElemType hsmoothingWeight, const CPUMatrix& label, - const CPUMatrix& dnnoutput, const CPUMatrix& gamma, ElemType alpha) - { - auto& us = *this; - foreach_coord(i, j, us) - us(i, j) += alpha * (label(i, j) - (1 - hsmoothingWeight)*dnnoutput(i, j) - hsmoothingWeight*gamma(i, j)); - return *this; - } - - // note: this function does not depend on the parameter - template - int CPUMatrix::SetNumThreads(int numThreads) - { - if (numThreads == 0) //use default - return numThreads; - - int mthreads = (int)std::thread::hardware_concurrency(); - - if (numThreads <= 0) - numThreads = max(1, mthreads + numThreads); - if (numThreads > mthreads) - numThreads = mthreads; - -#ifdef _OPENMP - omp_set_num_threads(numThreads); - numThreads = omp_get_max_threads(); - -#ifndef USE_MKL - acmlsetnumthreads(numThreads); -#else - mkl_set_num_threads(numThreads); -#endif -#endif - return numThreads; - } - - // ======================================================================= - // TensorView support - // ======================================================================= - - // To save time, this makes extensive use of templates and macros. - - // ----------------------------------------------------------------------- - // function to compute the value for a given output location (perform reduction if needed) - // ----------------------------------------------------------------------- - - // perform loop over reduction index m - // This function is declared inside a wrapper struct to allow partial specialization (m = -1). - template - struct TensorOpReduction - { - // reduction case (non-reduction case is specialized) - static inline ElemType Loop(array pointers, const OPFN & opfn, - const SmallVector & reducingOpDims, const array, N> & reducingStrides) - { - array strides; // N-1 because last one is the result pointer, which is unused in reduction - for (size_t i = 0; i < N - 1; i++) // N = a small constant, this will be unrolled - strides[i] = reducingStrides[i][(size_t)m]; - double/*ElemType*/ aggregate = 0; - for (size_t dim = reducingOpDims[(size_t)m]; dim-- > 0;) - { - // need to descend into one loop deeper - aggregate += TensorOpReduction::Loop(pointers, opfn, reducingOpDims, reducingStrides); - // advance the pointers - for (size_t i = 0; i < N - 1; i++) - pointers[i] += strides[i]; // note: last pointer (result) is unused and untouched here - } - return (ElemType)aggregate; - } - }; - - // perform loop over reduction index m - // This is the specialized version for m = -1, which terminates the recursion. - template - struct TensorOpReduction - { - static inline ElemType Loop(array pointers, const OPFN & opfn, - const SmallVector &, const array, N> &) - { - return opfn(pointers); // finally we are doing some work!!! - } - }; - - // ----------------------------------------------------------------------- - // perform loop over regular index k for N-nary operations (N counting the output) - // ----------------------------------------------------------------------- - - // perform loop over regular index k and reducing index m for N operands (counting the output) - template - struct TensorOpIteration - { - static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, - const SmallVector & regularOpDims, const array, N> & regularStrides, - const SmallVector & reducingOpDims, const array, N> & reducingStrides) - { - // non-scalar case: still nested result loops left - array strides; - for (size_t i = 0; i < N; i++) // N = a small constant, this will be unrolled - strides[i] = regularStrides[i][(size_t)k]; - for (size_t dim = regularOpDims[(size_t)k]; dim--> 0;) - { - // need to descend into one loop deeper - TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - // advance the pointers - for (size_t i = 0; i < N; i++) - pointers[i] += strides[i]; - } - } - }; - - // Special version for innermost loop with strides all being 1 and no further reduction. Compiler can use SSE. - // This is a very common case, e.g. adding vectors or computing the Sigmoid. - template - struct TensorOpIteration - { - static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, - const SmallVector & regularOpDims, const array, 3> & regularStrides, - const SmallVector & reducingOpDims, const array, 3> & reducingStrides) - { - ElemType* pa = pointers[0]; - ElemType* pb = pointers[1]; - ElemType* pc = pointers[2]; - size_t K = regularOpDims[0]; - // special-case beta and alpha to allow the compiler to short-circuit it - if (beta != 0) -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(beta, array { pa + k, pb + k, pc + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - else if (alpha != 1) -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(0, array { pa + k, pb + k, pc + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - else -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(0, array { pa + k, pb + k, pc + k }, 1, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - // TODO: According to Amit, the VS compiler is not able to vectorize into lambdas. Solution: change the lambda to take an N, or to implement the loop inside (with 1 element by default). - // TODO: The signedness of k (required for omp) causes an extra sign-extend. - // TODO: OMP adds LOTS of overhead. Do we need a guard, a min size when to use it? - } - }; - // and unary - template - struct TensorOpIteration - { - static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, - const SmallVector & regularOpDims, const array, 2> & regularStrides, - const SmallVector & reducingOpDims, const array, 2> & reducingStrides) - { - ElemType* pa = pointers[0]; - ElemType* pb = pointers[1]; - size_t K = regularOpDims[0]; - // special-case beta and alpha to allow the compiler to short-circuit it - if (beta != 0) -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(beta, array { pa + k, pb + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - else if (alpha != 1) -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(0, array { pa + k, pb + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - else -#pragma omp parallel for - for (int k = 0; k < (int)K; k++) - TensorOpIteration::Loop(0, array { pa + k, pb + k }, 1, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - } - }; - - template - struct TensorOpIteration - { - static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, - const SmallVector &, const array, N> &, - const SmallVector & reducingOpDims, const array, N> & reducingStrides) - { - // we are at element level for the result: perform the op (there may still be reduction) - ElemType val = TensorOpReduction::Loop(pointers, opfn, reducingOpDims, reducingStrides); - // scale - val *= alpha; - // combine with previous value in target matrix, then write it out - auto * pout = pointers.back(); - if (beta != 0) - val += beta * *pout; - // save - *pout = val; - return; - } - }; - - // ----------------------------------------------------------------------- - // map runtime parameters N to template parameters - // ----------------------------------------------------------------------- - - // tensor operation with k+1 dimensions (-1 means scalar) - template - static void TensorOpWithRegularLoop(ElemType beta, const array & pointers, ElemType alpha, const OPFN & opfn, - const SmallVector & regularOpDims, const array, N> & regularStrides, - const SmallVector & reducingOpDims, const array, N> & reducingStrides) - { - size_t dims = reducingOpDims.size(); - switch (dims) - { - case 2: return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 1: return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 0: - { - // if all leading dimensions are 1, we can let the compiler do some unrolling - bool leadingAllOne = true; - for (size_t i = 0; i < N; i++) - leadingAllOne &= k >= 0 && regularStrides[i][0] == 1; - if (leadingAllOne) // special version that uses a hard-coded increment of 1 for all leading dimensions - return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - else - return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - } - default: LogicError("TensorOp: %d non-flattened reduction dimensions are not supported.", (int)dims); - } - } - - // tensor operation, generalized in number of arguments, operation already provided as a lambda - // This function now expands into different k. - template - static void TensorOpWithFn(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, - const array & offsets, - const SmallVector & regularOpDims, const array, N> & regularStrides, - const SmallVector & reducingOpDims, const array, N> & reducingStrides) - { - for (size_t i = 0; i < N; i++) // N = a small constant, this will be unrolled - pointers[i] += offsets[i]; - size_t dims = regularOpDims.size(); - switch (dims) - { - case 4: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 3: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 2: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 1: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - case 0: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); - default: LogicError("TensorOp: %d non-flattened input dimensions are not supported.", (int)dims); - } - } - - // ----------------------------------------------------------------------- - // entry points from Matrix.cpp; also map op to a lambda - // ----------------------------------------------------------------------- - - // perform unary operation 'op' on a giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides - // This maps 'op' to a lambda. - template - void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 2> & regularStrides, - const SmallVector & reducingOpDims, const array, 2> & reducingStrides) - { - // TODO: Change the lambda to take a pointer and a number of elements, so that we can pass it 1 or 4 elements, in order for it to SSE-vectorize. - #define CaseUnaryTensorOp(oper) \ - case ElementWiseOperator::op ## oper: \ - return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) - - array pointers = { a.m_pArray, m_pArray }; - switch (op) - { - ForAllUnaryOps(CaseUnaryTensorOp); - default: LogicError("TensorUnaryOp: Unknown op code %d.", (int)op); - } - } - - // perform binary operation 'op' on a and b giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides - // This maps 'op' to a lambda. - template - void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, const CPUMatrix& b, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 3> & regularStrides, - const SmallVector & reducingOpDims, const array, 3> & reducingStrides) - { - #define CaseBinaryTensorOp(oper) \ - case ElementWiseOperator::op ## oper: \ - return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0])), (*(pp[1]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) - - array pointers = { a.m_pArray, b.m_pArray, m_pArray }; - switch (op) - { - ForAllBinaryOps(CaseBinaryTensorOp); - default: LogicError("TensorBinaryOp: Unknown op code %d.", (int)op); - } - } - - // perform ternary operation 'op' on a, and c giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides - // This maps 'op' to a lambda. - template - void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, const CPUMatrix& b, const CPUMatrix& c, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 4> & regularStrides, - const SmallVector & reducingOpDims, const array, 4> & reducingStrides) - { - #define CaseTernaryTensorOp(oper) \ - case ElementWiseOperator::op ## oper: \ - return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0])), (*(pp[1])), (*(pp[2]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) - - array pointers = { a.m_pArray, b.m_pArray, c.m_pArray, m_pArray }; - switch (op) - { - ForAllTernaryOps(CaseTernaryTensorOp); - default: LogicError("TensorTernaryOp: Unknown op code %d.", (int)op); - } - } - - // ======================================================================= - // explicit instantiations - // ======================================================================= - template class MATH_API CPUMatrix; - template class MATH_API CPUMatrix; - - // We use Matrix as the backing store for QuantizedMatrix - // Let's explicitly instantiate the methods we need for that purpose - template CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols); - template CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols, char* pArray, const size_t matrixFlags); - template CPUMatrix::CPUMatrix(); - template CPUMatrix::CPUMatrix(CPUMatrix const &); - template CPUMatrix::CPUMatrix(CPUMatrix&&); - template size_t CPUMatrix::LocateElement(size_t, size_t) const; - template CPUMatrix::~CPUMatrix(); - template CPUMatrix CPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const; - template CPUMatrix& CPUMatrix::operator=(CPUMatrix&&); - template void CPUMatrix::SetValue(const char); - template void CPUMatrix::SetValue(const size_t numRows, const size_t numCols, char *pArray, size_t matrixFlags); - template void CPUMatrix::SetValue(CPUMatrix const&); - template void CPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly); - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// CPUMatrix.cpp : full implementation of all matrix functions on the CPU side +// + +#include "stdafx.h" +#include "Basics.h" +#include "File.h" + +#include "CPUMatrix.h" +#include "TensorOps.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#ifdef _WIN32 +#include +#else +#ifndef max +#define max(a,b) (((a) > (b)) ? (a) : (b)) +#endif +#include +#endif + +#ifdef LEAKDETECT +#include +#endif + +#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this +#pragma warning (disable: 4702) // unreachable code; triggered for unknown reasons + +#ifndef USE_MKL +// use ACML as default. +// Download ACML 5.3.1 (e.g., acml5.3.1-ifort64.exe) or above +// from http://developer.amd.com/tools/cpu-development/amd-core-math-library-acml/acml-downloads-resources/ +// Install the ifort64_mp variant (compiled with intel compiler) of the library +// Set Environment variable ACML_PATH to C:\AMD\acml5.3.1\ifort64_mp or the folder you installed acml +// to point to your folder for the include file and link library +#include // requires ACML 5.3.1 and above +#else +// requires MKL 10.0 and above +#include +#endif + +#ifndef USE_MKL //MKL has one additional parameter for different matrix order +#define BLAS_COLMAJOR +#else +#define BLAS_COLMAJOR (int)MatrixOrder::ColMajor, +#endif + +#define SWAP(a,b) {(a) ^= (b); (b) ^= (a); (a) ^= (b);} +#define IDX2C(i,j,ld) (((j)*(ld))+(i)) // 0 based indexing +namespace Microsoft { namespace MSR { namespace CNTK { + +#pragma region Helpful Enum Definitions + enum class MatrixOrder + { + RowMajor = 101, // row-major arrays + ColMajor = 102 // column-major arrays + }; + + enum class MatrixTranspose : char + { + NoTrans = 'N', // trans='N' + Trans = 'T', // trans='T' + ConjTrans = 'C' // trans='C' + }; + + enum class SymMatrixType : char + { + Up = 'U', // symmetric matrix is stored in the upper part + Low = 'L', // symmetric matrix is stored in thelower part + Full = 'F', //full populated + NotSymmetric = 'N' //not a symmetric matrix + }; + + enum class MatrixOpSide : char + { + Left = 'L', // left multiply + Right = 'R', // right multiply + }; +#pragma endregion Helpful Enum Definitions + +#pragma region Constructors and Destructor + + //should only be used by constructors. + template + void CPUMatrix::ZeroInit() + { + m_computeDevice = CPUDEVICE; + m_pArray = nullptr; + m_numRows = 0; + m_numCols = 0; + m_elemSizeAllocated = 0; + m_matrixName=NULL; + m_format = matrixFormatDense; + m_externalBuffer = false; + } + + template + CPUMatrix::CPUMatrix() + { + ZeroInit(); + } + + //matrixName is used to verify that correct matrix is read. + template + CPUMatrix::CPUMatrix(FILE* f, const char * matrixName) + { + ZeroInit(); + ReadFromFile(f, matrixName); + } + + // helper to allocate an array of ElemType + // Use this instead of new[] to get NaN initialization for debugging. + template + static ElemType * NewArray(size_t n) + { + ElemType * p = new ElemType[n](); +#if 0//_DEBUG + ElemType nan = Matrix::MakeNan(__LINE__); + for (size_t i = 0; i < n; i++) + p[i] = nan; +#endif + return p; + } + + template + CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols) + { + ZeroInit(); + + m_numRows = numRows; + m_numCols = numCols; + m_elemSizeAllocated = GetNumElements(); + + if (m_elemSizeAllocated != 0) + m_pArray = NewArray(m_elemSizeAllocated); + } + + template + CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags) + { + ZeroInit(); + SetValue(numRows, numCols, pArray, matrixFlags); + } + + //copy constructor, deep copy + template + CPUMatrix::CPUMatrix(const CPUMatrix& deepCopyFrom) + { + ZeroInit(); + if (!deepCopyFrom.IsEmpty()) + SetValue(deepCopyFrom); + SetMatrixName(deepCopyFrom.m_matrixName); + } + + //assignment operator, deep copy + template + CPUMatrix& CPUMatrix::operator=(const CPUMatrix& deepCopyFrom) + { + Clear(); + if (!deepCopyFrom.IsEmpty()) + SetValue(deepCopyFrom); + SetMatrixName(deepCopyFrom.m_matrixName); + return *this; + } + + + //move constructor, shallow copy + template + CPUMatrix::CPUMatrix(CPUMatrix&& moveFrom) + { + m_computeDevice = moveFrom.m_computeDevice; + m_numRows = moveFrom.m_numRows; + m_numCols = moveFrom.m_numCols; + m_elemSizeAllocated = moveFrom.m_elemSizeAllocated; + m_pArray = moveFrom.m_pArray; //shallow copy the pointer + m_matrixName = moveFrom.m_matrixName; + m_format = moveFrom.m_format; + m_externalBuffer = moveFrom.m_externalBuffer; + //release the pointer from the source object so that the destructor won't release it twice + moveFrom.ZeroInit(); + } + + //move assignment operator, shallow copy + template + CPUMatrix& CPUMatrix::operator=(CPUMatrix&& moveFrom) + { + if (this != &moveFrom) + { + if (OwnBuffer() && m_pArray != nullptr) + delete[] m_pArray; //always delete the data pointer since we will use the pointer from moveFrom + + m_computeDevice = moveFrom.m_computeDevice; + m_numRows = moveFrom.m_numRows; + m_numCols = moveFrom.m_numCols; + m_elemSizeAllocated = moveFrom.m_elemSizeAllocated; + m_pArray = moveFrom.m_pArray; + m_format = moveFrom.m_format; + m_externalBuffer = moveFrom.m_externalBuffer; + + //release the pointer from the source object so that the destructor won't release it twice + moveFrom.ZeroInit(); + } + return *this; + } + + template + CPUMatrix::~CPUMatrix() + { + Clear(); + } + + template + void CPUMatrix::Clear() + { + if (m_pArray!=nullptr && OwnBuffer()) + { + delete [] m_pArray; + m_pArray = nullptr; + m_elemSizeAllocated = 0; + } + BaseMatrix::Clear(); + + ZeroInit(); + } + +#pragma endregion Constructors and Destructor + +#pragma region Basic Operators + + template + CPUMatrix CPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const + { + //if (numCols == 0) + // LogicError("The slice cannot have 0 columns."); + + if (startColumn + numCols > m_numCols) + InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int)startColumn, (int)numCols, (int)m_numCols); + + CPUMatrix slice; + + slice.m_externalBuffer = true; //memory of a slice is managed externally. + slice.m_numRows = m_numRows; + slice.m_numCols = numCols; + slice.m_elemSizeAllocated = slice.GetNumElements(); + slice.m_pArray = m_pArray + startColumn * m_numRows; + slice.m_format = m_format; + + return slice; + } + + // set this(:, 0:numCols-1) = fromMatrix(:, startColumn : startColumn+numCols-1) + // TODO: why not say *this = ColumnSlice()? + template + CPUMatrix& CPUMatrix::AssignColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols) + { + //if (numCols == 0) + // LogicError("The slice cannot have 0 columns."); + + if (startColumn + numCols > fromMatrix.m_numCols) + InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int)startColumn, (int)numCols, (int)fromMatrix.m_numCols); + + Clear(); + + SetOwnBuffer(false); //memory of a slice is managed externally. + m_numRows = fromMatrix.m_numRows; + m_numCols = numCols; + m_elemSizeAllocated = GetNumElements(); + m_pArray = fromMatrix.m_pArray + startColumn *m_numRows; + + return *this; + } + + // set this(: , startColumn:startColumn+numCols-1)= fromMatrix; + template + CPUMatrix& CPUMatrix::SetColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols) + { + //if (numCols == 0) + // LogicError("The slice cannot have 0 columns."); + if (startColumn + numCols > m_numCols) + LogicError("The slice is out of range of the destination matrix."); + if (numCols > fromMatrix.GetNumCols()) + InvalidArgument("The slice (%d) is out of range of the source matrix (%d).", (int)numCols, (int)fromMatrix.GetNumCols()); + if (m_numRows != fromMatrix.m_numRows) + LogicError("The number of rows in source and destination matrices do not match"); + + //SetOwnBuffer(false); + memcpy(m_pArray + startColumn*m_numRows, fromMatrix.m_pArray, numCols*m_numRows*sizeof(ElemType)); + + return *this; + } + + template + void CPUMatrix::CopyColumnsStrided(const CPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) + { + if ((((numCols - 1) * srcNumColsStride) + 1) > fromMatrix.m_numCols) + LogicError("The numCols to copy and srcNumColsStride specified is out of range of the source matrix."); + if ((((numCols - 1) * destNumColsStride) + 1) > m_numCols) + LogicError("The numCols to copy and srcNumColsStride specified is out of range of the destination matrix."); + if (m_numRows != fromMatrix.m_numRows) + LogicError("The number of rows in source and destination matrices do not match"); + + long n = (long)numCols, m = (long)m_numRows; + + auto& us = *this; + +#pragma omp parallel for + for (long j = 0; j + CPUMatrix& CPUMatrix::AssignToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) + { + if (a.GetNumRows() != numRows) + LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows."); + + if (startIndex + numRows > GetNumRows()) + LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows()."); + + if (a.GetNumCols() != GetNumCols()) + LogicError("AddToRowSliceValuesOf: columns does not match."); + + long n = (long)a.GetNumCols(), m = (long)numRows; + + auto& us = *this; + +#pragma omp parallel for + for (long j = 0; j + CPUMatrix& CPUMatrix::AssignRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) + { + if (startIndex + numRows > a.GetNumRows()) + LogicError("AssignRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows()."); + + Resize(numRows, a.GetNumCols()); + + long n = (long)a.GetNumCols(); // note: OpenMP requires loop indices to be long, not size_t + long k = (long)a.GetNumRows(); + +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::AddToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) + { + if (a.IsEmpty()) + LogicError("AddToRowSliceValuesOf: input matrix a is empty."); + + if (a.GetNumRows() != numRows) + LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows."); + + if (startIndex + numRows > GetNumRows()) + LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows()."); + + if (a.GetNumCols() != GetNumCols()) + LogicError("AddToRowSliceValuesOf: columns does not match."); + + long n=(long)a.GetNumCols(), m=(long)numRows; + + auto& us = *this; + +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::AddWithRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows) + { + if (a.IsEmpty()) + LogicError("AddWithRowSliceValuesOf: input matrix a is empty."); + + if (GetNumRows() != numRows) + LogicError("AddWithRowSliceValuesOf: GetNumRows() != numRows."); + + if (startIndex + numRows > a.GetNumRows()) + LogicError("AddWithRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows()."); + + if (a.GetNumCols() != GetNumCols()) + LogicError("AddWithRowSliceValuesOf: columns does not match."); + + long n = (long)a.GetNumCols(), m = (long)numRows; + + auto& us = *this; + +#pragma omp parallel for + for (long j = 0; j + CPUMatrix CPUMatrix::Diagonal() const + { + if (m_numRows != m_numCols) + LogicError("Diagonal can be called only for square matrix. (rows=%d, cols=%d)", (int)m_numRows, (int)m_numCols); + + CPUMatrix diag(1, m_numCols); + + auto& us = *this; + +#pragma omp parallel for + for (long i = 0; i < m_numRows; i++) + { + diag(0, (size_t)i) = us(i, i); + } + + return diag; + } + +#if 0 + //stack the columns in inputMatrices (starting from sliceStartCol for sliceNumCols columns) and assign it to [this] object. + template + CPUMatrix& CPUMatrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) + { + if (sliceNumCols == 0) + LogicError("AssignRowStackValuesOf: sliceNumCols should > 0."); + + size_t totalRows = 0; + size_t* startRowIndeces = new size_t[inputMatrices.size()]; + startRowIndeces[0] = 0; + for (int i = 0; i < inputMatrices.size(); i++) + { + const CPUMatrix& a = *inputMatrices[i]; + if (a.IsEmpty()) + LogicError("AssignRowStackValuesOf: input matrix (%d) is empty.", i); + + if (a.GetNumCols() < sliceStartCol + sliceNumCols) + LogicError("AssignRowStackValuesOf: input matrix (%d) GetNumCols() < sliceStartCol + sliceNumCols.", i); + + totalRows += a.GetNumRows(); + if (iGetNumRows() * sizeof(ElemType)); + } + } + + delete [] startRowIndeces; + + return *this; + } +#endif + + template + void CPUMatrix::MinusOneAt(CPUMatrix& c, const size_t position) + { + if (position < c.GetNumElements()) + c.m_pArray[position] -= 1.0; + else + RuntimeError("MinusOneAt: position is out of CPU matrix size"); + } + + template + CPUMatrix& CPUMatrix::AssignRepeatOf(const CPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats) + { + if (this == &a) + LogicError("AssignRepeatOf: a is the same as [this]. Does not support inplace repeat."); + + if (a.IsEmpty()) + LogicError("AssignRepeatOf: Matrix a is empty."); + + Resize(a.GetNumRows() * numRowRepeats, a.GetNumCols() * numColRepeats); + long n = (long)a.GetNumCols(), m = (long)a.GetNumRows(); + auto& us = *this; + +#pragma omp parallel for + for (long q = 0; q < numColRepeats; q++) + { + for (long p = 0; p < numRowRepeats; p++) + { + long colOffset = q*n; + + for (long j = 0; j < n; j++, colOffset++) + { + long rowOffset = p*m; + + //four-way unrolling + for (long i = 0; i < (m & ~3); i += 4, rowOffset += 4) + { + us(rowOffset, colOffset) = a(i, j); + us(rowOffset + 1, colOffset) = a(i + 1, j); + us(rowOffset + 2, colOffset) = a(i + 2, j); + us(rowOffset + 3, colOffset) = a(i + 3, j); + } + //handle remaining stuffs + for (long i = m & ~3; i < m; i++, rowOffset++) + { + us(rowOffset, colOffset) = a(i, j); + } + } + } + } + + return *this; + } + + template + CPUMatrix& CPUMatrix::AddToRowRepeatValuesOf(const CPUMatrix& a, const size_t numRepeats) + { + if (a.IsEmpty()) + LogicError("AddToRowRepeatValuesOf: input matrix a is empty."); + + if (a.GetNumRows() != GetNumRows() * numRepeats) + LogicError("AddToRowRepeatValuesOf: a.GetNumRows() != GetNumRows() * numRepeats."); + + long n = (long)a.GetNumCols(), m = (long)GetNumRows(); + + auto& us = *this; + +#pragma omp parallel for + for (long j = 0; j + CPUMatrix& CPUMatrix::AssignPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) + { + a; posNumber; negNumber; shiftNumber; + NOT_IMPLEMENTED; + } + + template + CPUMatrix& CPUMatrix::AddFoldedPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) + { + a; posNumber; negNumber; shiftNumber; + NOT_IMPLEMENTED; + } + + template + CPUMatrix CPUMatrix::Transpose() + { + if (IsEmpty()) + LogicError("Transpose: Matrix is empty."); + + CPUMatrix c; + c.AssignTransposeOf(*this); + return c; + } + + template + CPUMatrix& CPUMatrix::AssignTransposeOf (const CPUMatrix& a) + { + if (this == &a) + LogicError("AssignTransposeOf: a is the same as [this]. Does not support inplace transpose."); + + if (a.IsEmpty()) + LogicError("AssignTransposeOf: Matrix a is empty."); + + Resize(a.GetNumCols(), a.GetNumRows()); + long n=(long)a.GetNumCols(), m=(long)a.GetNumRows(); + + auto& us = *this; + +#pragma omp parallel for + for (long j=0; j + void CPUMatrix::SetValue(const ElemType v) + { + if (IsEmpty()) + LogicError("SetValue: Matrix is empty."); + bool isFinite = std::numeric_limits::is_integer || std::isfinite((double)v); + if (isFinite && v == 0) + { + memset(m_pArray, 0, sizeof(ElemType) * GetNumElements()); + } + else + { + long m=(long)GetNumElements(); + // 2-way thread parallelism is sufficient for the memory bound + // operation of just setting the values of an array. + const unsigned SETVALUE_NUM_THREADS = 2; +#pragma omp parallel for num_threads(SETVALUE_NUM_THREADS) + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + m_pArray[i] = v; + m_pArray[i+1] = v; + m_pArray[i+2] = v; + m_pArray[i+3] = v; + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::MaskColumnsValue(const CPUMatrix& columnsMask, ElemType val) + { + if (GetNumCols() != columnsMask.GetNumCols()) + RuntimeError("Matrix and column mask must have equal number of columns"); + + auto& us = *this; + long n = (long)GetNumCols(), m = (long)GetNumRows(); +#pragma omp parallel for + for (long j = 0; j + void CPUMatrix::SetColumn(const ElemType* colPointer, size_t j) + { + if (IsEmpty()) + LogicError("SetColumn: Matrix is empty."); + if (colPointer==NULL) + return; + + auto& us = *this; + long m=(long)GetNumRows(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + us(i,j) = colPointer[i]; + us(i+1,j) = colPointer[i+1]; + us(i+2,j) = colPointer[i+2]; + us(i+3,j) = colPointer[i+3]; + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetColumn(const ElemType val, size_t j) + { + if (IsEmpty()) + LogicError("SetColumn: Matrix is empty."); + + auto& us = *this; + long m=(long)GetNumRows(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + us(i,j) = val; + us(i+1,j) = val; + us(i+2,j) = val; + us(i+3,j) = val; + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetColumn(const CPUMatrix& valMat, size_t j) + { + if (IsEmpty()) + LogicError("SetColumn: Matrix is empty."); + assert(valMat.GetNumRows() == GetNumRows() && valMat.GetNumCols() == 1) ; + + auto& us = *this; + long m=(long)GetNumRows(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + us(i,j) = valMat(i,0); + us(i+1,j) = valMat(i+1,0); + us(i+2,j) = valMat(i+2,0); + us(i+3,j) = valMat(i+3,0); + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetValue(const CPUMatrix& deepCopyFrom) + { + if (this == &deepCopyFrom) + return; + + Resize(deepCopyFrom.GetNumRows(), deepCopyFrom.GetNumCols()); + memcpy(m_pArray, deepCopyFrom.m_pArray, deepCopyFrom.GetNumElements() * sizeof(ElemType)); + } + + template + void CPUMatrix::SetValue(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags) + { + if (pArray == nullptr) + InvalidArgument("Invalid pArray."); + + m_format = matrixFormatDense; + m_computeDevice = CPUDEVICE; + + // if it's externally managed, then populate the structure + if (matrixFlags&matrixFlagDontOwnBuffer) + { + // free previous array allocation if any before overwriting + if (m_pArray != nullptr) + delete [] m_pArray; + + m_pArray = pArray; + m_numRows = numRows; + m_numCols = numCols; + m_elemSizeAllocated = GetNumElements(); + m_externalBuffer = true; + } + else + { + Resize(numRows, numCols); + + if (IsEmpty()) + { + InvalidArgument("NumRows or NumCols is 0. Nothing to copy"); + } + else + { + if (!(matrixFlags&matrixFormatRowMajor)) //compatible to internal structure + { + memcpy(m_pArray, pArray, GetNumElements()*sizeof(ElemType)); + } + else //need to transpose + { + auto& us = *this; + if (sizeof(ElemType) == sizeof(double)) + { + #pragma omp parallel for + foreach_column(j, us) + { + #ifndef USE_MKL + dcopy((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); + #else + cblas_dcopy ((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); + #endif + } + } + else + { + #pragma omp parallel for + foreach_column(j, us) + { + { + #pragma warning (suppress: 4244) + #ifndef USE_MKL + scopy((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); + #else + cblas_scopy ((int)numRows, reinterpret_cast (pArray+j), (int)numCols, reinterpret_cast (m_pArray + LocateColumn(j)), 1); + #endif + } + } + } + } + } + } + } + + template + void CPUMatrix::SetDiagonalValue(const ElemType v) + { + if (IsEmpty()) + LogicError("SetDiagonalValue: Matrix is empty."); + + if (GetNumRows() != GetNumCols()) + LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); + + auto& us = *this; + long m=(long)GetNumRows(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + us(i,i) = v; + us(i+1,i+1) = v; + us(i+2,i+2) = v; + us(i+3,i+3) = v; + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetDiagonalValue(const CPUMatrix& vector) + { + if (IsEmpty() || vector.IsEmpty()) + LogicError("SetDiagonalValue: Matrix is empty."); + + if (GetNumRows() != GetNumCols()) + LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); + + if (vector.GetNumRows() != 1 && vector.GetNumCols() != 1) + LogicError("SetDiagonalValue: input vector must be a vector."); + + if (vector.GetNumElements() == 1) //reduce to simple form + SetDiagonalValue(vector(0,0)); + else if (vector.GetNumRows() != GetNumRows()) + LogicError("SetDiagonalValue: input vector's dimension does not agree with [this]."); + else + { + auto& us = *this; + + long m=(long)GetNumRows(); + if (vector.GetNumRows() == 1) //row vector + { +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + us(i,i) = vector(0, i); + us(i+1,i+1) = vector(0, i+1); + us(i+2,i+2) = vector(0, i+2); + us(i+3,i+3) = vector(0, i+3); + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) + { + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + +#ifdef _MSC_VER // TODO: check if available under GCC/Linux + std::ranlux64_base_01 generator; + generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); +#else + std::default_random_engine generator (seed); +#endif + std::uniform_real_distribution r(low, high); + + long m=(long)GetNumElements(); + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + m_pArray[i] = r(generator); + m_pArray[i+1] = r(generator); + m_pArray[i+2] = r(generator); + m_pArray[i+3] = r(generator); + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) + { + if (sigma <= 0) + InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); + + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + auto& us = *this; +#ifdef _MSC_VER // TODO: check if available under GCC/Linux + std::ranlux64_base_01 generator; + generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); +#else + std::default_random_engine generator (seed); +#endif + std::normal_distribution r(mean, sigma); + //#pragma omp parallel for //is it thread safe? + foreach_coord(i,j,us) + { + us(i,j) = r(generator); + } + } + + template + void CPUMatrix::AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) + { + if (sigma <= 0) + InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); + + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + auto& us = *this; +#ifdef _MSC_VER // TODO: check if available under GCC/Linux + std::ranlux64_base_01 generator; + generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); +#else + std::default_random_engine generator (seed); +#endif + std::normal_distribution r(mean, sigma); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); + for (long j=0; j + void CPUMatrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) + { + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + auto& us = *this; +#ifdef _MSC_VER // TODO: check if available under GCC/Linux + std::ranlux64_base_01 generator; + generator.seed(seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); +#else + std::default_random_engine generator (seed==USE_TIME_BASED_SEED ? (unsigned long) time(NULL) : seed); +#endif + std::uniform_real_distribution r(0, 1); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); + ElemType v; + for (long j=0; j + ElemType CPUMatrix::Adagrad(CPUMatrix& gradients, const bool needAveMultiplier) + { + ElemType aveMultiplier = 0; + + if (IsEmpty() || gradients.GetNumCols() != GetNumCols() || gradients.GetNumRows() != GetNumRows()) + { + Resize(gradients.GetNumRows(), gradients.GetNumCols()); + SetValue(0.0); + } + + assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == gradients.GetNumCols()); + + ElemType *a=m_pArray, *d_v=gradients.m_pArray; + size_t n = GetNumElements(); + + const ElemType floor = 1e-16f; + ElemType a0, a1, a2, a3; + + //disable omp here because aveMultiper needs to be added atomically. however, it seems the result is incorrect even if rmp atomic and amp critical are used. +//#pragma omp parallel for + for (long i = 0; i<(n & ~3); i += 4) //four-way unrolling + { + a[i] += d_v[i] * d_v[i]; + a[i+1] += d_v[i+1] * d_v[i+1]; + a[i+2] += d_v[i+2] * d_v[i+2]; + a[i+3] += d_v[i+3] * d_v[i+3]; + + a0 = sqrt(a[i] + floor); + a1 = sqrt(a[i + 1] + floor); + a2 = sqrt(a[i + 2] + floor); + a3 = sqrt(a[i + 3] + floor); + + d_v[i] /= a0; + d_v[i+1] /= a1; + d_v[i+2] /= a2; + d_v[i+3] /= a3; + + if (needAveMultiplier) + { + aveMultiplier += 1 / a0 + 1 / a1 + 1 / a2 + 1 / a3; + } + } + + // get the last few elements if any + for (long i = n & ~3; i 0) + return aveMultiplier / n; + else + return 1; + } + + template + void CPUMatrix::FSAdagrad(CPUMatrix& gradients, + CPUMatrix& functionValues, + ElemType learnRatePerSample, + ElemType momentum, + ElemType adaWeight, + ElemType adaMul) + { + size_t numColsNeeded = 2 * gradients.GetNumCols(); + + if (IsEmpty() || (GetNumCols() < numColsNeeded)) + { + Resize(gradients.GetNumRows(), numColsNeeded); + SetValue(0.0); + } + + assert((GetNumRows() == gradients.GetNumRows()) && (GetNumCols() == numColsNeeded)); + + size_t n = gradients.GetNumElements(); + ElemType* grad = gradients.m_pArray; + ElemType* smoothAda = m_pArray; + ElemType* smoothMom = m_pArray + n; + ElemType* val = functionValues.m_pArray; +#pragma omp parallel for + // TODO: Unroll 4-times for better performance leveraging vectorization + for (long i = 0; i < n; i++) + { + ElemType g = grad[i]; + ElemType adaSqr = adaWeight * smoothAda[i] + (1.0f - adaWeight) * g * g; + smoothAda[i] = adaSqr; + if (adaSqr != 0.0f) + { + ElemType ada = sqrt(adaSqr); + ElemType w = adaMul * ((ElemType)1.0 / ada); + + if (w > 10.0f) + w = 10.0f; + g *= w; + } + + if (momentum > 0.0f) + { + g = momentum * smoothMom[i] + (1.0f - momentum) * g; + smoothMom[i] = g; + } + + g *= learnRatePerSample; + val[i] -= g; + } + } + + template + ElemType CPUMatrix::RmsProp(CPUMatrix& gradients, + ElemType RMS_GAMMA, + ElemType RMS_WGT_INC, + ElemType RMS_WGT_MAX, + ElemType RMS_WGT_DEC, + ElemType RMS_WGT_MIN, + const bool needAveMultiplier + ) + { + const ElemType floor = 1e-6f; + + size_t n = gradients.GetNumElements(); + ElemType *curr_grad=gradients.m_pArray; + + if (IsEmpty() || GetNumCols() < gradients.GetNumCols() * 3) + { + Resize(gradients.GetNumRows(), gradients.GetNumCols() * 3); + SetValue(0.0); + + ElemType *avars=m_pArray; // accumulated variances for RMS scaling + ElemType *steps=m_pArray+2*n; // current step size + + // initialize moving average of gradient-squared + for( long i = 0; i < n; i++ ) + avars[i] = curr_grad[i]*curr_grad[i]; + + // initialize starting step size + for( long i = 0; i < n; i++ ) + steps[i] = ElemType(0.02); + } + + ElemType *avars=m_pArray; // accumulated variances for RMS scaling + ElemType *signs=m_pArray+n; // sign of previous gradient + ElemType *steps=m_pArray+2*n; // current step size + + assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == gradients.GetNumCols() * 3); + + ElemType ONE_MINUS_GAMMA = ElemType(1.0) - RMS_GAMMA; + //int upd[] = { + // 2,2,0, + // 2,2,0, + // 1,1,1, + // 2,2,0, + // 1,2,1, + // 0,2,2, + // 1,1,1, + // 0,2,2, + // 0,2,2, + //}; + + // for (long i=0; ineg, 1->zero, 2->pos + // const int grad_sign = 1 + (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); + + // // signs[i] contains three consecutive grad_sign + // signs[i] = 3*(int(signs[i]) % 9) + grad_sign; + + // switch(upd[int(signs[i])]) + // { + // case 0: + // steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); + // break; + // case 2: + // steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); + // break; + // } + // curr_grad[i] *= steps[i] / sqrt(avars[i] + floor); + // } + + ElemType aveMultiplier = 0, a; + for (long i=0; i 0 ) + steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); + else + steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); + + a = steps[i] / sqrt(avars[i] + floor); + curr_grad[i] *= a; + signs[i] = (ElemType)grad_sign; + + if (needAveMultiplier) + aveMultiplier += a; + } + + if (needAveMultiplier) + return aveMultiplier / n; + else + return 1; + } + + template + void CPUMatrix::Reshape(const size_t numRows, const size_t numCols) + { + assert (numRows*numCols == GetNumElements()); + if (numRows*numCols != GetNumElements()) + InvalidArgument("Reshape: Total number of elements does not match."); + + m_numRows = numRows; + m_numCols = numCols; + } + + // Resize() -- change matrix size + // This function is cheap if the matrix size does not change. + // Current content is not preserved. + // BUGBUG: There is code that relies on zero initialization (without, we get subtle variations of output). That is wrong--we should initialize to QNaN and see where it fails. + // If growOnly is true, resize will not reallocate memory if the current memory is large enough (i.e., will not shrink). + // If this object does not own its memory then new memory cannot be allocated (one can still shrink and/or reshape). + template + void CPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly /*=true*/) + { + if (m_numRows == numRows && m_numCols == numCols) + return; + + size_t numElements = numRows * numCols; + if (numElements > m_elemSizeAllocated || // grow allocation + (!growOnly && (numElements != m_elemSizeAllocated))) // shrink allocation (not if 'growOnly') + { + // reallocate buffer + ElemType * pArray = nullptr; + if (numElements > 0) + { + if (!OwnBuffer()) + LogicError("Resize: Resizing an matrix you don't own is not supported."); + pArray = NewArray(numElements); + } + // success: update the object + if (OwnBuffer()) + delete[] m_pArray; + else + assert(pArray == nullptr); // (if !OwnBuffer we can still resize to 0) + m_pArray = pArray; + m_elemSizeAllocated = numElements; + } + + // success + m_numRows = numRows; + m_numCols = numCols; + } + + // allocated by the callee but should be deleted by the caller + // TODO: change to use STL vector instead + template + ElemType* CPUMatrix::CopyToArray() const + { + size_t numElements = GetNumElements(); + if (numElements != 0) + { + ElemType* arrayCopyTo = NewArray(numElements); + memcpy(arrayCopyTo, m_pArray, sizeof(ElemType)*numElements); + return arrayCopyTo; + } + else + { + return nullptr; + } + } + + //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done + //return number of elements copied + template + size_t CPUMatrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const + { + size_t numElements = GetNumElements(); + + if (numElements > currentArraySize) + { + delete arrayCopyTo; + arrayCopyTo = NewArray(numElements); + currentArraySize = numElements; + } + + if (numElements != 0) + { + memcpy(arrayCopyTo, m_pArray, sizeof(ElemType)*numElements); + } + + return numElements; + } + + template + void CPUMatrix::CopySection(size_t /*numRows*/, size_t /*numCols*/, ElemType* /*dst*/, size_t /*colStride*/) const + { + // REVIEW alexeyk: currently not used by CPU, but implement when possible. + RuntimeError("Not implemented."); + } + + template + inline size_t CPUMatrix::LocateColumn(const size_t col) const + { + assert(col < m_numCols); + return col * m_numRows; // matrix in column-wise storage + } + + template + inline size_t CPUMatrix::LocateElement (const size_t row, const size_t col) const + { + assert (row < m_numRows); + return LocateColumn(col) + row; // matrix in column-wise storage + } + +#pragma endregion Basic Operators + +#pragma region Member BLAS Functions + + template + CPUMatrix& CPUMatrix::operator+= (ElemType alpha) + { + return AssignSumOf(alpha, *this); + } + + template + CPUMatrix CPUMatrix::operator+ (ElemType alpha) const + { + CPUMatrix c(GetNumRows(), GetNumCols()); + c.AssignSumOf(alpha, *this); + return c; + } + + template + CPUMatrix& CPUMatrix::AssignSumOf(const ElemType alpha, const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSumOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::operator+= (const CPUMatrix& a) + { + //if (a.GetNumElements() == 1) + // *this += a(0,0); + //else + ScaleAndAdd(1, a, *this); + + return *this; + } + + //if [this] and a have same dimension then OUTPUT=[this]+a + //if a is a column vector, add to all columns of [this] + //if a is a row vector, add to all rows of [this] + template + CPUMatrix CPUMatrix::operator+ (const CPUMatrix& a) const + { + if (GetNumElements() == 1) + { + CPUMatrix c(a); + c += (*this)(0,0); + return c; + } + else if (a.GetNumElements() == 1) + { + CPUMatrix c(*this); + c += a(0,0); + return c; + } + else + { + CPUMatrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code + c += a; + return c; + } + } + + template + CPUMatrix& CPUMatrix::AssignSumOf(const CPUMatrix& a, const CPUMatrix& b) + { + if (a.GetNumElements() == 1) + { + SetValue(b); + (*this) += a; + } + else + { + SetValue(a); + (*this) += b; + } + return *this; + } + + template + CPUMatrix& CPUMatrix::operator-= (ElemType alpha) + { + return AssignDifferenceOf(*this, alpha); + } + + template + CPUMatrix CPUMatrix::operator- (ElemType alpha) const + { + CPUMatrix c(GetNumRows(), GetNumCols()); + c.AssignDifferenceOf(*this, alpha); + return c; + } + + template + CPUMatrix& CPUMatrix::AssignDifferenceOf(const ElemType alpha, const CPUMatrix& a) + { + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::AssignDifferenceOf(const CPUMatrix& a, const ElemType alpha) + { + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::operator-= (const CPUMatrix& a) + { + ScaleAndAdd(-1, a, *this); + + return *this; + } + + //if [this] and a have same dimension then output=[this]-a + //if a is a column vector, minus it from all columns of [this] + //if a is a row vector, minus it from all rows of [this] + template + CPUMatrix CPUMatrix::operator- (const CPUMatrix& a) const + { + CPUMatrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code + c -= a; + return c; + } + + template + CPUMatrix& CPUMatrix::AssignDifferenceOf(const CPUMatrix& a, const CPUMatrix& b) + { + if (this != &a) + { + Resize(a.GetNumRows(), a.GetNumCols()); + SetValue(a); + } + (*this) -= b; + return *this; + } + + template + CPUMatrix& CPUMatrix::operator*= (ElemType alpha) + { + Scale(alpha, *this); + return *this; + } + + template + CPUMatrix CPUMatrix::operator* (ElemType alpha) const + { + CPUMatrix c(GetNumRows(), GetNumCols()); + Scale(alpha, *this, c); + return c; + } + + template + CPUMatrix& CPUMatrix::AssignProductOf(const ElemType alpha, const CPUMatrix& a) + { + Scale(alpha, a, *this); + return *this; + } + + // [this]=a*b + template + CPUMatrix& CPUMatrix::AssignProductOf (const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB) + { + if (a.GetNumElements() == 1) + { + if (transposeB) + AssignTransposeOf(b); + (*this) *= a(0,0); + } + else if (b.GetNumElements() == 1) + { + if (transposeA) + AssignTransposeOf(a); + (*this) *= b(0,0); + } + else + Multiply(a, transposeA, b, transposeB, *this); + + return *this; + } + + template + CPUMatrix CPUMatrix::operator* (const CPUMatrix& a) const + { + auto& us = *this; + if (GetNumElements() == 1) + { + CPUMatrix c; + c.AssignProductOf(us(0,0), a); + return c; + } + else if (a.GetNumElements() == 1) + { + CPUMatrix c; + c.AssignProductOf(a(0,0), us); + return c; + } + else + { + CPUMatrix c; + Multiply(*this, a, c); + return c; + } + } + + template + CPUMatrix& CPUMatrix::operator/= (ElemType alpha) + { + (*this) *= 1/alpha; + return (*this); + } + + template + CPUMatrix CPUMatrix::operator/ (ElemType alpha) const + { + return ((*this) * (1/alpha)); + } + + //element-wise power + template + CPUMatrix& CPUMatrix::operator^= (ElemType alpha) + { + auto& us = *this; + ElementWisePower(alpha, us, us); + return us; + } + + //element-wise power + template + CPUMatrix CPUMatrix::operator^ (ElemType alpha) const + { + CPUMatrix c(GetNumRows(), GetNumCols()); + ElementWisePower(alpha, *this, c); + return c; + } + + + template + CPUMatrix& CPUMatrix::AssignElementPowerOf(const CPUMatrix& a, const ElemType power) + { + ElementWisePower(power, a, *this); + return *this; + } + + //[this]=[this] .* a (we cannot override operator .* in c++) + template + CPUMatrix& CPUMatrix::ElementMultiplyWith (const CPUMatrix& a) + { + return AssignElementProductOf(*this, a); + } + + //[this]=[this] .* a (we cannot override operator .* in c++) + template + CPUMatrix& CPUMatrix::ElementDivideBy(const CPUMatrix& a) + { + return AssignElementDivisionOf(*this, a); + } + + //[this]=a .* b + template + CPUMatrix& CPUMatrix::AssignElementProductOf (const CPUMatrix& a, const CPUMatrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AssignElementProductOf: The input matrix dimensions do not match."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::AddElementProductOf (const CPUMatrix& a, const CPUMatrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AddElementProductOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AddElementProductOf : The input matrix dimensions do not match."); + + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == GetNumCols())) + InvalidArgument("AddElementProductOf : The input matrix dimensions do not match [this]."); + + auto& us=*this; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::AssignElementDivisionOf (const CPUMatrix& a, const CPUMatrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementDivisionOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AssignElementDivisionOf : The input matrix dimensions do not match."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + ElemType smallValue = EPS_IN_INVERSE; + +#pragma omp parallel for + foreach_coord(i,j,us) + { + ElemType v = b(i,j); + if (v >= 0 && v < smallValue) + us(i,j) = a(i,j) / smallValue; + else if (v < 0 && v > -smallValue) + us(i,j) = a(i,j) / (-smallValue); + else + us(i,j) = a(i,j) / v; + } + + return *this; + } + + template + CPUMatrix& CPUMatrix::ColumnElementMultiplyWith(const CPUMatrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("ColumnElementMultiplyWith: Matrix is empty."); + + assert (a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1); + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) + InvalidArgument("ColumnElementMultiplyWith: The input matrix should be a col vector and match [this]'s rows."); + + auto& us=*this; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::RowElementMultiplyWith(const CPUMatrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("RowElementMultiplyWith: Matrix is empty."); + + assert (a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()); + if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols())) + InvalidArgument("RowElementMultiplyWith: The input matrix should be a row vector and match [this]'s columns."); + + auto& us=*this; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::RowElementDivideBy(const CPUMatrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("RowElementDivideBy: Matrix is empty."); + + assert(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()); + if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols())) + InvalidArgument("RowElementDivideBy: The input matrix should be a row vector and match [this]'s columns."); + + auto& us = *this; + + long m = (long)GetNumRows(), n = (long)GetNumCols(); +#pragma omp parallel for + for (long j = 0; j= 0 && v < EPS_IN_INVERSE) + v = EPS_IN_INVERSE; + else if (v < 0 && v > -EPS_IN_INVERSE) + v = (-EPS_IN_INVERSE); + + //four-way unrolling + for (long i = 0; i<(m & ~3); i += 4) + { + us(i, j) /= v; + us(i + 1, j) /= v; + us(i + 2, j) /= v; + us(i + 3, j) /= v; + } + //handle remaining stuffs + for (long i = m & ~3; i + CPUMatrix& CPUMatrix::ColumnElementDivideBy(const CPUMatrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("ColumnElementDivideBy: Matrix is empty."); + + assert (a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1); + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) + InvalidArgument("ColumnElementDivideBy: The input matrix should be a col vector and match [this]'s rows."); + + auto& us=*this; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); + + ElemType smallValue = EPS_IN_INVERSE; +#pragma omp parallel for + for (long j=0; j= 0 && v < smallValue) + us(i,j) /= smallValue; + else if (v < 0 && v > -smallValue) + us(i,j) /= (-smallValue); + else + us(i,j) /= v; + } + } + + return *this; + } + + + //[this]=1 ./ a + template + CPUMatrix& CPUMatrix::ElementInverse () + { + return AssignElementInverseOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignElementInverseOf (const CPUMatrix& a) + { + ElemType smallValue = EPS_IN_INVERSE; + + if (a.IsEmpty()) + LogicError("AssignElementInverseOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,us) + { + if (a(i,j) <0 && a(i,j) > -smallValue) + us(i,j) = 1/(-smallValue); + else if (a(i,j) >=0 && a(i,j) < smallValue) + us(i,j) = 1/smallValue; + else + us(i,j) = 1 / a(i,j); + } + + return *this; + } + + //[this]=sigmoid([this]) element wise + template + CPUMatrix& CPUMatrix::InplaceSigmoid () + { + return AssignSigmoidOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignSigmoidOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSigmoidOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,us) + { + if (a(i,j) >= 0) + us(i,j) = 1 / (1+exp(-a(i,j))); + else + { + ElemType v = exp(a(i,j)); + us(i,j) = v / (1+v); + } + } + + return *this; + } + + template + CPUMatrix& CPUMatrix::InplaceLinearRectifierDerivative () + { + return AssignLinearRectifierDerivativeOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignLinearRectifierDerivativeOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignLinearRectifierDerivativeOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j 0.0f ? 1.0f : 0.0f; + us(i+1,j) = a(i+1,j) > 0.0f ? 1.0f : 0.0f; + us(i+2,j) = a(i+2,j) > 0.0f ? 1.0f : 0.0f; + us(i+3,j) = a(i+3,j) > 0.0f ? 1.0f : 0.0f; + } + //handle remaining stuffs + for (long i=m & ~3; i 0.0f ? 1.0f : 0.0f; + } + } + + + return *this; + } + + template + CPUMatrix& CPUMatrix::InplaceSigmoidDerivative () + { + return AssignSigmoidDerivativeOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignSigmoidDerivativeOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSigmoidDerivativeOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceTanh () + { + return AssignTanhOf(*this); + } + + + template + CPUMatrix& CPUMatrix::AssignTanhOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignTanhOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceLogSoftmax (const bool isColWise) + { + return AssignLogSoftmaxOf(*this, isColWise); + } + + template + CPUMatrix& CPUMatrix::AssignLogSoftmaxOf (const CPUMatrix& a, const bool isColWise) + { + if (a.IsEmpty()) + LogicError("AssignLogSoftmaxOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + if (isColWise) + { +#pragma omp parallel for + foreach_column(j,a) + { + //we need to extract max before applying exp to avoid overflow + ElemType maxV = a(0,j); + foreach_row(i, a) + maxV = max(maxV, a(i,j)); + + ElemType sum = 0; + foreach_row(i, a) + sum += exp(us(i,j) = a(i,j) - maxV); + sum = log(sum); + foreach_row(i, us) + us(i,j) -= sum; + } + + } + else + { +#pragma omp parallel for + foreach_row(i, a) + { + //we need to extract max before applying exp to avoid overflow + ElemType maxV = a(i,0); + foreach_column(j,a) + maxV = max(maxV, a(i,j)); + + ElemType sum = 0; + foreach_column(j,a) + sum += exp(us(i,j) = a(i,j) - maxV); + sum = log(sum); + foreach_column(j,us) + us(i,j) -= sum; + } + + } + + return *this; + } + + //[this]=hardmax([this]) + //the max element is 1 else is 0 + template + CPUMatrix& CPUMatrix::InplaceHardmax(const bool isColWise) + { + return AssignHardmaxOf(*this, isColWise); + } + + template + CPUMatrix& CPUMatrix::AssignHardmaxOf(const CPUMatrix& a, const bool isColWise) + { + if (a.IsEmpty()) + LogicError("AssignHardmaxOf: Matrix a is empty."); + + auto& us = *this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + if (isColWise) + { +#pragma omp parallel for + foreach_column(j, a) + { + //we need to extract max + ElemType maxV = a(0, j); + long maxI = 0; + foreach_row(i, a) + { + if (maxV < a(i, j)) + { + maxV = a(i, j); + maxI = i; + } + } + + foreach_row(i, us) + us(i, j) = (i == maxI) ? 1.0f : 0.0f; + } + + } + else + { +#pragma omp parallel for + foreach_row(i, a) + { + //we need to extract max + ElemType maxV = a(i, 0); + long maxJ = 0; + foreach_column(j, a) + { + if (maxV < a(i, j)) + { + maxV = a(i, j); + maxJ = j; + } + } + + foreach_column(j, us) + us(i, j) = (j == maxJ)? 1.0f : 0.0f; + } + + } + + return *this; + } + + //[this]=sqrt([this]) element wise + template + CPUMatrix& CPUMatrix::InplaceSqrt () + { + return AssignSqrtOf(*this); + } + + //to prevent negative values caused by floating operations, we force inputs to be >=0 + //this may, however, hide problems in the caller. + template + CPUMatrix& CPUMatrix::AssignSqrtOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSqrtOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceExp () + { + return AssignExpOf(*this); + } + + + template + CPUMatrix& CPUMatrix::AssignExpOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignExpOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceAbs () + { + return AssignAbsOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignAbsOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignAbsOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceLog () + { + return AssignLogOf(*this); + } + + //[this]=log([this]) element wise + template + CPUMatrix& CPUMatrix::InplaceLog10 () + { + return AssignLog10Of(*this); + } + + template + CPUMatrix& CPUMatrix::AssignLogOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignLogOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + const ElemType v = a(i,j); + if (v < EPS_IN_LOG) + { + us(i,j) = LOG_OF_EPS_IN_LOG; + } + else + us(i,j) = log(v); + } + + return *this; + } + + template + CPUMatrix& CPUMatrix::AssignLog10Of (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignLogOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + const ElemType v = a(i,j); + if (v <= 0) + LogicError("AssignLogOf: Log can only applied to numbers larger than 0."); + else if (v < EPS_IN_LOG) + { + us(i,j) = LOG10_OF_EPS_IN_LOG; + } + else + us(i,j) = log10(v); + } + + + return *this; + } + + //[this]=cos([this]) element wise + template + CPUMatrix& CPUMatrix::InplaceCosine () + { + return AssignCosineOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignCosineOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignCosineOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + const ElemType v = a(i,j); + us(i,j) = cos(v); + } + + return *this; + } + + //[this]=-sin([this]) element wise + template + CPUMatrix& CPUMatrix::InplaceNegativeSine () + { + return AssignNegativeSineOf(*this); + } + + template + CPUMatrix& CPUMatrix::AssignNegativeSineOf (const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignCosineOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + const ElemType v = a(i,j); + us(i,j) = -sin(v); + } + + return *this; + } + + + //Threshold truncating: this[i] = max( this[i], threshold ) + template + CPUMatrix& CPUMatrix::InplaceTruncateBottom (const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncateBottom: Matrix is empty."); + + auto& us=*this; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j + CPUMatrix& CPUMatrix::InplaceTruncate (const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncate: Matrix is empty."); + + auto& us=*this; + ElemType locThresholdPos = abs(threshold); + ElemType locTHresholdNeg = -locThresholdPos; + + long m=(long)GetNumRows(), n=(long)GetNumCols(); +#pragma omp parallel for + for (long j=0; j locThresholdPos ) + us(i,j) = locThresholdPos ; + else if (us(i,j) < locTHresholdNeg ) + us(i,j) = locTHresholdNeg ; + + if (us(i+1,j) > locThresholdPos ) + us(i+1,j) = locThresholdPos ; + else if (us(i+1,j) < locTHresholdNeg ) + us(i+1,j) = locTHresholdNeg ; + + if (us(i+2,j) > locThresholdPos ) + us(i+2,j) = locThresholdPos ; + else if (us(i+2,j) < locTHresholdNeg ) + us(i+2,j) = locTHresholdNeg ; + + if (us(i+3,j) > locThresholdPos ) + us(i+3,j) = locThresholdPos ; + else if (us(i+3,j) < locTHresholdNeg ) + us(i+3,j) = locTHresholdNeg ; + } + //handle remaining stuffs + for (long i=m & ~3; i locThresholdPos ) + us(i,j) = locThresholdPos ; + else if (us(i,j) < locTHresholdNeg ) + us(i,j) = locTHresholdNeg ; + } + } + + return *this; + } + + //x= x-threshold if x>threshold, x+threshold if x<-threshold, 0 otherwise + template + CPUMatrix& CPUMatrix::InplaceSoftThreshold(const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncate: Matrix is empty."); + + long m = (long)GetNumElements(); + +#pragma omp parallel for + for (long i = 0; i<(m & ~3); i += 4) //four-way unrolling + { + if (m_pArray[i] > threshold) + m_pArray[i] -= threshold; + else if (m_pArray[i] < -threshold) + m_pArray[i] += threshold; + else + m_pArray[i] = 0; + + if (m_pArray[i+1] > threshold) + m_pArray[i+1] -= threshold; + else if (m_pArray[i+1] < -threshold) + m_pArray[i+1] += threshold; + else + m_pArray[i+1] = 0; + + if (m_pArray[i+2] > threshold) + m_pArray[i+2] -= threshold; + else if (m_pArray[i+2] < -threshold) + m_pArray[i+2] += threshold; + else + m_pArray[i+2] = 0; + + if (m_pArray[i+3] > threshold) + m_pArray[i+3] -= threshold; + else if (m_pArray[i+3] < -threshold) + m_pArray[i+3] += threshold; + else + m_pArray[i+3] = 0; + } + //handle remaining stuffs + for (long i = m & ~3; i threshold) + m_pArray[i] -= threshold; + else if (m_pArray[i] < -threshold) + m_pArray[i] += threshold; + else + m_pArray[i] = 0; + } + + return *this; + } + + //Threshold truncating: this[i] = max( a[i], threshold ) + template + CPUMatrix& CPUMatrix::AssignTruncateBottomOf (const CPUMatrix& a, const ElemType threshold) + { + if (a.IsEmpty()) + LogicError("AssignTruncateBottomOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + if (a(i,j) < threshold) + us(i,j) = threshold; + else + us(i,j) = a(i,j); + } + + return *this; + } + + //Threshold truncating: this[i] = min( this[i], threshold ) + template + CPUMatrix& CPUMatrix::InplaceTruncateTop (const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncateTop: Matrix is empty."); + + auto& us=*this; + +#pragma omp parallel for + foreach_coord(i,j,us) + { + if (us(i,j) > threshold) + us(i,j) = threshold; + } + + return *this; + } + + //Threshold truncating: this[i] = min( a[i], threshold ) + template + CPUMatrix& CPUMatrix::AssignTruncateTopOf (const CPUMatrix& a, const ElemType threshold) + { + if (a.IsEmpty()) + LogicError("AssignTruncateTopOf: Matrix a is empty."); + + auto& us=*this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_coord(i,j,a) + { + if (a(i,j) > threshold) + us(i,j) = threshold; + else + us(i,j) = a(i,j); + } + + return *this; + } + //Threshold truncating: this[i] = 0 if abs(this[i] + CPUMatrix& CPUMatrix::SetToZeroIfAbsLessThan (const ElemType threshold) + { + if (IsEmpty()) + LogicError("SetToZeroIfAbsLessThan: Matrix is empty."); + + auto& us=*this; + +#pragma omp parallel for + foreach_coord(i,j,us) + { + if (abs(us(i,j)) < threshold) + us(i,j) = 0; + } + + return *this; + } + + //sum of all abs(elements) + template + ElemType CPUMatrix::SumOfAbsElements () const + { + if (IsEmpty()) + LogicError("SumOfAbsElements: Matrix is empty."); + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + return (ElemType)dasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); +#else + return (ElemType)cblas_dasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); +#endif + } + else + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + return sasum((int)GetNumElements(), reinterpret_cast (m_pArray), 1); +#else + return cblas_sasum ((int)GetNumElements(), reinterpret_cast (m_pArray), 1); +#endif + } + } + + //sum of all elements + template + ElemType CPUMatrix::SumOfElements () const + { + if (IsEmpty()) + LogicError("SumOfElements: Matrix is empty."); + + ElemType sum=0; + long m=(long)GetNumElements(); // note: OpenMP requires loop indices to be long, not size_t + + //four-way unrolling +#pragma omp parallel for reduction(+:sum) + for (long i=0; i<(m & ~3); i+=4) + { + sum += m_pArray[i] + m_pArray[i+1] + m_pArray[i+2] + m_pArray[i+3] ; + } + //handle remaining stuffs + for (long i=m & ~3; i + CPUMatrix& CPUMatrix::AssignSumOfElements(const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSumOfElements: Matrix a is empty."); + + auto& us=*this; + us.Resize(1,1); + us(0,0) =a.SumOfElements(); + + return *this; + } + + template + bool CPUMatrix::IsEqualTo(const CPUMatrix& a, const ElemType threshold /*= 1e-8*/) const + { + return AreEqual(*this, a, threshold); + } + + + template + void CPUMatrix::VectorSum(const CPUMatrix& a, CPUMatrix& c, const bool isColWise) + { + if (a.IsEmpty()) + LogicError("VectorSum: Input matrix a is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + + assert(m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + c.Resize(1, n); + +#pragma omp parallel for + foreach_column(j, a) + { + ElemType v = 0; + foreach_row(i, a) + { +#pragma omp atomic + v += a(i, j); + } + c(0, j) = v; + } + } + else + { + c.Resize(m, 1); + +#pragma omp parallel for + foreach_row(i, a) + { + ElemType v = 0; + foreach_column(j, a) + { +#pragma omp atomic + v += a(i, j); + } + c(i, 0) = v; + } + } + } + + template + void CPUMatrix::VectorNorm1(CPUMatrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNorm1: Matrix is empty."); + + auto& us=*this; + + const int m = (int)us.GetNumRows(); + const int n = (int)us.GetNumCols(); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + c.Resize(1, n); + +#pragma omp parallel for + foreach_column(j,us) + { + ElemType v = 0; + foreach_row(i,us) + { +#pragma omp atomic + v += abs(us(i,j)); + } + c(0,j) = v; + } + + + } + else + { + c.Resize(m, 1); + +#pragma omp parallel for + foreach_row(i,us) + { + ElemType v = 0; + foreach_column(j,us) + { +#pragma omp atomic + v += abs(us(i,j)); + } + c(i,0) = v; + } + + + } + } + + template + CPUMatrix& CPUMatrix::AssignVectorNorm1Of(CPUMatrix& a, const bool isColWise) + { + a.VectorNorm1(*this, isColWise); + return *this; + } + + template + void CPUMatrix::VectorNorm2(CPUMatrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNorm2: Matrix is empty."); + + auto& us=*this; + + const int m = (int)us.GetNumRows(); + const int n = (int)us.GetNumCols(); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + c.Resize(1, n); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_column(j,c) + { +#ifndef USE_MKL + c(0,j) = (ElemType) dnrm2(m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); +#else + c(0,j) = (ElemType) cblas_dnrm2 (m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); +#endif + } + } + else + { +#pragma omp parallel for + foreach_column(j,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + c(0,j) = snrm2(m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); +#else + c(0,j) = cblas_snrm2 (m, reinterpret_cast (us.m_pArray+us.LocateColumn(j)), 1); +#endif + } + } + } + else + { + c.Resize(m, 1); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_row(i,c) + { +#ifndef USE_MKL + c(i,0) = dnrm2(n, reinterpret_cast (us.m_pArray+i), m); +#else + c(i,0) = cblas_dnrm2 (n, reinterpret_cast (us.m_pArray+i), m); +#endif + } + } + else + { +#pragma omp parallel for + foreach_row(i,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + c(i,0) = snrm2(n, reinterpret_cast (us.m_pArray+i), m); +#else + c(i,0) = cblas_snrm2 (n, reinterpret_cast (us.m_pArray+i), m); +#endif + } + + } + } + } + + template + CPUMatrix& CPUMatrix::AssignVectorNorm2Of(CPUMatrix& a, const bool isColWise) + { + a.VectorNorm2(*this, isColWise); + return *this; + } + + template + void CPUMatrix::VectorNormInf(CPUMatrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNormInf: Matrix is empty."); + + auto& us=*this; + + const int m = (int)us.GetNumRows(); + const int n = (int)us.GetNumCols(); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + c.Resize(1, n); + + //#pragma omp parallel for + foreach_column(j,us) + { + ElemType v = 0; + foreach_row(i,us) + { + v = max( v, abs(us(i,j))); + } + c(0,j) = v; + } + + + } + else + { + c.Resize(m, 1); + + //#pragma omp parallel for + foreach_row(i,us) + { + ElemType v = 0; + foreach_column(j,us) + { + v = max( v, abs(us(i,j))); + } + c(i,0) = v; + } + + } + } + + template + CPUMatrix& CPUMatrix::AssignVectorNormInfOf(CPUMatrix& a, const bool isColWise) + { + a.VectorNormInf(*this, isColWise); + return *this; + } + + template + CPUMatrix& CPUMatrix::AssignInnerProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool isColWise) + { + InnerProduct (a, b, *this,isColWise); + return *this; + } + + //column-wise crossproduct + template + CPUMatrix& CPUMatrix::AssignKhatriRaoProductOf(const CPUMatrix& a, const CPUMatrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignKhatriRaoProductOf: Matrix is empty."); + + long cols = (long) a.GetNumCols(); + assert(cols == b.GetNumCols()); + if (cols != b.GetNumCols()) + InvalidArgument("a.GetNumCols() != b.GetNumCols()"); + + long rowsA = (long) a.GetNumRows(); + long rowsB = (long) b.GetNumRows(); + Resize(rowsA * rowsB, cols); + +#ifdef __INTEL_COMPILER // TODO: check this +#pragma simd statement +#endif +#pragma omp parallel for + for (long k=0; k + CPUMatrix& CPUMatrix::AddColumnReshapeProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool transposeAColumn) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AddColumnReshapeProductOf: Matrix is empty."); + + long cols = (long) a.GetNumCols(); + assert(cols == b.GetNumCols()); + if (cols != b.GetNumCols()) + InvalidArgument("AddColumnReshapeProductOf: a.GetNumCols() != b.GetNumCols()"); + + long rowsA = (long) a.GetNumRows(); + long rowsB = (long) b.GetNumRows(); + + if (rowsA % rowsB != 0) + InvalidArgument("AddColumnReshapeProductOf: number of rows in a should be multiples of that in b."); + + long rowsC = rowsA / rowsB; + if (rowsC != GetNumRows() || cols != GetNumCols()) + InvalidArgument("AddColumnReshapeProductOf: This matrix does not have the right size."); + + auto & us = *this; + + if (transposeAColumn) + { + //find nrows and ncols of tbe reshaped a + long nrows = rowsB; + long ncols = rowsC; + +#ifdef __INTEL_COMPILER // TODO: check this +#pragma simd statement +#endif +#pragma omp parallel for + foreach_column(t, a) + { + size_t k=0; + for (size_t j=0; j + CPUMatrix& CPUMatrix::AddWithScaleOf(ElemType alpha, const CPUMatrix& a) + { + ScaleAndAdd(alpha, a, *this); + return *this; + } + + template + ElemType CPUMatrix::FrobeniusNorm() const + { + if (IsEmpty()) + LogicError("FrobeniusNorm: Matrix is empty."); + + ElemType v = 0; + + long m=(long)GetNumElements(); + + //four-way unrolling +#pragma omp parallel for reduction(+:v) + for (long i=0; i<(m & ~3); i+=4) + { + v += m_pArray[i] * m_pArray[i] + m_pArray[i+1] * m_pArray[i+1] + m_pArray[i+2] * m_pArray[i+2] + m_pArray[i+3] * m_pArray[i+3]; + } + //handle remaining stuffs + for (long i=m & ~3; i + CPUMatrix& CPUMatrix::AssignFrobeniusNormOf(const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignFrobeniusNormOf: Matrix a is empty."); + + auto& us=*this; + us.Resize(1,1); + us(0,0) = a.FrobeniusNorm(); + + return us; + } + + template + ElemType CPUMatrix::MatrixNormInf() const + { + if (IsEmpty()) + LogicError("MatrixNormInf: Matrix is empty."); + + auto& us=*this; + + ElemType v = 0; +#pragma omp parallel for + foreach_coord(i,j,us) + { +#pragma omp critical + { + v = max( v, abs(us(i,j))); + } + } + return v; + } + + template + ElemType CPUMatrix::MatrixNorm0() const + { + if (IsEmpty()) + LogicError("MatrixNorm0: Matrix is empty."); + + auto& us=*this; + + ElemType v = 0; +#pragma omp parallel for + foreach_coord(i,j,us) + { + if (us(i,j)!=0) + { +#pragma omp critical + { + ++v; + } + } + } + return v; + } + + template + ElemType CPUMatrix::MatrixNorm1() const + { + if (IsEmpty()) + LogicError("MatrixNorm1: Matrix is empty."); + + auto& us=*this; + + ElemType sum = 0; +#pragma omp parallel for reduction(+:sum) + foreach_coord(i,j,us) + { + sum += abs(us(i,j)); + } + return sum; + } + + template + CPUMatrix& CPUMatrix::AssignSignOf(const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSignOf: Matrix a is empty."); + + auto& us = *this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_column(j, us) + { + foreach_row(i, us) + { + ElemType v = a(i, j); + if (!std::isnan(v)) + us(i, j) = (v == (ElemType)0 ? (ElemType)0 : (v > 0 ? (ElemType)1 : (ElemType)(-1))); + else + us(i, j) = v; + } + } + + return us; + } + + template + CPUMatrix& CPUMatrix::AddSignOf(const CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("AddSignOf: Matrix a is empty."); + + auto& us = *this; + if (this != &a) + Resize(a.GetNumRows(), a.GetNumCols()); + +#pragma omp parallel for + foreach_column(j, us) + { + foreach_row(i, us) + { + ElemType v = a(i, j); + if (!std::isnan(v)) + us(i, j) += (v == (ElemType)0 ? (ElemType)0 : (v > 0 ? (ElemType)1 : (ElemType)(-1))); + else + us(i, j) = v; + } + } + + return us; + } + //I decided to use CPUMatrix& maxIndexes instead of integer vector because the result may be used to do additional calculation + template + void CPUMatrix::VectorMax(CPUMatrix& maxIndexes, CPUMatrix& maxValues, const bool isColWise, int topK) const + { + if (IsEmpty()) + LogicError("VectorMax: Matrix is empty."); + + auto& us=*this; + const int m = (int)GetNumRows(); + const int n = (int)GetNumCols(); + assert(topK <= m); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + maxValues.Resize(topK, n); + maxIndexes.Resize(topK, n); + + if (topK == 1) + { +#pragma omp parallel for + for (int j = 0; j < n; j++) + { + ElemType v = us(0, j); + size_t index = 0; + foreach_row(i, us) + { + if (v < us(i, j)) + { + index = i; + v = us(i, j); + } + } + maxValues(0, j) = v; + maxIndexes(0, j) = (ElemType)index; + } + } + else + { + std::vector indices(m); + int i = 0; + std::generate(indices.begin(), indices.end(), [&i] { return i++; }); + + const ElemType* curVal = m_pArray; + ElemType* curIdx = maxIndexes.m_pArray; + ElemType* curMax = maxValues.m_pArray; + for (int icol = 0; icol < n; icol++, curVal += m, curIdx += topK, curMax += topK) + { + // Partial sort, descending order. + std::nth_element(indices.begin(), indices.begin() + topK, indices.end(), + [curVal](const int& a, const int& b) { return curVal[a] > curVal[b]; }); + // REVIEW alexeyk: the following produces warning (see SCL_SECURE_NO_WARNINGS) so use loop instead. + //std::transform(indices.begin(), indices.begin() + topK, curIdx, [](const int& a) { return static_cast(a); }); + for (int i = 0; i < topK; i++) + { + curIdx[i] = static_cast(indices[i]); + curMax[i] = curVal[indices[i]]; + } + } + } + } + else + { + if (topK > 1) + RuntimeError("Row-wise TopK max is not supported."); + + maxValues.Resize(m,1); + maxIndexes.Resize(m, 1); + +#pragma omp parallel for + for (int i=0; i + void CPUMatrix::VectorMin(CPUMatrix& minIndexes, CPUMatrix& minValues, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorMin: Matrix is empty."); + + auto& us=*this; + const int m = (int)GetNumRows(); + const int n = (int)GetNumCols(); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + + if (isColWise) //col-wise + { + minValues.Resize(1, n); + minIndexes.Resize(1, n); + +#pragma omp parallel for + for (int j=0; j us(i,j)) + { + index = i; + v = us(i,j); + } + + } + minValues(0,j) = v; + minIndexes(0,j) = (ElemType)index; + } + } + else + { + minValues.Resize(m,1); + minIndexes.Resize(m, 1); + +#pragma omp parallel for + for (int i=0; i us(i,j)) + { + index = j; + v = us(i,j); + } + + } + minValues(i,0) = v; + minIndexes(i,0) = (ElemType)index; + } + } + } + + template + CPUMatrix& CPUMatrix::AssignNumOfDiff(const CPUMatrix& a, const CPUMatrix& b, bool searchInCol) + { + if (a.GetNumCols() != b.GetNumCols()) + throw std::invalid_argument("AssignNumOfDiff: a and b must have the same number of columns."); + if (!searchInCol && a.GetNumRows() != b.GetNumRows()) + throw std::invalid_argument("AssignNumOfDiff: a and b must have the same number of rows."); + + ElemType n = 0; + if (!searchInCol) + { + foreach_coord(i, j, a) + { + n += (a(i, j) != b(i, j)); + } + } + else + { + size_t crow = b.GetNumRows(); + const ElemType* curCol = b.m_pArray; + for (size_t icol = 0; icol < a.GetNumCols(); icol++, curCol += crow) + { + auto res = std::find(curCol, curCol + crow, a(0, icol)); + if (res == curCol + crow) + n++; + } + } + + Resize(1, 1); //result should be one element + (*this)(0, 0) = n; + + return *this; + } + + +#pragma endregion Member BLAS Functions + +#pragma region Other helper Functions + + template + void CPUMatrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const + { + if (IsEmpty()) + LogicError("Print: Matrix is empty."); + + if (rowEnd >= GetNumRows() || colEnd >= GetNumCols()) + InvalidArgument("Index out of range."); + + if (matrixName != nullptr) + fprintf (stderr, "\n###### %s (%lu, %lu) ######\n", matrixName, GetNumRows(), GetNumCols()); + else + fprintf (stderr, "\n###### Unnamed Matrix (%lu, %lu) ######\n", GetNumRows(), GetNumCols()); + + fprintf (stderr, "\n------ Print Range (%lu:%lu, %lu:%lu) ------\n", rowStart, rowEnd, colStart, colEnd); + + const auto& us = *this; + for (size_t i = rowStart; i <= rowEnd; i++) + { + for (size_t j = colStart; j <= colEnd; j++) + fprintf(stderr, "%.10f\t", us(i, j)); + fprintf (stderr, "\n"); + } + } + + template + void CPUMatrix::Print(const char* matrixName /*=nullptr*/) const + { + Print(matrixName, 0, GetNumRows()-1, 0, GetNumCols()-1); + } + + // file I/O + //matrixName is used to verify that correct matrix is read. + template + void CPUMatrix::ReadFromFile(FILE*, const char * /*matrixName*/) + { + RuntimeError("not implemented."); + } + + //matrixName is used to verify that correct matrix is read. + template + void CPUMatrix::WriteToFile(FILE*, const char * /*matrixName*/) + { + RuntimeError("not implemented."); + } + + //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) + template + CPUMatrix& CPUMatrix::AssignPackedConvolutionInput(const CPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t /*outputChannels*/, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) + { + assert (verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth); + + const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; + const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel + const size_t inputDim = inputWidth*inputHeight*inputChannels; + const size_t smallBatchSize = inputSubBatch.GetNumCols(); + const long inputHeightTimesChannel = (long) (inputHeight * inputChannels); + Resize(packedInputRows, packedInputColsPerSample * smallBatchSize); + if (zeroPadding) + SetValue((ElemType)0); + + const long halfKernelWidth = (long) kernelWidth/2; + const long halfKernelHeight = (long) kernelHeight/2; + + +#pragma omp parallel for //each input element is copied to many places + for (long sample = 0; sample =0 && x1=0 && y1=0; wcol++, posyInKernel -= (long) horizontalSubsample) + { + long packRowBase = (long) (c * kernelWidth * kernelHeight + posyInKernel * kernelHeight); + for (long wrow = x0, posxInKernel = x1; wrow < (long) outputHeight && posxInKernel>=0; wrow++, posxInKernel -= (long) verticalSubsample) + { + const long packRow = packRowBase + posxInKernel; + const long packCol = packColBase + wrow; + (*this)(packRow, packCol) = currentInputValue; + } + packColBase += (long) outputHeight; + } + } + } + + return *this; + } + //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) + template + CPUMatrix& CPUMatrix::UnpackConvolutionInput(CPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t /*outputChannels*/, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) const + { + assert (verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth); + + const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel + const size_t inputDim = inputWidth*inputHeight*inputChannels; + const size_t smallBatchSize = inputSubBatch.GetNumCols(); + const long inputHeightTimesChannel = (long) (inputHeight * inputChannels); + + const long halfKernelWidth = (long) kernelWidth/2; + const long halfKernelHeight = (long) kernelHeight/2; + +#pragma omp parallel for //each input element is copied to many places + for (long sample = 0; sample =0 && x1=0 && y1=0; wcol++, posyInKernel -= (long) horizontalSubsample) + { + long packRowBase = (long) (c * kernelWidth * kernelHeight + posyInKernel * kernelHeight); + for (long wrow = x0, posxInKernel = x1; wrow < (long) outputHeight && posxInKernel>=0; wrow++, posxInKernel -= (long) verticalSubsample) + { + const long packRow = packRowBase + posxInKernel; + const long packCol = packColBase + wrow; + currentInputValue += (*this)(packRow, packCol); + } + packColBase += (long) outputHeight; + } + inputSubBatch(id, sample) = currentInputValue; + } + } + + return inputSubBatch; + } + + //assume each column is an input sample. Each sample is stored in (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) + template + CPUMatrix& CPUMatrix::AssignMaxPoolingResult(const CPUMatrix& inputBatch, const size_t channels, + const size_t /*inputWidth*/, const size_t inputHeight, const size_t /*inputSizePerSample*/, + const size_t /*outputWidth*/, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + const long inputHeightTimesChannel = (long) (inputHeight * channels); + const long outputHeightTimesChannel = (long) (outputHeight * channels); + const size_t batchSize = inputBatch.GetNumCols(); + Resize(outputSizePerSample, batchSize); + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + +#pragma omp parallel for + for (long sample = 0; sample < (long) batchSize; sample ++) + { + for (long outputIndexWithinSample=0; outputIndexWithinSample + CPUMatrix& CPUMatrix::AddMaxPoolingGradient(const CPUMatrix& outputGradientBatch, const CPUMatrix& inputBatch, const CPUMatrix& outputBatch, + const size_t channels, + const size_t /*inputWidth*/, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t /*outputSizePerSample*/, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + size_t batchSize = inputBatch.GetNumCols(); + const long inputHeightTimesChannel = (long) (inputHeight * channels); + const long outputHeightTimesChannel = (long) (outputHeight * channels); + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + +#pragma omp parallel for + for (long sample = 0; sample < batchSize; sample ++) + { + for (long inputIndexWithinSample=0; inputIndexWithinSample + CPUMatrix& CPUMatrix::AssignAveragePoolingResult(const CPUMatrix& inputBatch, const size_t channels, + const size_t /*inputWidth*/, const size_t inputHeight, const size_t /*inputSizePerSample*/, + const size_t /*outputWidth*/, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + const long inputHeightTimesChannel = (long) (inputHeight * channels); + const long outputHeightTimesChannel = (long) (outputHeight * channels); + const size_t batchSize = inputBatch.GetNumCols(); + const size_t windowSize = windowWidth * windowHeight; + Resize(outputSizePerSample, batchSize); + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + +#pragma omp parallel for + for (long sample = 0; sample < batchSize; sample ++) + { + for (long outputIndexWithinSample=0; outputIndexWithinSample + CPUMatrix& CPUMatrix::AddAveragePoolingGradient(const CPUMatrix& outputGradientBatch, + const size_t channels, + const size_t /*inputWidth*/, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t /*outputSizePerSample*/, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + size_t batchSize = outputGradientBatch.GetNumCols(); + const long inputHeightTimesChannel = (long)(inputHeight * channels); + const long outputHeightTimesChannel = (long)(outputHeight * channels); + const long windowSize = (long) (windowWidth * windowHeight); + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + +#pragma omp parallel for + for (long sample = 0; sample < batchSize; sample ++) + { + for (long inputIndexWithinSample=0; inputIndexWithinSampleMatrix-matrix multiply with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c + /// Scalar + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Scalar + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, + ElemType beta, CPUMatrix& c) + { + if (a.IsEmpty() || b.IsEmpty()) + return; + + int m, n, k, l; + int lda, ldb, ldc; +#ifndef USE_MKL + char transA, transB; +#else + CBLAS_TRANSPOSE mklTransA; + CBLAS_TRANSPOSE mklTransB; +#endif + + if (transposeA) + { + m = (int)a.GetNumCols(); + k = (int)a.GetNumRows(); + lda = k; +#ifndef USE_MKL + transA = (char)MatrixTranspose::Trans; +#else + mklTransA = CBLAS_TRANSPOSE::CblasTrans; +#endif + } + else + { + m = (int)a.GetNumRows(); + k = (int)a.GetNumCols(); + lda = m; +#ifndef USE_MKL + transA = (char)MatrixTranspose::NoTrans; +#else + mklTransA = CBLAS_TRANSPOSE::CblasNoTrans; +#endif + } + + if (transposeB) + { + l = (int)b.GetNumCols(); + n = (int)b.GetNumRows(); + ldb = n; +#ifndef USE_MKL + transB = (char)MatrixTranspose::Trans; +#else + mklTransB = CBLAS_TRANSPOSE::CblasTrans; +#endif + } + else + { + l = (int)b.GetNumRows(); + n = (int)b.GetNumCols(); + ldb = l; +#ifndef USE_MKL + transB = (char)MatrixTranspose::NoTrans; +#else + mklTransB = CBLAS_TRANSPOSE::CblasNoTrans; +#endif + } + + assert (m>0 && k>0 && l>0 && n>0); //converting from size_t to int may cause overflow + assert (k == l); + if (k != l) + InvalidArgument("CPUMatrix::MultiplyAndWeightedAdd : The inner dimensions of a and b must match."); + + if (beta == 0) + c.Resize(m, n); + else + c.VerifySize(m, n); // Can't resize if beta != 0 + + ldc = (int)c.GetNumRows(); + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + dgemm(transA, transB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); +#else + cblas_dgemm ((CBLAS_ORDER) BLAS_COLMAJOR mklTransA, mklTransB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); +#endif + } + else + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + sgemm(BLAS_COLMAJOR transA, transB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); +#else + cblas_sgemm ((CBLAS_ORDER) BLAS_COLMAJOR mklTransA, mklTransB, m, n, k, alpha, reinterpret_cast (a.m_pArray), lda, reinterpret_cast (b.m_pArray), ldb, beta, reinterpret_cast (c.m_pArray), ldc); +#endif + } + } + + template + void CPUMatrix::Multiply1x1AndWeightedAdd(ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, + ElemType beta, CPUMatrix& c) + { + assert(a.GetNumElements() == 1); // a is a scalar + + ElemType f = alpha * a.Get00Element(); + if (beta == 0) // don't even read the memory if beta is 0 +#pragma omp parallel for + foreach_coord(i, j, c) + c(i, j) = b(i, j) * f; + else +#pragma omp parallel for + foreach_coord(i, j, c) + c(i, j) = b(i, j) * f + c(i, j) * beta; + } + + /* compute singular value decomposition as + A = U*SIGMA*VT + W is used as temp working memory + */ + template + void CPUMatrix::SVD(const CPUMatrix& A, CPUMatrix& SIGMA, CPUMatrix& U, CPUMatrix& VT, CPUMatrix& W) + { + if (A.IsEmpty()) + LogicError("SVD: input matrix is empty."); + + int info; + int m, n, lda, ldu, ldvt; + m = (int)A.GetNumRows(); + n = (int)A.GetNumCols(); + W.GetNumRows(); //W is used as temp working memory + lda = m; + ldu = m; + ldvt = n; + U.Resize(m, m); + SIGMA.Resize(min(m, n), 1); + VT.Resize(n, n); + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + dgesvd('A', 'A', (int)m, (int)n, reinterpret_cast (A.m_pArray), (int)lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), (int)ldu, reinterpret_cast (VT.m_pArray), (int)ldvt, &info); +#else + double wkopt; + int lwork = -1; + dgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, &wkopt, &lwork, &info); + lwork = (int)wkopt; + W.Resize(lwork, 1); + dgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, reinterpret_cast (W.m_pArray), &lwork, &info); +#endif + } + else + { +#ifndef USE_MKL +#pragma warning (suppress: 4244) + sgesvd('A', 'A', (int)m, (int)n, reinterpret_cast (A.m_pArray), (int)lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), (int)ldu, reinterpret_cast (VT.m_pArray), (int)ldvt, &info); +#else + float wkopt; + int lwork = -1; + sgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, &wkopt, &lwork, &info); + lwork = (int)wkopt; + W.Resize(lwork, 1); + sgesvd("All", "All", &m, &n, reinterpret_cast (A.m_pArray), &lda, reinterpret_cast (SIGMA.m_pArray), reinterpret_cast (U.m_pArray), &ldu, reinterpret_cast (VT.m_pArray), &ldvt, reinterpret_cast (W.m_pArray), &lwork, &info); +#endif + } + + if (info > 0) + { + RuntimeError("The algorithm computing SVD failed to converge.\n"); + } + } + + /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + c + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::MultiplyAndAdd(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, + CPUMatrix& c) + { + return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 1.0, c); + } + template + void CPUMatrix::AssignSoftmaxSum(const CPUMatrix& softmax, CPUMatrix& c) + { + ElemType log_likelihood = 0.0; + size_t batch_size = this->GetNumCols(); +#pragma omp parallel for reduction(+:log_likelihood) + for (int instance_id = 0; instance_id < batch_size; instance_id++) + { + int sample = (int)(*this)(0, instance_id); + log_likelihood += softmax(instance_id, sample); + } + c(0, 0) = -log_likelihood; + } + + template + void CPUMatrix::AssignNCEUnnormalizedEval(const CPUMatrix& a, + const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& c) + //this: samples+probs + // a: hidden + // b: embedding + // tmp: softmax + // c: loglikelihood + { + ElemType log_likelihood = 0.0; + size_t batch_size = this->GetNumCols(); +#pragma omp parallel for reduction(+:log_likelihood) + for (int instance_id = 0; instance_id < batch_size; instance_id++) + { + int sample = -(int)(*this)(0, instance_id); + ElemType score = bias(sample, 0); + for (int dim = 0; dim < b.GetNumRows(); dim++) + score += b(dim, sample)* a(dim, instance_id); + log_likelihood += score; + } + c(0, 0) = -log_likelihood; + } + + //samples+prob gradient hidden embedding embedding/hidden + //a.m_CPUMatrix->AssignNCEDerivative(*tmp.m_CPUMatrix, *a.m_CPUMatrix, *b.m_CPUMatrix, inputIndex, *c.m_CPUMatrix); + template + CPUMatrix& CPUMatrix::AssignNCEDerivative(const CPUMatrix& tmp, const CPUMatrix& a, const CPUMatrix& b, size_t inputIndex, CPUMatrix& c) + { + size_t sample_size = this->GetNumRows() / 2; + size_t batch_size = this->GetNumCols(); + if (inputIndex == 1) + { +#pragma omp parallel for + for (int instance_id = 0; instance_id < batch_size; instance_id++) + for (int sample_id = 0; sample_id < sample_size; sample_id++) + { + int sample = (int)(*this)(2 * sample_id, instance_id); + for (int dim = 0; dim < b.GetNumRows(); dim++) + c(dim, instance_id) -= b(dim, sample)* tmp(sample_id, instance_id); + } + } + else if (inputIndex == 2) + { + int i_blocks = omp_get_num_threads() * 16; + // Assume only one block in k direction. + // We don't need to explicitly block in the j direction. +#pragma omp parallel for + for (int ib = 0; ib < i_blocks; ib++) + for (int instance_id = 0; instance_id < batch_size; instance_id++) + for (int sample_id = 0; sample_id < sample_size; sample_id++) + { + int sample =(int) (*this)(2 * sample_id, instance_id); + if (sample % i_blocks == ib) + for (int dim = 0; dim < b.GetNumRows(); dim++) + c(dim, sample) -= a(dim, instance_id)* tmp(sample_id, instance_id); + } + } + else + { + assert(inputIndex == 3); + // Assume only one block in k direction. + // We don't need to explicitly block in the j direction. + for (int instance_id = 0; instance_id < batch_size; instance_id++) + for (int sample_id = 0; sample_id < sample_size; sample_id++) + { + int sample =(int) (*this)(2 * sample_id, instance_id); + c(0, sample) -= tmp(sample_id, instance_id); + } + } + return *this; + } + + template + void CPUMatrix::AssignNoiseContrastiveEstimation(const CPUMatrix& a, + const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& tmp, CPUMatrix& c) + //this: samples+probs + // a: hidden + // b: embedding + // tmp: softmax + // c: loglikelihood + { + double log_likelihood = 0.0; + size_t sample_size = this->GetNumRows() / 2; + size_t batch_size = this->GetNumCols(); + size_t num_noise_samples = sample_size - 1; + double log_num_noise_samples = std::log(num_noise_samples); +#pragma omp parallel for reduction(+:log_likelihood) + for (int instance_id = 0; instance_id < batch_size; instance_id++) + for (int sample_id = 0; sample_id < sample_size; sample_id++) + { + int sample = (int)(*this)(2 * sample_id, instance_id); + double score = bias(0, sample); + for (int dim = 0; dim < b.GetNumRows(); dim++) + score += a(dim, instance_id)* b(dim, sample); + double sample_prob = -(*this)(2 * sample_id + 1, instance_id); + if (sample_id == 0) + sample_prob = -sample_prob; + double score_noise = log_num_noise_samples + sample_prob; + double z = LogAdd(score, score_noise); + double logprob = score - z; + double logprob_noise = score_noise - z; + tmp(sample_id, instance_id) = (ElemType)-std::exp(logprob); + if (sample_id == 0) + tmp(sample_id, instance_id) += 1; + log_likelihood += sample_id == 0 ? logprob : logprob_noise; + } + c(0, 0) = (ElemType)-log_likelihood; + } + + /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::Multiply(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, + CPUMatrix& c) + { + return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 0.0, c); + } + + /// Matrix-matrix multiply with col-major matrices (a and b are not transposed): c = a * b + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::Multiply(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) + { + return CPUMatrix::MultiplyAndWeightedAdd(1.0, a, false, b, false, 0.0, c); + } + + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c + /// if a is a column vector, add to all columns of c + /// if a is a row vector, add to all rows of c + /// if a is a scalar, add to all rows of c + /// Scalar + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::ScaleAndAdd(ElemType alpha, const CPUMatrix& a, CPUMatrix& c) + { + if (a.IsEmpty() || c.IsEmpty()) + LogicError("ScaleAndAdd: one of the input matrices is empty."); + + if (a.GetNumRows() != 1 && a.GetNumCols() != 1) // a is not a col or row vector + { + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int len = m * n; + const int incx = 1; + const int incy = 1; + + assert (m>0 && n>0 && len>0); //converting from size_t to int may cause overflow + assert ((int)c.GetNumRows() == m && (int)c.GetNumCols() == n); + if ((int)c.GetNumRows() != m || (int)c.GetNumCols() != n) + InvalidArgument("Dimension of matrix c does not match dimension of matrix a."); + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + daxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); +#else + cblas_daxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); +#endif + } + else + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + saxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); +#else + cblas_saxpy(len, alpha, reinterpret_cast (a.m_pArray), incx, reinterpret_cast (c.m_pArray), incy); +#endif + } + } + else if (a.GetNumElements() == 1) //scalar, add to all elements + { + ElemType v = alpha*a(0,0); + long m=(long)c.GetNumRows(), n=(long)c.GetNumCols(); +#pragma omp parallel for + for (long j=0; j(a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); +#else + cblas_daxpy (m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); +#endif + } + } + else + { +#pragma omp parallel for + foreach_column(j,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + saxpy(m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); +#else + cblas_saxpy (m, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+c.LocateColumn(j)), 1); +#endif + } + } + } + else //row vector, add it to all rows + { + int m = (int)c.GetNumRows(); + int n = (int)c.GetNumCols(); + assert (n == (int)a.GetNumCols()); + if (n != (int)a.GetNumCols()) + InvalidArgument("To add row vector, cols should match."); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_row(i,c) + { +#ifndef USE_MKL + daxpy(n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); +#else + cblas_daxpy (n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); +#endif + } + } + else + { +#pragma omp parallel for + foreach_row(i,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + saxpy(n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); +#else + cblas_saxpy (n, alpha, reinterpret_cast (a.m_pArray), 1, reinterpret_cast (c.m_pArray+i), m); +#endif + + } + } + } + } + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::AddScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) + { + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() && + a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols()); + + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() && + a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols())) + { + InvalidArgument("AddScaledDifference: a, b, and c must have same dimension."); + } + + if (a.IsEmpty()) + LogicError("AddScaledDifference: Input matrix a is empty."); + + long m=(long)c.GetNumElements(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + c.m_pArray[i] += alpha * (a.m_pArray[i]-b.m_pArray[i]); + c.m_pArray[i+1] += alpha * (a.m_pArray[i+1]-b.m_pArray[i+1]); + c.m_pArray[i+2] += alpha * (a.m_pArray[i+2]-b.m_pArray[i+2]); + c.m_pArray[i+3] += alpha * (a.m_pArray[i+3]-b.m_pArray[i+3]); + } + //handle remaining stuffs + for (long i=m & ~3; i c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::AssignScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) + { + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols() ); + + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + { + InvalidArgument("AssignScaledDifference: a, b must have same dimension."); + } + + if (a.IsEmpty()) + LogicError("AssignScaledDifference: Input matrix a is empty."); + + if (&c != &a && &c != &b) + c.Resize(a.GetNumRows(), a.GetNumCols()); + + long m=(long)c.GetNumElements(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(m & ~3); i+=4) + { + c.m_pArray[i] = alpha * (a.m_pArray[i]-b.m_pArray[i]); + c.m_pArray[i+1] = alpha * (a.m_pArray[i+1]-b.m_pArray[i+1]); + c.m_pArray[i+2] = alpha * (a.m_pArray[i+2]-b.m_pArray[i+2]); + c.m_pArray[i+3] = alpha * (a.m_pArray[i+3]-b.m_pArray[i+3]); + } + //handle remaining stuffs + for (long i=m & ~3; i + void CPUMatrix::AddElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) + { + if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || + ci >= c.GetNumRows() || cj >=c.GetNumCols()) + InvalidArgument("AddElementToElement: index out of range."); + + c(ci, cj) += a(ai, aj); + } + + ////c[ci,cj] += a[ai,aj] + //template + //void CPUMatrix::AddLogElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) + //{ + // if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || + // ci >= c.GetNumRows() || cj >=c.GetNumCols()) + // InvalidArgument("AddElementToElement: index out of range."); + // + // ElemType v = a(ai,aj); + // c(ci, cj) += ((v < EPS_IN_LOG) ? LOG_OF_EPS_IN_LOG : log(v)); + //} + + //c[ci,cj] = a[ai,aj] + template + void CPUMatrix::AssignElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj) + { + if (ai >= a.GetNumRows() || aj >=a.GetNumCols() || + ci >= c.GetNumRows() || cj >=c.GetNumCols()) + InvalidArgument("AssignElementToElement: index out of range."); + + c(ci, cj) = a(ai, aj); + } + + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// 1X1 matrix + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::AddScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) + { + assert(alpha.GetNumElements() == 1); + if (!(alpha.GetNumElements() == 1)) + InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix."); + + AddScaledDifference(alpha(0,0), a, b, c); + } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// 1X1 matrix + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::AssignScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c) + { + assert(alpha.GetNumElements() == 1); + if (!(alpha.GetNumElements() == 1)) + InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix."); + + AssignScaledDifference(alpha(0,0), a, b, c); + } + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + /// Scalar + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void CPUMatrix::Scale(ElemType alpha, const CPUMatrix& a, CPUMatrix& c) + { + if (a.IsEmpty()) + LogicError("Scale: Input matrix a is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + + assert (m>0 && n>0); //converting from size_t to int may cause overflow + c.Resize(m,n); + + long size=(long)c.GetNumElements(); +#pragma omp parallel for + //four-way unrolling + for (long i=0; i<(size & ~3); i+=4) + { + c.m_pArray[i] = alpha * a.m_pArray[i]; + c.m_pArray[i+1] = alpha * a.m_pArray[i+1]; + c.m_pArray[i+2] = alpha * a.m_pArray[i+2]; + c.m_pArray[i+3] = alpha * a.m_pArray[i+3]; + } + //handle remaining stuffs + for (long i=size & ~3; iMatrix-scalar multiply with col-major matrices: a = alpha * a + /// Scalar + /// Input matrix + template + void CPUMatrix::Scale(ElemType alpha, CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("Scale: Input matrix a is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int len = m * n; + const int incx = 1; + + assert (m>0 && n>0 && len>0); //converting from size_t to int may cause overflow + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + dscal(len, alpha, reinterpret_cast (a.m_pArray), incx); +#else + cblas_dscal(len, alpha, reinterpret_cast (a.m_pArray), incx); +#endif + } + else + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + sscal(len, alpha, reinterpret_cast (a.m_pArray), incx); +#else + cblas_sscal (len, alpha, reinterpret_cast (a.m_pArray), incx); +#endif + } + } + + /// Matrix multiply with col-major matrices: a = alpha[1,1] * a + /// 1x1 matrix + /// Input matrix + template + void CPUMatrix::Scale(CPUMatrix alpha, CPUMatrix& a) + { + if (a.IsEmpty()) + LogicError("Scale: Input matrix a is empty."); + if (alpha.GetNumElements()!=1) + LogicError("Matrix alpha must be 1x1"); + CPUMatrix::Scale(alpha(0,0),a); + } + + template + void CPUMatrix::InnerProduct (const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrices is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int k = (int)b.GetNumRows(); + const int l = (int)b.GetNumCols(); + + assert (m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow + assert (m==k && n==l); //converting from size_t to int may cause overflow + if (m!=k || n!=l) + InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); + + if ((isColWise && m == 1) || !isColWise && n == 1) //in this case it's equivalent to element-wise product + { + c.AssignElementProductOf(a, b); + } + else if (isColWise) //col-wise + { + c.Resize(1,n); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_column(j,c) + { +#ifndef USE_MKL + c(0,j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); +#else + c(0,j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); +#endif + } + } + else + { +#pragma omp parallel for + foreach_column(j,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + c(0,j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); +#else + c(0,j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray+a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray+b.LocateColumn(j)), 1); +#endif + } + } + } + else + { + c.Resize(m, 1); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_row(i,c) + { +#ifndef USE_MKL + c(i,0) = ddot(n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); +#else + c(i,0) = cblas_ddot (n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); +#endif + } + } + else + { +#pragma omp parallel for + foreach_row(i,c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + c(i,0) = sdot(n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); +#else + c(i,0) = cblas_sdot (n, reinterpret_cast (a.m_pArray+i), m, reinterpret_cast (b.m_pArray+i), m); +#endif + } + } + } + } + + // treat matrices as vectors. do vec(a)^T vec(b) + template + ElemType CPUMatrix::InnerProductOfMatrices(const CPUMatrix& a, const CPUMatrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProductOfMatrices: one of the input matrices is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int k = (int)b.GetNumRows(); + const int l = (int)b.GetNumCols(); + + assert (m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow + assert (m==k && n==l); //converting from size_t to int may cause overflow + if (m!=k || n!=l) + InvalidArgument("InnerProductOfMatrices: Matrices a and b should have same dimension."); + + if (sizeof(ElemType) == sizeof(double)) + { +#ifndef USE_MKL + return (ElemType)ddot((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); +#else + return (ElemType)cblas_ddot ((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); +#endif + } + else + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + return (ElemType)sdot((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); +#else + return (ElemType)cblas_sdot ((int)a.GetNumElements(), reinterpret_cast (a.m_pArray), 1, reinterpret_cast (b.m_pArray), 1); +#endif + } + } + + template + void CPUMatrix::ElementWisePower (ElemType alpha, const CPUMatrix& a, CPUMatrix& c) + { + if (a.IsEmpty()) + LogicError("Scale: The input matrix a is empty."); + + c.Resize(a.GetNumRows(), a.GetNumCols()); + + if (alpha == 2) + { +#pragma omp parallel for + foreach_coord(i,j,c) + { + c(i,j) = a(i,j) * a(i,j); + } + } + else if (alpha == 3) + { +#pragma omp parallel for + foreach_coord(i,j,c) + { + c(i,j) = a(i,j) * a(i,j) * a(i,j); + } + } + else + { +#pragma omp parallel for + foreach_coord(i,j,c) + { + c(i,j) = pow(a(i,j), alpha); + } + } + } + + template + bool CPUMatrix::AreEqual(const CPUMatrix& a, const CPUMatrix& b, const ElemType threshold /*= 1e-8*/) + { + if (a.GetNumRows() != b.GetNumRows() || a.GetNumCols() != b.GetNumCols()) + return false; + + bool result=true; +#pragma omp parallel for + foreach_coord(i, j, a) + { + if (abs(a(i,j)-b(i,j)) > threshold) + { + result = false; + break; + } + } + + return result; + } + + // see Matrix::TensorShuffleScaleAndAdd() for comments + template + void CPUMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const CPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const CPUMatrix& b, CPUMatrix& c) + { + size_t N = D * S * M * K * T; + const ElemType * pa = a.m_pArray; + const ElemType * pb = b.m_pArray; + ElemType * pc = c.m_pArray; + // Note: This code is written to match a GPU implementation. It is not super-efficient on the CPU. + for (size_t na = 0; na < N; na++) // loop over all elements + { + // recover the 5 indices from the loop counter + size_t d = na % D; + size_t s = (na / D ) % S; + size_t m = (na / D / S ) % M; + size_t k = (na / D / S / M ) % K; + size_t t = (na / D / S / M / K) % T; + // compute index for the a and b/c tensors + assert(na== (((t * K + k) * M + m) * S + s) * D + d); // input tensor of dimension (D x S x M x K x T) + size_t nb = (((t * S + s) * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S x T): k/K and s/S swapped + assert(nb < N); + // perform the computation + ElemType cval = keepWeight ? keepWeight * pb[nb] : 0; // if weight is 0 then don't bother to read memory (efficiency) or to multiply (NaN-safe) + cval += scaleFactor * pa[na]; + pc[nb] = cval; + } + } + + template + CPUMatrix CPUMatrix::Ones(const size_t rows, const size_t cols) + { + CPUMatrix c(rows, cols); //will initialize to 0 + c.SetValue(1); + return c; + } + + template + CPUMatrix CPUMatrix::Zeros(const size_t rows, const size_t cols) + { + CPUMatrix c(rows, cols); //will initialize to 0 + c.SetValue(0); + return c; + } + + template + CPUMatrix CPUMatrix::Eye(const size_t rows) + { + CPUMatrix c(rows, rows); //will initialize to 0 + c.SetDiagonalValue(1); + return c; + } + + template + CPUMatrix CPUMatrix::RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed) + { + CPUMatrix c(rows, cols); //will initialize to 0 + c.SetUniformRandomValue(low, high, seed); + return c; + } + + template + CPUMatrix CPUMatrix::RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed) + { + CPUMatrix c(rows, cols); //will initialize to 0 + c.SetGaussianRandomValue(mean, sigma, seed); + return c; + } + + template + bool CPUMatrix::HasElement(const CPUMatrix& mat, const ElemType v) + { + bool bHas = false; + + bool isvFinite = std::isfinite(v); +#pragma omp parallel for + for (long j = 0; j < mat.GetNumElements(); j++) + { +#pragma omp flush(bHas) + if (!bHas) + { + ElemType cur = mat.m_pArray[j]; + if (isvFinite && std::isfinite(cur)) + { + if (cur == v) + bHas = true; + } + else if (std::isnan(v) && std::isnan(cur)) + bHas = true; + else if (std::isinf(v) && std::isinf(cur) && std::signbit(v) == std::signbit(cur)) + bHas = true; + } + } + + return bHas; + } + + // CPUMatrix& AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber); + //[this]=a .* b + // here, a and b must be two row vectors of the same size, i.e. [1,m] + // the inputs are two rwo vectors + // the output is a matrix of size(neg+1, col) + template + CPUMatrix& CPUMatrix::AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); + + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix dimensions do not match."); + + if (a.GetNumRows() != 1) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); + + auto& us = *this; + if (this != &a) + { + Resize(negnumber + 1, a.GetNumCols()); + // Resize(a.GetNumRows(), a.GetNumCols()); + } + + long m = (long)GetNumRows(), n = (long)GetNumCols(); // a and b are of size (1,n) + //#pragma omp parallel for + + for (long j = 0; j < n; j++) + { + us(0, j) = a(0, j) * b(0, j); + } + for (long j = 0; j + void CPUMatrix::InnerProductWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise, size_t shift, size_t negnumber) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrices is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int k = (int)b.GetNumRows(); + const int l = (int)b.GetNumCols(); + + assert(m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow + assert(m == k && n == l); //converting from size_t to int may cause overflow + if (m != k || n != l) + InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); + + if ((isColWise && m == 1) || !isColWise && n == 1) //in this case it's equivalent to element-wise product + { + InvalidArgument("InnerProduct: Both matrices should be normal ones, not vectors"); + // c.AssignElementProductOf(a, b); + } + else if (isColWise) //col-wise + { + c.Resize(negnumber + 1, n); // this line ischanged + + if (sizeof(ElemType) == sizeof(double)) + { + for (long j = 0; j < n; j++) + { +#ifndef USE_MKL + c(0, j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); +#else + c(0, j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); +#endif + } + for (long j = 0; j < n; j++) + { + for (long i = 1; i < negnumber + 1; i++) + { +#ifndef USE_MKL + c(i, j) = (ElemType)ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); +#else + c(i, j) = (ElemType)cblas_ddot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); +#endif + } + } + + } + else + { + for (long j = 0; j < n; j++) + { +#ifndef USE_MKL + c(0, j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); +#else + c(0, j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn(j)), 1); +#endif + } + for (long j = 0; j < n; j++) + { + for (long i = 1; i < negnumber + 1; i++) + { +#ifndef USE_MKL + c(i, j) = (ElemType)sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); +#else + c(i, j) = (ElemType)cblas_sdot(m, reinterpret_cast (a.m_pArray + a.LocateColumn(j)), 1, reinterpret_cast (b.m_pArray + b.LocateColumn((j + shift + i - 1) % n)), 1); +#endif + } + } + } + } + else + { + InvalidArgument("InnerProduct: Rowwise is not supported yet"); + + c.Resize(m, 1); + + if (sizeof(ElemType) == sizeof(double)) + { +#pragma omp parallel for + foreach_row(i, c) + { +#ifndef USE_MKL + c(i, 0) = (ElemType)ddot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); +#else + c(i, 0) = (ElemType)cblas_ddot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); +#endif + } + } + else + { +#pragma omp parallel for + foreach_row(i, c) + { +#pragma warning (suppress: 4244) +#ifndef USE_MKL + c(i, 0) = sdot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); +#else + c(i, 0) = cblas_sdot(n, reinterpret_cast (a.m_pArray + i), m, reinterpret_cast (b.m_pArray + i), m); +#endif + } + } + } + } + + + template + CPUMatrix& CPUMatrix::GetARowByIndex(const CPUMatrix& a, size_t index) + { + if (a.IsEmpty()) + LogicError("GetARowByIndex: the input matrices is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + + if (index <0 || index >= m) + LogicError("GetARowByIndex: the row index is out of range."); + + assert(m>0 && n>0); //converting from size_t to int may cause overflow + + auto& us = *this; + this->Resize(1, n); + for (long j = 0; j < n; j++) + { + us(0, j) = a(index, j); + } + + return *this; + + } + + + // input: a, a row vector + // input: b, a matrix. b.col == a.col + // input firstmatrixfixed: If true, keep a's order. Otherwise, keep b's order + // output: c, a matrix. c.size == b.size + /* + Example, a = [a1 a2 a3] + b = [b11 b12 b13; + b21 b22 b23 ] + + if true: + shift = 1 + + then c = [a1*b12 a2*b13 a3*b11 + a1*b22 a2*b23 a3*b21] + + if shift = 2 + then c = [ a1*b13 a2*b11 a3*b12 + a1*b23 a2*b21 a3*b22] + i.e. we do column-wise shift + + if false: + shift = 1 + + then c = [a2*b11 a3*b12 a1*b13 + a2*b21 a3*b22 a1*b23] + + shift = 2 + + then c = [ a3*b11 a1*b12 a2*b13 + a3*b21 a1*b22 a2*b23] + + + */ + template + void CPUMatrix::ConductRowElementMultiplyWithShift(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, size_t shift, bool bFirstmatrixfixed) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrices is empty."); + + const int m = (int)a.GetNumRows(); + const int n = (int)a.GetNumCols(); + const int k = (int)b.GetNumRows(); + const int l = (int)b.GetNumCols(); + + assert(m>0 && n>0 && k>0 && l>0); //converting from size_t to int may cause overflow + assert(m == 1 && n == l); //converting from size_t to int may cause overflow + if (m != 1 || n != l) + InvalidArgument("InnerProduct: Matrices a and b should have same dimension."); + + c.Resize(k, l); // c must the the same size of b + + if (bFirstmatrixfixed) + { + for (long j = 0; j < l; j++) + { + for (long i = 0; i < k; i++) + { + c(i, j) = a(0, j) * b(i, (j + shift) % l); + } + } + } + else + { + for (long j = 0; j < l; j++) + { + for (long i = 0; i < k; i++) + { + c(i, j) = a(0, (j + shift) % l) * b(i, j); + } + } + } + + + + } + + + // CPUMatrix& AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, size_t shift); + //[this]=a .* b + // here, a and b must be two row vectors of the same size, i.e. [1,m]. We will do element product with shift. + // inputs are 2 row vectors + // output is a row vector + template + CPUMatrix& CPUMatrix::AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, size_t shift) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); + + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix dimensions do not match."); + + if (a.GetNumRows() != 1) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); + + auto& us = *this; + if (this != &a) + { + Resize(1, a.GetNumCols()); + // Resize(a.GetNumRows(), a.GetNumCols()); + } + + //long m = (long)GetNumRows(), n = (long)GetNumCols(); // a and b are of size (1,n) + long n = (long)GetNumCols(); // a and b are of size (1,n) +#pragma omp parallel for + for (long j = 0; j + ElemType CPUMatrix::LogAddSumOfElements() const + { + ElemType fAlpha = (ElemType)LZERO; + for (int k = 0; k < GetNumElements(); k++) + fAlpha = (ElemType) LogAddD(fAlpha, m_pArray[k]); + return fAlpha; + } + + template + void CPUMatrix::RCRFBackwardCompute(const CPUMatrix& alpha, CPUMatrix& beta, + const CPUMatrix& lbls, + const CPUMatrix& pair_scores) + { + int iNumPos = (int)lbls.GetNumCols(); + int iNumLab = (int)lbls.GetNumRows(); + + int lastLbl = -1; + for (int ik = 0; ik < lbls.GetNumRows(); ik++) + if (lbls(ik, iNumPos - 1) != 0){ + lastLbl = ik; break; + } + + beta.Resize(iNumLab, iNumPos); + + for (int t = iNumPos - 1; t >= 0; t--) + { +#pragma omp parallel for + for (int k = 0; k < iNumLab; k++) + { + _rcrfBackwardCompute(t, k, alpha, beta, pair_scores); + } + } + + }; + + /// the kernel function for RCRF backward computation + template + void CPUMatrix::_rcrfBackwardCompute(size_t t, size_t k, const CPUMatrix& alpha, + CPUMatrix& beta, + const CPUMatrix& pair_scores) + { + size_t iNumLab = alpha.GetNumRows(); + size_t iNumPos = alpha.GetNumCols(); + + ElemType fSum; + ElemType fTmp = (ElemType)LZERO; + if (t == iNumPos - 1) + { + fSum = (ElemType)LZERO; + for (int j = 0; j < iNumLab; j++) + { + fSum = (ElemType)LogAddD(fSum, alpha(j, t)); + } + + fTmp = alpha(k, t) - fSum; + beta(k, t) = fTmp; + } + else + { + for (int j = 0; j < iNumLab; j++) + { + fSum = (ElemType)LZERO; + for (int m = 0; m < iNumLab; m++) + { + fSum = (ElemType)LogAddD(fSum, alpha(m, t) + pair_scores(j, m)); + } + + fTmp = (ElemType)LogAddD(fTmp, beta(j, t + 1) + alpha(k, t) + pair_scores(j, k) - fSum); + } + beta(k, t) = fTmp; + } + } + + template + void CPUMatrix::RCRFTransGrdCompute(const CPUMatrix& lbls, + const CPUMatrix& alpha, + const CPUMatrix& beta, + const CPUMatrix& pair_scores, + CPUMatrix& grd) + { + int iNumPos = (int)alpha.GetNumCols(); + int iNumLab = (int)alpha.GetNumRows(); + + int firstLbl = -1; + for (int ik = 0; ik < lbls.GetNumRows(); ik++) + if (lbls(ik, 0) != 0){ + firstLbl = ik; break; + } + + for (size_t tPos = 0; tPos < iNumPos; tPos++) + { + CPUMatrix b = beta.ColumnSlice(tPos, 1); + CPUMatrix a; + if (tPos > 0) + a = alpha.ColumnSlice(tPos - 1, 1); + +#pragma omp parallel for + for (int i = 0; i < iNumLab; i++){ + _rcrfTransGrdCompute(i, lbls, alpha, beta, pair_scores, grd, tPos); + } + + /// transition score + int i = -1; + if (tPos == 0) i = firstLbl; + else { + for (int ik = 0; ik < lbls.GetNumRows(); ik++) + if (lbls(ik, tPos - 1) != 0){ + i = ik; break; + } + } + + int j = -1; + for (int ik = 0; ik < lbls.GetNumRows(); ik++){ + if (lbls(ik, tPos) != 0){ + j = ik; break; + } + } + + grd(j, i) -= 1.0; + } + }; + + template + void CPUMatrix::_rcrfTransGrdCompute(size_t i, + const CPUMatrix& lbls, + const CPUMatrix& alpha, + const CPUMatrix& beta, + const CPUMatrix& pair_scores, + CPUMatrix& grd, + const size_t tPos /// position + ) + { + int iNumLab = (int)alpha.GetNumRows(); + + int firstLbl = -1; + for (int ik = 0; ik < lbls.GetNumRows(); ik++) + if (lbls(ik, 0) != 0){ + firstLbl = ik; break; + } + + CPUMatrix b = beta.ColumnSlice(tPos, 1); + CPUMatrix a; + if (tPos > 0) + a = alpha.ColumnSlice(tPos - 1, 1); + + { + ElemType fTmp = (ElemType)LZERO; + for (int j = 0; j < iNumLab; j++){ + if (tPos == 0){ + if (i == firstLbl){ + fTmp = 0; + } + else{ + fTmp = (ElemType)LZERO; + } + } + else{ + fTmp = a(i, 0); + } + fTmp += pair_scores(j, i); + + + ElemType fSum = (ElemType)LZERO; + for (int k = 0; k < iNumLab; k++){ + ElemType fTmp2; + if (tPos == 0){ + if (k == firstLbl){ + fTmp2 = 0; + } + else{ + fTmp2 = (ElemType)LZERO; + } + } + else{ + fTmp2 = a(k, 0); + } + fSum = (ElemType)LogAddD(fSum, fTmp2 + pair_scores(j, k)); + } + + fTmp -= fSum; + fTmp += b(j, 0); + + grd(j, i) += exp(fTmp); + } + } + }; + template + CPUMatrix& CPUMatrix::DropFrame(const CPUMatrix& label, const CPUMatrix& gamma, const ElemType & threshhold) + { + auto& us = *this; + if (us.GetNumCols() != gamma.GetNumCols() || us.GetNumRows() != gamma.GetNumRows()) + LogicError("DropFrame: target matrix is not in the same size as gamm matrix."); + +#pragma omp parallel for + foreach_column(j, label) + { + + bool dropframe = false; + foreach_row(i, label) + { + if (fabs(label(i, j) - 1.0f) < 0.1) + { + if (gamma(i, j) < threshhold) + dropframe = true; + break; + } + } + + foreach_row(i, label) + { + us(i, j) = 0.0f; + } + } + + return *this; + } + + template + CPUMatrix& CPUMatrix::AssignSequenceError(const ElemType hsmoothingWeight, const CPUMatrix& label, + const CPUMatrix& dnnoutput, const CPUMatrix& gamma, ElemType alpha) + { + auto& us = *this; + foreach_coord(i, j, us) + us(i, j) += alpha * (label(i, j) - (1 - hsmoothingWeight)*dnnoutput(i, j) - hsmoothingWeight*gamma(i, j)); + return *this; + } + + // note: this function does not depend on the parameter + template + int CPUMatrix::SetNumThreads(int numThreads) + { + if (numThreads == 0) //use default + return numThreads; + + int mthreads = (int)std::thread::hardware_concurrency(); + + if (numThreads <= 0) + numThreads = max(1, mthreads + numThreads); + if (numThreads > mthreads) + numThreads = mthreads; + +#ifdef _OPENMP + omp_set_num_threads(numThreads); + numThreads = omp_get_max_threads(); + +#ifndef USE_MKL + acmlsetnumthreads(numThreads); +#else + mkl_set_num_threads(numThreads); +#endif +#endif + return numThreads; + } + + // ======================================================================= + // TensorView support + // ======================================================================= + + // To save time, this makes extensive use of templates and macros. + + // ----------------------------------------------------------------------- + // function to compute the value for a given output location (perform reduction if needed) + // ----------------------------------------------------------------------- + + // perform loop over reduction index m + // This function is declared inside a wrapper struct to allow partial specialization (m = -1). + template + struct TensorOpReduction + { + // reduction case (non-reduction case is specialized) + static inline ElemType Loop(array pointers, const OPFN & opfn, + const SmallVector & reducingOpDims, const array, N> & reducingStrides) + { + array strides; // N-1 because last one is the result pointer, which is unused in reduction + for (size_t i = 0; i < N - 1; i++) // N = a small constant, this will be unrolled + strides[i] = reducingStrides[i][(size_t)m]; + double/*ElemType*/ aggregate = 0; + for (size_t dim = reducingOpDims[(size_t)m]; dim-- > 0;) + { + // need to descend into one loop deeper + aggregate += TensorOpReduction::Loop(pointers, opfn, reducingOpDims, reducingStrides); + // advance the pointers + for (size_t i = 0; i < N - 1; i++) + pointers[i] += strides[i]; // note: last pointer (result) is unused and untouched here + } + return (ElemType)aggregate; + } + }; + + // perform loop over reduction index m + // This is the specialized version for m = -1, which terminates the recursion. + template + struct TensorOpReduction + { + static inline ElemType Loop(array pointers, const OPFN & opfn, + const SmallVector &, const array, N> &) + { + return opfn(pointers); // finally we are doing some work!!! + } + }; + + // ----------------------------------------------------------------------- + // perform loop over regular index k for N-nary operations (N counting the output) + // ----------------------------------------------------------------------- + + // perform loop over regular index k and reducing index m for N operands (counting the output) + template + struct TensorOpIteration + { + static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, + const SmallVector & regularOpDims, const array, N> & regularStrides, + const SmallVector & reducingOpDims, const array, N> & reducingStrides) + { + // non-scalar case: still nested result loops left + array strides; + for (size_t i = 0; i < N; i++) // N = a small constant, this will be unrolled + strides[i] = regularStrides[i][(size_t)k]; + for (size_t dim = regularOpDims[(size_t)k]; dim--> 0;) + { + // need to descend into one loop deeper + TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + // advance the pointers + for (size_t i = 0; i < N; i++) + pointers[i] += strides[i]; + } + } + }; + + // Special version for innermost loop with strides all being 1 and no further reduction. Compiler can use SSE. + // This is a very common case, e.g. adding vectors or computing the Sigmoid. + template + struct TensorOpIteration + { + static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, + const SmallVector & regularOpDims, const array, 3> & regularStrides, + const SmallVector & reducingOpDims, const array, 3> & reducingStrides) + { + ElemType* pa = pointers[0]; + ElemType* pb = pointers[1]; + ElemType* pc = pointers[2]; + size_t K = regularOpDims[0]; + // special-case beta and alpha to allow the compiler to short-circuit it + if (beta != 0) +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(beta, array { pa + k, pb + k, pc + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + else if (alpha != 1) +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(0, array { pa + k, pb + k, pc + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + else +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(0, array { pa + k, pb + k, pc + k }, 1, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + // TODO: According to Amit, the VS compiler is not able to vectorize into lambdas. Solution: change the lambda to take an N, or to implement the loop inside (with 1 element by default). + // TODO: The signedness of k (required for omp) causes an extra sign-extend. + // TODO: OMP adds LOTS of overhead. Do we need a guard, a min size when to use it? + } + }; + // and unary + template + struct TensorOpIteration + { + static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, + const SmallVector & regularOpDims, const array, 2> & regularStrides, + const SmallVector & reducingOpDims, const array, 2> & reducingStrides) + { + ElemType* pa = pointers[0]; + ElemType* pb = pointers[1]; + size_t K = regularOpDims[0]; + // special-case beta and alpha to allow the compiler to short-circuit it + if (beta != 0) +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(beta, array { pa + k, pb + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + else if (alpha != 1) +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(0, array { pa + k, pb + k }, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + else +#pragma omp parallel for + for (int k = 0; k < (int)K; k++) + TensorOpIteration::Loop(0, array { pa + k, pb + k }, 1, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + } + }; + + template + struct TensorOpIteration + { + static inline void Loop(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, + const SmallVector &, const array, N> &, + const SmallVector & reducingOpDims, const array, N> & reducingStrides) + { + // we are at element level for the result: perform the op (there may still be reduction) + ElemType val = TensorOpReduction::Loop(pointers, opfn, reducingOpDims, reducingStrides); + // scale + val *= alpha; + // combine with previous value in target matrix, then write it out + auto * pout = pointers.back(); + if (beta != 0) + val += beta * *pout; + // save + *pout = val; + return; + } + }; + + // ----------------------------------------------------------------------- + // map runtime parameters N to template parameters + // ----------------------------------------------------------------------- + + // tensor operation with k+1 dimensions (-1 means scalar) + template + static void TensorOpWithRegularLoop(ElemType beta, const array & pointers, ElemType alpha, const OPFN & opfn, + const SmallVector & regularOpDims, const array, N> & regularStrides, + const SmallVector & reducingOpDims, const array, N> & reducingStrides) + { + size_t dims = reducingOpDims.size(); + switch (dims) + { + case 2: return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 1: return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 0: + { + // if all leading dimensions are 1, we can let the compiler do some unrolling + bool leadingAllOne = true; + for (size_t i = 0; i < N; i++) + leadingAllOne &= k >= 0 && regularStrides[i][0] == 1; + if (leadingAllOne) // special version that uses a hard-coded increment of 1 for all leading dimensions + return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + else + return TensorOpIteration::Loop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + } + default: LogicError("TensorOp: %d non-flattened reduction dimensions are not supported.", (int)dims); + } + } + + // tensor operation, generalized in number of arguments, operation already provided as a lambda + // This function now expands into different k. + template + static void TensorOpWithFn(ElemType beta, array pointers, ElemType alpha, const OPFN & opfn, + const array & offsets, + const SmallVector & regularOpDims, const array, N> & regularStrides, + const SmallVector & reducingOpDims, const array, N> & reducingStrides) + { + for (size_t i = 0; i < N; i++) // N = a small constant, this will be unrolled + pointers[i] += offsets[i]; + size_t dims = regularOpDims.size(); + switch (dims) + { + case 4: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 3: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 2: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 1: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + case 0: return TensorOpWithRegularLoop(beta, pointers, alpha, opfn, regularOpDims, regularStrides, reducingOpDims, reducingStrides); + default: LogicError("TensorOp: %d non-flattened input dimensions are not supported.", (int)dims); + } + } + + // ----------------------------------------------------------------------- + // entry points from Matrix.cpp; also map op to a lambda + // ----------------------------------------------------------------------- + + // perform unary operation 'op' on a giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides + // This maps 'op' to a lambda. + template + void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 2> & regularStrides, + const SmallVector & reducingOpDims, const array, 2> & reducingStrides) + { + // TODO: Change the lambda to take a pointer and a number of elements, so that we can pass it 1 or 4 elements, in order for it to SSE-vectorize. + #define CaseUnaryTensorOp(oper) \ + case ElementWiseOperator::op ## oper: \ + return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) + + array pointers = { a.m_pArray, m_pArray }; + switch (op) + { + ForAllUnaryOps(CaseUnaryTensorOp); + default: LogicError("TensorUnaryOp: Unknown op code %d.", (int)op); + } + } + + // perform binary operation 'op' on a and b giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides + // This maps 'op' to a lambda. + template + void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, const CPUMatrix& b, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 3> & regularStrides, + const SmallVector & reducingOpDims, const array, 3> & reducingStrides) + { + #define CaseBinaryTensorOp(oper) \ + case ElementWiseOperator::op ## oper: \ + return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0])), (*(pp[1]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) + + array pointers = { a.m_pArray, b.m_pArray, m_pArray }; + switch (op) + { + ForAllBinaryOps(CaseBinaryTensorOp); + default: LogicError("TensorBinaryOp: Unknown op code %d.", (int)op); + } + } + + // perform ternary operation 'op' on a, and c giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides + // This maps 'op' to a lambda. + template + void CPUMatrix::TensorOp(ElemType beta, const CPUMatrix& a, const CPUMatrix& b, const CPUMatrix& c, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 4> & regularStrides, + const SmallVector & reducingOpDims, const array, 4> & reducingStrides) + { + #define CaseTernaryTensorOp(oper) \ + case ElementWiseOperator::op ## oper: \ + return TensorOpWithFn(beta, pointers, alpha, [](const array & pp) { return Op ## oper((*(pp[0])), (*(pp[1])), (*(pp[2]))); }, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides) + + array pointers = { a.m_pArray, b.m_pArray, c.m_pArray, m_pArray }; + switch (op) + { + ForAllTernaryOps(CaseTernaryTensorOp); + default: LogicError("TensorTernaryOp: Unknown op code %d.", (int)op); + } + } + + // ======================================================================= + // explicit instantiations + // ======================================================================= + template class MATH_API CPUMatrix; + template class MATH_API CPUMatrix; + + // We use Matrix as the backing store for QuantizedMatrix + // Let's explicitly instantiate the methods we need for that purpose + template CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols); + template CPUMatrix::CPUMatrix(const size_t numRows, const size_t numCols, char* pArray, const size_t matrixFlags); + template CPUMatrix::CPUMatrix(); + template CPUMatrix::CPUMatrix(CPUMatrix const &); + template CPUMatrix::CPUMatrix(CPUMatrix&&); + template size_t CPUMatrix::LocateElement(size_t, size_t) const; + template CPUMatrix::~CPUMatrix(); + template CPUMatrix CPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const; + template CPUMatrix& CPUMatrix::operator=(CPUMatrix&&); + template void CPUMatrix::SetValue(const char); + template void CPUMatrix::SetValue(const size_t numRows, const size_t numCols, char *pArray, size_t matrixFlags); + template void CPUMatrix::SetValue(CPUMatrix const&); + template void CPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly); + +}}} diff --git a/Source/Math/CPUMatrix.h b/Source/Math/CPUMatrix.h index 204f3b809286..cc5f5d658284 100644 --- a/Source/Math/CPUMatrix.h +++ b/Source/Math/CPUMatrix.h @@ -1,339 +1,339 @@ - // -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once -#include "Basics.h" // for RuntimeError() -#include "Matrix.h" -#include "File.h" -#include "Helpers.h" -#include "CommonMatrix.h" -#include -#include -#include -#include - -// NOTE NOTE NOTE: -// use CPUSingleMatrix and CPUDoubleMatrix instead of using the template directly -/////////////////////////////////////////////// - - -// This class is exported from the Math.dll -namespace Microsoft { namespace MSR { namespace CNTK { - - double logadd(double x, double y); - - //To compy with BLAS libraries matrices are stored in ColMajor. However, by default C/C++/C# use RowMajor - //convertion is need when passing data between CPUMatrix and C++ matrices - template - class MATH_API CPUMatrix : public BaseMatrix - { - typedef BaseMatrix B; using B::m_numRows; using B::m_numCols; using B::m_pArray; using B::m_computeDevice; using B::m_elemSizeAllocated; - using B::m_externalBuffer; using B::m_format; using B::m_matrixName; // without this, base members would require to use thi-> in GCC - public: - CPUMatrix(); - CPUMatrix(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. - CPUMatrix(const size_t numRows, const size_t numCols); - CPUMatrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags=matrixFlagNormal); - CPUMatrix(const CPUMatrix& deepCopyFrom); //copy constructor, deep copy - CPUMatrix& operator=(const CPUMatrix& deepCopyFrom); //assignment operator, deep copy - CPUMatrix(CPUMatrix&& moveFrom); //move constructor, shallow copy - CPUMatrix& operator=(CPUMatrix&& moveFrom); //move assignment operator, shallow copy - - ~CPUMatrix(); - - public: - using B::OwnBuffer; using B::GetNumElements; using B::IsEmpty; using B::GetNumRows; using B::GetNumCols; using B::SetOwnBuffer; using B::SetMatrixName; - - size_t BufferSize() const { return m_numRows*m_numCols*sizeof(ElemType); } - ElemType* BufferPointer() const {return m_pArray;} - - CPUMatrix ColumnSlice(size_t startColumn, size_t numCols) const; - CPUMatrix& AssignColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols); - CPUMatrix& SetColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols); - - void CopyColumnsStrided(const CPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); - - CPUMatrix Diagonal() const; - - ElemType Adagrad(CPUMatrix& gradients, const bool needAveMultiplier); - void FSAdagrad(CPUMatrix& gradients, CPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul); - ElemType RmsProp(CPUMatrix& gradients, - ElemType RMS_GAMMA, - ElemType RMS_WGT_INC, - ElemType RMS_WGT_MAX, - ElemType RMS_WGT_DEC, - ElemType RMS_WGT_MIN, - const bool needAveMultiplier - ); - - void Reshape(const size_t numRows, const size_t numCols); - void Resize(const size_t numRows, const size_t numCols, bool growOnly = true); //by default we only reallocate if need to grow - ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller - size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller - void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; - - inline ElemType& operator() (const size_t row, const size_t col) - { - return m_pArray[LocateElement(row, col)]; - } - inline const ElemType& operator() (const size_t row, const size_t col) const - { - return m_pArray[LocateElement(row, col)]; - } - inline ElemType Get00Element() const - { - return m_pArray[0]; - } - - void SetValue(const ElemType v); - void SetValue(const CPUMatrix& deepCopyFrom); - void SetValue(const size_t numRows, const size_t numCols, ElemType *pArray, size_t matrixFlags=matrixFlagNormal); - - void MaskColumnsValue(const CPUMatrix& columnsMask, ElemType val); - - void SetColumn(const ElemType* colPointer, size_t colInd); - void SetColumn(const CPUMatrix& valMat, size_t colInd); - void SetColumn(const ElemType val, size_t j); - - void SetDiagonalValue(const ElemType v); - void SetDiagonalValue(const CPUMatrix& vector); - void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); - void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); - void AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - - CPUMatrix Transpose(); - CPUMatrix& AssignTransposeOf (const CPUMatrix& a); - - CPUMatrix& operator+= (const ElemType alpha); - CPUMatrix operator+ (const ElemType alpha) const; - CPUMatrix& AssignSumOf(const ElemType alpha, const CPUMatrix& a); - - CPUMatrix& operator+= (const CPUMatrix& a); - CPUMatrix operator+ (const CPUMatrix& a) const; - CPUMatrix& AssignSumOf(const CPUMatrix& a, const CPUMatrix& b); - - CPUMatrix& operator-= (const ElemType alpha); - CPUMatrix operator- (const ElemType alpha) const; - CPUMatrix& AssignDifferenceOf(const ElemType alpha, const CPUMatrix& a); - CPUMatrix& AssignDifferenceOf(const CPUMatrix& a, const ElemType alpha); - - CPUMatrix& operator-= (const CPUMatrix& a); - CPUMatrix operator- (const CPUMatrix& a) const; - CPUMatrix& AssignDifferenceOf(const CPUMatrix& a, const CPUMatrix& b); - - CPUMatrix& operator*= (const ElemType alpha); - CPUMatrix operator* (const ElemType alpha) const; - CPUMatrix& AssignProductOf(const ElemType alpha, const CPUMatrix& a); - - CPUMatrix operator* (const CPUMatrix& a) const; - CPUMatrix& AssignProductOf (const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB); - - CPUMatrix& operator/= (ElemType alpha); - CPUMatrix operator/ (ElemType alpha) const; - - CPUMatrix& operator^= (ElemType alpha); //element-wise power - CPUMatrix operator^ (ElemType alpha) const; //element-wise power - CPUMatrix& AssignElementPowerOf(const CPUMatrix& a, const ElemType power); - - CPUMatrix& ElementMultiplyWith (const CPUMatrix& a); - CPUMatrix& AssignElementProductOf (const CPUMatrix& a, const CPUMatrix& b); - CPUMatrix& AddElementProductOf (const CPUMatrix& a, const CPUMatrix& b); - - CPUMatrix& AssignElementDivisionOf (const CPUMatrix& a, const CPUMatrix& b); - CPUMatrix& ElementDivideBy(const CPUMatrix& a); - - CPUMatrix& ColumnElementMultiplyWith(const CPUMatrix& a); - CPUMatrix& RowElementMultiplyWith(const CPUMatrix& a); - - CPUMatrix& ColumnElementDivideBy(const CPUMatrix& a); - CPUMatrix& RowElementDivideBy(const CPUMatrix& a); - - CPUMatrix& ElementInverse (); - CPUMatrix& AssignElementInverseOf (const CPUMatrix& a); - - CPUMatrix& InplaceSigmoid (); - CPUMatrix& AssignSigmoidOf (const CPUMatrix& a); - - CPUMatrix& InplaceLinearRectifierDerivative(); - CPUMatrix& AssignLinearRectifierDerivativeOf (const CPUMatrix& a); - - CPUMatrix& InplaceSigmoidDerivative(); - CPUMatrix& AssignSigmoidDerivativeOf (const CPUMatrix& a); - - CPUMatrix& InplaceTanh (); - CPUMatrix& AssignTanhOf (const CPUMatrix& a); - - CPUMatrix& InplaceLogSoftmax (const bool isColWise); - CPUMatrix& AssignLogSoftmaxOf (const CPUMatrix& a, const bool isColWise); - - CPUMatrix& InplaceHardmax(const bool isColWise); - CPUMatrix& AssignHardmaxOf(const CPUMatrix& a, const bool isColWise); - - //sequence training - CPUMatrix& DropFrame(const CPUMatrix& label, const CPUMatrix& gamma, const ElemType & threshhold); - CPUMatrix& AssignSequenceError(const ElemType hsmoothingWeight, const CPUMatrix& label, const CPUMatrix& dnnoutput, const CPUMatrix& gamma, ElemType alpha); - CPUMatrix& InplaceSqrt (); - CPUMatrix& AssignSqrtOf (const CPUMatrix& a); - - CPUMatrix& InplaceExp (); - CPUMatrix& AssignExpOf (const CPUMatrix& a); - - CPUMatrix& InplaceLog (); - CPUMatrix& AssignLogOf (const CPUMatrix& a); - - CPUMatrix& InplaceLog10 (); - CPUMatrix& AssignLog10Of (const CPUMatrix& a); - - CPUMatrix& InplaceCosine (); - CPUMatrix& AssignCosineOf (const CPUMatrix& a); - - CPUMatrix& InplaceNegativeSine (); - CPUMatrix& AssignNegativeSineOf (const CPUMatrix& a); - - CPUMatrix& InplaceAbs (); - CPUMatrix& AssignAbsOf (const CPUMatrix& a); - - CPUMatrix& InplaceTruncateBottom (const ElemType threshold); - CPUMatrix& AssignTruncateBottomOf (const CPUMatrix& a, const ElemType threshold); - CPUMatrix& InplaceTruncateTop (const ElemType threshold); - CPUMatrix& AssignTruncateTopOf (const CPUMatrix& a, const ElemType threshold); - CPUMatrix& InplaceTruncate (const ElemType threshold); - CPUMatrix& InplaceSoftThreshold(const ElemType threshold); - - CPUMatrix& SetToZeroIfAbsLessThan (const ElemType threshold); - - ElemType SumOfAbsElements () const; //sum of all abs(elements) - ElemType SumOfElements () const; //sum of all elements - CPUMatrix& AssignSumOfElements(const CPUMatrix& a); - - bool IsEqualTo(const CPUMatrix& a, const ElemType threshold = 1e-8) const; - - static void VectorSum(const CPUMatrix& a, CPUMatrix& c, const bool isColWise); - - void VectorNorm1(CPUMatrix& c, const bool isColWise) const; - CPUMatrix& AssignVectorNorm1Of(CPUMatrix& a, const bool isColWise); - - void VectorNorm2(CPUMatrix& c, const bool isColWise) const; - CPUMatrix& AssignVectorNorm2Of(CPUMatrix& a, const bool isColWise); - - void AssignNoiseContrastiveEstimation(const CPUMatrix& a, const CPUMatrix& b, const CPUMatrix& bias, - CPUMatrix& tmp, CPUMatrix& c); - - void AssignSoftmaxSum(const CPUMatrix& a, CPUMatrix& softmax); - - void AssignNCEUnnormalizedEval(const CPUMatrix& a, - const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& c); - - CPUMatrix& AssignNCEDerivative(const CPUMatrix& tmp, const CPUMatrix& a, const CPUMatrix& b, size_t inputIndex, CPUMatrix& c); - - void VectorNormInf(CPUMatrix& c, const bool isColWise) const; - CPUMatrix& AssignVectorNormInfOf(CPUMatrix& a, const bool isColWise); - - CPUMatrix& AssignInnerProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool isColWise); - CPUMatrix& AssignKhatriRaoProductOf(const CPUMatrix& a, const CPUMatrix& b); - CPUMatrix& AddColumnReshapeProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool transposeAColumn); - - CPUMatrix& AddWithScaleOf(ElemType alpha, const CPUMatrix& a); - - ElemType FrobeniusNorm() const; - CPUMatrix& AssignFrobeniusNormOf(const CPUMatrix& a); - - ElemType MatrixNormInf() const; - ElemType MatrixNorm1() const; - ElemType MatrixNorm0() const; //number of non-zero elemets - CPUMatrix& AssignSignOf(const CPUMatrix& a); - CPUMatrix& AddSignOf(const CPUMatrix& a); - - CPUMatrix& AssignRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); - CPUMatrix& AddToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); - CPUMatrix& AddWithRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); - //CPUMatrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); - - CPUMatrix& AssignToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); - - CPUMatrix& AssignRepeatOf(const CPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats); - CPUMatrix& AddToRowRepeatValuesOf(const CPUMatrix& a, const size_t numRowRepeats); - - CPUMatrix& AssignPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - CPUMatrix& AddFoldedPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - - void VectorMax(CPUMatrix& maxIndexes, CPUMatrix& maxValues, const bool isColWise, int topK = 1) const; - void VectorMin(CPUMatrix& minIndexes, CPUMatrix& minValues, const bool isColWise) const; - - CPUMatrix& AssignNumOfDiff(const CPUMatrix& a, const CPUMatrix& b, bool searchInCol = false); - - void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; - void Print(const char* matrixName = nullptr) const; //print whole matrix. can be expensive - - void ReadFromFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. - void WriteToFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. - - CPUMatrix& AssignPackedConvolutionInput(const CPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding = false); - CPUMatrix& UnpackConvolutionInput(CPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding = false) const; - CPUMatrix& AssignMaxPoolingResult(const CPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - CPUMatrix& AddMaxPoolingGradient(const CPUMatrix& outputGradientBatch, const CPUMatrix& inputBatch, const CPUMatrix& outputBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - CPUMatrix& AssignAveragePoolingResult(const CPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - CPUMatrix& AddAveragePoolingGradient(const CPUMatrix& outputGradientBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - - public: - static int SetNumThreads(int numThreads); // note: this does not depend on , i.e. you can call it on any - - //static BLAS functions - static void SVD(const CPUMatrix& A, CPUMatrix& SIGMA, CPUMatrix& U, CPUMatrix& VT, CPUMatrix& W); - - static void MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, ElemType beta, CPUMatrix& c); - static void MultiplyAndAdd(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, CPUMatrix& c); - static void Multiply(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, CPUMatrix& c); - static void Multiply(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); - static void Multiply1x1AndWeightedAdd(ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, ElemType beta, CPUMatrix& c); - - static void ScaleAndAdd(ElemType alpha, const CPUMatrix& a, CPUMatrix& c); - static void AddScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); - static void AssignScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); - static void AddScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); //alpha must be 1X1 - static void AssignScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); //alpha must be 1X1 - - static void AddElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); - //static void AddLogElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); - static void AssignElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); - - static void MinusOneAt(CPUMatrix& c, const size_t position); - - static void Scale(ElemType alpha, CPUMatrix& a); - static void Scale(CPUMatrix alpha, CPUMatrix& a); //In this case Matrix alpha must be 1x1 - static void Scale(ElemType alpha, const CPUMatrix& a, CPUMatrix& c); - static void InnerProduct (const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise); - static ElemType InnerProductOfMatrices(const CPUMatrix& a, const CPUMatrix& b); - static void ElementWisePower (ElemType alpha, const CPUMatrix& a, CPUMatrix& c); - - static bool AreEqual(const CPUMatrix& a, const CPUMatrix& b, const ElemType threshold = 1e-8); - - static void TensorShuffleScaleAndAdd(ElemType keepWeight, const CPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const CPUMatrix& b, CPUMatrix& c); + // +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once +#include "Basics.h" // for RuntimeError() +#include "Matrix.h" +#include "File.h" +#include "Helpers.h" +#include "CommonMatrix.h" +#include +#include +#include +#include + +// NOTE NOTE NOTE: +// use CPUSingleMatrix and CPUDoubleMatrix instead of using the template directly +/////////////////////////////////////////////// + + +// This class is exported from the Math.dll +namespace Microsoft { namespace MSR { namespace CNTK { + + double logadd(double x, double y); + + //To compy with BLAS libraries matrices are stored in ColMajor. However, by default C/C++/C# use RowMajor + //convertion is need when passing data between CPUMatrix and C++ matrices + template + class MATH_API CPUMatrix : public BaseMatrix + { + typedef BaseMatrix B; using B::m_numRows; using B::m_numCols; using B::m_pArray; using B::m_computeDevice; using B::m_elemSizeAllocated; + using B::m_externalBuffer; using B::m_format; using B::m_matrixName; // without this, base members would require to use thi-> in GCC + public: + CPUMatrix(); + CPUMatrix(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. + CPUMatrix(const size_t numRows, const size_t numCols); + CPUMatrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags=matrixFlagNormal); + CPUMatrix(const CPUMatrix& deepCopyFrom); //copy constructor, deep copy + CPUMatrix& operator=(const CPUMatrix& deepCopyFrom); //assignment operator, deep copy + CPUMatrix(CPUMatrix&& moveFrom); //move constructor, shallow copy + CPUMatrix& operator=(CPUMatrix&& moveFrom); //move assignment operator, shallow copy + + ~CPUMatrix(); + + public: + using B::OwnBuffer; using B::GetNumElements; using B::IsEmpty; using B::GetNumRows; using B::GetNumCols; using B::SetOwnBuffer; using B::SetMatrixName; + + size_t BufferSize() const { return m_numRows*m_numCols*sizeof(ElemType); } + ElemType* BufferPointer() const {return m_pArray;} + + CPUMatrix ColumnSlice(size_t startColumn, size_t numCols) const; + CPUMatrix& AssignColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols); + CPUMatrix& SetColumnSlice(const CPUMatrix& fromMatrix, size_t startColumn, size_t numCols); + + void CopyColumnsStrided(const CPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); + + CPUMatrix Diagonal() const; + + ElemType Adagrad(CPUMatrix& gradients, const bool needAveMultiplier); + void FSAdagrad(CPUMatrix& gradients, CPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul); + ElemType RmsProp(CPUMatrix& gradients, + ElemType RMS_GAMMA, + ElemType RMS_WGT_INC, + ElemType RMS_WGT_MAX, + ElemType RMS_WGT_DEC, + ElemType RMS_WGT_MIN, + const bool needAveMultiplier + ); + + void Reshape(const size_t numRows, const size_t numCols); + void Resize(const size_t numRows, const size_t numCols, bool growOnly = true); //by default we only reallocate if need to grow + ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller + size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller + void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; + + inline ElemType& operator() (const size_t row, const size_t col) + { + return m_pArray[LocateElement(row, col)]; + } + inline const ElemType& operator() (const size_t row, const size_t col) const + { + return m_pArray[LocateElement(row, col)]; + } + inline ElemType Get00Element() const + { + return m_pArray[0]; + } + + void SetValue(const ElemType v); + void SetValue(const CPUMatrix& deepCopyFrom); + void SetValue(const size_t numRows, const size_t numCols, ElemType *pArray, size_t matrixFlags=matrixFlagNormal); + + void MaskColumnsValue(const CPUMatrix& columnsMask, ElemType val); + + void SetColumn(const ElemType* colPointer, size_t colInd); + void SetColumn(const CPUMatrix& valMat, size_t colInd); + void SetColumn(const ElemType val, size_t j); + + void SetDiagonalValue(const ElemType v); + void SetDiagonalValue(const CPUMatrix& vector); + void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); + void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); + void AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + + CPUMatrix Transpose(); + CPUMatrix& AssignTransposeOf (const CPUMatrix& a); + + CPUMatrix& operator+= (const ElemType alpha); + CPUMatrix operator+ (const ElemType alpha) const; + CPUMatrix& AssignSumOf(const ElemType alpha, const CPUMatrix& a); + + CPUMatrix& operator+= (const CPUMatrix& a); + CPUMatrix operator+ (const CPUMatrix& a) const; + CPUMatrix& AssignSumOf(const CPUMatrix& a, const CPUMatrix& b); + + CPUMatrix& operator-= (const ElemType alpha); + CPUMatrix operator- (const ElemType alpha) const; + CPUMatrix& AssignDifferenceOf(const ElemType alpha, const CPUMatrix& a); + CPUMatrix& AssignDifferenceOf(const CPUMatrix& a, const ElemType alpha); + + CPUMatrix& operator-= (const CPUMatrix& a); + CPUMatrix operator- (const CPUMatrix& a) const; + CPUMatrix& AssignDifferenceOf(const CPUMatrix& a, const CPUMatrix& b); + + CPUMatrix& operator*= (const ElemType alpha); + CPUMatrix operator* (const ElemType alpha) const; + CPUMatrix& AssignProductOf(const ElemType alpha, const CPUMatrix& a); + + CPUMatrix operator* (const CPUMatrix& a) const; + CPUMatrix& AssignProductOf (const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB); + + CPUMatrix& operator/= (ElemType alpha); + CPUMatrix operator/ (ElemType alpha) const; + + CPUMatrix& operator^= (ElemType alpha); //element-wise power + CPUMatrix operator^ (ElemType alpha) const; //element-wise power + CPUMatrix& AssignElementPowerOf(const CPUMatrix& a, const ElemType power); + + CPUMatrix& ElementMultiplyWith (const CPUMatrix& a); + CPUMatrix& AssignElementProductOf (const CPUMatrix& a, const CPUMatrix& b); + CPUMatrix& AddElementProductOf (const CPUMatrix& a, const CPUMatrix& b); + + CPUMatrix& AssignElementDivisionOf (const CPUMatrix& a, const CPUMatrix& b); + CPUMatrix& ElementDivideBy(const CPUMatrix& a); + + CPUMatrix& ColumnElementMultiplyWith(const CPUMatrix& a); + CPUMatrix& RowElementMultiplyWith(const CPUMatrix& a); + + CPUMatrix& ColumnElementDivideBy(const CPUMatrix& a); + CPUMatrix& RowElementDivideBy(const CPUMatrix& a); + + CPUMatrix& ElementInverse (); + CPUMatrix& AssignElementInverseOf (const CPUMatrix& a); + + CPUMatrix& InplaceSigmoid (); + CPUMatrix& AssignSigmoidOf (const CPUMatrix& a); + + CPUMatrix& InplaceLinearRectifierDerivative(); + CPUMatrix& AssignLinearRectifierDerivativeOf (const CPUMatrix& a); + + CPUMatrix& InplaceSigmoidDerivative(); + CPUMatrix& AssignSigmoidDerivativeOf (const CPUMatrix& a); + + CPUMatrix& InplaceTanh (); + CPUMatrix& AssignTanhOf (const CPUMatrix& a); + + CPUMatrix& InplaceLogSoftmax (const bool isColWise); + CPUMatrix& AssignLogSoftmaxOf (const CPUMatrix& a, const bool isColWise); + + CPUMatrix& InplaceHardmax(const bool isColWise); + CPUMatrix& AssignHardmaxOf(const CPUMatrix& a, const bool isColWise); + + //sequence training + CPUMatrix& DropFrame(const CPUMatrix& label, const CPUMatrix& gamma, const ElemType & threshhold); + CPUMatrix& AssignSequenceError(const ElemType hsmoothingWeight, const CPUMatrix& label, const CPUMatrix& dnnoutput, const CPUMatrix& gamma, ElemType alpha); + CPUMatrix& InplaceSqrt (); + CPUMatrix& AssignSqrtOf (const CPUMatrix& a); + + CPUMatrix& InplaceExp (); + CPUMatrix& AssignExpOf (const CPUMatrix& a); + + CPUMatrix& InplaceLog (); + CPUMatrix& AssignLogOf (const CPUMatrix& a); + + CPUMatrix& InplaceLog10 (); + CPUMatrix& AssignLog10Of (const CPUMatrix& a); + + CPUMatrix& InplaceCosine (); + CPUMatrix& AssignCosineOf (const CPUMatrix& a); + + CPUMatrix& InplaceNegativeSine (); + CPUMatrix& AssignNegativeSineOf (const CPUMatrix& a); + + CPUMatrix& InplaceAbs (); + CPUMatrix& AssignAbsOf (const CPUMatrix& a); + + CPUMatrix& InplaceTruncateBottom (const ElemType threshold); + CPUMatrix& AssignTruncateBottomOf (const CPUMatrix& a, const ElemType threshold); + CPUMatrix& InplaceTruncateTop (const ElemType threshold); + CPUMatrix& AssignTruncateTopOf (const CPUMatrix& a, const ElemType threshold); + CPUMatrix& InplaceTruncate (const ElemType threshold); + CPUMatrix& InplaceSoftThreshold(const ElemType threshold); + + CPUMatrix& SetToZeroIfAbsLessThan (const ElemType threshold); + + ElemType SumOfAbsElements () const; //sum of all abs(elements) + ElemType SumOfElements () const; //sum of all elements + CPUMatrix& AssignSumOfElements(const CPUMatrix& a); + + bool IsEqualTo(const CPUMatrix& a, const ElemType threshold = 1e-8) const; + + static void VectorSum(const CPUMatrix& a, CPUMatrix& c, const bool isColWise); + + void VectorNorm1(CPUMatrix& c, const bool isColWise) const; + CPUMatrix& AssignVectorNorm1Of(CPUMatrix& a, const bool isColWise); + + void VectorNorm2(CPUMatrix& c, const bool isColWise) const; + CPUMatrix& AssignVectorNorm2Of(CPUMatrix& a, const bool isColWise); + + void AssignNoiseContrastiveEstimation(const CPUMatrix& a, const CPUMatrix& b, const CPUMatrix& bias, + CPUMatrix& tmp, CPUMatrix& c); + + void AssignSoftmaxSum(const CPUMatrix& a, CPUMatrix& softmax); + + void AssignNCEUnnormalizedEval(const CPUMatrix& a, + const CPUMatrix& b, const CPUMatrix& bias, CPUMatrix& c); + + CPUMatrix& AssignNCEDerivative(const CPUMatrix& tmp, const CPUMatrix& a, const CPUMatrix& b, size_t inputIndex, CPUMatrix& c); + + void VectorNormInf(CPUMatrix& c, const bool isColWise) const; + CPUMatrix& AssignVectorNormInfOf(CPUMatrix& a, const bool isColWise); + + CPUMatrix& AssignInnerProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool isColWise); + CPUMatrix& AssignKhatriRaoProductOf(const CPUMatrix& a, const CPUMatrix& b); + CPUMatrix& AddColumnReshapeProductOf(const CPUMatrix& a, const CPUMatrix& b, const bool transposeAColumn); + + CPUMatrix& AddWithScaleOf(ElemType alpha, const CPUMatrix& a); + + ElemType FrobeniusNorm() const; + CPUMatrix& AssignFrobeniusNormOf(const CPUMatrix& a); + + ElemType MatrixNormInf() const; + ElemType MatrixNorm1() const; + ElemType MatrixNorm0() const; //number of non-zero elemets + CPUMatrix& AssignSignOf(const CPUMatrix& a); + CPUMatrix& AddSignOf(const CPUMatrix& a); + + CPUMatrix& AssignRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); + CPUMatrix& AddToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); + CPUMatrix& AddWithRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); + //CPUMatrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); + + CPUMatrix& AssignToRowSliceValuesOf(const CPUMatrix& a, const size_t startIndex, const size_t numRows); + + CPUMatrix& AssignRepeatOf(const CPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats); + CPUMatrix& AddToRowRepeatValuesOf(const CPUMatrix& a, const size_t numRowRepeats); + + CPUMatrix& AssignPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + CPUMatrix& AddFoldedPositiveAndShiftedNegSample(const CPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + + void VectorMax(CPUMatrix& maxIndexes, CPUMatrix& maxValues, const bool isColWise, int topK = 1) const; + void VectorMin(CPUMatrix& minIndexes, CPUMatrix& minValues, const bool isColWise) const; + + CPUMatrix& AssignNumOfDiff(const CPUMatrix& a, const CPUMatrix& b, bool searchInCol = false); + + void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; + void Print(const char* matrixName = nullptr) const; //print whole matrix. can be expensive + + void ReadFromFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. + void WriteToFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. + + CPUMatrix& AssignPackedConvolutionInput(const CPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding = false); + CPUMatrix& UnpackConvolutionInput(CPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding = false) const; + CPUMatrix& AssignMaxPoolingResult(const CPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + CPUMatrix& AddMaxPoolingGradient(const CPUMatrix& outputGradientBatch, const CPUMatrix& inputBatch, const CPUMatrix& outputBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + CPUMatrix& AssignAveragePoolingResult(const CPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + CPUMatrix& AddAveragePoolingGradient(const CPUMatrix& outputGradientBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + + public: + static int SetNumThreads(int numThreads); // note: this does not depend on , i.e. you can call it on any + + //static BLAS functions + static void SVD(const CPUMatrix& A, CPUMatrix& SIGMA, CPUMatrix& U, CPUMatrix& VT, CPUMatrix& W); + + static void MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, ElemType beta, CPUMatrix& c); + static void MultiplyAndAdd(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, CPUMatrix& c); + static void Multiply(const CPUMatrix& a, const bool transposeA, const CPUMatrix& b, const bool transposeB, CPUMatrix& c); + static void Multiply(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); + static void Multiply1x1AndWeightedAdd(ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, ElemType beta, CPUMatrix& c); + + static void ScaleAndAdd(ElemType alpha, const CPUMatrix& a, CPUMatrix& c); + static void AddScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); + static void AssignScaledDifference(const ElemType alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); + static void AddScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); //alpha must be 1X1 + static void AssignScaledDifference(const CPUMatrix& alpha, const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c); //alpha must be 1X1 + + static void AddElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); + //static void AddLogElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); + static void AssignElementToElement(const CPUMatrix& a, const size_t ai, const size_t aj, CPUMatrix& c, const size_t ci, const size_t cj); + + static void MinusOneAt(CPUMatrix& c, const size_t position); + + static void Scale(ElemType alpha, CPUMatrix& a); + static void Scale(CPUMatrix alpha, CPUMatrix& a); //In this case Matrix alpha must be 1x1 + static void Scale(ElemType alpha, const CPUMatrix& a, CPUMatrix& c); + static void InnerProduct (const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise); + static ElemType InnerProductOfMatrices(const CPUMatrix& a, const CPUMatrix& b); + static void ElementWisePower (ElemType alpha, const CPUMatrix& a, CPUMatrix& c); + + static bool AreEqual(const CPUMatrix& a, const CPUMatrix& b, const ElemType threshold = 1e-8); + + static void TensorShuffleScaleAndAdd(ElemType keepWeight, const CPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const CPUMatrix& b, CPUMatrix& c); void TensorOp(ElemType beta, const CPUMatrix& a, ElemType alpha, ElementWiseOperator op, const std::array & offsets, @@ -347,104 +347,104 @@ namespace Microsoft { namespace MSR { namespace CNTK { const std::array & offsets, const SmallVector & regularOpDims, const std::array, 4> & regularStrides, const SmallVector & reducingOpDims, const std::array, 4> & reducingStrides); - - static CPUMatrix Ones(const size_t rows, const size_t cols); - static CPUMatrix Zeros(const size_t rows, const size_t cols); - static CPUMatrix Eye(const size_t rows); - static CPUMatrix RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); - static CPUMatrix RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - - /// return true if v is an element in matrix c - static bool HasElement(const CPUMatrix& a, const ElemType v = 0.0); - - public: - CPUMatrix& AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber); - static void InnerProductWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise, size_t shift, size_t negnumber); - // extract out a row from a, assign it to [this]. - CPUMatrix& GetARowByIndex(const CPUMatrix& a, const size_t index); - static void ConductRowElementMultiplyWithShift(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const size_t shift, bool bFirstmatrixfixed); - CPUMatrix& AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, const size_t shift); - - - - public: - friend File& operator>>(File& stream, CPUMatrix& us) - { - stream.GetMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); - size_t elsize; - stream>>elsize; - if (sizeof(ElemType)!=elsize) - RuntimeError("Template argument size doesn't match those in file"); - std::wstring matrixName; - size_t numRows, numCols; - int format; - stream>>matrixName>>format>>numRows>>numCols; - ElemType* d_array = new ElemType[numRows*numCols]; - for (size_t i=0;i>d_array[i]; - stream.GetMarker(fileMarkerEndSection, std::wstring(L"EMAT")); - us.SetValue(numRows,numCols,d_array, matrixFlagNormal); - if (us.m_matrixName ) - delete [] us.m_matrixName ; - us.m_matrixName = new wchar_t[matrixName.length()+1]; - wmemcpy(us.m_matrixName,matrixName.c_str(),matrixName.length()+1); - - delete[] d_array; - return stream; - } - friend File& operator<<(File& stream, const CPUMatrix& us) - { - stream.PutMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); - stream<& alpha, CPUMatrix& beta, - const CPUMatrix& lbls, - const CPUMatrix& pair_scores); - static void _rcrfBackwardCompute(size_t t, size_t k, const CPUMatrix& alpha, - CPUMatrix& beta, - const CPUMatrix& pair_scores); - - static void RCRFTransGrdCompute(const CPUMatrix& lbls, - const CPUMatrix& alpha, - const CPUMatrix& beta, - const CPUMatrix& pair_scores, - CPUMatrix& grd); - - static void _rcrfTransGrdCompute(size_t i, - const CPUMatrix& lbls, - const CPUMatrix& alpha, - const CPUMatrix& beta, - const CPUMatrix& pair_scores, - CPUMatrix& grd, - const size_t tPos /// position - ); - - protected: - size_t LocateElement (const size_t i, const size_t j) const; - size_t LocateColumn (const size_t j) const; - - private: - void ZeroInit(); //should only be used by constructors. - void Clear(); - }; - - typedef CPUMatrix CPUSingleMatrix; - typedef CPUMatrix CPUDoubleMatrix; - -}}} + + static CPUMatrix Ones(const size_t rows, const size_t cols); + static CPUMatrix Zeros(const size_t rows, const size_t cols); + static CPUMatrix Eye(const size_t rows); + static CPUMatrix RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); + static CPUMatrix RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + + /// return true if v is an element in matrix c + static bool HasElement(const CPUMatrix& a, const ElemType v = 0.0); + + public: + CPUMatrix& AssignElementProductOfWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, size_t shift, size_t negnumber); + static void InnerProductWithShiftNeg(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const bool isColWise, size_t shift, size_t negnumber); + // extract out a row from a, assign it to [this]. + CPUMatrix& GetARowByIndex(const CPUMatrix& a, const size_t index); + static void ConductRowElementMultiplyWithShift(const CPUMatrix& a, const CPUMatrix& b, CPUMatrix& c, const size_t shift, bool bFirstmatrixfixed); + CPUMatrix& AssignElementProductOfWithShift(const CPUMatrix& a, const CPUMatrix& b, const size_t shift); + + + + public: + friend File& operator>>(File& stream, CPUMatrix& us) + { + stream.GetMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); + size_t elsize; + stream>>elsize; + if (sizeof(ElemType)!=elsize) + RuntimeError("Template argument size doesn't match those in file"); + std::wstring matrixName; + size_t numRows, numCols; + int format; + stream>>matrixName>>format>>numRows>>numCols; + ElemType* d_array = new ElemType[numRows*numCols]; + for (size_t i=0;i>d_array[i]; + stream.GetMarker(fileMarkerEndSection, std::wstring(L"EMAT")); + us.SetValue(numRows,numCols,d_array, matrixFlagNormal); + if (us.m_matrixName ) + delete [] us.m_matrixName ; + us.m_matrixName = new wchar_t[matrixName.length()+1]; + wmemcpy(us.m_matrixName,matrixName.c_str(),matrixName.length()+1); + + delete[] d_array; + return stream; + } + friend File& operator<<(File& stream, const CPUMatrix& us) + { + stream.PutMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); + stream<& alpha, CPUMatrix& beta, + const CPUMatrix& lbls, + const CPUMatrix& pair_scores); + static void _rcrfBackwardCompute(size_t t, size_t k, const CPUMatrix& alpha, + CPUMatrix& beta, + const CPUMatrix& pair_scores); + + static void RCRFTransGrdCompute(const CPUMatrix& lbls, + const CPUMatrix& alpha, + const CPUMatrix& beta, + const CPUMatrix& pair_scores, + CPUMatrix& grd); + + static void _rcrfTransGrdCompute(size_t i, + const CPUMatrix& lbls, + const CPUMatrix& alpha, + const CPUMatrix& beta, + const CPUMatrix& pair_scores, + CPUMatrix& grd, + const size_t tPos /// position + ); + + protected: + size_t LocateElement (const size_t i, const size_t j) const; + size_t LocateColumn (const size_t j) const; + + private: + void ZeroInit(); //should only be used by constructors. + void Clear(); + }; + + typedef CPUMatrix CPUSingleMatrix; + typedef CPUMatrix CPUDoubleMatrix; + +}}} diff --git a/Source/Math/CPUSparseMatrix.h b/Source/Math/CPUSparseMatrix.h index f1453dcc71be..93ccab0b659a 100644 --- a/Source/Math/CPUSparseMatrix.h +++ b/Source/Math/CPUSparseMatrix.h @@ -1,189 +1,189 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - -#include -#include "CPUMatrix.h" -#include -#include - -#ifdef _WIN32 -#ifdef MATH_EXPORTS -#define MATH_API __declspec(dllexport) -#else -#define MATH_API __declspec(dllimport) -#endif -#endif /* Linux - already defined in CPUMatrix.h */ - -namespace Microsoft { namespace MSR { namespace CNTK { - - template - class MATH_API CPUSparseMatrix : public BaseMatrix - { - typedef BaseMatrix B; using B::m_elemSizeAllocated; using B::m_computeDevice; using B::m_externalBuffer; using B::m_format; using B::m_matrixName; - using B::m_numCols; using B::m_numRows; using B::m_nz; using B::m_pArray; // without this, base members would require to use thi-> in GCC - using B::OwnBuffer; - using B::Clear; - public: - using B::SetMatrixName; - - private: - void ZeroInit(); - void CheckInit(const MatrixFormat format); - void ReleaseMemory(); - - public: - CPUSparseMatrix(const MatrixFormat format); - CPUSparseMatrix(const MatrixFormat format, const size_t numRows, const size_t numCols, const size_t size); - CPUSparseMatrix(const CPUSparseMatrix& deepCopyFrom); //copy constructor, deep copy - CPUSparseMatrix& operator=(const CPUSparseMatrix& deepCopyFrom); //assignment operator, deep copy - CPUSparseMatrix(CPUSparseMatrix&& moveFrom); //move constructor, shallow copy - CPUSparseMatrix& operator=(CPUSparseMatrix&& moveFrom); //move assignment operator, shallow copy - ~CPUSparseMatrix(); - - public: - using B::GetNumCols; using B::GetNumRows; - - void SetValue(const size_t row, const size_t col, ElemType val); - void SetValue(const CPUSparseMatrix& /*val*/); - - void ShiftBy(int /*numShift*/) { NOT_IMPLEMENTED; } - - size_t BufferSize() const {return m_elemSizeAllocated*sizeof(ElemType);} - ElemType* BufferPointer() const; - inline size_t GetNumElemAllocated() const { return m_elemSizeAllocated; } - - CPUSparseMatrix ColumnSlice(size_t startColumn, size_t numCols) const; - CPUMatrix CopyColumnSliceToDense(size_t startColumn, size_t numCols) const; - - CPUMatrix DiagonalToDense() const; - - void SetGaussianRandomValue(const ElemType /*mean*/, const ElemType /*sigma*/, unsigned long /*seed*/) { NOT_IMPLEMENTED; } - - void SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, - const size_t nz, const size_t numRows, const size_t numCols); - - static void MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& lhs, const bool transposeA, - const CPUSparseMatrix& rhs, const bool transposeB, ElemType beta, CPUMatrix& c); - - static void MultiplyAndAdd(ElemType alpha, const CPUMatrix& lhs, const bool transposeA, - const CPUSparseMatrix& rhs, const bool transposeB, CPUSparseMatrix& c); - - static void ScaleAndAdd(const ElemType alpha, const CPUSparseMatrix& lhs, CPUMatrix& c); - - static bool AreEqual(const CPUSparseMatrix& a, const CPUSparseMatrix& b, const ElemType threshold = 1e-8); - - /// sum(vec(a).*vec(b)) - static ElemType InnerProductOfMatrices(const CPUSparseMatrix& /*a*/, const CPUMatrix& /*b*/) { NOT_IMPLEMENTED; } - - static void AddScaledDifference(const ElemType /*alpha*/, const CPUSparseMatrix& /*a*/, const CPUMatrix& /*b*/, CPUMatrix& /*c*/, - bool /*bDefaultZero*/ ) { NOT_IMPLEMENTED; } - static void AddScaledDifference(const ElemType /*alpha*/, const CPUMatrix& /*a*/, const CPUSparseMatrix& /*b*/, CPUMatrix& /*c*/, - bool /*bDefaultZero*/ ) { NOT_IMPLEMENTED; } - - int GetComputeDeviceId() const {return -1;} - - void Resize(const size_t numRows, const size_t numCols, size_t numNZElemToReserve = 10000, const bool growOnly = true, bool keepExistingValues = false); - void Reset(); - - const ElemType operator() (const size_t row, const size_t col) const - { - if (col >= m_numCols || row >= m_numRows) - { - RuntimeError("Position outside matrix dimensions"); - } - - if (m_format == MatrixFormat::matrixFormatSparseCSC) - { - size_t start = m_compIndex[col]; - size_t end = m_compIndex[col + 1]; - for (size_t p = start; p < end; p++) - { - size_t i = m_unCompIndex[p]; - if (i == row) - { - return m_pArray[p]; - } - } - - return 0; - } - else - { - NOT_IMPLEMENTED; - } - } - - public: - void NormalGrad(CPUMatrix& c, const ElemType momentum); - ElemType Adagrad(CPUMatrix& c, const bool needAveMultiplier); - - public: - CPUSparseMatrix& InplaceTruncateTop(const ElemType threshold); - CPUSparseMatrix& InplaceTruncateBottom(const ElemType threshold); - CPUSparseMatrix& InplaceTruncate (const ElemType threshold); - CPUSparseMatrix& InplaceSoftThreshold(const ElemType threshold); - - ElemType FrobeniusNorm() const; //useful for comparing CPU and GPU results - - ElemType SumOfAbsElements() const; //sum of all abs(elements) - ElemType SumOfElements() const; //sum of all elements - - public: - //void Print(const char* /*matrixName*/) const { NOT_IMPLEMENTED; } - void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; - void Print(const char* matrixName = NULL) const; //print whole matrix. can be expensive - - - public: - const ElemType* NzValues() const { return m_nzValues; } - inline ElemType* NzValues() { return m_nzValues; } - size_t NzSize() const { return sizeof(ElemType)*m_nz; } // actual number of element bytes in use - - CPUSPARSE_INDEX_TYPE* MajorIndexLocation() const { return m_unCompIndex; } //this is the major index, row/col ids in CSC/CSR format - size_t MajorIndexCount() const { return m_nz; } - size_t MajorIndexSize() const { return sizeof(CPUSPARSE_INDEX_TYPE)*MajorIndexCount(); } // actual number of major index bytes in use - - CPUSPARSE_INDEX_TYPE* SecondaryIndexLocation() const { return m_compIndex; } //this is the compressed index, col/row in CSC/CSR format - size_t SecondaryIndexCount() const - { - if (m_format&matrixFormatCompressed) - { - size_t cnt = (m_format&matrixFormatRowMajor) ? m_numRows : m_numCols; - if (cnt > 0) cnt++; // add an extra element on the end for the "max" value - return cnt; - } - else - return m_nz; // COO format - } - // get size for compressed index - size_t SecondaryIndexSize() const { return (SecondaryIndexCount())*sizeof(CPUSPARSE_INDEX_TYPE); } - - // the column and row locations will swap based on what format we are in. Full index always follows the data array - CPUSPARSE_INDEX_TYPE* RowLocation() const { return (m_format&matrixFormatRowMajor) ? SecondaryIndexLocation() : MajorIndexLocation(); } - size_t RowSize() const { return (m_format&matrixFormatRowMajor) ? SecondaryIndexSize() : MajorIndexSize(); } - CPUSPARSE_INDEX_TYPE* ColLocation() const { return (m_format&matrixFormatRowMajor) ? MajorIndexLocation() : SecondaryIndexLocation(); } - size_t ColSize() const { return (m_format&matrixFormatRowMajor) ? MajorIndexSize() : SecondaryIndexSize(); } // actual number of bytes in use - - private: - int m_colIdx; //used to SetValue() - size_t m_compIndexSize; - ElemType* m_nzValues; - - //non-zero values are stored in m_pArray - CPUSPARSE_INDEX_TYPE *m_unCompIndex; //row/col ids in CSC/CSR format - CPUSPARSE_INDEX_TYPE *m_compIndex; //begin ids of col/row in CSC/CSR format - - size_t m_blockSize; //block size - size_t *m_blockIds; //block ids - size_t m_blockIdShift; //used to get efficient slice, actual col = blockIds[j] - m_blockIdShift - }; - - typedef CPUSparseMatrix CPUSingleSparseMatrix; - typedef CPUSparseMatrix CPUDoubleSparseMatrix; - -}}} - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + +#include +#include "CPUMatrix.h" +#include +#include + +#ifdef _WIN32 +#ifdef MATH_EXPORTS +#define MATH_API __declspec(dllexport) +#else +#define MATH_API __declspec(dllimport) +#endif +#endif /* Linux - already defined in CPUMatrix.h */ + +namespace Microsoft { namespace MSR { namespace CNTK { + + template + class MATH_API CPUSparseMatrix : public BaseMatrix + { + typedef BaseMatrix B; using B::m_elemSizeAllocated; using B::m_computeDevice; using B::m_externalBuffer; using B::m_format; using B::m_matrixName; + using B::m_numCols; using B::m_numRows; using B::m_nz; using B::m_pArray; // without this, base members would require to use thi-> in GCC + using B::OwnBuffer; + using B::Clear; + public: + using B::SetMatrixName; + + private: + void ZeroInit(); + void CheckInit(const MatrixFormat format); + void ReleaseMemory(); + + public: + CPUSparseMatrix(const MatrixFormat format); + CPUSparseMatrix(const MatrixFormat format, const size_t numRows, const size_t numCols, const size_t size); + CPUSparseMatrix(const CPUSparseMatrix& deepCopyFrom); //copy constructor, deep copy + CPUSparseMatrix& operator=(const CPUSparseMatrix& deepCopyFrom); //assignment operator, deep copy + CPUSparseMatrix(CPUSparseMatrix&& moveFrom); //move constructor, shallow copy + CPUSparseMatrix& operator=(CPUSparseMatrix&& moveFrom); //move assignment operator, shallow copy + ~CPUSparseMatrix(); + + public: + using B::GetNumCols; using B::GetNumRows; + + void SetValue(const size_t row, const size_t col, ElemType val); + void SetValue(const CPUSparseMatrix& /*val*/); + + void ShiftBy(int /*numShift*/) { NOT_IMPLEMENTED; } + + size_t BufferSize() const {return m_elemSizeAllocated*sizeof(ElemType);} + ElemType* BufferPointer() const; + inline size_t GetNumElemAllocated() const { return m_elemSizeAllocated; } + + CPUSparseMatrix ColumnSlice(size_t startColumn, size_t numCols) const; + CPUMatrix CopyColumnSliceToDense(size_t startColumn, size_t numCols) const; + + CPUMatrix DiagonalToDense() const; + + void SetGaussianRandomValue(const ElemType /*mean*/, const ElemType /*sigma*/, unsigned long /*seed*/) { NOT_IMPLEMENTED; } + + void SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, + const size_t nz, const size_t numRows, const size_t numCols); + + static void MultiplyAndWeightedAdd(ElemType alpha, const CPUMatrix& lhs, const bool transposeA, + const CPUSparseMatrix& rhs, const bool transposeB, ElemType beta, CPUMatrix& c); + + static void MultiplyAndAdd(ElemType alpha, const CPUMatrix& lhs, const bool transposeA, + const CPUSparseMatrix& rhs, const bool transposeB, CPUSparseMatrix& c); + + static void ScaleAndAdd(const ElemType alpha, const CPUSparseMatrix& lhs, CPUMatrix& c); + + static bool AreEqual(const CPUSparseMatrix& a, const CPUSparseMatrix& b, const ElemType threshold = 1e-8); + + /// sum(vec(a).*vec(b)) + static ElemType InnerProductOfMatrices(const CPUSparseMatrix& /*a*/, const CPUMatrix& /*b*/) { NOT_IMPLEMENTED; } + + static void AddScaledDifference(const ElemType /*alpha*/, const CPUSparseMatrix& /*a*/, const CPUMatrix& /*b*/, CPUMatrix& /*c*/, + bool /*bDefaultZero*/ ) { NOT_IMPLEMENTED; } + static void AddScaledDifference(const ElemType /*alpha*/, const CPUMatrix& /*a*/, const CPUSparseMatrix& /*b*/, CPUMatrix& /*c*/, + bool /*bDefaultZero*/ ) { NOT_IMPLEMENTED; } + + int GetComputeDeviceId() const {return -1;} + + void Resize(const size_t numRows, const size_t numCols, size_t numNZElemToReserve = 10000, const bool growOnly = true, bool keepExistingValues = false); + void Reset(); + + const ElemType operator() (const size_t row, const size_t col) const + { + if (col >= m_numCols || row >= m_numRows) + { + RuntimeError("Position outside matrix dimensions"); + } + + if (m_format == MatrixFormat::matrixFormatSparseCSC) + { + size_t start = m_compIndex[col]; + size_t end = m_compIndex[col + 1]; + for (size_t p = start; p < end; p++) + { + size_t i = m_unCompIndex[p]; + if (i == row) + { + return m_pArray[p]; + } + } + + return 0; + } + else + { + NOT_IMPLEMENTED; + } + } + + public: + void NormalGrad(CPUMatrix& c, const ElemType momentum); + ElemType Adagrad(CPUMatrix& c, const bool needAveMultiplier); + + public: + CPUSparseMatrix& InplaceTruncateTop(const ElemType threshold); + CPUSparseMatrix& InplaceTruncateBottom(const ElemType threshold); + CPUSparseMatrix& InplaceTruncate (const ElemType threshold); + CPUSparseMatrix& InplaceSoftThreshold(const ElemType threshold); + + ElemType FrobeniusNorm() const; //useful for comparing CPU and GPU results + + ElemType SumOfAbsElements() const; //sum of all abs(elements) + ElemType SumOfElements() const; //sum of all elements + + public: + //void Print(const char* /*matrixName*/) const { NOT_IMPLEMENTED; } + void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; + void Print(const char* matrixName = NULL) const; //print whole matrix. can be expensive + + + public: + const ElemType* NzValues() const { return m_nzValues; } + inline ElemType* NzValues() { return m_nzValues; } + size_t NzSize() const { return sizeof(ElemType)*m_nz; } // actual number of element bytes in use + + CPUSPARSE_INDEX_TYPE* MajorIndexLocation() const { return m_unCompIndex; } //this is the major index, row/col ids in CSC/CSR format + size_t MajorIndexCount() const { return m_nz; } + size_t MajorIndexSize() const { return sizeof(CPUSPARSE_INDEX_TYPE)*MajorIndexCount(); } // actual number of major index bytes in use + + CPUSPARSE_INDEX_TYPE* SecondaryIndexLocation() const { return m_compIndex; } //this is the compressed index, col/row in CSC/CSR format + size_t SecondaryIndexCount() const + { + if (m_format&matrixFormatCompressed) + { + size_t cnt = (m_format&matrixFormatRowMajor) ? m_numRows : m_numCols; + if (cnt > 0) cnt++; // add an extra element on the end for the "max" value + return cnt; + } + else + return m_nz; // COO format + } + // get size for compressed index + size_t SecondaryIndexSize() const { return (SecondaryIndexCount())*sizeof(CPUSPARSE_INDEX_TYPE); } + + // the column and row locations will swap based on what format we are in. Full index always follows the data array + CPUSPARSE_INDEX_TYPE* RowLocation() const { return (m_format&matrixFormatRowMajor) ? SecondaryIndexLocation() : MajorIndexLocation(); } + size_t RowSize() const { return (m_format&matrixFormatRowMajor) ? SecondaryIndexSize() : MajorIndexSize(); } + CPUSPARSE_INDEX_TYPE* ColLocation() const { return (m_format&matrixFormatRowMajor) ? MajorIndexLocation() : SecondaryIndexLocation(); } + size_t ColSize() const { return (m_format&matrixFormatRowMajor) ? MajorIndexSize() : SecondaryIndexSize(); } // actual number of bytes in use + + private: + int m_colIdx; //used to SetValue() + size_t m_compIndexSize; + ElemType* m_nzValues; + + //non-zero values are stored in m_pArray + CPUSPARSE_INDEX_TYPE *m_unCompIndex; //row/col ids in CSC/CSR format + CPUSPARSE_INDEX_TYPE *m_compIndex; //begin ids of col/row in CSC/CSR format + + size_t m_blockSize; //block size + size_t *m_blockIds; //block ids + size_t m_blockIdShift; //used to get efficient slice, actual col = blockIds[j] - m_blockIdShift + }; + + typedef CPUSparseMatrix CPUSingleSparseMatrix; + typedef CPUSparseMatrix CPUDoubleSparseMatrix; + +}}} + diff --git a/Source/Math/GPUMatrix.h b/Source/Math/GPUMatrix.h index 5943b133fec3..8a2e7b5ff513 100644 --- a/Source/Math/GPUMatrix.h +++ b/Source/Math/GPUMatrix.h @@ -1,413 +1,413 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#pragma once -#include "Platform.h" -#include "File.h" -#include "Helpers.h" -#include "CommonMatrix.h" -#include "TensorShape.h" // only for SmallVector; I was hoping to keep this out -#include "DebugUtil.h" -#include "BestGpu.h" // for CPUONLY macro -#include "ConcStack.h" -#include -#include -#include -#include -#include // for cout/cerr -#include // for unique_ptr -#include // for ULONG_MAX - -// predeclare cublasHandle_t -struct cublasContext; -typedef struct cublasContext *cublasHandle_t; -struct CUstream_st; -typedef struct CUstream_st *cudaStream_t; - -#ifdef _WIN32 -#ifndef MATH_API -#ifdef MATH_EXPORTS -#define MATH_API __declspec(dllexport) -#else -#define MATH_API __declspec(dllimport) -#endif -#endif /* MATH_API */ -#else // no DLLs in Linux -#define MATH_API -#endif - -#ifndef USE_TIME_BASED_SEED -#define USE_TIME_BASED_SEED ULONG_MAX -#endif - -// Stream management functions -void MATH_API SetStream(cudaStream_t stream); -cudaStream_t MATH_API GetStream(); - -namespace Microsoft { namespace MSR { namespace CNTK { - - // ----------------------------------------------------------------------- - // DeviceBoundNumber -- This class represents a number which resides on a particular device. Use it to avoid unnecessary transfers between CPU and GPU - // ----------------------------------------------------------------------- - - template - class MATH_API DeviceBoundNumber - { - private: - DEVICEID_TYPE m_computeDevice; - ElemType* m_data; - public: - DeviceBoundNumber() {m_data=NULL;}; - DeviceBoundNumber(const DeviceBoundNumber &deepCopy); - DeviceBoundNumber(DeviceBoundNumber &&shallowCopy); - ~DeviceBoundNumber(); - DEVICEID_TYPE GetDeviceId() const { return m_computeDevice; } - ElemType* ExposePointer2Value() const {return m_data;} - //performs shallow copy only - void ShallowCopyFrom(ElemType* newVal,int newValsDevceId); - }; - - - // ----------------------------------------------------------------------- - // GPUMatrix - // ----------------------------------------------------------------------- - - void PrepareDevice(DEVICEID_TYPE deviceId); - - template - class MATH_API GPUMatrix : public BaseMatrix - { - typedef BaseMatrix B; using B::m_numRows; using B::m_numCols; using B::m_pArray; // without this, base members would require to use thi-> in GCC - - template - friend class GPUMatrix; - - public: - static const int MaxGpus = 8; // support up to 8 GPUs - using BaseMatrix::m_computeDevice; - using BaseMatrix::m_elemSizeAllocated; - using BaseMatrix::m_matrixName; - using BaseMatrix::m_format; - using BaseMatrix::m_externalBuffer; - using BaseMatrix::m_nz; - using BaseMatrix::OwnBuffer; - using BaseMatrix::GetNumElements; - using BaseMatrix::IsEmpty; - using BaseMatrix::GetArray; - using BaseMatrix::GetNumRows; - using BaseMatrix::GetNumCols; - using BaseMatrix::SetMatrixName; - private: - static cublasHandle_t s_cuHandle[MaxGpus]; - static void *s_curandGenerator; - - // Have to use naked pointer to avoid issues with __declspec(dllexport) on Windows (C4251). - // Cannot use atomic for the same reason either. - mutable conc_stack>>* m_workspace; - - private: - - void performElementWiseFunction(const ElementWiseOperator kind, const ElemType *src); - size_t LocateElement (const size_t i, const size_t j) const; - size_t LocateColumn (const size_t j) const; - void Clear(); - void ZeroInit(int deviceId); - - std::unique_ptr> GetOrCreateWorkspace() const; - void ReleaseWorkspace(std::unique_ptr> src) const; - - public: - GPUMatrix(int deviceId); - GPUMatrix(FILE* f, const char * matrixName, int deviceId); - GPUMatrix(const size_t numRows, const size_t numCols, int deviceId); - GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags = matrixFlagNormal); - GPUMatrix(const GPUMatrix& deepCopyFrom); - GPUMatrix& operator=(const GPUMatrix& deepCopyFrom); //assignment operator, deep copy - GPUMatrix(GPUMatrix&& moveFrom); - GPUMatrix& operator=(GPUMatrix&& moveFrom); //move assignment operator, shallow copy - ~GPUMatrix(void); - - static void SetDevice(DEVICEID_TYPE deviceId); - static DEVICEID_TYPE GetBestGPUDeviceId(); - int GetComputeDeviceId() const; - DEVICEID_TYPE PrepareDevice(DEVICEID_TYPE deviceId = -1) const; - - static cublasHandle_t GetCublasHandle(int computeDevice=-1); - ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller - size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller - void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; - - void ChangeDeviceTo(DEVICEID_TYPE to_id); - - public: - - GPUMatrix ColumnSlice(size_t startColumn, size_t numCols) const; - GPUMatrix& AssignColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols); - GPUMatrix& SetColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols); - - void CopyColumnsStrided(const GPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); - - GPUMatrix Diagonal() const; - - size_t BufferSize() const {return m_numRows*m_numCols*sizeof(ElemType);} - ElemType* BufferPointer() const {return m_pArray;} - - ElemType Adagrad(GPUMatrix& gradients, const bool needAveMultiplier); - void FSAdagrad(GPUMatrix& gradients, GPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul); - ElemType RmsProp(GPUMatrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier); - - void Reshape(const size_t numRows, const size_t numCols); - void Resize(const size_t numRows, const size_t numCols, bool growOnly = true); //by default we only reallocate if need to grow - - ElemType& operator() (const size_t /*row*/, const size_t /*col*/) { LogicError("GPUMatrix doesn't support this"); } - const ElemType& operator() (const size_t /*row*/, const size_t /*col*/) const { LogicError("GPUMatrix doesn't support this"); } - ElemType Get00Element() const; - - void SetValue(const ElemType v); - void SetValue(const ElemType* d_v); //d_v is pointer to the the value in GPU memory - void SetColumn(const ElemType* colPointer, size_t colInd); - void SetColumn(const GPUMatrix& valMat, size_t colInd); - - void MaskColumnsValue(const GPUMatrix& columnsMask, ElemType val); - - void SetValue(const GPUMatrix& deepCopyFrom); - void SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, size_t matrixFlags = matrixFlagNormal); - - void SetDiagonalValue(const ElemType v); - void SetDiagonalValue(const GPUMatrix& vector); - void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); - void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); - - GPUMatrix Transpose() const; - GPUMatrix& AssignTransposeOf (const GPUMatrix& a); - - GPUMatrix& operator+= (const ElemType alpha); - GPUMatrix operator+ (const ElemType alpha) const; - GPUMatrix& AssignSumOf(const ElemType alpha, const GPUMatrix& a); - - GPUMatrix& operator+= (const GPUMatrix& a); - GPUMatrix operator+ (const GPUMatrix& a) const; - GPUMatrix& AssignSumOf(const GPUMatrix& a, const GPUMatrix& b); - - GPUMatrix& operator-= (const ElemType alpha); - GPUMatrix operator- (const ElemType alpha) const; - GPUMatrix& AssignDifferenceOf(const ElemType alpha, const GPUMatrix& a); - GPUMatrix& AssignDifferenceOf(const GPUMatrix& a, const ElemType alpha); - - GPUMatrix& operator-= (const GPUMatrix& a); - GPUMatrix operator- (const GPUMatrix& a) const; - GPUMatrix& AssignDifferenceOf(const GPUMatrix& a, const GPUMatrix& b); - - GPUMatrix& operator*= (const ElemType alpha); - GPUMatrix operator* (const ElemType alpha) const; - GPUMatrix& AssignProductOf(const ElemType alpha, const GPUMatrix& a); - - GPUMatrix operator* (const GPUMatrix& a) const; - GPUMatrix& AssignProductOf(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB); - - GPUMatrix& operator/= (ElemType alpha); - GPUMatrix operator/ (ElemType alpha) const; - - GPUMatrix& operator^= (ElemType alpha); //element-wise power - GPUMatrix operator^ (ElemType alpha) const; //element-wise power - GPUMatrix& AssignElementPowerOf(const GPUMatrix& a, const ElemType power); - - GPUMatrix& ElementMultiplyWith (const GPUMatrix& a); - GPUMatrix& AssignElementProductOf (const GPUMatrix& a, const GPUMatrix& b); - GPUMatrix& AddElementProductOf (const GPUMatrix& a, const GPUMatrix& b); - - GPUMatrix& AssignElementDivisionOf (const GPUMatrix& a, const GPUMatrix& b); - GPUMatrix& ElementDivideBy(const GPUMatrix& a); - - GPUMatrix& ColumnElementMultiplyWith(const GPUMatrix& a); - GPUMatrix& RowElementMultiplyWith(const GPUMatrix& a); - - GPUMatrix& ColumnElementDivideBy(const GPUMatrix& a); - GPUMatrix& RowElementDivideBy(const GPUMatrix& a); - - GPUMatrix& ElementInverse (); - GPUMatrix& AssignElementInverseOf (const GPUMatrix& a); - - GPUMatrix& InplaceLinearRectifierDerivative(); - GPUMatrix& AssignLinearRectifierDerivativeOf (const GPUMatrix& a); - - GPUMatrix& InplaceSigmoidDerivative(); - GPUMatrix& AssignSigmoidDerivativeOf (const GPUMatrix& a); - - GPUMatrix& InplaceSigmoid (); - GPUMatrix& AssignSigmoidOf (const GPUMatrix& a); - - GPUMatrix& InplaceTanh (); - GPUMatrix& AssignTanhOf (const GPUMatrix& a); - - GPUMatrix& InplaceLogSoftmax (const bool isColWise); - GPUMatrix& AssignLogSoftmaxOf (const GPUMatrix& a, const bool isColWise); - - GPUMatrix& InplaceHardmax(const bool isColWise); - GPUMatrix& AssignHardmaxOf(const GPUMatrix& a, const bool isColWise); - - //sequence training - GPUMatrix& DropFrame(const GPUMatrix& label, const GPUMatrix& gamma, const ElemType & threshhold); - GPUMatrix& AssignSequenceError(const ElemType hsmoothingWeight, const GPUMatrix& label, const GPUMatrix& dnnoutput, const GPUMatrix& gamma, ElemType alpha); - - GPUMatrix& InplaceSqrt (); - GPUMatrix& AssignSqrtOf (const GPUMatrix& a); - - GPUMatrix& InplaceExp (); - GPUMatrix& AssignExpOf (const GPUMatrix& a); - - GPUMatrix& InplaceLog (); - GPUMatrix& AssignLogOf (const GPUMatrix& a); - - GPUMatrix& InplaceCosine (); - GPUMatrix& AssignCosineOf (const GPUMatrix& a); - - GPUMatrix& InplaceNegativeSine (); - GPUMatrix& AssignNegativeSineOf (const GPUMatrix& a); - - GPUMatrix& InplaceAbs (); - GPUMatrix& AssignAbsOf (const GPUMatrix& a); - - GPUMatrix& InplaceTruncateBottom (const ElemType threshold); - GPUMatrix& AssignTruncateBottomOf (const GPUMatrix& a, const ElemType threshold); - GPUMatrix& InplaceTruncateTop (const ElemType threshold); - GPUMatrix& AssignTruncateTopOf (const GPUMatrix& a, const ElemType threshold); - GPUMatrix& InplaceTruncate(const ElemType threshold); - GPUMatrix& InplaceSoftThreshold(const ElemType threshold); - - GPUMatrix& SetToZeroIfAbsLessThan (const ElemType threshold); - - DeviceBoundNumber Sum_AsDeviceBoundNum() const; - ElemType SumOfAbsElements () const; //sum of all abs(elements) - ElemType SumOfElements () const; //sum of all elements - GPUMatrix& AssignSumOfElements(const GPUMatrix& a); - - ElemType Max () const; - bool IsEqualTo(const GPUMatrix& a, const ElemType threshold = 1e-8) const; - - static void VectorSum(const GPUMatrix& a, GPUMatrix& c, const bool isColWise); - - void VectorNorm1(GPUMatrix& c, const bool isColWise) const; - GPUMatrix& AssignVectorNorm1Of(GPUMatrix& a, const bool isColWise); - - void VectorNorm2(GPUMatrix& c, const bool isColWise) const; - GPUMatrix& AssignVectorNorm2Of(GPUMatrix& a, const bool isColWise); - - void VectorNormInf(GPUMatrix& c, const bool isColWise) const; - GPUMatrix& AssignVectorNormInfOf(GPUMatrix& a, const bool isColWise); - - GPUMatrix& AssignInnerProductOf(const GPUMatrix& a, const GPUMatrix& b, const bool isColWise); - GPUMatrix& AssignKhatriRaoProductOf(const GPUMatrix& a, const GPUMatrix& b); - GPUMatrix& AddColumnReshapeProductOf(const GPUMatrix& a, const GPUMatrix& b, const bool transposeAColumn); - - GPUMatrix& AddWithScaleOf(ElemType alpha, const GPUMatrix& a); - - ElemType FrobeniusNorm() const; - GPUMatrix& AssignFrobeniusNormOf(const GPUMatrix& a); - - ElemType MatrixNormInf() const; - ElemType MatrixNorm1() const; - ElemType MatrixNorm0() const; //number of non-zero elemets - GPUMatrix& AssignSignOf(const GPUMatrix& a); - GPUMatrix& AddSignOf(const GPUMatrix& a); - - GPUMatrix& AssignToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); - GPUMatrix& AssignRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); - GPUMatrix& AddToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); - GPUMatrix& AddWithRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); - //GPUMatrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); - - GPUMatrix& AssignRepeatOf(const GPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats); - GPUMatrix& AddToRowRepeatValuesOf(const GPUMatrix& a, const size_t numRowRepeats); - - GPUMatrix& AssignPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - GPUMatrix& AddFoldedPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - - void VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise) const; - void VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise, int topK) const; - void VectorMin(GPUMatrix& minIndexes, GPUMatrix& minValues, const bool isColWise) const; - - GPUMatrix& AssignNumOfDiff(const GPUMatrix& a, const GPUMatrix& b, bool searchInCol = false); - - - GPUMatrix& AssignInnerProductOfMatrices(const GPUMatrix& a, const GPUMatrix& b); - - - void AssignNoiseContrastiveEstimation(const GPUMatrix& a, const GPUMatrix& b, const GPUMatrix& bias, - size_t sampleCount, GPUMatrix& tmp, GPUMatrix& c); - void AssignNCEDerivative(GPUMatrix& tmp, const GPUMatrix& a, const GPUMatrix& b, size_t inputIndex, GPUMatrix& c); - void AssignNCEUnnormalizedEval(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - void AssignSoftmaxSum(const GPUMatrix& a, GPUMatrix& softmax); - - void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; - void Print(const char* matrixName = NULL) const; //print whole matrix. can be expensive - - void ReadFromFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. - void WriteToFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. - - GPUMatrix& AssignPackedConvolutionInput(const GPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding = false); - GPUMatrix& UnpackConvolutionInput(GPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - bool zeroPadding = false) const; - GPUMatrix& AssignMaxPoolingResult(const GPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - GPUMatrix& AddMaxPoolingGradient(const GPUMatrix& outputGradientBatch, const GPUMatrix& inputBatch, const GPUMatrix& outputBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - GPUMatrix& AssignAveragePoolingResult(const GPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - GPUMatrix& AddAveragePoolingGradient(const GPUMatrix& outputGradientBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - public: - //static BLAS functions - static void MultiplyAndWeightedAdd(ElemType alpha,const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, ElemType beta, GPUMatrix& c); - static void MultiplyAndAdd(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, GPUMatrix& c); - static void Multiply(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, GPUMatrix& c); - static void Multiply(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - static void Multiply1x1AndWeightedAdd(ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, ElemType beta, GPUMatrix& c); - - static void ScaleAndAdd(ElemType alpha, const GPUMatrix& a, GPUMatrix& c); - static void ScaleAndAdd(ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - static void AddScaledDifference(const ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - static void AssignScaledDifference(const ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - static void AddScaledDifference(const GPUMatrix& alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - static void AssignScaledDifference(const GPUMatrix& alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); - - static void AddElementToElement(const GPUMatrix& a, const size_t ai, const size_t aj, GPUMatrix& c, const size_t ci, const size_t cj); - - /// minus one at a specific position - static void MinusOneAt(GPUMatrix& c, const size_t position); - - static void Scale(ElemType alpha, const GPUMatrix& a, GPUMatrix& c); - static void Scale(GPUMatrix &alpha, GPUMatrix& a); //In this case matrix alpha must be 1x1 - static void Scale(ElemType alpha, GPUMatrix& a); - static void InnerProduct (const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const bool isColWise); - static ElemType InnerProductOfMatrices(const GPUMatrix& a, const GPUMatrix& b); - static void ElementWisePower (ElemType alpha, const GPUMatrix& a, GPUMatrix& c); - - static bool AreEqual(const GPUMatrix& a, const GPUMatrix& b, const ElemType threshold = 1e-8); - - static void TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUMatrix& b, GPUMatrix& c); - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#pragma once +#include "Platform.h" +#include "File.h" +#include "Helpers.h" +#include "CommonMatrix.h" +#include "TensorShape.h" // only for SmallVector; I was hoping to keep this out +#include "DebugUtil.h" +#include "BestGpu.h" // for CPUONLY macro +#include "ConcStack.h" +#include +#include +#include +#include +#include // for cout/cerr +#include // for unique_ptr +#include // for ULONG_MAX + +// predeclare cublasHandle_t +struct cublasContext; +typedef struct cublasContext *cublasHandle_t; +struct CUstream_st; +typedef struct CUstream_st *cudaStream_t; + +#ifdef _WIN32 +#ifndef MATH_API +#ifdef MATH_EXPORTS +#define MATH_API __declspec(dllexport) +#else +#define MATH_API __declspec(dllimport) +#endif +#endif /* MATH_API */ +#else // no DLLs in Linux +#define MATH_API +#endif + +#ifndef USE_TIME_BASED_SEED +#define USE_TIME_BASED_SEED ULONG_MAX +#endif + +// Stream management functions +void MATH_API SetStream(cudaStream_t stream); +cudaStream_t MATH_API GetStream(); + +namespace Microsoft { namespace MSR { namespace CNTK { + + // ----------------------------------------------------------------------- + // DeviceBoundNumber -- This class represents a number which resides on a particular device. Use it to avoid unnecessary transfers between CPU and GPU + // ----------------------------------------------------------------------- + + template + class MATH_API DeviceBoundNumber + { + private: + DEVICEID_TYPE m_computeDevice; + ElemType* m_data; + public: + DeviceBoundNumber() {m_data=NULL;}; + DeviceBoundNumber(const DeviceBoundNumber &deepCopy); + DeviceBoundNumber(DeviceBoundNumber &&shallowCopy); + ~DeviceBoundNumber(); + DEVICEID_TYPE GetDeviceId() const { return m_computeDevice; } + ElemType* ExposePointer2Value() const {return m_data;} + //performs shallow copy only + void ShallowCopyFrom(ElemType* newVal,int newValsDevceId); + }; + + + // ----------------------------------------------------------------------- + // GPUMatrix + // ----------------------------------------------------------------------- + + void PrepareDevice(DEVICEID_TYPE deviceId); + + template + class MATH_API GPUMatrix : public BaseMatrix + { + typedef BaseMatrix B; using B::m_numRows; using B::m_numCols; using B::m_pArray; // without this, base members would require to use thi-> in GCC + + template + friend class GPUMatrix; + + public: + static const int MaxGpus = 8; // support up to 8 GPUs + using BaseMatrix::m_computeDevice; + using BaseMatrix::m_elemSizeAllocated; + using BaseMatrix::m_matrixName; + using BaseMatrix::m_format; + using BaseMatrix::m_externalBuffer; + using BaseMatrix::m_nz; + using BaseMatrix::OwnBuffer; + using BaseMatrix::GetNumElements; + using BaseMatrix::IsEmpty; + using BaseMatrix::GetArray; + using BaseMatrix::GetNumRows; + using BaseMatrix::GetNumCols; + using BaseMatrix::SetMatrixName; + private: + static cublasHandle_t s_cuHandle[MaxGpus]; + static void *s_curandGenerator; + + // Have to use naked pointer to avoid issues with __declspec(dllexport) on Windows (C4251). + // Cannot use atomic for the same reason either. + mutable conc_stack>>* m_workspace; + + private: + + void performElementWiseFunction(const ElementWiseOperator kind, const ElemType *src); + size_t LocateElement (const size_t i, const size_t j) const; + size_t LocateColumn (const size_t j) const; + void Clear(); + void ZeroInit(int deviceId); + + std::unique_ptr> GetOrCreateWorkspace() const; + void ReleaseWorkspace(std::unique_ptr> src) const; + + public: + GPUMatrix(int deviceId); + GPUMatrix(FILE* f, const char * matrixName, int deviceId); + GPUMatrix(const size_t numRows, const size_t numCols, int deviceId); + GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags = matrixFlagNormal); + GPUMatrix(const GPUMatrix& deepCopyFrom); + GPUMatrix& operator=(const GPUMatrix& deepCopyFrom); //assignment operator, deep copy + GPUMatrix(GPUMatrix&& moveFrom); + GPUMatrix& operator=(GPUMatrix&& moveFrom); //move assignment operator, shallow copy + ~GPUMatrix(void); + + static void SetDevice(DEVICEID_TYPE deviceId); + static DEVICEID_TYPE GetBestGPUDeviceId(); + int GetComputeDeviceId() const; + DEVICEID_TYPE PrepareDevice(DEVICEID_TYPE deviceId = -1) const; + + static cublasHandle_t GetCublasHandle(int computeDevice=-1); + ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller + size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller + void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; + + void ChangeDeviceTo(DEVICEID_TYPE to_id); + + public: + + GPUMatrix ColumnSlice(size_t startColumn, size_t numCols) const; + GPUMatrix& AssignColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols); + GPUMatrix& SetColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols); + + void CopyColumnsStrided(const GPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); + + GPUMatrix Diagonal() const; + + size_t BufferSize() const {return m_numRows*m_numCols*sizeof(ElemType);} + ElemType* BufferPointer() const {return m_pArray;} + + ElemType Adagrad(GPUMatrix& gradients, const bool needAveMultiplier); + void FSAdagrad(GPUMatrix& gradients, GPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul); + ElemType RmsProp(GPUMatrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier); + + void Reshape(const size_t numRows, const size_t numCols); + void Resize(const size_t numRows, const size_t numCols, bool growOnly = true); //by default we only reallocate if need to grow + + ElemType& operator() (const size_t /*row*/, const size_t /*col*/) { LogicError("GPUMatrix doesn't support this"); } + const ElemType& operator() (const size_t /*row*/, const size_t /*col*/) const { LogicError("GPUMatrix doesn't support this"); } + ElemType Get00Element() const; + + void SetValue(const ElemType v); + void SetValue(const ElemType* d_v); //d_v is pointer to the the value in GPU memory + void SetColumn(const ElemType* colPointer, size_t colInd); + void SetColumn(const GPUMatrix& valMat, size_t colInd); + + void MaskColumnsValue(const GPUMatrix& columnsMask, ElemType val); + + void SetValue(const GPUMatrix& deepCopyFrom); + void SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, size_t matrixFlags = matrixFlagNormal); + + void SetDiagonalValue(const ElemType v); + void SetDiagonalValue(const GPUMatrix& vector); + void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); + void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); + + GPUMatrix Transpose() const; + GPUMatrix& AssignTransposeOf (const GPUMatrix& a); + + GPUMatrix& operator+= (const ElemType alpha); + GPUMatrix operator+ (const ElemType alpha) const; + GPUMatrix& AssignSumOf(const ElemType alpha, const GPUMatrix& a); + + GPUMatrix& operator+= (const GPUMatrix& a); + GPUMatrix operator+ (const GPUMatrix& a) const; + GPUMatrix& AssignSumOf(const GPUMatrix& a, const GPUMatrix& b); + + GPUMatrix& operator-= (const ElemType alpha); + GPUMatrix operator- (const ElemType alpha) const; + GPUMatrix& AssignDifferenceOf(const ElemType alpha, const GPUMatrix& a); + GPUMatrix& AssignDifferenceOf(const GPUMatrix& a, const ElemType alpha); + + GPUMatrix& operator-= (const GPUMatrix& a); + GPUMatrix operator- (const GPUMatrix& a) const; + GPUMatrix& AssignDifferenceOf(const GPUMatrix& a, const GPUMatrix& b); + + GPUMatrix& operator*= (const ElemType alpha); + GPUMatrix operator* (const ElemType alpha) const; + GPUMatrix& AssignProductOf(const ElemType alpha, const GPUMatrix& a); + + GPUMatrix operator* (const GPUMatrix& a) const; + GPUMatrix& AssignProductOf(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB); + + GPUMatrix& operator/= (ElemType alpha); + GPUMatrix operator/ (ElemType alpha) const; + + GPUMatrix& operator^= (ElemType alpha); //element-wise power + GPUMatrix operator^ (ElemType alpha) const; //element-wise power + GPUMatrix& AssignElementPowerOf(const GPUMatrix& a, const ElemType power); + + GPUMatrix& ElementMultiplyWith (const GPUMatrix& a); + GPUMatrix& AssignElementProductOf (const GPUMatrix& a, const GPUMatrix& b); + GPUMatrix& AddElementProductOf (const GPUMatrix& a, const GPUMatrix& b); + + GPUMatrix& AssignElementDivisionOf (const GPUMatrix& a, const GPUMatrix& b); + GPUMatrix& ElementDivideBy(const GPUMatrix& a); + + GPUMatrix& ColumnElementMultiplyWith(const GPUMatrix& a); + GPUMatrix& RowElementMultiplyWith(const GPUMatrix& a); + + GPUMatrix& ColumnElementDivideBy(const GPUMatrix& a); + GPUMatrix& RowElementDivideBy(const GPUMatrix& a); + + GPUMatrix& ElementInverse (); + GPUMatrix& AssignElementInverseOf (const GPUMatrix& a); + + GPUMatrix& InplaceLinearRectifierDerivative(); + GPUMatrix& AssignLinearRectifierDerivativeOf (const GPUMatrix& a); + + GPUMatrix& InplaceSigmoidDerivative(); + GPUMatrix& AssignSigmoidDerivativeOf (const GPUMatrix& a); + + GPUMatrix& InplaceSigmoid (); + GPUMatrix& AssignSigmoidOf (const GPUMatrix& a); + + GPUMatrix& InplaceTanh (); + GPUMatrix& AssignTanhOf (const GPUMatrix& a); + + GPUMatrix& InplaceLogSoftmax (const bool isColWise); + GPUMatrix& AssignLogSoftmaxOf (const GPUMatrix& a, const bool isColWise); + + GPUMatrix& InplaceHardmax(const bool isColWise); + GPUMatrix& AssignHardmaxOf(const GPUMatrix& a, const bool isColWise); + + //sequence training + GPUMatrix& DropFrame(const GPUMatrix& label, const GPUMatrix& gamma, const ElemType & threshhold); + GPUMatrix& AssignSequenceError(const ElemType hsmoothingWeight, const GPUMatrix& label, const GPUMatrix& dnnoutput, const GPUMatrix& gamma, ElemType alpha); + + GPUMatrix& InplaceSqrt (); + GPUMatrix& AssignSqrtOf (const GPUMatrix& a); + + GPUMatrix& InplaceExp (); + GPUMatrix& AssignExpOf (const GPUMatrix& a); + + GPUMatrix& InplaceLog (); + GPUMatrix& AssignLogOf (const GPUMatrix& a); + + GPUMatrix& InplaceCosine (); + GPUMatrix& AssignCosineOf (const GPUMatrix& a); + + GPUMatrix& InplaceNegativeSine (); + GPUMatrix& AssignNegativeSineOf (const GPUMatrix& a); + + GPUMatrix& InplaceAbs (); + GPUMatrix& AssignAbsOf (const GPUMatrix& a); + + GPUMatrix& InplaceTruncateBottom (const ElemType threshold); + GPUMatrix& AssignTruncateBottomOf (const GPUMatrix& a, const ElemType threshold); + GPUMatrix& InplaceTruncateTop (const ElemType threshold); + GPUMatrix& AssignTruncateTopOf (const GPUMatrix& a, const ElemType threshold); + GPUMatrix& InplaceTruncate(const ElemType threshold); + GPUMatrix& InplaceSoftThreshold(const ElemType threshold); + + GPUMatrix& SetToZeroIfAbsLessThan (const ElemType threshold); + + DeviceBoundNumber Sum_AsDeviceBoundNum() const; + ElemType SumOfAbsElements () const; //sum of all abs(elements) + ElemType SumOfElements () const; //sum of all elements + GPUMatrix& AssignSumOfElements(const GPUMatrix& a); + + ElemType Max () const; + bool IsEqualTo(const GPUMatrix& a, const ElemType threshold = 1e-8) const; + + static void VectorSum(const GPUMatrix& a, GPUMatrix& c, const bool isColWise); + + void VectorNorm1(GPUMatrix& c, const bool isColWise) const; + GPUMatrix& AssignVectorNorm1Of(GPUMatrix& a, const bool isColWise); + + void VectorNorm2(GPUMatrix& c, const bool isColWise) const; + GPUMatrix& AssignVectorNorm2Of(GPUMatrix& a, const bool isColWise); + + void VectorNormInf(GPUMatrix& c, const bool isColWise) const; + GPUMatrix& AssignVectorNormInfOf(GPUMatrix& a, const bool isColWise); + + GPUMatrix& AssignInnerProductOf(const GPUMatrix& a, const GPUMatrix& b, const bool isColWise); + GPUMatrix& AssignKhatriRaoProductOf(const GPUMatrix& a, const GPUMatrix& b); + GPUMatrix& AddColumnReshapeProductOf(const GPUMatrix& a, const GPUMatrix& b, const bool transposeAColumn); + + GPUMatrix& AddWithScaleOf(ElemType alpha, const GPUMatrix& a); + + ElemType FrobeniusNorm() const; + GPUMatrix& AssignFrobeniusNormOf(const GPUMatrix& a); + + ElemType MatrixNormInf() const; + ElemType MatrixNorm1() const; + ElemType MatrixNorm0() const; //number of non-zero elemets + GPUMatrix& AssignSignOf(const GPUMatrix& a); + GPUMatrix& AddSignOf(const GPUMatrix& a); + + GPUMatrix& AssignToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); + GPUMatrix& AssignRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); + GPUMatrix& AddToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); + GPUMatrix& AddWithRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows); + //GPUMatrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); + + GPUMatrix& AssignRepeatOf(const GPUMatrix& a, const size_t numRowRepeats, const size_t numColRepeats); + GPUMatrix& AddToRowRepeatValuesOf(const GPUMatrix& a, const size_t numRowRepeats); + + GPUMatrix& AssignPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + GPUMatrix& AddFoldedPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + + void VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise) const; + void VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise, int topK) const; + void VectorMin(GPUMatrix& minIndexes, GPUMatrix& minValues, const bool isColWise) const; + + GPUMatrix& AssignNumOfDiff(const GPUMatrix& a, const GPUMatrix& b, bool searchInCol = false); + + + GPUMatrix& AssignInnerProductOfMatrices(const GPUMatrix& a, const GPUMatrix& b); + + + void AssignNoiseContrastiveEstimation(const GPUMatrix& a, const GPUMatrix& b, const GPUMatrix& bias, + size_t sampleCount, GPUMatrix& tmp, GPUMatrix& c); + void AssignNCEDerivative(GPUMatrix& tmp, const GPUMatrix& a, const GPUMatrix& b, size_t inputIndex, GPUMatrix& c); + void AssignNCEUnnormalizedEval(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + void AssignSoftmaxSum(const GPUMatrix& a, GPUMatrix& softmax); + + void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; + void Print(const char* matrixName = NULL) const; //print whole matrix. can be expensive + + void ReadFromFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. + void WriteToFile(FILE* f, const char * matrixName); //matrixName is used to verify that correct matrix is read. + + GPUMatrix& AssignPackedConvolutionInput(const GPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding = false); + GPUMatrix& UnpackConvolutionInput(GPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + bool zeroPadding = false) const; + GPUMatrix& AssignMaxPoolingResult(const GPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + GPUMatrix& AddMaxPoolingGradient(const GPUMatrix& outputGradientBatch, const GPUMatrix& inputBatch, const GPUMatrix& outputBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + GPUMatrix& AssignAveragePoolingResult(const GPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + GPUMatrix& AddAveragePoolingGradient(const GPUMatrix& outputGradientBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + public: + //static BLAS functions + static void MultiplyAndWeightedAdd(ElemType alpha,const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, ElemType beta, GPUMatrix& c); + static void MultiplyAndAdd(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, GPUMatrix& c); + static void Multiply(const GPUMatrix& a, const bool transposeA, const GPUMatrix& b, const bool transposeB, GPUMatrix& c); + static void Multiply(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + static void Multiply1x1AndWeightedAdd(ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, ElemType beta, GPUMatrix& c); + + static void ScaleAndAdd(ElemType alpha, const GPUMatrix& a, GPUMatrix& c); + static void ScaleAndAdd(ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + static void AddScaledDifference(const ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + static void AssignScaledDifference(const ElemType alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + static void AddScaledDifference(const GPUMatrix& alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + static void AssignScaledDifference(const GPUMatrix& alpha, const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c); + + static void AddElementToElement(const GPUMatrix& a, const size_t ai, const size_t aj, GPUMatrix& c, const size_t ci, const size_t cj); + + /// minus one at a specific position + static void MinusOneAt(GPUMatrix& c, const size_t position); + + static void Scale(ElemType alpha, const GPUMatrix& a, GPUMatrix& c); + static void Scale(GPUMatrix &alpha, GPUMatrix& a); //In this case matrix alpha must be 1x1 + static void Scale(ElemType alpha, GPUMatrix& a); + static void InnerProduct (const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const bool isColWise); + static ElemType InnerProductOfMatrices(const GPUMatrix& a, const GPUMatrix& b); + static void ElementWisePower (ElemType alpha, const GPUMatrix& a, GPUMatrix& c); + + static bool AreEqual(const GPUMatrix& a, const GPUMatrix& b, const ElemType threshold = 1e-8); + + static void TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUMatrix& b, GPUMatrix& c); + void TensorOp(ElemType beta, const GPUMatrix& a, ElemType alpha, ElementWiseOperator op, const std::array & offsets, const SmallVector & regularOpDims, const std::array, 2> & regularStrides, @@ -420,110 +420,110 @@ namespace Microsoft { namespace MSR { namespace CNTK { const std::array & offsets, const SmallVector & regularOpDims, const std::array, 4> & regularStrides, const SmallVector & reducingOpDims, const std::array, 4> & reducingStrides); - - static void CreateCurandObject(unsigned long seed, const char *caller); - static void ResetCurandObject(unsigned long seed, const char *caller); - static GPUMatrix Ones(const size_t rows, const size_t cols, int deviceId); - static GPUMatrix Zeros(const size_t rows, const size_t cols, int deviceId); - static GPUMatrix Eye(const size_t rows, int deviceId); - static GPUMatrix RandomUniform(const size_t rows, const size_t cols, int deviceId, const ElemType low, const ElemType high, unsigned long seed = USE_TIME_BASED_SEED); - static GPUMatrix RandomGaussian(const size_t rows, const size_t cols, int deviceId, const ElemType mean, const ElemType sigma, unsigned long seed = USE_TIME_BASED_SEED); - - static bool HasElement(const GPUMatrix& a, const ElemType v = 0.0); - - static ElemType GetLearnRateForBlock_Helper(const GPUMatrix &Gradients, const GPUMatrix &SmoothedGradients); - - ElemType LogAddSumOfElements() const; - - public: - GPUMatrix& AssignElementProductOfWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, const size_t shift, const size_t nt); - static void InnerProductWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const size_t nt); - GPUMatrix& GetARowByIndex(const GPUMatrix& a, const size_t m); - static void ConductRowElementMultiplyWithShift(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const bool isafixed); - - GPUMatrix& AssignElementProductOfWithShift(const GPUMatrix& a, const GPUMatrix& b, const size_t shift); - - public: - static void RCRFBackwardCompute( - const GPUMatrix& alpha, GPUMatrix& beta, - const GPUMatrix& lbls, - const GPUMatrix& pos_scores, const GPUMatrix& pair_scores, const int shift = 1); - static void RCRFTransGrdCompute(const GPUMatrix& lbls, - const GPUMatrix& alpha, - const GPUMatrix& beta, - const GPUMatrix& pair_scores, - GPUMatrix& grd, - const int startLbl, /// the time 0 start symbol in the output layer - const int shift); - - public: - friend File& operator>>(File& stream, GPUMatrix& us) - { - stream.GetMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); - size_t elsize; - stream>>elsize; - if (sizeof(ElemType)!=elsize) - LogicError("Template argument size doesn't match those in file"); - std::wstring matrixName; - size_t numRows, numCols; - int format; - stream>>matrixName>>format>>numRows>>numCols; - ElemType* d_array = new ElemType[numRows*numCols]; - for (size_t i=0;i>d_array[i]; - stream.GetMarker(fileMarkerEndSection, std::wstring(L"EMAT")); - us.SetValue(numRows, numCols, us.GetComputeDeviceId(), d_array, matrixFlagNormal | format); - delete[] d_array; - us.m_matrixName = new wchar_t[matrixName.length()+1]; - wmemcpy(us.m_matrixName,matrixName.c_str(),matrixName.length()+1); - //us.m_matrixName = matrixName; - return stream; - } - friend File& operator<<(File& stream, const GPUMatrix& us) - { - stream.PutMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); - stream< GPUSingleMatrix; - -}}} - -// Error handling -template const char * CudaErrString(ERRTYPE x); // actual error function is defined inside .cu files -template static void CudaCall(ERRTYPE retCode, const char * exprString, const char * libName, ERRTYPE successCode) -{ - if (retCode != successCode) - { - try - { - const char * hostname = getenv("COMPUTERNAME"); // TODO: This is the easy way for Windows; likely different on Linux. - Microsoft::MSR::CNTK::RuntimeError("%s failure %d: %s ; GPU=%d ; hostname=%s ; expr=%s", libName, (int)retCode, CudaErrString(retCode), Microsoft::MSR::CNTK::GPUMatrix::GetBestGPUDeviceId(), hostname ? hostname : "?", exprString); - } - catch (const std::exception & e) // catch, log, and rethrow since CUDA code sometimes hangs in destruction, so we'd never get to see the error - { - std::cerr << e.what() << std::endl; - throw; - } - } -} - -#define CUDA_CALL(expr) (CudaCall((expr), #expr, "CUDA", cudaSuccess)) -#define CUBLAS_CALL(expr) (CudaCall((expr), #expr, "CUBLAS", CUBLAS_STATUS_SUCCESS)) -#define CUSPARSE_CALL(expr) (CudaCall((expr), #expr, "CUSPARSE", CUSPARSE_STATUS_SUCCESS)) -#define CURAND_CALL(expr) (CudaCall((expr), #expr, "CURAND", CURAND_STATUS_SUCCESS)) + + static void CreateCurandObject(unsigned long seed, const char *caller); + static void ResetCurandObject(unsigned long seed, const char *caller); + static GPUMatrix Ones(const size_t rows, const size_t cols, int deviceId); + static GPUMatrix Zeros(const size_t rows, const size_t cols, int deviceId); + static GPUMatrix Eye(const size_t rows, int deviceId); + static GPUMatrix RandomUniform(const size_t rows, const size_t cols, int deviceId, const ElemType low, const ElemType high, unsigned long seed = USE_TIME_BASED_SEED); + static GPUMatrix RandomGaussian(const size_t rows, const size_t cols, int deviceId, const ElemType mean, const ElemType sigma, unsigned long seed = USE_TIME_BASED_SEED); + + static bool HasElement(const GPUMatrix& a, const ElemType v = 0.0); + + static ElemType GetLearnRateForBlock_Helper(const GPUMatrix &Gradients, const GPUMatrix &SmoothedGradients); + + ElemType LogAddSumOfElements() const; + + public: + GPUMatrix& AssignElementProductOfWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, const size_t shift, const size_t nt); + static void InnerProductWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const size_t nt); + GPUMatrix& GetARowByIndex(const GPUMatrix& a, const size_t m); + static void ConductRowElementMultiplyWithShift(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const bool isafixed); + + GPUMatrix& AssignElementProductOfWithShift(const GPUMatrix& a, const GPUMatrix& b, const size_t shift); + + public: + static void RCRFBackwardCompute( + const GPUMatrix& alpha, GPUMatrix& beta, + const GPUMatrix& lbls, + const GPUMatrix& pos_scores, const GPUMatrix& pair_scores, const int shift = 1); + static void RCRFTransGrdCompute(const GPUMatrix& lbls, + const GPUMatrix& alpha, + const GPUMatrix& beta, + const GPUMatrix& pair_scores, + GPUMatrix& grd, + const int startLbl, /// the time 0 start symbol in the output layer + const int shift); + + public: + friend File& operator>>(File& stream, GPUMatrix& us) + { + stream.GetMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); + size_t elsize; + stream>>elsize; + if (sizeof(ElemType)!=elsize) + LogicError("Template argument size doesn't match those in file"); + std::wstring matrixName; + size_t numRows, numCols; + int format; + stream>>matrixName>>format>>numRows>>numCols; + ElemType* d_array = new ElemType[numRows*numCols]; + for (size_t i=0;i>d_array[i]; + stream.GetMarker(fileMarkerEndSection, std::wstring(L"EMAT")); + us.SetValue(numRows, numCols, us.GetComputeDeviceId(), d_array, matrixFlagNormal | format); + delete[] d_array; + us.m_matrixName = new wchar_t[matrixName.length()+1]; + wmemcpy(us.m_matrixName,matrixName.c_str(),matrixName.length()+1); + //us.m_matrixName = matrixName; + return stream; + } + friend File& operator<<(File& stream, const GPUMatrix& us) + { + stream.PutMarker(fileMarkerBeginSection, std::wstring(L"BMAT")); + stream< GPUSingleMatrix; + +}}} + +// Error handling +template const char * CudaErrString(ERRTYPE x); // actual error function is defined inside .cu files +template static void CudaCall(ERRTYPE retCode, const char * exprString, const char * libName, ERRTYPE successCode) +{ + if (retCode != successCode) + { + try + { + const char * hostname = getenv("COMPUTERNAME"); // TODO: This is the easy way for Windows; likely different on Linux. + Microsoft::MSR::CNTK::RuntimeError("%s failure %d: %s ; GPU=%d ; hostname=%s ; expr=%s", libName, (int)retCode, CudaErrString(retCode), Microsoft::MSR::CNTK::GPUMatrix::GetBestGPUDeviceId(), hostname ? hostname : "?", exprString); + } + catch (const std::exception & e) // catch, log, and rethrow since CUDA code sometimes hangs in destruction, so we'd never get to see the error + { + std::cerr << e.what() << std::endl; + throw; + } + } +} + +#define CUDA_CALL(expr) (CudaCall((expr), #expr, "CUDA", cudaSuccess)) +#define CUBLAS_CALL(expr) (CudaCall((expr), #expr, "CUBLAS", CUBLAS_STATUS_SUCCESS)) +#define CUSPARSE_CALL(expr) (CudaCall((expr), #expr, "CUSPARSE", CUSPARSE_STATUS_SUCCESS)) +#define CURAND_CALL(expr) (CudaCall((expr), #expr, "CURAND", CURAND_STATUS_SUCCESS)) #define CUDNN_CALL(expr) (CudaCall((expr), #expr, "cuDNN", CUDNN_STATUS_SUCCESS)) diff --git a/Source/Math/GPUMatrixCUDAKernels.cuh b/Source/Math/GPUMatrixCUDAKernels.cuh index 2c02b58be257..4d7143c9ee01 100644 --- a/Source/Math/GPUMatrixCUDAKernels.cuh +++ b/Source/Math/GPUMatrixCUDAKernels.cuh @@ -1,5066 +1,5066 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#pragma once - -#include "BestGpu.h" - -#ifndef CPUONLY - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#pragma once + +#include "BestGpu.h" + +#ifndef CPUONLY + #pragma push_macro("TENSOR_OPS_DECL") #define TENSOR_OPS_DECL __device__ __host__ -#include "CommonMatrix.h" +#include "CommonMatrix.h" #include "GPUMatrix.h" #include "TensorOps.h" // for exp_() etc. -#include "device_functions.h" -#include -#include -#include +#include "device_functions.h" +#include +#include +#include #pragma pop_macro("TENSOR_OPS_DECL") - -// REVIEW alexeyk: disable warnings properly for GCC/clang -#ifdef _MSC_VER -#pragma warning (push) -#pragma warning (disable: 4100) -#pragma warning (disable: 4127) -#pragma warning (disable: 4201) -#pragma warning (disable: 4515) -#endif -#include -#ifdef _MSC_VER -#pragma warning (pop) -#endif - -// We would like to use 64-bit integer to support large matrices. However, CUDA seems to support only 32-bit integer -// For now, use int32_t to ensure that both Linux and Windows see this as 32 bit integer type. - -#ifndef CUDA_LONG -#define CUDA_LONG int32_t -#endif - -#define IDX2C(i,j,ld) (((j)*(ld))+(i)) // 0 based indexing - -// CUDA atomicAdd() only exists for 'float'. This is the 'double' version. -static __inline__ __device__ double atomicAdd(double* address, double val) -{ - unsigned long long int* address_as_ull = (unsigned long long int*)address; - unsigned long long int old = *address_as_ull, assumed; - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val + __longlong_as_double(assumed))); - } while (assumed != old); - return __longlong_as_double(old); -} - -// TODO: replace this with TensorOps.h LogAdd(). It differs in using ElemType throughout, while this one seems to use 'double' versions of exp() and log(). -// The 'k' in the name is to avoid naming conflicts with various versions of logadd() that are defined throughout the codebase. -template -static inline __device__ __host__ ElemType logaddk(ElemType x, ElemType y) -{ - ElemType temp, diff, z; - - if (x < y) - { - temp = x; x = y; y = temp; - } - diff = y - x; - if (diff < MINLOGEXP) - { - return (x < LSMALL) ? LZERO : x; - } - else - { - z = exp(diff); - return x + log(1.0 + z); - } -} + +// REVIEW alexeyk: disable warnings properly for GCC/clang +#ifdef _MSC_VER +#pragma warning (push) +#pragma warning (disable: 4100) +#pragma warning (disable: 4127) +#pragma warning (disable: 4201) +#pragma warning (disable: 4515) +#endif +#include +#ifdef _MSC_VER +#pragma warning (pop) +#endif + +// We would like to use 64-bit integer to support large matrices. However, CUDA seems to support only 32-bit integer +// For now, use int32_t to ensure that both Linux and Windows see this as 32 bit integer type. + +#ifndef CUDA_LONG +#define CUDA_LONG int32_t +#endif + +#define IDX2C(i,j,ld) (((j)*(ld))+(i)) // 0 based indexing + +// CUDA atomicAdd() only exists for 'float'. This is the 'double' version. +static __inline__ __device__ double atomicAdd(double* address, double val) +{ + unsigned long long int* address_as_ull = (unsigned long long int*)address; + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); +} + +// TODO: replace this with TensorOps.h LogAdd(). It differs in using ElemType throughout, while this one seems to use 'double' versions of exp() and log(). +// The 'k' in the name is to avoid naming conflicts with various versions of logadd() that are defined throughout the codebase. +template +static inline __device__ __host__ ElemType logaddk(ElemType x, ElemType y) +{ + ElemType temp, diff, z; + + if (x < y) + { + temp = x; x = y; y = temp; + } + diff = y - x; + if (diff < MINLOGEXP) + { + return (x < LSMALL) ? LZERO : x; + } + else + { + z = exp(diff); + return x + log(1.0 + z); + } +} namespace Microsoft { namespace MSR { namespace CNTK { - -// --------------------------------------------------------------------------- -// GridDim -- helper to choose the CUDA grid dimensions -// --------------------------------------------------------------------------- - -template -static INT CeilDiv(INT a, INT2 b) // ceil(a/b) -{ - return (INT)(((size_t)a + (size_t)b - 1) / (size_t)b); // these size_t casts are necessary since b may be INT_MAX (for maxGridSize[]) -} - -struct GridDim -{ - static const CUDA_LONG maxThreadsPerBlock = 512; // use this many threads per block - static const CUDA_LONG maxWarpsPerBlock = 16; // use this many warps per block - - // use these for launching - // GridDim grid(NN); - // kernel<<>>(...) - int m_blocksPerGrid, m_threadsPerBlock; // (these may in the future be extended to multi-dimensional ones) - CUDA_LONG m_N; - - GridDim(CUDA_LONG N) // linear grid - { - m_N = N; - if (N == 0) // CUDA will fail to launch with 0 blocks - N = 1; - - // get device information - const auto & props = GetDeviceProps(); - CUDA_LONG numProcs = props.multiProcessorCount; - CUDA_LONG warpSize = props.warpSize; - - // distribute warps evenly over processors - CUDA_LONG warpsPerProc = CeilDiv(N, numProcs * warpSize); - - // if too many warps per block then reduce #warps - if (warpsPerProc > maxWarpsPerBlock) - { - CUDA_LONG overBy = CeilDiv(warpsPerProc, maxWarpsPerBlock); // we are over by this factor - warpsPerProc = CeilDiv(warpsPerProc, overBy); - } - - // put it back together - m_threadsPerBlock = warpsPerProc * warpSize; - m_blocksPerGrid = CeilDiv(N, m_threadsPerBlock); - if (m_blocksPerGrid == 1) - m_threadsPerBlock = N; // don't launch more than necessary --TODO: Does this make a difference at all? - assert(m_blocksPerGrid * m_threadsPerBlock >= N); - } - - static std::vector CacheDeviceProps() - { - int numDevices; - CUDA_CALL(cudaGetDeviceCount(&numDevices)); - std::vector props(numDevices); - for (int i = 0; i < numDevices; i++) - CUDA_CALL(cudaGetDeviceProperties(&props[i], i)); -#if 0 // on Linux, maxGridSize[0] gets reported as 0 - for (int i = 0; i < numDevices; i++) - fprintf(stderr, "%d procs %d warps %d %d %d max grid on %s\n", (int)props[i].multiProcessorCount, (int)props[i].warpSize, (int)props[i].maxGridSize[0], (int)props[i].maxGridSize[1], (int)props[i].maxGridSize[2], props[i].name); -#endif - return props; - } - - // get device properties of current device - static const cudaDeviceProp & GetDeviceProps() - { - static std::vector props = CacheDeviceProps(); // thread-safe according to C++ standard - int deviceId; - cudaGetDevice(&deviceId); - return props[deviceId]; - } - - // compute our location on the grid - static __device__ CUDA_LONG GetLinearThreadId() - { - return blockDim.x * blockIdx.x + threadIdx.x; - } -}; - -#define CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) CUDA_LONG id = GridDim::GetLinearThreadId(); if (id>=N) return; - -#ifdef __GNUC__ -#define UNUSED_FUNCTION_ATTRIBUTE __attribute__ ((unused)) -#else -#define UNUSED_FUNCTION_ATTRIBUTE -#endif - -// =========================================================================== -// CUDA kernels follow, lots of them -// =========================================================================== - -// _elementWise*() kernels -// -// Designed to operate on contiguous blocks of memory, where the output is a simple function of the inputs. -// The first parameters of every function are inputs, and the last two arguments to each function are always -// (ElemenType *res, CUDA_LONG N), a pointer and length of the output block. Each thread computes a function -// of the inputs for one value in the output. - -template -__global__ void _elementWisePowerOnCuda( - const ElemType alpha, - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - if (alpha==0) - { - res[id]=1; - } - else if (alpha==1) - { - res[id]=a[id]; - } - else if (alpha==2) - { - res[id]=a[id]*a[id]; - } - else if (alpha==3) - { - res[id]=a[id]*a[id]*a[id]; - } - else - { - if (sizeof(ElemType)==sizeof(double)) - { - res[id]=pow(a[id],alpha); - } - else - { - res[id]=powf(a[id],alpha); - } - } -}; - -// Note that this code is inefficient on CUDA due to diverging code paths. -// Use Sigmoid() in TensorOps.h instead, which solves this problem. -template -__global__ void _elementWiseSigmoidOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); -#if 0 // this computes the same thing but is twice as fast on CUDA - res[id] = Microsoft::MSR::CNTK::Sigmoid(a[id]); -#else - if (a[id] >= 0) - { - ElemType e = exp_(-a[id]); - res[id] = 1 / (1 + e); - } - else - { - ElemType e = exp_(a[id]); - res[id] = e / (1 + e); - } -#endif -}; - -template -__global__ void _assignSigmoidOf( - const ElemType* a, - ElemType* res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - - // This function computes 1 / (1 + e^(-x)) which yields 1 / (1 + e^|x|) if x is negative, - // and e^x / (1 + e^x) if x is positive. - // BUGBUG: This does not invert the calculation when the exp argument becomes large, potentially causing overflows. - // There is a second version of this function that does. That should be used. -#if 0 // this has the same speed now, although not identical accuracy - res[id] = Microsoft::MSR::CNTK::Sigmoid(a[id]); -#else - ElemType negElem = -a[id]; - ElemType e = exp_(negElem); - - res[id] = 1 / (e + 1); -#endif -}; - -template -__global__ void _elementWiseLinRectDerivativeOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = (a[id] <= 0) ? 0 : 1; -} - -template -__global__ void _elementWiseSigmoidDerivativeOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = a[id] * (1-a[id]); -} - -template -__global__ void _elementWiseTanhOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = tanh_(a[id]); -}; - -//to prevent negative values caused by floating operations, we force inputs to be >=0 -//this may, however, hide problems in the caller. -template -__global__ void _elementWiseSqrtOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = sqrt_(max((ElemType)0, a[id])); -}; - -template -__global__ void _elementWiseExpOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = exp_(a[id]); -}; - -template -__global__ void _elementWiseLogOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = (a[id] < EPS_IN_LOG) ? LOG_OF_EPS_IN_LOG : log_(a[id]); -}; - -template -__global__ void _elementWiseAbsOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = fabs_(a[id]); -}; - -template -__global__ void _elementWiseCosineOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = cos_(a[id]); -}; - -template -__global__ void _elementWiseNegativeSineOnCuda( - const ElemType *a, - ElemType *res, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - res[id] = -sin_(a[id]); -}; - -template -__global__ void _setValue( - ElemType* a, - const ElemType v, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]=v; -}; - -template -__global__ void _setValue( - ElemType* a, - const ElemType* d_v, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]=d_v[0]; -}; - -template -__global__ void _copyColumnsStrided(ElemType * dest, ElemType * src, CUDA_LONG N, CUDA_LONG numRows, CUDA_LONG destNumColsStride, CUDA_LONG srcNumColsStride) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG denseColIdx = id / numRows; - CUDA_LONG rowIdx = id - (denseColIdx * numRows); - - dest[(denseColIdx*destNumColsStride*numRows) + rowIdx] = src[(denseColIdx*srcNumColsStride*numRows) + rowIdx]; -} - -template -__global__ void _assignToRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG col = id / srcRows; - CUDA_LONG row = id - (col * srcRows); - - dest[col*destRows + row + startIndex] = src[id]; -} - -template -__global__ void _assignRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - CUDA_LONG col = id / destRows; - CUDA_LONG row = id - (col * destRows); - - //dest[id] = src[col*srcRows + row + startIndex]; - dest[id] = src[IDX2C(row + startIndex, col, srcRows)]; -} - -template -__global__ void _addToRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - CUDA_LONG col = id / srcRows; //src is the full matrix, rowslice is taken from the dest - CUDA_LONG row = id - (col * srcRows); - - //dest[col*destRows + row + startIndex] += src[id]; - dest[IDX2C(row + startIndex, col, destRows)] += src[id]; -} - -template -__global__ void _addWithRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG col = id / destRows; //dest is the full matrix, rowslice is taken from the src - CUDA_LONG row = id - (col * destRows); - - dest[id] += src[IDX2C(row + startIndex, col, srcRows)]; -} - -template -__global__ void _assignToDiagonalValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcCols) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG col = id / srcCols; - CUDA_LONG row = id - (col * srcCols); - - if (row == col) - dest[row] = src[id]; -} - -template -__global__ void _assignRowStackValuesOf(ElemType * dest, ElemType ** srces, size_t* startRowIndeces, const CUDA_LONG numSrces, const CUDA_LONG N, const CUDA_LONG destRows, const CUDA_LONG destCols) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG col = id / destRows; //dest is the full matrix, rowslice is taken from the src - CUDA_LONG row = id - (col * destRows); - - //can we replace the for loop with something better? - int srcId = 0; - for (; srcId < numSrces; srcId++) - { - if (startRowIndeces[srcId + 1]>row) - break; - } - - dest[id] = srces[srcId][IDX2C(row - startRowIndeces[srcId], col, startRowIndeces[srcId+1] - startRowIndeces[srcId])]; -} - -template -__global__ void _assignRepeatOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG destCol = id / destRows; - CUDA_LONG destRow = id - (destCol * destRows); - - CUDA_LONG srcRow = destRow % srcRows; - CUDA_LONG srcCol = destCol % srcCols; - - dest[id] = src[IDX2C(srcRow,srcCol,srcRows)]; -} - -template -__global__ void _addToRowRepeatValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG col = id / srcRows; - CUDA_LONG row = (id - (col * srcRows)) % destRows; - - //dest[col*destRows + row + startIndex] += src[id]; - dest[IDX2C(row, col, destRows)] += src[id]; -} - -template -__global__ void _assignPositiveAndShiftedNegSample(ElemType * dest, const ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows, const CUDA_LONG posNumber, const CUDA_LONG shiftNumber) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - CUDA_LONG destCol = id / destRows; - CUDA_LONG destRow = id - (destCol * destRows); - - CUDA_LONG sampleInDestCol = destRow / srcRows; - CUDA_LONG srcRow = destRow - srcRows * sampleInDestCol; - CUDA_LONG srcCol = sampleInDestCol < posNumber ? destCol : (destCol + shiftNumber + sampleInDestCol - posNumber) % srcCols; - - dest[id] = src[IDX2C(srcRow, srcCol, srcRows)]; -} - -template -__global__ void _addFoldedPositiveAndShiftedNegSample(ElemType * folded, const ElemType * unfolded, const CUDA_LONG unfoldedN, const CUDA_LONG unfoldedRows, const CUDA_LONG unfoldedCols, const CUDA_LONG foldedRows, const CUDA_LONG posNumber, const CUDA_LONG shiftNumber) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= unfoldedN) - return; - - CUDA_LONG unfoldedCol = id / unfoldedRows; - CUDA_LONG unfoldedRow = id - (unfoldedCol * unfoldedRows); - - CUDA_LONG sampleInUnfoldedCol = unfoldedRow / foldedRows; - CUDA_LONG foldedRow = unfoldedRow - foldedRows * sampleInUnfoldedCol; - CUDA_LONG foldedCol = sampleInUnfoldedCol < posNumber ? unfoldedCol : (unfoldedCol + shiftNumber + sampleInUnfoldedCol - posNumber) % unfoldedCols; - - atomicAdd(&folded[IDX2C(foldedRow, foldedCol, foldedRows)], unfolded[id]); -} - -template -__global__ void _assignDifferenceOf1( - ElemType* us, - const ElemType alpha, - const ElemType* a, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - us[id]=alpha-a[id]; -}; - -template -__global__ void _assignDifferenceOf2( - ElemType* us, - const ElemType alpha, - const ElemType* a, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - us[id]=a[id]-alpha; -}; - -///a is a scalar -template -__global__ void _scaleAndAddScalar( - ElemType* c, - const CUDA_LONG N, - const ElemType alpha, - const ElemType *a, - const ElemType *b -) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - c[id] = alpha*a[0] + b[id]; -}; - -template -__global__ void _multiply1x1AndWeightedAdd( - ElemType alpha, const ElemType* a, const ElemType* b, ElemType beta, ElemType* c, CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - ElemType f = alpha * *a; // scalar matrix - if (beta == 0) // don't even read the memory if beta is 0 - c[id] = b[id] * f; - else - c[id] = b[id] * f + c[id] * beta; -} - -template -__global__ void _addValue( - ElemType* a, - const ElemType v, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]+=v; -}; - -template -__global__ void _addValue( - ElemType* a, - const ElemType* d_v, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]+=d_v[0]; -}; - - -template -__global__ void _elemMul( - ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]*=b[id]; -}; - -template -__global__ void _assignElementProductOf( - ElemType* us, - const ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - us[id]=a[id]*b[id]; -} - -template -__global__ void _assignKhatriRaoProductOf( - ElemType* us, - const ElemType* a, - const ElemType* b, - const CUDA_LONG rowsA, - const CUDA_LONG rowsB, - const CUDA_LONG cols) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - - const CUDA_LONG rows = rowsA * rowsB; - const CUDA_LONG col = id / rows; - if (col >= cols) - return; - - const CUDA_LONG row = id % rows; - const CUDA_LONG rowB = row / rowsA; - const CUDA_LONG rowA = row % rowsA; - - us[id] = a[rowA + col * rowsA] * b[rowB + col * rowsB]; -} - -template -__global__ void _addColumnReshapeProductOf( - ElemType* us, - const ElemType* a, - const ElemType* b, - const CUDA_LONG rowsB, - const CUDA_LONG rowsC, - const CUDA_LONG cols, - const bool transposeAColumn) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - - const CUDA_LONG col = id / rowsC; - if (col >= cols) - return; - - const CUDA_LONG row = id % rowsC; - CUDA_LONG bBase = col * rowsB; - CUDA_LONG aBase = bBase * rowsC; - ElemType v = 0; - - if (transposeAColumn) - { - aBase += row * rowsB; - for (CUDA_LONG i=0; i -__global__ void _assignElementDivisionOf( - ElemType* us, - const ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - ElemType smallValue = EPS_IN_INVERSE; - - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - ElemType v = b[id]; - - if (v <0 && v > -smallValue) - us[id] = a[id]/(-smallValue); - else if (v >=0 && v < smallValue) - us[id] = a[id]/smallValue; - else - us[id]=a[id]/v; -} - -template -__global__ void _elemInverse( - ElemType* us, - const CUDA_LONG N) -{ - ElemType smallValue = EPS_IN_INVERSE; - - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - if (us[id] <0 && us[id] > -smallValue) - us[id] = 1/-smallValue; - else if (us[id] >=0 && us[id] < smallValue) - us[id] = 1/smallValue; - else - us[id]=1/us[id]; -} - -template -__global__ void _logSoftMaxColWise( - ElemType *a, - const CUDA_LONG m_numCols, - const CUDA_LONG m_numRows) //ld -{ - int col_id = blockDim.x * blockIdx.x + threadIdx.x; - if (col_id >= m_numCols) - return; - - __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; - __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; - maxV[threadIdx.x] = a[IDX2C(0, col_id, m_numRows)]; - Sum[threadIdx.x] = 0; - - for (CUDA_LONG i = 0; imaxV[threadIdx.x]) - { - maxV[threadIdx.x] = a[IDX2C(i, col_id, m_numRows)]; - } - } - - for (CUDA_LONG i = 0; i -//__global__ void _assignColumnwiseSoftmaxOf( -// const ElemType *a, -// ElemType* us, -// const CUDA_LONG m_numCols, -// const CUDA_LONG m_numRows) //thead per column -//{ -// int col_id = blockDim.x * blockIdx.x + threadIdx.x; -// if (col_id>=m_numCols) -// return; -// -// __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; -// __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; -// maxV[threadIdx.x]=a[IDX2C(0,col_id,m_numRows)]; -// Sum[threadIdx.x]=0; -// -// for (CUDA_LONG i=0;imaxV[threadIdx.x]) -// { -// maxV[threadIdx.x]=a[IDX2C(i,col_id,m_numRows)]; -// } -// } -// -// for (CUDA_LONG i=0;i -__global__ void _assignColumnwiseLogSoftmaxOf( - const ElemType *a, - ElemType* us, - const CUDA_LONG m_numCols, - const CUDA_LONG m_numRows) -{ - // We first find max per column - __shared__ ElemType colMax[1]; - __shared__ ElemType partials[512]; - colMax[0] = -10000000; - partials[threadIdx.x] = -10000000; - - for (int i = threadIdx.x; i < m_numRows; i += 512) - { - partials[threadIdx.x] = max(partials[threadIdx.x], a[IDX2C(i, blockIdx.x, m_numRows)]); - } - __syncthreads(); - - if (threadIdx.x < 256) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 256], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 128) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 128], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 64) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 64], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 32) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 32], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 16) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 16], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 8) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 8], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x < 4) - { - partials[threadIdx.x] = max(partials[threadIdx.x + 4], partials[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x == 0) - { - colMax[0] = max(max(partials[0], partials[1]), max(partials[2], partials[3])); - } - partials[threadIdx.x] = 0.0f; - __syncthreads(); - - // Now start finding sums - __shared__ ElemType colSum[1]; - colSum[0] = 0.0f; - for (int i = threadIdx.x; i < m_numRows; i += 512) - { - ElemType tmp = a[IDX2C(i, blockIdx.x, m_numRows)] - colMax[0]; - us[IDX2C(i, blockIdx.x, m_numRows)] = tmp; - partials[threadIdx.x] += (sizeof(ElemType) == sizeof(float)) ? expf(tmp) : exp(tmp); - } - __syncthreads(); - - if (threadIdx.x < 256) - { - partials[threadIdx.x] += partials[threadIdx.x + 256]; - } - __syncthreads(); - - if (threadIdx.x < 128) - { - partials[threadIdx.x] += partials[threadIdx.x + 128]; - } - __syncthreads(); - - if (threadIdx.x < 64) - { - partials[threadIdx.x] += partials[threadIdx.x + 64]; - } - __syncthreads(); - - if (threadIdx.x < 32) - { - partials[threadIdx.x] += partials[threadIdx.x + 32]; - } - __syncthreads(); - - if (threadIdx.x < 16) - { - partials[threadIdx.x] += partials[threadIdx.x + 16]; - } - __syncthreads(); - - if (threadIdx.x < 8) - { - partials[threadIdx.x] += partials[threadIdx.x + 8]; - } - __syncthreads(); - - if (threadIdx.x < 4) - { - partials[threadIdx.x] += partials[threadIdx.x + 4]; - } - __syncthreads(); - - if (threadIdx.x == 0) - { - colSum[0] = partials[0] + partials[1] + partials[2] + partials[3]; - colSum[0] = (sizeof(ElemType) == sizeof(float)) ? logf(colSum[0]) : log(colSum[0]); - } - __syncthreads(); - - for (int i = threadIdx.x; i < m_numRows; i += 512) - { - us[IDX2C(i, blockIdx.x, m_numRows)] -= colSum[0]; - } -} - -template -__global__ void _logSoftMaxRowWise( - ElemType *a, - const CUDA_LONG m_numCols, - const CUDA_LONG m_numRows) //ld -{ - int row_id = blockDim.x * blockIdx.x + threadIdx.x; - if (row_id >= m_numRows) - return; - - __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; - __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; - maxV[threadIdx.x] = a[IDX2C(row_id, 0, m_numRows)]; - Sum[threadIdx.x] = 0; - - for (CUDA_LONG j = 0; jmaxV[threadIdx.x]) - { - maxV[threadIdx.x] = a[IDX2C(row_id, j, m_numRows)]; - } - } - - for (CUDA_LONG j = 0; j -__global__ void _assignColumnwiseHardmaxOf( - const ElemType *a, - ElemType* us, - const CUDA_LONG m_numCols, - const CUDA_LONG m_numRows) -{ - // We first find max per column - __shared__ ElemType partials[512]; - __shared__ int colMaxI[512]; - int row = threadIdx.x % m_numRows; - colMaxI[threadIdx.x] = row; - partials[threadIdx.x] = a[IDX2C(row, blockIdx.x, m_numRows)]; - - for (int i = threadIdx.x; i < m_numRows; i += 512) - { - if (partials[threadIdx.x] < a[IDX2C(i, blockIdx.x, m_numRows)]) - { - partials[threadIdx.x] = a[IDX2C(i, blockIdx.x, m_numRows)]; - colMaxI[threadIdx.x] = i; - } - } - __syncthreads(); - - if (m_numRows > 256) - { - if (threadIdx.x < 256) - { - int other = threadIdx.x + 256; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 128) - { - if (threadIdx.x < 128) - { - int other = threadIdx.x + 128; - - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 64) - { - if (threadIdx.x < 64) - { - int other = threadIdx.x + 64; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 32) - { - if (threadIdx.x < 32) - { - int other = threadIdx.x + 32; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 16) - { - if (threadIdx.x < 16) - { - int other = threadIdx.x + 16; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 8) - { - if (threadIdx.x < 8) - { - int other = threadIdx.x + 8; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (m_numRows > 4) - { - if (threadIdx.x < 4) - { - int other = threadIdx.x + 4; - if (partials[threadIdx.x] < partials[other]) - { - partials[threadIdx.x] = partials[other]; - colMaxI[threadIdx.x] = colMaxI[other]; - } - } - __syncthreads(); - } - - if (threadIdx.x == 0) - { - for (int i = 1; i < 4 && i < m_numRows; i++) - { - if (partials[0] < partials[i]) - { - partials[0] = partials[i]; - colMaxI[0] = colMaxI[i]; - } - } - } - __syncthreads(); - - for (int i = threadIdx.x; i < m_numRows; i += 512) - { - us[IDX2C(i, blockIdx.x, m_numRows)] = (i == colMaxI[0]) ? 1 : 0; - } -} - -#if 0 -template -__global__ void _inplaceTruncateBottom( - ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - if (a[id] -__global__ void _assignTruncateBottom( - ElemType* us, - const ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - us[id] = a[id] < threshold ? threshold : a[id]; -} - -#if 0 -template -__global__ void _inplaceTruncateTop( - ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - if (a[id]>threshold) - a[id]=threshold; -} -#endif - -template -__global__ void _assignTruncateTop( - ElemType* us, - const ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - us[id] = a[id] > threshold ? threshold : a[id]; -} - -template -__global__ void _setToZeroIfAbsLessThan( - ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - if (sizeof(ElemType)==sizeof(float)) - { - if (fabsf(a[id]) -__global__ void _areEqual( - const ElemType* a, - const ElemType* b, - const CUDA_LONG N, - const ElemType threshold, - long *d_res) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - if (sizeof(ElemType)==sizeof(float)) - { - if (fabsf(a[id]-b[id]) > threshold) - { - d_res[0]=0; - } - } - else - { - if (fabs(1.0*a[id]-1.0*b[id]) > threshold) - { - d_res[0]=0; - } - } -} - -// see Matrix::TensorShuffleScaleAndAdd() for comments -template -__global__ void _tensorShuffleScaleAndAdd( - ElemType keepWeight, const ElemType* pa, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const ElemType* pb, ElemType* pc) -{ - size_t N = D * S * M * K * T; - CUDA_LONG na = blockDim.x * blockIdx.x + threadIdx.x; // input tensor of dimension (D x S x M x K x T) - if (na >= N) - return; - // recover the 5 indices from the loop counter - size_t d = na % D; - size_t s = (na / D ) % S; - size_t m = (na / D / S ) % M; - size_t k = (na / D / S / M ) % K; - size_t t = (na / D / S / M / K) % T; - // compute index for the a and b/c tensors - size_t nb = (((t * S + s) * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S x T): k/K and s/S swapped - // perform the computation - ElemType cval = keepWeight ? keepWeight * pb[nb] : 0; // if weight is 0 then don't bother to read memory (efficiency) or to multiply (NaN-safe) - cval += scaleFactor * pa[na]; - pc[nb] = cval; -} - -// see Matrix::TensorShuffleScaleAndAdd() for comments -template -__global__ void _tensorShuffleScaleAndAddRowSparse( - const ElemType* anzValues, //source nz values - const GPUSPARSE_INDEX_TYPE* aRowIndex, - const GPUSPARSE_INDEX_TYPE* aColCSCIndex, - ElemType* cnzValues, //target nz values - GPUSPARSE_INDEX_TYPE* cRowIndex, - GPUSPARSE_INDEX_TYPE* cColCSCIndex, - size_t D, size_t S, size_t M, size_t K, size_t T, - size_t nz) -{ - CUDA_LONG N = blockDim.x * blockIdx.x + threadIdx.x; // input tensor of dimension (D x S x M x K x T) - if (N < aColCSCIndex[0] || N >= aColCSCIndex[T]) - return; - - size_t col; - for (col = 0; col < T; col++) - { - if (aColCSCIndex[col + 1] > N) - break; - } - - size_t na = aRowIndex[N]; - int start = aColCSCIndex[col]; - int end = aColCSCIndex[col + 1]; - - // recover the 5 indices from the loop counter - size_t d = (na ) % D; - size_t s = (na / D ) % S; - size_t m = (na / D / S ) % M; - size_t k = (na / D / S / M ) % K; - - // compute index for the a and b/c tensors - size_t nc = ((s * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S): k/K and s/S swapped - - int rowIdx = start; - for (size_t j = start; j < end; j++) - { - // recover the 5 indices from the loop counter - size_t na_i = aRowIndex[j]; - size_t d_i = (na_i ) % D; - size_t s_i = (na_i / D ) % S; - size_t m_i = (na_i / D / S ) % M; - size_t k_i = (na_i / D / S / M ) % K; - - // compute index for the a and b/c tensors - size_t nc_i = ((s_i * M + m_i) * K + k_i) * D + d_i; // output tensor of dimension (D x K x M x S): k/K and s/S swapped - if (nc_i < nc) - { - rowIdx++; - } - } - - cnzValues[rowIdx] = anzValues[N]; - cRowIndex[rowIdx] = nc; - - if (N == 0) - { - for (int i = 0; i <= T; i++) - { - cColCSCIndex[i] = aColCSCIndex[i]; - } - } -} - -template -__global__ void _hasElement( - const ElemType* a, - const CUDA_LONG N, - ElemType *d_res /// [2x1] vector. The first is the value to be compared and the second is the 0/1 to return - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - if (a[id] == d_res[0]) - { - d_res[1] = 1; - } -} - -template -__global__ void _setDiagonalValue( - ElemType* a, - const ElemType v, - const CUDA_LONG N, - const CUDA_LONG ld) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[IDX2C(id,id,ld)]=v; - -} - -template -__global__ void _setDiagonalValueFromVector( - ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[IDX2C(id,id,N)]=b[id]; -} - -template -__global__ void _adagrad( - ElemType* a, - ElemType* d_v, - const CUDA_LONG N, - ElemType* multipliers) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - const ElemType floor = 1e-16f; - - a[id] += d_v[id] * d_v[id]; - ElemType temp = sqrt(a[id]+floor); - d_v[id] /= temp; - - if (multipliers != nullptr) - multipliers[id] = 1/temp; -} - -template -__global__ void _adagrad4BlockSparse( - ElemType* a, //dense - const size_t numRows, //number of rows in a and in d_v - ElemType* d_v, //block sparse - const GPUSPARSE_INDEX_TYPE* blockId2ColOrRow, - ElemType* multipliers, - const bool colMajor, - const size_t len, //major dim, numRows in colMajor and numcols in rowMajor - const CUDA_LONG N) //total number of non-zero values -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - const ElemType floor = 1e-16f; - CUDA_LONG blockid = id / len; - CUDA_LONG row = colMajor ? id - blockid*len : blockId2ColOrRow[blockid]; - CUDA_LONG col = colMajor ? blockId2ColOrRow[blockid] : id - blockid*len; - - size_t indexInA = row + col*numRows; - a[indexInA] += d_v[id] * d_v[id]; - ElemType temp = sqrt(a[indexInA] + floor); - d_v[id] /= temp; - - if (multipliers != nullptr) - multipliers[id] = 1 / temp; -} - -template -__global__ void _fsadagrad(CUDA_LONG size, ElemType* grad, ElemType* smoothAda, ElemType* smoothMom, ElemType* val, - ElemType lr, ElemType mom, ElemType adaWeight, ElemType adaMul) -{ - CUDA_LONG idx = blockIdx.x * blockDim.x + threadIdx.x; - CUDA_LONG stride = blockDim.x * gridDim.x; - for (; idx < size; idx += stride) - { - ElemType g = grad[idx]; - ElemType adaSqr = adaWeight * smoothAda[idx] + (1.0f - adaWeight) * g * g; - smoothAda[idx] = adaSqr; - if (adaSqr != 0.0f) - { - ElemType w; - if (sizeof(ElemType) == sizeof(double)) - { - w = adaMul * rsqrt(adaSqr); - } - else - { - w = adaMul * rsqrtf(adaSqr); - } - - if (w > 10.0f) - w = 10.0f; - g *= w; - } - - if (mom > 0.0f) - { - g = mom * smoothMom[idx] + (1.0f - mom) * g; - smoothMom[idx] = g; - } - - g *= lr; - val[idx] -= g; - } -} - -template -__global__ void _rmsprop_init( - ElemType* avars, ElemType* signs, ElemType* steps, - ElemType* curr_grad, - const CUDA_LONG N - ) -{ - CUDA_LONG i = blockDim.x * blockIdx.x + threadIdx.x; - if (i >= N) - return; - - ElemType tmp = curr_grad[i]; - avars[i] = tmp * tmp; - signs[i] = ElemType(0.0); - steps[i] = ElemType(0.02); -} - -template -__global__ void _rmsprop( - ElemType* avars, ElemType* signs, ElemType* steps, - ElemType* curr_grad, - const CUDA_LONG N, - ElemType RMS_GAMMA,ElemType RMS_WGT_INC,ElemType RMS_WGT_MAX,ElemType RMS_WGT_DEC,ElemType RMS_WGT_MIN, - ElemType floor, - ElemType *upd_gpu, - ElemType* multipliers - ) -{ - CUDA_LONG i = blockDim.x * blockIdx.x + threadIdx.x; - if (i >= N) - return; - - avars[i] = RMS_GAMMA * avars[i] + (ElemType(1.0)-RMS_GAMMA)* (curr_grad[i] * curr_grad[i]); - - //// grad sign base 3: 0->neg, 1->zero, 2->pos - //const int grad_sign = 1 + (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); - - //// signs[i] contains three consecutive grad_sign - //signs[i] = 3*(int(signs[i]) % 9) + grad_sign; - - //// update according to the following table: - //// (!pos,!pos,!pos) or (!neg,!neg,!neg): RMS_WGT_INC - //// (!neg,!neg,neg) or (!pos,!pos,pos): RMS_WGT_DEC - //// otherwise: no action - - //switch(int(upd_gpu[int(signs[i])])) - //{ - //case 0: - // steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); - // break; - //case 2: - // steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); - // break; - //} - //curr_grad[i] *= steps[i] / sqrt(avars[i] + floor); - - const int grad_sign = (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); - - if( signs[i] * grad_sign > 0 ) - steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); - else - steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); - - ElemType temp = steps[i] / sqrt(avars[i] + floor); - curr_grad[i] *= temp; - signs[i] = grad_sign; - - if (multipliers != nullptr) - multipliers[i] = temp; -} - -template -__global__ void _rescaleToRange( - ElemType* a, - const CUDA_LONG N, - const ElemType low, - const ElemType high) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]=a[id]*(high-low)+low; -} - -template -__global__ void _setMaskAndScale( - ElemType* a, - const CUDA_LONG N, - const ElemType maskRate, - const ElemType scaleValue) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - a[id]=a[id]<=maskRate? 0 : scaleValue; -} - -template -__global__ void _vectorSum( - ElemType* c, //output - const ElemType* a, //input - const CUDA_LONG n, //a.numRows - const CUDA_LONG m, //a.numCols - const bool isColWise) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if ((isColWise && id >= m) || (!isColWise && id >= n)) - return; - - ElemType sum = 0; - - if (isColWise) - { - for (CUDA_LONG i = 0; i -__global__ void _vectorNorm1( - ElemType* c, //output - const ElemType* a, //input - const CUDA_LONG n, //a.numRows - const CUDA_LONG m, //a.numCols - const bool isColWise) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if ((isColWise && id>=m)||(!isColWise && id>=n)) - return; - - ElemType sum = 0; - - if (isColWise) - { - for (CUDA_LONG i=0;i -__global__ void _vectorNorm2( - ElemType* c, //output - const ElemType* a, //input - const CUDA_LONG N, //a.GetNumRows(); - const CUDA_LONG M, //a.GetNumCols(); - const bool isColWise) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if ((isColWise && id>=M) || (!isColWise && id>=N)) - return; - - ElemType sum = 0; - if (isColWise) - { - for (CUDA_LONG i=0;i -__global__ void _convertInd2ValsAdjustInd( - ElemType* inds, - const ElemType* M, - ElemType* vals, - const CUDA_LONG n, //number of cols - const CUDA_LONG m, //number of rows - const bool isColWise) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if ((isColWise && id>=n)||(!isColWise && id>=m)) - return; - inds[id]--; - if (isColWise) - { - vals[id]=M[IDX2C((int)inds[id],id,m)]; - } - else - { - vals[id]=M[IDX2C(id,(int)inds[id],m)]; - } -} - - - //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) -template -__global__ void _assignPackedConvolutionInput(ElemType * packedMatrix, const ElemType * inputSubBatch, const CUDA_LONG batchSize, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputChannels, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputChannels, - const CUDA_LONG kernelWidth, const CUDA_LONG kernelHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample, const bool zeroPadding) -{ - const CUDA_LONG inputHeightTimesChannel = inputHeight * inputChannels; - const size_t inputDim = inputWidth*inputHeightTimesChannel; - - const CUDA_LONG idall = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = idall / inputDim; - if (sample >= batchSize) - return; - - const CUDA_LONG id = idall % inputDim; - const CUDA_LONG y = id / inputHeightTimesChannel; //inputCol - - const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; - const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * inputChannels) - // IN_ELEM_COLPOS = sample - - const CUDA_LONG nXC = id % inputHeightTimesChannel; //channel + inputRow*inputChannels - const CUDA_LONG x = nXC / inputChannels; //inputRow - const CUDA_LONG c = nXC % inputChannels; //channel - - ElemType currentInputValue = inputSubBatch[id + sample*inputDim]; - - CUDA_LONG x0 = 0, y0 = 0, x1 = 0, y1 = 0; - if (zeroPadding) - { - const CUDA_LONG halfKernelWidth = kernelWidth/2; - const CUDA_LONG halfKernelHeight = kernelHeight/2; - - x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1.0f+halfKernelHeight)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in - x1 = x+halfKernelHeight-x0*verticalSubsample; //first posxInKernel - y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1.0f+halfKernelWidth)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in - y1 = y+halfKernelWidth-y0*horizontalSubsample; //first posyInKernel - } - else - { - x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in - x1 = x-x0*verticalSubsample; //first posxInKernel - y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in - y1 = y-y0*horizontalSubsample; //first posyInKernel - } - - // PACK_ELEM_ROWPOS(channel, posxInKernel, posyInKernel) = (channel * kernelWidth * kernelHeight + posxInKernel + posyInKernel * kernelHeight) - // PACK_ELEM_COLPOS(sample, wrow, wcol) = (sample*packedInputColsPerSample + outputHeight*wcol + wrow - - CUDA_LONG packColBase = sample*packedInputColsPerSample + y0*outputHeight; - for (CUDA_LONG wcol = y0, posyInKernel = y1; wcol < outputWidth && posyInKernel>=0; wcol++, posyInKernel -= horizontalSubsample) - { - CUDA_LONG packRowBase = c * kernelWidth * kernelHeight + posyInKernel * kernelHeight; - for (CUDA_LONG wrow = x0, posxInKernel = x1; wrow < outputHeight && posxInKernel>=0; wrow++, posxInKernel -= verticalSubsample) - { - const CUDA_LONG packRow = packRowBase + posxInKernel; - const CUDA_LONG packCol = packColBase + wrow; - packedMatrix[packRow + packCol*packedInputRows] = currentInputValue; - } - packColBase += outputHeight; - } -} - - //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) -template -__global__ void _unpackConvolutionInput(const ElemType * packedMatrix, ElemType * inputSubBatch, const CUDA_LONG batchSize, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputChannels, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputChannels, - const CUDA_LONG kernelWidth, const CUDA_LONG kernelHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample, const bool zeroPadding) -{ - const CUDA_LONG inputHeightTimesChannel = inputHeight * inputChannels; - const size_t inputDim = inputWidth*inputHeightTimesChannel; - - const CUDA_LONG idall = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = idall / inputDim; - if (sample >= batchSize) - return; - - const CUDA_LONG id = idall % inputDim; - const CUDA_LONG y = id / inputHeightTimesChannel; //inputCol - - const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; - const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * inputChannels) - // IN_ELEM_COLPOS = sample - - const CUDA_LONG nXC = id % inputHeightTimesChannel; //channel + inputRow*inputChannels - const CUDA_LONG x = nXC / inputChannels; //inputRow - const CUDA_LONG c = nXC % inputChannels; //channel - - CUDA_LONG x0 = 0, y0 = 0, x1 = 0, y1 = 0; - if (zeroPadding) - { - const CUDA_LONG halfKernelWidth = kernelWidth/2; - const CUDA_LONG halfKernelHeight = kernelHeight/2; - - x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1.0f+halfKernelHeight)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in - x1 = x+halfKernelHeight-x0*verticalSubsample; //first posxInKernel - y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1.0f+halfKernelWidth)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in - y1 = y+halfKernelWidth-y0*horizontalSubsample; //first posyInKernel - } - else - { - x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in - x1 = x-x0*verticalSubsample; //first posxInKernel - y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in - y1 = y-y0*horizontalSubsample; //first posyInKernel - } - - // PACK_ELEM_ROWPOS(channel, posxInKernel, posyInKernel) = (channel * kernelWidth * kernelHeight + posxInKernel + posyInKernel * kernelHeight) - // PACK_ELEM_COLPOS(sample, wrow, wcol) = (sample*packedInputColsPerSample + outputHeight*wcol + wrow - - ElemType currentInputValue = inputSubBatch[id + sample*inputDim]; - CUDA_LONG packColBase = sample*packedInputColsPerSample + y0*outputHeight; - for (CUDA_LONG wcol = y0, posyInKernel = y1; wcol < outputWidth && posyInKernel>=0; wcol++, posyInKernel -= horizontalSubsample) - { - CUDA_LONG packRowBase = c * kernelWidth * kernelHeight + posyInKernel * kernelHeight; - for (CUDA_LONG wrow = x0, posxInKernel = x1; wrow < outputHeight && posxInKernel>=0; wrow++, posxInKernel -= verticalSubsample) - { - const CUDA_LONG packRow = packRowBase + posxInKernel; - const CUDA_LONG packCol = packColBase + wrow; - currentInputValue += packedMatrix[packRow + packCol*packedInputRows]; - } - packColBase += outputHeight; - } - - inputSubBatch[id + sample*inputDim] = currentInputValue; -} - -template -__global__ void _assignMaxPoolingResult(ElemType * outputBatch, const ElemType * inputBatch, const CUDA_LONG batchSize, const CUDA_LONG channels, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, - const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) -{ - const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = outputIndex / outputSizePerSample; - if (sample >= batchSize) - return; - - const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; - const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; - const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; - - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - - const CUDA_LONG y = outputIndexWithinSample / outputHeightTimesChannel; //wcol - const CUDA_LONG nXC = outputIndexWithinSample % outputHeightTimesChannel; //channel + wrow*channels - const CUDA_LONG x = nXC / channels; //wrow - const CUDA_LONG c = nXC % channels; //channel - - const ElemType *inputBatchBase4Sample = inputBatch + sample*inputSizePerSample; - register ElemType maxVal = -FLT_MAX; - const CUDA_LONG rowInWindowBase = (x*verticalSubsample + y*horizontalSubsample*inputHeight)*channels+c; - for (CUDA_LONG colInWindow=0; colInWindow -__global__ void _addMaxPoolingGradient(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, const ElemType * inputBatch, const ElemType * outputBatch, - const CUDA_LONG batchSize, const CUDA_LONG channels, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, - const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) -{ - const CUDA_LONG inputIndex = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = inputIndex / inputSizePerSample; - if (sample >= batchSize) - return; - - const CUDA_LONG inputIndexWithinSample = inputIndex % inputSizePerSample; - - const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; - const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - - const CUDA_LONG y = inputIndexWithinSample / inputHeightTimesChannel; //col in input - const CUDA_LONG nXC = inputIndexWithinSample % inputHeightTimesChannel; //channel + row*chanels - const CUDA_LONG x = nXC / channels; //row in input - const CUDA_LONG c = nXC % channels; //channel - - CUDA_LONG startOutX = max(0.0f, ceil((x-(ElemType)windowHeight+1)/ (ElemType)verticalSubsample)); //inclusive start - CUDA_LONG endOutX = (x/verticalSubsample < outputHeight-1)? x/verticalSubsample : outputHeight-1; //inclusive end - CUDA_LONG startOutY = max(0.0f, ceil((y-(ElemType)windowWidth+1)/(ElemType)horizontalSubsample)); //inclusive start - CUDA_LONG endOutY = (y/horizontalSubsample < outputWidth-1)? y/horizontalSubsample : outputWidth-1; //inclusive end - - - ElemType *inputGradientBatchBase4Sample = inputGradientBatch + sample*inputSizePerSample; - const ElemType *outputGradientBatchBase4Sample = outputGradientBatch + sample*outputSizePerSample; - const ElemType * outputBatchBase4Sample = outputBatch + sample*outputSizePerSample; - - ElemType inputValue = inputBatch[inputIndexWithinSample + sample*inputSizePerSample]; - for (CUDA_LONG outY=startOutY; outY<=endOutY; outY++) - { - for (CUDA_LONG outX=startOutX; outX<=endOutX; outX++) - { - CUDA_LONG outputIndex = outY * outputHeightTimesChannel + outX * channels + c; - if (inputValue == outputBatchBase4Sample[outputIndex]) - inputGradientBatchBase4Sample[inputIndexWithinSample] += outputGradientBatchBase4Sample[outputIndex]; - } - } -} -template -__global__ void _assignAveragePoolingResult(ElemType * outputBatch, const ElemType * inputBatch, const CUDA_LONG batchSize, const CUDA_LONG channels, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, - const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) -{ - const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = outputIndex / outputSizePerSample; - if (sample >= batchSize) - return; - - const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; - const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; - const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; - - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - - const CUDA_LONG y = outputIndexWithinSample / outputHeightTimesChannel; //wcol - const CUDA_LONG nXC = outputIndexWithinSample % outputHeightTimesChannel; //channel + wrow*channels - const CUDA_LONG x = nXC / channels; //wrow - const CUDA_LONG c = nXC % channels; //channel - - const ElemType *inputBatchBase4Sample = inputBatch + sample*inputSizePerSample; - - register ElemType average = 0; - const CUDA_LONG rowInWindowBase = (x*verticalSubsample + y*horizontalSubsample*inputHeight)*channels+c; - for (CUDA_LONG colInWindow=0; colInWindow -__global__ void _addAveragePoolingGradient(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, - const CUDA_LONG batchSize, const CUDA_LONG channels, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, - const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) -{ - const CUDA_LONG inputIndex = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = inputIndex / inputSizePerSample; - if (sample >= batchSize) - return; - - const CUDA_LONG inputIndexWithinSample = inputIndex % inputSizePerSample; - - const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; - const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; - const CUDA_LONG windowSize = windowWidth * windowHeight; - - // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) - // IN_ELEM_COLPOS = sample - - // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) - // OUT_ELEM_COLPOS = sample - - const CUDA_LONG y = inputIndexWithinSample / inputHeightTimesChannel; //col in input - const CUDA_LONG nXC = inputIndexWithinSample % inputHeightTimesChannel; //channel + row*chanels - const CUDA_LONG x = nXC / channels; //row in input - const CUDA_LONG c = nXC % channels; //channel - - CUDA_LONG startOutX = max(0.0f, ceil((x-(ElemType)windowHeight+1)/ (ElemType)verticalSubsample)); //inclusive start - CUDA_LONG endOutX = (x/verticalSubsample < outputHeight-1)? x/verticalSubsample : outputHeight-1; //inclusive end - CUDA_LONG startOutY = max(0.0f, ceil((y-(ElemType)windowWidth+1)/(ElemType)horizontalSubsample)); //inclusive start - CUDA_LONG endOutY = (y/horizontalSubsample < outputWidth-1)? y/horizontalSubsample : outputWidth-1; //inclusive end - - ElemType *inputGradientBatchBase4Sample = inputGradientBatch + sample*inputSizePerSample; - const ElemType *outputGradientBatchBase4Sample = outputGradientBatch + sample*outputSizePerSample; - - for (CUDA_LONG outY=startOutY; outY<=endOutY; outY++) - { - for (CUDA_LONG outX=startOutX; outX<=endOutX; outX++) - { - CUDA_LONG outputIndex = outY * outputHeightTimesChannel + outX * channels + c; - inputGradientBatchBase4Sample[inputIndexWithinSample] += outputGradientBatchBase4Sample[outputIndex]/windowSize; - } - } -} - -template -__global__ void _addMaxPoolingGradientLoopOut(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, const ElemType * inputBatch, const ElemType * outputBatch, - const CUDA_LONG batchSize, const CUDA_LONG channels, - const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, - const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, - const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) -{ - const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG sample = outputIndex / outputSizePerSample; - if (sample >= batchSize) - return; - - const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; - const CUDA_LONG inputWidthTimesChannel = inputWidth * channels; - const CUDA_LONG outputWidthTimesChannel = outputWidth * channels; - const CUDA_LONG y = outputIndexWithinSample / outputWidthTimesChannel; - const CUDA_LONG nXC = outputIndexWithinSample % outputWidthTimesChannel; - const CUDA_LONG x = nXC / channels; - const CUDA_LONG c = nXC % channels; - - const CUDA_LONG offset0 = sample*inputSizePerSample + y*verticalSubsample*inputWidthTimesChannel + x*horizontalSubsample*channels; - const ElemType *pCurWindow4Input = inputBatch + offset0; // pooling to current window's first input pixel - ElemType *pCurWindow4InGradient = inputGradientBatch + offset0; - for (CUDA_LONG yy=0; yy -__global__ void _addElementProductOf( - ElemType* us, - const ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - us[id]+=(a[id]*b[id]); -} - -template -__global__ void _columnElementMultiplyWith( - ElemType* us, - const ElemType* a, - const CUDA_LONG N, //a.GetNumRows(); - const CUDA_LONG M) //us.GetNumCols(); -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; - //_a[threadIdx.x]=a[id]; - ElemType mul=a[id]; - for (CUDA_LONG j=0;j -__global__ void _rowElementMultiplyWith( - ElemType* us, - const ElemType* a, - const CUDA_LONG N, //us.GetNumRows(); - const CUDA_LONG M) //a.GetNumCols(); -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=M) - return; - - //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; - //_a[threadIdx.x]=a[id]; - ElemType mul=a[id]; - for (CUDA_LONG i=0;i -__global__ void _rowElementDivideBy( - ElemType* us, - const ElemType* a, - const CUDA_LONG N, //us.GetNumRows(); - const CUDA_LONG M) //a.GetNumCols(); -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= M) - return; - - //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; - //_a[threadIdx.x]=a[id]; - ElemType v = a[id]; - if (v >= 0 && v < EPS_IN_INVERSE) - v = EPS_IN_INVERSE; - else if (v < 0 && v > -EPS_IN_INVERSE) - v = (-EPS_IN_INVERSE); - - for (CUDA_LONG i = 0; i -__global__ void _ColumnElementDivideBy( - ElemType* us, - const ElemType* a, - const CUDA_LONG N, //a.GetNumRows(); - const CUDA_LONG M) //us.GetNumCols(); -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - - ElemType smallValue = EPS_IN_INVERSE; - - //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; - //_a[threadIdx.x]=a[id]; - ElemType v=a[id]; - for (CUDA_LONG j=0;j -smallValue) - us[IDX2C(id,j,N)] /= (-smallValue); - else if (v >=0 && v < smallValue) - us[IDX2C(id,j,N)] /= smallValue; - else - us[IDX2C(id,j,N)] /= v; - } - -} - - -template -__global__ void _innerProduct( - ElemType* c, - const ElemType* a, - const ElemType* b, - const CUDA_LONG N, //a.GetNumRows(); - const CUDA_LONG M, //a.GetNumCols(); - const bool isColWise) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if ((isColWise && id>=M) || (!isColWise && id>=N)) - return; - - ElemType sum = 0; - CUDA_LONG index; - if (isColWise) - { - for (CUDA_LONG i=0; i -__global__ void _assignSignOf( - ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - ElemType v = b[id]; - a[id] = (v == (ElemType)0? (ElemType)0 : (v > 0? (ElemType)1 : (ElemType)(-1))); -} - -template -__global__ void _addSignOf( - ElemType* a, - const ElemType* b, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - ElemType v = b[id]; - a[id] += (v == (ElemType)0? (ElemType)0 : (v > 0? (ElemType)1 : (ElemType)(-1))); -} - -// This function processes 1 column per block. this function needs 512 threads -template -__global__ void _vectorMaxMinReduce( - const ElemType* us, - ElemType* Indexes, - ElemType* Values, - const CUDA_LONG numRows, - const CUDA_LONG numCols) -{ - //we first find max per column - __shared__ ElemType partials[512]; - __shared__ int partialsInd[512]; - if (IsMax) - { - partials[threadIdx.x]=-10000000; - } - else - { - partials[threadIdx.x]=10000000; - } - partialsInd[threadIdx.x]=-1; - - for (int i = threadIdx.x; i < numRows; i += 512) - { - if ((IsMax ? (us[IDX2C(i, blockIdx.x, numRows)] > partials[threadIdx.x]) : (us[IDX2C(i, blockIdx.x, numRows)] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = us[IDX2C(i, blockIdx.x, numRows)]; - partialsInd[threadIdx.x]=i; - } - } - __syncthreads(); - - if (threadIdx.x < 256) - { - if ((IsMax ? (partials[threadIdx.x + 256] > partials[threadIdx.x]) : (partials[threadIdx.x + 256] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 256]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 256]; - } - } - __syncthreads(); - - if (threadIdx.x < 128) - { - if ((IsMax ? (partials[threadIdx.x + 128] > partials[threadIdx.x]) : (partials[threadIdx.x + 128] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 128]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 128]; - } - } - __syncthreads(); - - if (threadIdx.x < 64) - { - if ((IsMax ? (partials[threadIdx.x + 64] > partials[threadIdx.x]) : (partials[threadIdx.x + 64] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 64]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 64]; - } - } - __syncthreads(); - - if (threadIdx.x < 32) - { - if ((IsMax ? (partials[threadIdx.x + 32] > partials[threadIdx.x]) : (partials[threadIdx.x + 32] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 32]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 32]; - } - } - __syncthreads(); - - if (threadIdx.x < 16) - { - if ((IsMax ? (partials[threadIdx.x + 16] > partials[threadIdx.x]) : (partials[threadIdx.x + 16] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 16]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 16]; - } - } - __syncthreads(); - - if (threadIdx.x < 8) - { - if ((IsMax ? (partials[threadIdx.x + 8] > partials[threadIdx.x]) : (partials[threadIdx.x + 8] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 8]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 8]; - } - } - __syncthreads(); - - if (threadIdx.x < 4) - { - if ((IsMax ? (partials[threadIdx.x + 4] > partials[threadIdx.x]) : (partials[threadIdx.x + 4] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) - { - partials[threadIdx.x] = partials[threadIdx.x + 4]; - partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 4]; - } - } - __syncthreads(); - - if (threadIdx.x == 0) - { - ElemType mx = partials[0]; - int ind = partialsInd[0]; - if ((IsMax ? (mx < partials[1]) : (mx > partials[1])) || (ind == -1)) - { - mx = partials[1]; - ind = partialsInd[1]; - } - if ((IsMax ? (mx < partials[2]) : (mx > partials[2])) || (ind == -1)) - { - mx = partials[2]; - ind = partialsInd[2]; - } - if ((IsMax ? (mx < partials[3]) : (mx > partials[3])) || (ind == -1)) - { - mx = partials[3]; - ind = partialsInd[3]; - } - Values[blockIdx.x] = mx; - Indexes[blockIdx.x] = ind; - } -} - -template -__global__ void _vectorMax( - const ElemType* us, - ElemType* maxIndexes, - ElemType* maxValues, - const CUDA_LONG m, //number of rows - const CUDA_LONG n, //number of cols - const bool isColWise) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG maxInd = -1; - ElemType maxVal = -100000; - - if (isColWise) - { - if (id>=n) - return; - - for (CUDA_LONG i=0;i=maxVal) - { - maxInd = i; - maxVal = us[IDX2C(i,id,m)]; - } - } - } - else - { - if (id>=m) - return; - - for (CUDA_LONG j=0;j=maxVal) - { - maxInd = j; - maxVal = us[IDX2C(id,j,m)]; - } - } - } - maxIndexes[id]=maxInd; - maxValues[id]=maxVal; -} - -template -__global__ void _vectorMin( - const ElemType* us, - ElemType* minIndexes, - ElemType* minValues, - const CUDA_LONG m, //number of rows - const CUDA_LONG n, //number of cols - const bool isColWise) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG minInd = -1; - ElemType minVal = -100000; - - if (isColWise) - { - if (id>=n) - return; - - for (CUDA_LONG i=0;i=m) - return; - - for (CUDA_LONG j=0;j -__global__ void _matrixMatrixAddOnCuda( - const ElemType alpha, - const ElemType *a, - const ElemType *b, - ElemType *c, - const CUDA_LONG N - ) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - c[id] = alpha * a[id] + b[id]; -} - -template -__global__ void _matrixVectorRowWiseAddWithThreadPerElem( - const ElemType *a, - const ElemType *b, - ElemType* us, - ElemType alpha, - const CUDA_LONG m, //number of rows - const CUDA_LONG n) //number of cols -{ - CUDA_LONG N = m*n; // used in CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) macro - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - - CUDA_LONG col = id / m; - - us[id] = alpha*a[col] + b[id]; -} - -//this implementation uses more threads but also more memory access -template -__global__ void _matrixVectorColumnWiseAddWithThreadPerElem( - const ElemType *a, - const ElemType *b, - ElemType* us, - ElemType alpha, - const CUDA_LONG m, //number of rows - const CUDA_LONG n) //number of cols -{ - CUDA_LONG N = m*n; // used in CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) macro - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); - - CUDA_LONG col = id / m; - CUDA_LONG row = id - col*m; - - us[id] = alpha*a[row] + b[id]; -} - -template -__global__ void _matrixVectorColumnWiseAddWithThreadPerRow( - const ElemType *a, - ElemType* us, - ElemType alpha, - const CUDA_LONG m, //number of rows - const CUDA_LONG n) //number of cols -{ -#ifdef VALIDATION - if (blockDim.x * blockIdx.x + threadIdx.x == 0) - { - printf("** _matrixVectorColumnWiseAdd on device:\na = %p, us = %p, alpha = %f, m = %ld, n = %ld\n", - a,us,alpha,m,n); - printf("us[0] = %f\n", us[0]); - printf("a[0] = %f\n", a[0]); - } -#endif - int id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=m) - return; - ElemType tmp = a[id]; -#ifdef VALIDATION - printf(" a[%d] = %f\n", id, tmp); -#endif - for (CUDA_LONG j = 0; j < n; ++j ) - { - us[j*m+id] += alpha*tmp; - } - -} - - -template -__global__ void _matrixVectorColumnWiseAddBlockPerRow( - const ElemType* a, - ElemType* us, - ElemType alpha, - const CUDA_LONG m, //number of rows - const CUDA_LONG n) //number of cols -{ - ElemType tmp; - - if (threadIdx.x==0) - { - tmp = a[blockIdx.x]; - } - __syncthreads(); - - int loadPerThread = n/blockDim.x; - - for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? n : (threadIdx.x+1)*loadPerThread);++i) - { - us[m*blockIdx.x + i] += alpha*tmp; - } -} - - - -template -__global__ void _addScaledDifference( - ElemType alpha, - ElemType *a, - ElemType *b, - ElemType *c, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - c[id] = c[id] + (a[id]-b[id]) * (alpha); -} - -template -__global__ void _assignScaledDifference( - ElemType alpha, - ElemType *a, - ElemType *b, - ElemType *c, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - c[id] = (a[id]-b[id]) * (alpha); -} - -template -__global__ void _addScaledDifference( - ElemType *alpha, - ElemType *a, - ElemType *b, - ElemType *c, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - c[id] = c[id] + (a[id]-b[id]) * alpha[0]; -} - -template -__global__ void _assignScaledDifference( - ElemType *alpha, - ElemType *a, - ElemType *b, - ElemType *c, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - c[id] = (a[id]-b[id]) * alpha[0]; -} - -template -__global__ void _addElementToElement( - const ElemType *a, CUDA_LONG indexA, - ElemType *c, CUDA_LONG indexC) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>0) - return; - c[indexC] += a[indexA]; -} - -template -__global__ void _assignNumOfDiff( - const ElemType *a, - const ElemType *b, - ElemType *c, - CUDA_LONG N) -{ - __shared__ ElemType partialSums[1024]; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG loadPerThread = N/blockDim.x; - for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - partialSums[threadIdx.x]+=(a[i] != b[i]); - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - c[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; - } -} - - -/*template -__global__ void _assignNumOfDiff( -ElemType *a, -ElemType *b, -ElemType *c, -CUDA_LONG N) -{ -//TO DO: replace atomic operation with reduction - -__shared__ int totalSum; -if (threadIdx.x == 0) totalSum = 0; -__syncthreads(); - -int id = blockDim.x * blockIdx.x + threadIdx.x; -if (id>=N) -return; - -int localVal = (a[id] != b[id]); -atomicAdd(&totalSum, localVal); -__syncthreads(); - -c[id] = totalSum; -}*/ - -template -__global__ void _scaleArray( - ElemType alpha, - ElemType *us, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=N) - return; - us[id]=us[id]*alpha; -} - - -template -__global__ void _sparseCSRPlusDense( - ElemType alpha, - const ElemType* m_dVal, - const int* m_dRow, - const int* m_dCol, - ElemType* pArrayDev, - CUDA_LONG M) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=M) - return; - int start = m_dRow[id]; - int end = m_dRow[id+1]; - for (int _i=start;_i -__global__ void _sparseCSRElemMulDense( - const ElemType* m_dVal, - const int* m_dRow, - const int* m_dCol, - const ElemType* b, - ElemType* c, - CUDA_LONG M) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id>=M) - return; - int start = m_dRow[id]; - int end = m_dRow[id+1]; - for (int _i=start;_i -__global__ void _isValid( - const GPUSPARSE_INDEX_TYPE* rowIndex, - const GPUSPARSE_INDEX_TYPE* colCSCIndex, - const int rows, - const int cols, - const int nz, - long* d_res - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= cols || d_res[0] <= 0) - return; - - int start = colCSCIndex[id]; - int end = colCSCIndex[id + 1]; - - if (start > end) - { - if (d_res[0] > 0) - { - d_res[0] = -1; - d_res[1] = id; - d_res[2] = start; - d_res[3] = end; - } - } - else if (end > nz) - { - if (d_res[0] > 0) - { - d_res[0] = -2; - d_res[1] = id + 1; - d_res[2] = end; - d_res[3] = nz; - } - } - else - { - for (int j = start; j < end; j++) //j points to the value - { - if (rowIndex[j] >= rows) - { - if (d_res[0] > 0) - { - d_res[0] = -3; - d_res[1] = j; - d_res[2] = rowIndex[j]; - d_res[3] = rows; - break; - } - } - if (j > start && rowIndex[j] < rowIndex[j - 1]) - { - if (d_res[0] > 0) - { - d_res[0] = -4; - d_res[1] = id; - d_res[2] = j; - d_res[3] = rowIndex[j]; - break; - } - } - } - } -} - -template -__global__ void _shiftColCSCIndexFromSliceViewToAbsolute( - GPUSPARSE_INDEX_TYPE* colCSCIndex, - const int cols, - const int nz - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= cols) - return; - - colCSCIndex[id] = colCSCIndex[id] - colCSCIndex[0]; - - if (id == cols - 1) - colCSCIndex[cols] = nz; -} - -//c = alpha * op(a) * op(b) + beta*c -// TODO: This function can be further improved by loading the kernel in shared memory -template -__global__ void _dense1DConvMultSparseCSCAndWeightedAddToDense( - const int m, // rowDense - const int k, // colDense - const int n, // colSparse - const int numChannels, // input num channels - const int numSteps, // convolution num steps - const int horizontalSubsample,// convolution step size - const bool channelwise, // pixelwise for normal multiplication and channelwise for convolution operation - const ElemType alpha, - const ElemType* a, //dense - const bool transposeA, - const ElemType* bnzValues, //sparse nz values - const GPUSPARSE_INDEX_TYPE* rowIndex, - const GPUSPARSE_INDEX_TYPE* colCSCIndex, - const ElemType beta, - ElemType* c //dense target - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= m*numSteps*n) - return; - - int colInC = id / (m * numSteps); - int rowInC = id % (m * numSteps); - int stepIdx = rowInC / m; - - int start = colCSCIndex[colInC]; - int end = colCSCIndex[colInC + 1]; - - ElemType s = 0; - for (int j = start; j < end; j++) //j points to the value - { - int i = rowIndex[j] - (horizontalSubsample * numChannels * stepIdx); // offset row index by the convolution step - - if (i >= 0) - { - if (i >= k) - break; - - // Convert to channelwise index. - // This is similar to rowwise to columnwise conversion - if (channelwise) - { - int pixel = i / numChannels; - int channel = i % numChannels; - int numPixels = k / numChannels; - i = channel * numPixels + pixel; - } - - if (!transposeA) - s += a[IDX2C(rowInC % m, i, m)] * bnzValues[j]; - else - s += a[IDX2C(i, rowInC % m, k)] * bnzValues[j]; - } - } - - c[IDX2C(rowInC, colInC, m * numSteps)] = alpha * s + (beta == 0 ? 0 : beta * c[IDX2C(rowInC, colInC, m * numSteps)]); // If beta is zero then don't lookup c -} - -/// c += alpha * a * b^T -template -__global__ void _dense1DConvMultSparseCSCTransposeAndAddToDense( - int m, // rowDense - int k, // colDense - int n, // colSparse - int numChannels, // input num channels - int numSteps, // convolution num steps - int horizontalSubsample, // convolution step size - bool channelwise, // pixelwise for normal multiplication and channelwise for convolution operation - int rowInB, // row index of the sparse matrix - ElemType alpha, - const ElemType* a, //dense - bool transposeA, - const ElemType* bnzValues, //sparse nz values - const GPUSPARSE_INDEX_TYPE* rowIndex, - const GPUSPARSE_INDEX_TYPE* colCSCIndex, - ElemType* c //dense target - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= m*numSteps) - return; - - int rowInC = id; - int stepIdx = rowInC / m; - int i = rowInB - (horizontalSubsample * numChannels * stepIdx); // offset row index by the convolution step - - if (i < 0 || i >= k) - return; - - // Convert to channelwise index. - // This is similar to rowwise to columnwise conversion - if (channelwise) - { - int pixel = i / numChannels; - int channel = i % numChannels; - int numPixels = k / numChannels; - i = channel * numPixels + pixel; - } - - int start = colCSCIndex[rowInB]; - int end = colCSCIndex[rowInB + 1]; - - ElemType s = 0; - for (int j = start; j -__global__ void _reshape( - const int oldNumRows, // old row count - const int oldNumCols, // old col count - const int newNumRows, // new row count - const int newNumCols, // new col count - const GPUSPARSE_INDEX_TYPE* oldRowIndex, // old row index array - const GPUSPARSE_INDEX_TYPE* oldColumnIndex, // old column index array - GPUSPARSE_INDEX_TYPE* newRowIndex, // new row index array - GPUSPARSE_INDEX_TYPE* newColumnIndex // new column index array - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= newNumCols) - return; - - int currentCol = id; - int oldColLower = (newNumRows * currentCol) / oldNumRows; - - // initialize to the end and then scan in the right direction in the for-loop - int currentColStart = oldColumnIndex[oldNumCols]; - - for (int oldCol = oldColLower; oldCol < oldNumCols; oldCol++) - { - int start = oldColumnIndex[oldCol]; - int end = oldColumnIndex[oldCol + 1]; - bool done = false; - - for (int j = start; j < end; j++) //j points to the value - { - int oldRow = oldRowIndex[j]; - int index = (oldCol * oldNumRows + oldRow); - int newCol = index / newNumRows; - int newRow = index % newNumRows; - - if (newCol == currentCol) - newRowIndex[j] = newRow; - - if (newCol >= currentCol && currentColStart > j) - currentColStart = j; - - if (newCol > currentCol) - { - done = true; - break; - } - } - - if (done) - break; - } - - newColumnIndex[currentCol] = currentColStart; - - if (currentCol == (newNumCols - 1)) - newColumnIndex[newNumCols] = oldColumnIndex[oldNumCols]; // set end pointer -} - -//called before _determineBlockIds and _denseMulSparseCSCTransposeToSparseBlockCol to determine which columns have values and -//what's the mapping from the column id in the resulted SparseBlockCol format to the column id in the dense format -//input: rowIndexes: the row indexes of the CSC sparse matrix to be multiplied with -//blockIDs: the blockID mapping in the resulting matrix; -//nnz: number of nonzero value or the size of rowIndexes; -template -__global__ void _findColsWithValues( - const GPUSPARSE_INDEX_TYPE* rowIndexes, GPUSPARSE_INDEX_TYPE* blockIds, const size_t nnz) -{ - const size_t index = blockIdx.x * blockDim.x + threadIdx.x; - if (index >= nnz) - return; - - blockIds[rowIndexes[index]] = 1; //this row has value. -} - -//called before _denseMulSparseCSCTransposeToSparseBlockCol and after _findColsWithValuesto determine which columns have values and -//what's the mapping from the column id in the resulted SparseBlockCol format to the column id in the dense format -//input: rowIndexes: the row indexes of the CSC sparse matrix to be multiplied with -//blockId2Col: the blockID to colum id mapping in the resulting matrix; -//col2BlockId: the col2BlockId to blockID mapping in the resulting matrix; -//numCols: number of columns in the resulting matrix or the size of blockIDs -//blockSize: return the blockSize with values, *blockSize must be zero before passed in. -template -__global__ void _determineBlockIds( - GPUSPARSE_INDEX_TYPE* blockId2Col, GPUSPARSE_INDEX_TYPE*col2BlockId, const size_t numCols, size_t* blockSize) -{ - const size_t index = blockIdx.x * blockDim.x + threadIdx.x; - if (index >= numCols) - return; - - size_t blockIndex = numCols; - if (blockId2Col[index] > 0) - { - blockIndex = atomicAdd((unsigned int *)blockSize, (unsigned int)1); - col2BlockId[index] = blockIndex; - } - - __syncthreads(); - - if (blockIndex < numCols) - blockId2Col[blockIndex] = index; -} - -// backward pass from hidden layer to feature weight -//result (sparse BlockCol)= alpha * (lhs (dense) X rhs^T (sparse CSC) -//assume resultValues are 0-initialized -template -__global__ void _denseMulSparseCSCTransposeToSparseBlockCol2( - const ElemType alpha, - const ElemType* lhsValues, - const size_t numRowsLhs, - const size_t numColsRhs, - const ElemType* rhsNZValues, - const GPUSPARSE_INDEX_TYPE* rhsRows, - const GPUSPARSE_INDEX_TYPE* rhsCols, - const GPUSPARSE_INDEX_TYPE* col2blockIds, - ElemType* resultValues) -{ - const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG lhsCol = index / numRowsLhs; //rhsCol == lhsCol - if (lhsCol >= numColsRhs) - return; - const CUDA_LONG lhsRow = index - numRowsLhs*lhsCol; //resultRow == lhsRow - - //each thread handles one [row, col] combination - ElemType lhsValue = alpha*lhsValues[IDX2C(lhsRow, lhsCol, numRowsLhs)]; - - CUDA_LONG start = rhsCols[lhsCol]; //rhsCol == lhsCol - CUDA_LONG end = rhsCols[lhsCol + 1]; - - for (CUDA_LONG p = start; p < end; p++) - { - CUDA_LONG rhsRow = rhsRows[p]; - ElemType rhsVal = rhsNZValues[p]; - CUDA_LONG resultCol = col2blockIds[rhsRow]; //resultCol == rhsRow maps to columnid - - //assume resultValues are 0-initialized - atomicAdd(&resultValues[IDX2C(lhsRow, resultCol, numRowsLhs)], lhsValue * rhsVal); - } -} - -// backward pass from hidden layer to feature weight -//result (sparse BlockCol)= alpha * (lhs (dense) X rhs^T (sparse CSC) -//assume resultValues are 0-initialized -template -__global__ void _denseMulSparseCSCTransposeToSparseBlockCol( - const ElemType alpha, - const ElemType* lhsValues, - const size_t numRowsLhs, - const size_t numColsRhs, - const ElemType* rhsNZValues, - const GPUSPARSE_INDEX_TYPE* rhsRows, - const GPUSPARSE_INDEX_TYPE* rhsCols, - const GPUSPARSE_INDEX_TYPE* rhsRowIdx, - ElemType* resultValues, - GPUSPARSE_INDEX_TYPE* resultBlockIds) -{ - const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; - const CUDA_LONG lhsCol = index / numRowsLhs; //rhsCol == lhsCol - if (lhsCol >= numColsRhs) - return; - const CUDA_LONG lhsRow = index - numRowsLhs*lhsCol; //resultRow == lhsRow - - //each thread handles one [row, col] combination - ElemType lhsValue = alpha*lhsValues[IDX2C(lhsRow, lhsCol, numRowsLhs)]; - - CUDA_LONG start = rhsCols[lhsCol]; //rhsCol == lhsCol - CUDA_LONG end = rhsCols[lhsCol + 1]; - - for (CUDA_LONG p = start; p < end; p++) - { - CUDA_LONG rhsRow = rhsRows[p]; - ElemType rhsVal = rhsNZValues[p]; - CUDA_LONG resultCol = rhsRowIdx[p]; //resultCol == rhsRow maps to columnid - resultBlockIds[resultCol] = rhsRow; //indicate which colmn it actually points to - - //assume resultValues are 0-initialized - atomicAdd(&resultValues[IDX2C(lhsRow, resultCol, numRowsLhs)], lhsValue * rhsVal); - } -} - - -// gradients update -template -__global__ void _scaleSparseBlockAndAddToDense( - const ElemType alpha, - const bool blockCol, //true if blockRow - const size_t numRows, - const size_t numCols, - const size_t numBlocks, - const ElemType* lhsValues, //lhs is blockCol or blockRow - const GPUSPARSE_INDEX_TYPE* blockIds, - ElemType* rhs) -{ - const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; - CUDA_LONG row, col; - if (blockCol) - { - const CUDA_LONG blockId = index / numRows; - if (blockId >= numBlocks) - return; - row = index - numRows* blockId; - col = blockIds[blockId]; - } - else - { - const CUDA_LONG blockId = index / numCols; - if (blockId >= numBlocks) - return; - col = index - numCols* blockId; - row = blockIds[blockId]; - } - rhs[IDX2C(row, col, numRows)] += alpha * lhsValues[index]; -} - -// compute predictions in cross entory node -template -__global__ void _computePrediction( - int nv, - const ElemType* a, - int numrows, - const ElemType* weight, - int nrs, - int labelSize, - const GPUSPARSE_INDEX_TYPE* labelRow, - const size_t* block2Id, - const ElemType* cls, - const ElemType* idx2cls, - ElemType* val, - GPUSPARSE_INDEX_TYPE* row, - GPUSPARSE_INDEX_TYPE* pb) -{ - // get label block id - int id = -1; - int offset = -1; - for(int i = 1; i < labelSize; i++) - { - if (blockIdx.x < block2Id[i]) - { - id = i-1; - offset = blockIdx.x - block2Id[i-1]; - break; - } - } - if( id == -1) - { - id = labelSize-1; - offset = blockIdx.x - block2Id[labelSize-1]; - } - - int t = labelRow[id]; - int iStt; - int iEnd; - if(t < nv) - { - int clsid = idx2cls[t]; - iStt = cls[IDX2C(0, clsid, 2)]; - iEnd = cls[IDX2C(1, clsid, 2)]; - } - else - { - iStt = nv; - iEnd = nrs; - } - int i = iStt + offset; - int j = id /2; - - int loadPerThread = (numrows+blockDim.x-1)/blockDim.x; - int tStart = loadPerThread * threadIdx.x; - int tEnd = min((int)numrows, loadPerThread + tStart); - - ElemType v = 0.0; - for (int h = tStart; h < tEnd; h++) - { - v += weight[IDX2C(i,h,nrs)] * a[IDX2C(h,j,numrows)]; - } - atomicAdd(&val[blockIdx.x], v); - row[blockIdx.x] = i; - - if(blockIdx.x == 0 && threadIdx.x == 0) - pb[0] = 0; - - if((threadIdx.x == 0) && (i == iEnd-1) && (i >= nv)) - pb[j+1] = blockIdx.x+1; -} - -// normalize predictions in cross entropy node -template -__global__ void _normalizePrediction( - const size_t labelSize, - const size_t expandedLabelSize, - const GPUSPARSE_INDEX_TYPE* labelRow, - const size_t* block2Id, - const GPUSPARSE_INDEX_TYPE* row, - ElemType* val, - ElemType* entropyScore) -{ - __shared__ ElemType partials[512]; - partials[threadIdx.x] = 0; - - int p = blockIdx.x; - int t = labelRow[p]; - int start = block2Id[p]; - int end; - if(p == labelSize -1) - { - end = expandedLabelSize; - } - else - { - end = block2Id[p+1]; - } - int len = end - start; - - int loadPerThread = (len+blockDim.x-1)/blockDim.x; - int tStart = loadPerThread * threadIdx.x; - int tLen = min((int)len, loadPerThread + tStart); - - for(int i = start + tStart; i < start + tLen; i++) - { - partials[threadIdx.x] += exp(val[i]); - } - - __syncthreads(); - - // now sum up the objective function - int nTotalThreads = blockDim.x; - - while (nTotalThreads >1) - { - int halfPoint = (nTotalThreads >> 1); - - if (threadIdx.x < halfPoint) - partials[threadIdx.x] += partials[threadIdx.x+halfPoint]; - - __syncthreads(); - - nTotalThreads = (nTotalThreads>>1); - } - - for(int i = start + tStart; i < start + tLen; i++) - { - val[i] = log(exp(val[i])/partials[0]); - if(row[i] == t) - { - atomicAdd(entropyScore, -val[i]); - val[i] *= -1; - } - } -} - -// compute prediction error in cross entropy node -template -__global__ void _computePredictionError( - ElemType* val, - int N) -{ - int p = blockDim.x * blockIdx.x + threadIdx.x; - if (p>=N) - return; - - if(val[p] < 0) - val[p] = exp(val[p]); //negative; - else - val[p] = exp(-val[p])-1; //positive -} - -// compute gradients of input in cross entropy node -template -__global__ void _computeGradientOfInput( - const ElemType* val, - const GPUSPARSE_INDEX_TYPE* row, - const GPUSPARSE_INDEX_TYPE* pb, - ElemType* weight, - size_t nrs, - ElemType* grd, - size_t numrows) -{ - int h = blockIdx.x%numrows; - int j = blockIdx.x/numrows; - - int start = pb[j]; - int end = pb[j+1]; - int len = end - start; - - int load = (len+blockDim.x-1)/blockDim.x; - int pStart = start + load * threadIdx.x; - int pEnd = start + min(len, load * (threadIdx.x+1)); - - ElemType sum = 0; - for(int p = pStart; p < pEnd; p++) - { - int i = row[p]; - sum += val[p] * weight[IDX2C(i, h, nrs)]; - } - - atomicAdd(&grd[IDX2C(h,j,numrows)], sum); -} - - -template -__global__ void computeNCEForwardProp( - const ElemType* val, - const int* col, - int numRows, - int sampleCount, - const ElemType* a, - int numCols_a, - const ElemType* b, - ElemType* res) -{ - // val and col are in CSR format - // val is an array contains log_Pn(w). To differentiate positive and negative samples, - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // res is the buffer to store computed output (sparse) - - // follow the convention, this kernel must be run on 512 threads per block - __shared__ ElemType partials[512]; - partials[threadIdx.x] = 0; - - // determine the elements to be handled by this block - int total = numRows * sampleCount; - int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; - - int start = loadPerBlock * blockIdx.x; - int end = min(total, loadPerBlock * (blockIdx.x + 1)); - - for (int i = start; i < end; i++) - { - int colIndex = col[i]; - int rowIndex = i / sampleCount; - - int loadPerThread = (numCols_a + blockDim.x - 1) / blockDim.x; - int tstart = loadPerThread * threadIdx.x; - int tend = min(numCols_a, loadPerThread * (threadIdx.x + 1)); - - for (int j = tstart; j < tend; j++) - partials[threadIdx.x] = a[IDX2C(rowIndex, j, numRows)] * b[IDX2C(j, colIndex, numCols_a)]; - - __syncthreads(); - - // sum up - int nTotalThreads = blockDim.x; - - while (nTotalThreads >1) - { - int halfPoint = (nTotalThreads >> 1); - - if (threadIdx.x < halfPoint) - partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; - - __syncthreads(); - - nTotalThreads = (nTotalThreads >> 1); - } - - if (threadIdx.x == 0) - res[i] = partials[0]; - } -} - -template -__global__ void _computeNceOutput( - const ElemType* col, - int numRows, - int sampleCount, - const ElemType* a, - int numCols_a, - const ElemType* b, - const ElemType* bias, - ElemType* res) -{ - // val and col are in CSR format - // val is an array contains log_Pn(w). To differentiate positive and negative samples, - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // res is the buffer to store computed output (sparse) - - // follow the convention, this kernel must be run on 512 threads per block - __shared__ ElemType partials[512]; - partials[threadIdx.x] = 0; - - //threadIdx.x range from[0 ~ 512) - //blockIdx.x range from[0 ~ nnz) - //blockDim.x equal to 512 - //gridDim.x equal to nnz - - // determine the elements to be handled by this block - int total = numRows * sampleCount; - int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; - - int start = loadPerBlock * blockIdx.x; - int end = min(total, loadPerBlock * (blockIdx.x + 1)); - - for (int i = start; i < end; i++) - { - int wid = (int)col[2 * i]; - int batchid = i / sampleCount; - - int loadPerThread = (numCols_a + blockDim.x - 1) / blockDim.x; - int tstart = loadPerThread * threadIdx.x; - int tend = min(numCols_a, loadPerThread * (threadIdx.x + 1)); - - for (int j = tstart; j < tend; j++) - partials[threadIdx.x] = a[IDX2C(j, batchid, numCols_a)] * b[IDX2C(j, wid, numCols_a)]; - - __syncthreads(); - - // sum up - int nTotalThreads = blockDim.x; - - while (nTotalThreads >1) - { - int halfPoint = (nTotalThreads >> 1); - - if (threadIdx.x < halfPoint) - partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; - - __syncthreads(); - - nTotalThreads = (nTotalThreads >> 1); - } - - if (threadIdx.x == 0) - res[i] = partials[0] + bias[wid]; - } -} - - -template -__global__ void _assignSoftmaxSum( - const ElemType* softmax, - int sampleCount, - const ElemType* a, - ElemType* c) // run on 512 threads per block -{ - // val and col are in CSR format - // val is an array contains log_Pn(w). To differentiate positive and negative samples, - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // tmp is the buffer that stores NCE output calculated from _computeNceOutput - // c is the matrix to store objective - - __shared__ ElemType partials[512]; - partials[threadIdx.x] = 0; - - int total = sampleCount; - int loadPerThread = (total + blockDim.x - 1) / blockDim.x; - - // find out the items this thread is responsible for - int start = loadPerThread * threadIdx.x; - int end = min(total, loadPerThread * (threadIdx.x + 1)); - for (int i = start; i < end; i++) - { - int wid = (int)a[i]; - partials[threadIdx.x] += softmax[IDX2C(i, wid, sampleCount)]; - } - - __syncthreads(); - - // now sum up the objective function - int nTotalThreads = blockDim.x; - - while (nTotalThreads >1) - { - int halfPoint = (nTotalThreads >> 1); - - if (threadIdx.x < halfPoint) - partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; - - __syncthreads(); - - nTotalThreads = (nTotalThreads >> 1); - } - - if (threadIdx.x == 0) - c[0] = -partials[0]; -} - -template -__global__ void _assignNoiseContrastiveEstimation( - const ElemType* val, - int numRows, - int sampleCount, - const ElemType* a, - int width, // number of columns in a - const ElemType* b, - ElemType* tmp, - ElemType* c) // run on 512 threads per block -{ - // val and col are in CSR format - // val is an array contains log_Pn(w). To differentiate positive and negative samples, - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // tmp is the buffer that stores NCE output calculated from _computeNceOutput - // c is the matrix to store objective - - __shared__ ElemType partials[512]; - partials[threadIdx.x] = 0; - - int total = numRows * sampleCount; - int loadPerThread = (total + blockDim.x - 1) / blockDim.x; - - // find out the items this thread is responsible for - int start = loadPerThread * threadIdx.x; - int end = min(total, loadPerThread * (threadIdx.x + 1)); - - ElemType log_num_noise_samples = log((ElemType)(sampleCount - 1)); - for (int i = start; i < end; i++) - { - ElemType prob = -val[2 * i + 1]; - bool positive = (prob > 0); - if (positive) - prob = -prob; - ElemType score_noise = log_num_noise_samples + prob; - ElemType z = logaddk(tmp[i], score_noise); - ElemType logprob = tmp[i] - z; - ElemType logprob_noise = score_noise - z; - tmp[i] = -exp(logprob); - if (positive) - tmp[i] += 1; - if (positive) - partials[threadIdx.x] += logprob; - else - partials[threadIdx.x] += logprob_noise; - } - - __syncthreads(); - - // now sum up the objective function - int nTotalThreads = blockDim.x; - - while (nTotalThreads >1) - { - int halfPoint = (nTotalThreads >> 1); - - if (threadIdx.x < halfPoint) - partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; - - __syncthreads(); - - nTotalThreads = (nTotalThreads >> 1); - } - - if (threadIdx.x == 0) - c[0] = -partials[0]; -} - -template -__global__ void _assignNceDerivative( - const ElemType* val, - int numRows, - int sampleCount, - const ElemType* a, - int width, // number of columns in a - const ElemType* b, - const ElemType* tmp, - ElemType* c, - size_t inputIndex) -{ - // val and col are CSR format sparse matrix for label - // val is an array contains log_Pn(w). To differentiate positive and negative samples - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // tmp is a matrix of precalculated error - // c is the output matrix to store calculated gradients - - int total = numRows * sampleCount; - int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; - - // find out the items this block is responsible for - int start = loadPerBlock * blockIdx.x; - int end = min(total, loadPerBlock * (blockIdx.x + 1)); - - for (int i = start; i < end; i++) - { - int wid = (int)val[2 * i]; - int batchId = i / sampleCount; - - ElemType er = tmp[i]; // precalculated error for this output node - - // calculate gradients - int loadPerThread = (width + blockDim.x - 1) / blockDim.x; - int tstart = loadPerThread * threadIdx.x; - int tend = min(width, loadPerThread*(threadIdx.x + 1)); - - if (inputIndex == 1) // hidden layer output - { - for (int j = tstart; j < tend; j++) - { - ElemType val = -er * b[IDX2C(j, wid, width)]; - atomicAdd(&c[IDX2C(j, batchId, width)], val); - //c[IDX2C(j, batchId, width)] += val; - //c[IDX2C(batchId, j, numRows)] += val; - } - } - else if (inputIndex == 2) // weight - { - for (int j = tstart; j < tend; j++) - { - ElemType val = -er * a[IDX2C(j, batchId, width)]; - atomicAdd(&c[IDX2C(j, wid, width)], val); - //c[IDX2C(j, wid, width)] += val; - } - } - else //bias vector - { - //ElemType val = -er; - atomicAdd(&c[wid], -er); - //c[wid] -= er; - } - } -} - -template -__global__ void _assignNceDerivativeNew( - const ElemType* val, - int numRows, - int sampleCount, - const ElemType* a, - int width, // number of columns in a - const ElemType* b, - const ElemType* tmp, - ElemType* c, - size_t inputIndex) -{ - // val and col are CSR format sparse matrix for label - // val is an array contains log_Pn(w). To differentiate positive and negative samples - // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples - // col is an array contains index of the word samples - // a is a matrix in column major format contains output from hidden layer - // b is the weight matrix for output layer - // tmp is a matrix of precalculated error - // c is the output matrix to store calculated gradients - - // logical single index for this thread - int n = threadIdx.x + blockDim.x* blockIdx.x; - - int batchId = n / sampleCount; - int total = numRows * sampleCount; - // is thread in range for the addition - if (n < total) - { - int wid = (int)val[2 * n]; - ElemType er = tmp[n]; - if (inputIndex == 1) - { - for (int i = 0; i < width; i++) - { - int j = (i + n) % width; //introduce randomization to avoid conflicts - ElemType val = -er * b[IDX2C(j, wid, width)]; - atomicAdd(&c[IDX2C(j, batchId, width)], val); - } - } - else if (inputIndex == 2) - { - for (int i = 0; i < width; i++) - { - int j = (i + n) % width; //introduce randomization to avoid conflicts - ElemType val = -er * a[IDX2C(j, batchId, width)]; - atomicAdd(&c[IDX2C(j, wid, width)], val); - } - } - else - atomicAdd(&c[wid], -er); - } -} -// compute gradients of weights in cross entropy node -template -__global__ void _computeGradientOfWeight( - const ElemType* val, - const GPUSPARSE_INDEX_TYPE* row, - const GPUSPARSE_INDEX_TYPE* pb, - size_t mb, - size_t nv, - const GPUSPARSE_INDEX_TYPE* labelRow, - const size_t* labelBlock2UniqId, - const ElemType* cls, - const ElemType* idx2cls, - ElemType* input, - size_t nrs, - ElemType* blockVal, - GPUSPARSE_INDEX_TYPE* blockIds) -{ - int p = blockIdx.x; - ElemType v = val[p]; - int i = row[p]; - int j = -1; - for(int k = 1; k < mb; k++) - { - if( p < pb[k]) - { - j = k-1; - break; - } - } - if( j == -1) - { - j = mb-1; - } - - //figure out blocks - int bId = i < nv ? 2*j : 2*j+1; - int t = labelRow[bId]; - int iStt; - if(t < nv) - { - int clsid = idx2cls[t]; - iStt = cls[IDX2C(0, clsid, 2)]; - } - else - { - iStt = nv; - } - int offset = i - iStt; - int ii = labelBlock2UniqId[bId] + offset; - - int load = (nrs+blockDim.x-1)/blockDim.x; - int pStart = load * threadIdx.x; - int pEnd = min((int)nrs, load + pStart); - - for(int h = pStart; h < pEnd; h++) - { - ElemType temp = v * input[IDX2C(h, j, nrs)]; - atomicAdd(&blockVal[ii*nrs+h], temp); - blockIds[ii] = i; - } -} - -// used in clipping gradients -template -__global__ void _inplaceTruncate( - ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) - ElemType locThresholdPos = abs(threshold); - ElemType locTHresholdNeg = -locThresholdPos; - if (a[id] > locThresholdPos) - { - a[id] = locThresholdPos; - } - else if(a[id] < locTHresholdNeg) - { - a[id] = locTHresholdNeg; - } -} - -template -__global__ void _inplaceSoftThreshold( - ElemType* a, - const ElemType threshold, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - if (a[id] > threshold) - { - a[id] -= threshold; - } - else if (a[id] < -threshold) - { - a[id] += threshold; - } - else - a[id] = 0; -} - - -template -__global__ void _normalGradForSparseBlock( - const ElemType momentum, - const bool blockCol, //true if blockRow - const size_t numRows, - const size_t numCols, - const size_t numBlocks, - ElemType* lhsValues, //lhs is blockCol or blockRow - const GPUSPARSE_INDEX_TYPE* blockIds, - ElemType* rhs) -{ - const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; - CUDA_LONG row, col; - if (blockCol) - { - const CUDA_LONG blockId = index / numRows; - if (blockId >= numBlocks) - return; - row = index - numRows* blockId; - col = blockIds[blockId]; - } - else - { - const CUDA_LONG blockId = index / numCols; - if (blockId >= numBlocks) - return; - col = index - numCols* blockId; - row = blockIds[blockId]; - } - rhs[IDX2C(row, col, numRows)] = (1 - momentum)*lhsValues[index] + momentum*rhs[IDX2C(row, col, numRows)]; - lhsValues[index] = rhs[IDX2C(row, col, numRows)]; -} - -//This function should be called with 1024 threads per block and 1 block -//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! -template -__global__ void _reductionSum( - const ElemType* data, - ElemType *sum, - CUDA_LONG N) -{ - - __shared__ ElemType partialSums[1024]; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG loadPerThread = N/blockDim.x; - for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - partialSums[threadIdx.x]+=data[i]; - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - sum[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; - } -} - -//This function should be called with 1024 threads per block and 1 block -//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! -template -__global__ void _reductionSumAndAssign( - ElemType* toAssign, - const ElemType* data, - CUDA_LONG N, //length of data - CUDA_LONG M) //length of toAssign -{ - __shared__ ElemType partialSums[1024]; - __shared__ ElemType res; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG loadPerThread = N/blockDim.x; - for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - partialSums[threadIdx.x]+=data[i]; - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - res = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; - for (CUDA_LONG i=0;i -__global__ void _reductionSum2( - const ElemType* data, - ElemType *sum, - CUDA_LONG N, - bool takeSqrt=false) -{ - - __shared__ ElemType partialSums[1024]; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG loadPerThread = N/blockDim.x; - for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - //for (int i= threadIdx.x*loadPerThread; i<(threadIdx.x+1)*loadPerThread;++i) - { - partialSums[threadIdx.x]+=(data[i]*data[i]); - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - sum[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; - if (takeSqrt) - { - if (sizeof(ElemType)==sizeof(float)) - sum[0] = sqrtf(sum[0]); - else - sum[0] = sqrt(sum[0]); - } - } -} - - -//This function should be called with 1024 threads per block and 1 block -//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! -template -__global__ void _reductionMatrixNormInf( - const ElemType* data, - ElemType *maxAbs, - CUDA_LONG N) -{ - - __shared__ ElemType partialSums[1024]; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - int loadPerThread = N/blockDim.x; - for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - if (sizeof(ElemType)==sizeof(float)) - { - partialSums[threadIdx.x]=max(fabsf(data[i]),partialSums[threadIdx.x]); - } - else - { - partialSums[threadIdx.x]=max(fabs(data[i]),partialSums[threadIdx.x]); - } - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+512],partialSums[threadIdx.x]); - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+256],partialSums[threadIdx.x]); - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+128],partialSums[threadIdx.x]); - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+64],partialSums[threadIdx.x]); - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+32],partialSums[threadIdx.x]); - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+16],partialSums[threadIdx.x]); - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+8],partialSums[threadIdx.x]); - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]=max(partialSums[threadIdx.x+4],partialSums[threadIdx.x]); - } - __syncthreads(); - - if (threadIdx.x==0) - { - maxAbs[0] = max(max(partialSums[0],partialSums[1]),max(partialSums[2],partialSums[3])); - } -} - -//This function should be called with 1024 threads per block and 1 block -//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! -template -__global__ void _reductionMatrixNorm0( - const ElemType* data, - ElemType *nz, - CUDA_LONG N) -{ - - __shared__ ElemType partialSums[1024]; - partialSums[threadIdx.x]=0; - //int id = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG loadPerThread = N/blockDim.x; - for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - if (data[i]!=0) - ++partialSums[threadIdx.x]; - } - __syncthreads(); - - //512 - if (threadIdx.x<512) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+512]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //256 - if (threadIdx.x<256) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+256]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+128]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+64]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+32]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+16]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+8]+partialSums[threadIdx.x]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums[threadIdx.x]=partialSums[threadIdx.x+4]+partialSums[threadIdx.x]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - nz[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; - } -} - - -template -__global__ void _getSparseVectorRepresntationForCSCMatrix( - const int* m_dRow, - const int* m_dCol, - int* vectArray, - const CUDA_LONG M, - const CUDA_LONG N) -{ - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i>=M) - return; - int start = m_dRow[i]; - int end = m_dRow[i+1]; - for (int _i=start;_i -__global__ void _lrHelper( - const ElemType* data1, - const ElemType* data2, - const CUDA_LONG N, - ElemType* d_res) -{ - __shared__ ElemType partialSums1[512]; - __shared__ ElemType partialSums2[512]; - partialSums1[threadIdx.x]=0; - partialSums2[threadIdx.x]=0; - - //int id = blockDim.x * blockIdx.x + threadIdx.x; - int loadPerThread = N/blockDim.x; - for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) - { - partialSums1[threadIdx.x]+=(data1[i]*data1[i]); - partialSums2[threadIdx.x]+=(data2[i]*data2[i]); - } - __syncthreads(); - - /* - //512 - if (threadIdx.x<512) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+512]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+512]; - } - __syncthreads();*/ - - //256 - if (threadIdx.x<256) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+256]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+256]; - } - __syncthreads(); - - //128 - if (threadIdx.x<128) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+128]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+128]; - } - __syncthreads(); - - //64 - if (threadIdx.x<64) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+64]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+64]; - } - __syncthreads(); - - //32 - if (threadIdx.x<32) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+32]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+32]; - } - __syncthreads(); - - //16 - if (threadIdx.x<16) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+16]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+16]; - } - __syncthreads(); - - //8 - if (threadIdx.x<8) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+8]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+8]; - } - __syncthreads(); - - //4 - if (threadIdx.x<4) - { - partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+4]; - partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+4]; - } - __syncthreads(); - - if (threadIdx.x==0) - { - ElemType fns1 = partialSums1[0]+partialSums1[1]+partialSums1[2]+partialSums1[3]; - ElemType fns2 = partialSums2[0]+partialSums2[1]+partialSums2[2]+partialSums2[3]; - if (sizeof(ElemType)==sizeof(float)) - { - d_res[0] = max((ElemType)0, d_res[0]/max((ElemType)1.0e-10,sqrtf(fns1))/max((ElemType)1.0e-10,sqrtf(fns2))); - } - else - { - d_res[0] = max((ElemType)0, d_res[0]/max((ElemType)1.0e-10,sqrt(fns1))/max((ElemType)1.0e-10,sqrt(fns2))); - } - } -} - -/* -template -__global__ void _lrHelper( -ElemType* d_tmp) -{ -if (sizeof(ElemType)==sizeof(float)) -{ -d_tmp[0] = max((ElemType)0, d_tmp[0]/max((ElemType)1.0e-10,sqrtf(d_tmp[1]))/max((ElemType)1.0e-10,sqrtf(d_tmp[2]))); -} -else -{ -d_tmp[0] = max((ElemType)0, d_tmp[0]/max((ElemType)1.0e-10,sqrt(d_tmp[1]))/max((ElemType)1.0e-10,sqrt(d_tmp[2]))); -} -} -*/ - - -template -__global__ void _assignElementProductOfWithShiftNeg( - ElemType* us, - const ElemType* a, - const ElemType* b, - const int shift, - const int NTPlusOne, - const int BS) -{ - CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; - - if (idx >= NTPlusOne || idy >= BS) - return; - - if (idx == 0) - { - // this is row-0. No need to shift - us[IDX2C(idx, idy, NTPlusOne)] = a[idy] * b[idy]; - } - else - { - int cs = shift + idx - 1; - int tmpidy = (idy + cs) % BS; - us[IDX2C(idx, idy, NTPlusOne)] = a[idy] * b[tmpidy]; - } -} - -template -__global__ void _innerProductWithShiftNeg( - ElemType* c, - const ElemType* a, - const ElemType* b, - const CUDA_LONG N, //a.GetNumRows(); - const CUDA_LONG M, //a.GetNumCols(); - const CUDA_LONG shift, - const CUDA_LONG NTPlusOne - ) -{ - CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; - - if (idx >= NTPlusOne || idy >= M) - return; - - ElemType sum = 0; - CUDA_LONG index_a = 0; - CUDA_LONG index_b = 0; - CUDA_LONG col_a = 0; - CUDA_LONG col_b = 0; - if (idx == 0) - { - // this is row 0. No need to shift - // the product of a(:,idy) dot b(:,idy) - col_a = idy; - for (CUDA_LONG i = 0; i < N; ++i) - { - index_a = IDX2C(i, col_a, N); - sum += a[index_a] * b[index_a]; - } - } - else - { - int cs = shift + idx - 1; - col_a = idy; - col_b = (idy + cs) % M; - for (int i = 0; i < N; ++i) - { - index_a = IDX2C(i, col_a, N); - index_b = IDX2C(i, col_b, N); - sum += a[index_a] * b[index_b]; - } - } - c[IDX2C(idx, idy, NTPlusOne)] = sum; - -} - -template -__global__ void _getARowByIndex( - ElemType* us, - const ElemType* a, - const int O, // a's rows - const int P, // a's cols - const int m // the m-th row of a - ) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= P) - return; - // us[id] = a[id] * b[id]; - us[id] = a[IDX2C(m, id, O)]; -} - - -template -__global__ void _conductRowElementMultiplyWithShift( - ElemType* us, - const ElemType* a, - const ElemType* b, - const int O, // b's rows - const int P, // b's cols - const int shift, - const bool isafixed) -{ - CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; - CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; - - if (idx >= O || idy >= P) - return; - - int tmpidy = (idy + shift) % P; - if (isafixed) - { - // we fix a, and shift b - us[IDX2C(idx, idy, O)] = a[idy] * b[IDX2C(idx, tmpidy, O)]; - } - else - { - // we fix b, but shift a - us[IDX2C(idx, idy, O)] = a[tmpidy] * b[IDX2C(idx, idy, O)]; - } - -} - -template -__global__ void _assignElementProductOfWithShift( - ElemType* us, - const ElemType* a, - const ElemType* b, - const int shift, - const CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - - int tmpidb = (id + shift) % N; - us[id] = a[id] * b[tmpidb]; -} - - -/// minus 1 at a specific position -template -__global__ void _minusOneAt( - ElemType *c, - CUDA_LONG position, - CUDA_LONG N) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - if (id == position) - c[id] = c[id] - 1.0; -} - - -/// the kernel function for RCRF backward computation -/// assume a column slice of input and output -template -__global__ void _rcrfBackwardCompute( - const size_t iNumPos, - const ElemType* galpha, /// column slice at current time t - ElemType* gbeta, /// column slices with [row, 2] at current time t for [ - const ElemType* gpair_scores, - const size_t iNumLab, const int shift) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - - extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * - /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) - - ElemType * alpha = (ElemType*)(sh_alpha_and_beta); - ElemType * pair_scores = alpha + iNumPos * iNumLab; - ElemType * beta = alpha + iNumPos * iNumLab + iNumLab * iNumLab; - - if (id < 0 || id >= iNumLab) - return; - - /// copy global memory to shared memory to save time - for (int t = iNumPos - 1; t >= 0; t--) - { - alpha[IDX2C(id, t, iNumLab)] = galpha[IDX2C(id, t, iNumLab)]; - } - - for (int j = 0; j < iNumLab; j++) - pair_scores[IDX2C(id, j, iNumLab)] = gpair_scores[IDX2C(id, j, iNumLab)]; - - __syncthreads(); - - for (int t = iNumPos - 1; t >= 0; t--) - { - ElemType fSum; - ElemType fTmp = LZERO; - if (t == iNumPos - 1) - { - fSum = LZERO; - for (int j = 0; j < iNumLab; j++) - { - fSum = logaddk(fSum, alpha[IDX2C(j, t, iNumLab)]); - } - - fTmp = alpha[IDX2C(id, t, iNumLab)] - fSum; - } - else - { - for (int j = 0; j < iNumLab; j++) - { - fSum = LZERO; - for (int m = 0; m < iNumLab; m++) - { - fSum = logaddk(fSum, alpha[IDX2C(m, t, iNumLab)] + pair_scores[IDX2C(j, m, iNumLab)]); - } - - fTmp = logaddk(fTmp, beta[IDX2C(j, t + 1, iNumLab)] + alpha[IDX2C(id, t, iNumLab)] + pair_scores[IDX2C(j, id, iNumLab)] - fSum); - } - } - - beta[IDX2C(id, t, iNumLab)] = fTmp; - __syncthreads(); - } - - /// copy from shared memory to global memory to pass values - for (int t = iNumPos - 1; t >= 0; t--) - { - gbeta[IDX2C(id, t, iNumLab)] = beta[IDX2C(id, t, iNumLab)]; - } - // __syncthreads(); -} - -/// the kernel function for RCRF backward computation -/// assume a column slice of input and output -template -__global__ void _rcrfBackwardCompute( - const size_t t, /// time position - const size_t iNumPos, - const ElemType* galpha, /// column slice at current time t - ElemType* gbeta, /// column slices with [row, 2] at current time t for [ - const ElemType* gzeta, /// column slices with [row, 2] at current time t for [ - const ElemType* gpair_scores, /// column slice at current time t - const size_t iNumLab, const int shift) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - - extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * - /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) - - ElemType * alpha = (ElemType*)(sh_alpha_and_beta); - ElemType * beta_t1 = (ElemType*)(alpha + iNumLab); - ElemType * zeta = (ElemType*)(beta_t1 + iNumLab); - ElemType pair_scores[1024]; - - if (id < 0 || id >= iNumLab) - return; - - /// copy global memory to shared memory to save time - alpha[id] = galpha[IDX2C(id, t, iNumLab)]; - if (t < iNumPos - 1) - beta_t1[id] = gbeta[IDX2C(id, t + 1, iNumLab)]; - zeta[id] = gzeta[id]; - - __syncthreads(); - - for (int j = 0; j < iNumLab; j++) - pair_scores[j] = gpair_scores[IDX2C(j, id, iNumLab)]; - - ElemType fTmp = LZERO; - if (t == iNumPos - 1) - { - fTmp = alpha[id] - zeta[id]; - } - else - { - for (int j = 0; j < iNumLab; j++) - { - fTmp = logaddk(fTmp, beta_t1[j] + alpha[id] + pair_scores[j] - zeta[j]); - } - } - - gbeta[IDX2C(id, t, iNumLab)] = fTmp; - -} - -/// $\zeta_t(j) = {\sum_k exp(\delta_{t-1}(k) + a_{kj}(t))}$. -template -__global__ void _rcrfBackwardComputeZeta( - const size_t t, /// time position - const size_t iNumPos, - const ElemType* galpha, /// column slice at current time t - ElemType* gzeta, /// column slices with [row, 2] at current time t for [ - const ElemType* gpair_scores, - const size_t iNumLab, const int shift) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - - extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * - /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) - - ElemType * alpha = (ElemType*)(sh_alpha_and_beta); - ElemType pair_scores[1024]; - - if (id < 0 || id >= iNumLab) - return; - - /// copy global memory to shared memory to save time - alpha[id] = galpha[IDX2C(id, t, iNumLab)]; - - __syncthreads(); - - for (int j = 0; j < iNumLab; j++) - pair_scores[j] = gpair_scores[IDX2C(id, j, iNumLab)]; - - ElemType fSum = LZERO; - for (int m = 0; m < iNumLab; m++) - { - if (t == iNumPos - 1) - fSum = logaddk(fSum, alpha[IDX2C(m, 0, iNumLab)]); - else - fSum = logaddk(fSum, alpha[IDX2C(m, 0, iNumLab)] + pair_scores[m]); - } - - gzeta[id] = fSum; - -} - -/// $\zeta_t(j) = {\sum_k exp(\delta_{t-1}(k) + a_{kj}(t))}$. -template -__global__ void _rcrfTransGrdComputeZeta( - const int t, /// time position - const size_t iNumPos, - const ElemType* galpha, /// column slice at current time t - ElemType* gzeta, /// column slices with [row, 2] at current time t for [ - const ElemType* gpair_scores, - const size_t iNumLab, - const size_t start_lbl, - const int shift) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - - extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * - /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) - - ElemType * alpha = (ElemType*)(sh_alpha_and_beta); - ElemType pair_scores[1024]; - - if (id < 0 || id >= iNumLab) - return; - - /// copy global memory to shared memory to save time - if (t >= 0) - alpha[id] = galpha[IDX2C(id, t, iNumLab)]; - - __syncthreads(); - - for (int j = 0; j < iNumLab; j++) - pair_scores[j] = gpair_scores[IDX2C(id, j, iNumLab)]; - - ElemType fSum = LZERO; - ElemType fTmp; - for (int m = 0; m < iNumLab; m++) - { - if (t < 0) - { - if (m == start_lbl) - fTmp = 0; - else fTmp = LZERO; - } - else - fTmp = alpha[m]; - - fSum = logaddk(fSum, pair_scores[m] + fTmp); - } - - gzeta[id] = fSum; - -} - -template -__global__ void _rcrfTransGrdCompute( - int t, - const size_t start_lbl, - const ElemType* galpha, - const ElemType* gbeta, - const ElemType* gzeta, - const ElemType* gpair_scores, - const ElemType * lbls, - ElemType* grd, - const size_t iNumPos, - const size_t iNumLab, - const int shift) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - - extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * - /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) - - ElemType * alpha = (ElemType*)(sh_alpha_and_beta); - ElemType * beta = (ElemType*)(alpha + iNumLab); - ElemType * zeta = (ElemType*)(beta + iNumLab); - ElemType pair_scores[1024]; - - if (id < 0 || id >= iNumLab) - return; - - /// copy global memory to shared memory to save time - if (t > 0) - alpha[id] = galpha[IDX2C(id, t - 1, iNumLab)]; - beta[id] = gbeta[IDX2C(id, t, iNumLab)]; - zeta[id] = gzeta[id]; - - __syncthreads(); - - for (int j = 0; j < iNumLab; j++) - pair_scores[j] = gpair_scores[IDX2C(j, id, iNumLab)]; - - ElemType fTmp; - ElemType fTmp2; - for (int j = 0; j < iNumLab; j++){ - if (t == 0) - { - if (id == start_lbl) - fTmp = 0; - else - fTmp = LZERO; - } - else - fTmp = alpha[id]; - - fTmp2 = fTmp + pair_scores[j] - zeta[j]; - assert(fTmp2 <= 0.0); - fTmp2 += beta[j]; - - fTmp = exp(fTmp2); - grd[IDX2C(j, id, iNumLab)] += fTmp; - } - - if ((t == 0 && id == start_lbl) || (t > 0 && t < iNumPos && lbls[IDX2C(id, t - 1, iNumLab)] != 0)) - { - for (int ik = 0; ik < iNumLab; ik++) - { - if (lbls[IDX2C(ik, t, iNumLab)] != 0) - grd[IDX2C(ik, id, iNumLab)] -= 1.0; - } - } - -}; - -template -__global__ void _reductionLogAddSum( - const ElemType* data, - ElemType *sum, - const size_t sum_size, - CUDA_LONG N) -{ - - __shared__ ElemType partialLogAddSum[GridDim::maxThreadsPerBlock]; - - int id = blockDim.x * blockIdx.x + threadIdx.x; - int tid = threadIdx.x; - - if (id < N) - partialLogAddSum[tid] = data[id]; - else - partialLogAddSum[tid] = LZERO; - - __syncthreads(); - - /// do reduction on the shared memory - size_t start_width = ceil((N + 0.0) / 2.0); - for (size_t s = start_width; s > 0; s >>= 1) - { - ElemType lSum = LZERO; - if (tid < s){ - lSum = logaddk(partialLogAddSum[tid], partialLogAddSum[tid + s]); - partialLogAddSum[tid] = lSum; - } - } - __syncthreads(); - - - if (tid == 0) - sum[0] = partialLogAddSum[0]; -} - -// set the value of certain columns to be zero -// the column is decided by threshhold value -// TODO: This kernel has very poor performace and needs to -// be optimized -template -__global__ void _DropFrame( - ElemType *a, - const ElemType *label, - const ElemType *gamma, - const ElemType framedropthreshhold, - const long m_numCols, - const long m_numRows) //ld -{ - int col_id = blockDim.x * blockIdx.x + threadIdx.x; - if (col_id >= m_numCols) - return; - bool dropframe = false; - // find the 1 in the one-hot representation of the labels - // This is a linear scan--bad perf! - for (long i = 0; i -__global__ void _AssignSequenceError(const ElemType hsmoothingWeight, ElemType *error, const ElemType *label, - const ElemType *dnnoutput, const ElemType *gamma, ElemType alpha, const long N) -{ - int id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= N) - return; - error[id] -= alpha * (label[id] - (1.0 - hsmoothingWeight)*dnnoutput[id] - hsmoothingWeight * gamma[id]); - //change to ce - //error[id] -= alpha * (label[id] - dnnoutput[id] ); - -} - -template -__global__ void _copyTopKResults(const uint64_t* indexes, const ElemType* values, ElemType* maxIndexes, ElemType* maxValues, - CUDA_LONG crow, CUDA_LONG ccol, int topK) -{ - CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; - if (id >= topK * ccol) - return; - CUDA_LONG irow = id % topK; - CUDA_LONG icol = id / topK; - maxIndexes[id] = static_cast(indexes[icol * crow + irow] >> 32); - maxValues[id] = values[icol * crow + irow]; -} - -template -__global__ void _assignNumOfDiffCol(const ElemType *a, const ElemType *b, ElemType *c, CUDA_LONG crowB, CUDA_LONG ccol) -{ - assert(gridDim.x == 1 && gridDim.y == 1 && gridDim.z == 1); - - int cur = 0; - CUDA_LONG icol = threadIdx.x; - for (; icol < ccol; icol += blockDim.x) - { - ElemType key = a[icol]; - CUDA_LONG idxB = icol * crowB; - CUDA_LONG irow = 0; - for (; irow < crowB; irow++, idxB++) - { - if (b[idxB] == key) - break; - } - - cur += (irow == crowB); - } - - using BlockReduceT = cub::BlockReduce; - __shared__ typename BlockReduceT::TempStorage tmp; - - int res = BlockReduceT(tmp).Sum(cur); - if (threadIdx.x == 0) - *c = res; -} - -template -__global__ void _maskColumnsValue(ElemType *a, const char *columnsMask, CUDA_LONG numCols, CUDA_LONG numRows, ElemType val) -{ - CUDA_LONG colIdx = blockIdx.x; - if (colIdx > numCols) - return; - - if (columnsMask[IDX2C(0, colIdx, 1)] == 1) - return; - - CUDA_LONG rowIdx = threadIdx.x; - for (; rowIdx < numRows; rowIdx += blockDim.x) - { - a[IDX2C(rowIdx, colIdx, numRows)] = val; - } -} - -}}} - -#endif // !CPUONLY + +// --------------------------------------------------------------------------- +// GridDim -- helper to choose the CUDA grid dimensions +// --------------------------------------------------------------------------- + +template +static INT CeilDiv(INT a, INT2 b) // ceil(a/b) +{ + return (INT)(((size_t)a + (size_t)b - 1) / (size_t)b); // these size_t casts are necessary since b may be INT_MAX (for maxGridSize[]) +} + +struct GridDim +{ + static const CUDA_LONG maxThreadsPerBlock = 512; // use this many threads per block + static const CUDA_LONG maxWarpsPerBlock = 16; // use this many warps per block + + // use these for launching + // GridDim grid(NN); + // kernel<<>>(...) + int m_blocksPerGrid, m_threadsPerBlock; // (these may in the future be extended to multi-dimensional ones) + CUDA_LONG m_N; + + GridDim(CUDA_LONG N) // linear grid + { + m_N = N; + if (N == 0) // CUDA will fail to launch with 0 blocks + N = 1; + + // get device information + const auto & props = GetDeviceProps(); + CUDA_LONG numProcs = props.multiProcessorCount; + CUDA_LONG warpSize = props.warpSize; + + // distribute warps evenly over processors + CUDA_LONG warpsPerProc = CeilDiv(N, numProcs * warpSize); + + // if too many warps per block then reduce #warps + if (warpsPerProc > maxWarpsPerBlock) + { + CUDA_LONG overBy = CeilDiv(warpsPerProc, maxWarpsPerBlock); // we are over by this factor + warpsPerProc = CeilDiv(warpsPerProc, overBy); + } + + // put it back together + m_threadsPerBlock = warpsPerProc * warpSize; + m_blocksPerGrid = CeilDiv(N, m_threadsPerBlock); + if (m_blocksPerGrid == 1) + m_threadsPerBlock = N; // don't launch more than necessary --TODO: Does this make a difference at all? + assert(m_blocksPerGrid * m_threadsPerBlock >= N); + } + + static std::vector CacheDeviceProps() + { + int numDevices; + CUDA_CALL(cudaGetDeviceCount(&numDevices)); + std::vector props(numDevices); + for (int i = 0; i < numDevices; i++) + CUDA_CALL(cudaGetDeviceProperties(&props[i], i)); +#if 0 // on Linux, maxGridSize[0] gets reported as 0 + for (int i = 0; i < numDevices; i++) + fprintf(stderr, "%d procs %d warps %d %d %d max grid on %s\n", (int)props[i].multiProcessorCount, (int)props[i].warpSize, (int)props[i].maxGridSize[0], (int)props[i].maxGridSize[1], (int)props[i].maxGridSize[2], props[i].name); +#endif + return props; + } + + // get device properties of current device + static const cudaDeviceProp & GetDeviceProps() + { + static std::vector props = CacheDeviceProps(); // thread-safe according to C++ standard + int deviceId; + cudaGetDevice(&deviceId); + return props[deviceId]; + } + + // compute our location on the grid + static __device__ CUDA_LONG GetLinearThreadId() + { + return blockDim.x * blockIdx.x + threadIdx.x; + } +}; + +#define CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) CUDA_LONG id = GridDim::GetLinearThreadId(); if (id>=N) return; + +#ifdef __GNUC__ +#define UNUSED_FUNCTION_ATTRIBUTE __attribute__ ((unused)) +#else +#define UNUSED_FUNCTION_ATTRIBUTE +#endif + +// =========================================================================== +// CUDA kernels follow, lots of them +// =========================================================================== + +// _elementWise*() kernels +// +// Designed to operate on contiguous blocks of memory, where the output is a simple function of the inputs. +// The first parameters of every function are inputs, and the last two arguments to each function are always +// (ElemenType *res, CUDA_LONG N), a pointer and length of the output block. Each thread computes a function +// of the inputs for one value in the output. + +template +__global__ void _elementWisePowerOnCuda( + const ElemType alpha, + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + if (alpha==0) + { + res[id]=1; + } + else if (alpha==1) + { + res[id]=a[id]; + } + else if (alpha==2) + { + res[id]=a[id]*a[id]; + } + else if (alpha==3) + { + res[id]=a[id]*a[id]*a[id]; + } + else + { + if (sizeof(ElemType)==sizeof(double)) + { + res[id]=pow(a[id],alpha); + } + else + { + res[id]=powf(a[id],alpha); + } + } +}; + +// Note that this code is inefficient on CUDA due to diverging code paths. +// Use Sigmoid() in TensorOps.h instead, which solves this problem. +template +__global__ void _elementWiseSigmoidOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); +#if 0 // this computes the same thing but is twice as fast on CUDA + res[id] = Microsoft::MSR::CNTK::Sigmoid(a[id]); +#else + if (a[id] >= 0) + { + ElemType e = exp_(-a[id]); + res[id] = 1 / (1 + e); + } + else + { + ElemType e = exp_(a[id]); + res[id] = e / (1 + e); + } +#endif +}; + +template +__global__ void _assignSigmoidOf( + const ElemType* a, + ElemType* res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + + // This function computes 1 / (1 + e^(-x)) which yields 1 / (1 + e^|x|) if x is negative, + // and e^x / (1 + e^x) if x is positive. + // BUGBUG: This does not invert the calculation when the exp argument becomes large, potentially causing overflows. + // There is a second version of this function that does. That should be used. +#if 0 // this has the same speed now, although not identical accuracy + res[id] = Microsoft::MSR::CNTK::Sigmoid(a[id]); +#else + ElemType negElem = -a[id]; + ElemType e = exp_(negElem); + + res[id] = 1 / (e + 1); +#endif +}; + +template +__global__ void _elementWiseLinRectDerivativeOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = (a[id] <= 0) ? 0 : 1; +} + +template +__global__ void _elementWiseSigmoidDerivativeOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = a[id] * (1-a[id]); +} + +template +__global__ void _elementWiseTanhOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = tanh_(a[id]); +}; + +//to prevent negative values caused by floating operations, we force inputs to be >=0 +//this may, however, hide problems in the caller. +template +__global__ void _elementWiseSqrtOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = sqrt_(max((ElemType)0, a[id])); +}; + +template +__global__ void _elementWiseExpOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = exp_(a[id]); +}; + +template +__global__ void _elementWiseLogOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = (a[id] < EPS_IN_LOG) ? LOG_OF_EPS_IN_LOG : log_(a[id]); +}; + +template +__global__ void _elementWiseAbsOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = fabs_(a[id]); +}; + +template +__global__ void _elementWiseCosineOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = cos_(a[id]); +}; + +template +__global__ void _elementWiseNegativeSineOnCuda( + const ElemType *a, + ElemType *res, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + res[id] = -sin_(a[id]); +}; + +template +__global__ void _setValue( + ElemType* a, + const ElemType v, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]=v; +}; + +template +__global__ void _setValue( + ElemType* a, + const ElemType* d_v, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]=d_v[0]; +}; + +template +__global__ void _copyColumnsStrided(ElemType * dest, ElemType * src, CUDA_LONG N, CUDA_LONG numRows, CUDA_LONG destNumColsStride, CUDA_LONG srcNumColsStride) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG denseColIdx = id / numRows; + CUDA_LONG rowIdx = id - (denseColIdx * numRows); + + dest[(denseColIdx*destNumColsStride*numRows) + rowIdx] = src[(denseColIdx*srcNumColsStride*numRows) + rowIdx]; +} + +template +__global__ void _assignToRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG col = id / srcRows; + CUDA_LONG row = id - (col * srcRows); + + dest[col*destRows + row + startIndex] = src[id]; +} + +template +__global__ void _assignRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + CUDA_LONG col = id / destRows; + CUDA_LONG row = id - (col * destRows); + + //dest[id] = src[col*srcRows + row + startIndex]; + dest[id] = src[IDX2C(row + startIndex, col, srcRows)]; +} + +template +__global__ void _addToRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + CUDA_LONG col = id / srcRows; //src is the full matrix, rowslice is taken from the dest + CUDA_LONG row = id - (col * srcRows); + + //dest[col*destRows + row + startIndex] += src[id]; + dest[IDX2C(row + startIndex, col, destRows)] += src[id]; +} + +template +__global__ void _addWithRowSliceValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG startIndex, const CUDA_LONG destRows, const CUDA_LONG srcRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG col = id / destRows; //dest is the full matrix, rowslice is taken from the src + CUDA_LONG row = id - (col * destRows); + + dest[id] += src[IDX2C(row + startIndex, col, srcRows)]; +} + +template +__global__ void _assignToDiagonalValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcCols) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG col = id / srcCols; + CUDA_LONG row = id - (col * srcCols); + + if (row == col) + dest[row] = src[id]; +} + +template +__global__ void _assignRowStackValuesOf(ElemType * dest, ElemType ** srces, size_t* startRowIndeces, const CUDA_LONG numSrces, const CUDA_LONG N, const CUDA_LONG destRows, const CUDA_LONG destCols) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG col = id / destRows; //dest is the full matrix, rowslice is taken from the src + CUDA_LONG row = id - (col * destRows); + + //can we replace the for loop with something better? + int srcId = 0; + for (; srcId < numSrces; srcId++) + { + if (startRowIndeces[srcId + 1]>row) + break; + } + + dest[id] = srces[srcId][IDX2C(row - startRowIndeces[srcId], col, startRowIndeces[srcId+1] - startRowIndeces[srcId])]; +} + +template +__global__ void _assignRepeatOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG destCol = id / destRows; + CUDA_LONG destRow = id - (destCol * destRows); + + CUDA_LONG srcRow = destRow % srcRows; + CUDA_LONG srcCol = destCol % srcCols; + + dest[id] = src[IDX2C(srcRow,srcCol,srcRows)]; +} + +template +__global__ void _addToRowRepeatValuesOf(ElemType * dest, ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG col = id / srcRows; + CUDA_LONG row = (id - (col * srcRows)) % destRows; + + //dest[col*destRows + row + startIndex] += src[id]; + dest[IDX2C(row, col, destRows)] += src[id]; +} + +template +__global__ void _assignPositiveAndShiftedNegSample(ElemType * dest, const ElemType * src, const CUDA_LONG N, const CUDA_LONG srcRows, const CUDA_LONG srcCols, const CUDA_LONG destRows, const CUDA_LONG posNumber, const CUDA_LONG shiftNumber) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + CUDA_LONG destCol = id / destRows; + CUDA_LONG destRow = id - (destCol * destRows); + + CUDA_LONG sampleInDestCol = destRow / srcRows; + CUDA_LONG srcRow = destRow - srcRows * sampleInDestCol; + CUDA_LONG srcCol = sampleInDestCol < posNumber ? destCol : (destCol + shiftNumber + sampleInDestCol - posNumber) % srcCols; + + dest[id] = src[IDX2C(srcRow, srcCol, srcRows)]; +} + +template +__global__ void _addFoldedPositiveAndShiftedNegSample(ElemType * folded, const ElemType * unfolded, const CUDA_LONG unfoldedN, const CUDA_LONG unfoldedRows, const CUDA_LONG unfoldedCols, const CUDA_LONG foldedRows, const CUDA_LONG posNumber, const CUDA_LONG shiftNumber) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= unfoldedN) + return; + + CUDA_LONG unfoldedCol = id / unfoldedRows; + CUDA_LONG unfoldedRow = id - (unfoldedCol * unfoldedRows); + + CUDA_LONG sampleInUnfoldedCol = unfoldedRow / foldedRows; + CUDA_LONG foldedRow = unfoldedRow - foldedRows * sampleInUnfoldedCol; + CUDA_LONG foldedCol = sampleInUnfoldedCol < posNumber ? unfoldedCol : (unfoldedCol + shiftNumber + sampleInUnfoldedCol - posNumber) % unfoldedCols; + + atomicAdd(&folded[IDX2C(foldedRow, foldedCol, foldedRows)], unfolded[id]); +} + +template +__global__ void _assignDifferenceOf1( + ElemType* us, + const ElemType alpha, + const ElemType* a, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + us[id]=alpha-a[id]; +}; + +template +__global__ void _assignDifferenceOf2( + ElemType* us, + const ElemType alpha, + const ElemType* a, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + us[id]=a[id]-alpha; +}; + +///a is a scalar +template +__global__ void _scaleAndAddScalar( + ElemType* c, + const CUDA_LONG N, + const ElemType alpha, + const ElemType *a, + const ElemType *b +) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + c[id] = alpha*a[0] + b[id]; +}; + +template +__global__ void _multiply1x1AndWeightedAdd( + ElemType alpha, const ElemType* a, const ElemType* b, ElemType beta, ElemType* c, CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + ElemType f = alpha * *a; // scalar matrix + if (beta == 0) // don't even read the memory if beta is 0 + c[id] = b[id] * f; + else + c[id] = b[id] * f + c[id] * beta; +} + +template +__global__ void _addValue( + ElemType* a, + const ElemType v, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]+=v; +}; + +template +__global__ void _addValue( + ElemType* a, + const ElemType* d_v, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]+=d_v[0]; +}; + + +template +__global__ void _elemMul( + ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]*=b[id]; +}; + +template +__global__ void _assignElementProductOf( + ElemType* us, + const ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + us[id]=a[id]*b[id]; +} + +template +__global__ void _assignKhatriRaoProductOf( + ElemType* us, + const ElemType* a, + const ElemType* b, + const CUDA_LONG rowsA, + const CUDA_LONG rowsB, + const CUDA_LONG cols) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + + const CUDA_LONG rows = rowsA * rowsB; + const CUDA_LONG col = id / rows; + if (col >= cols) + return; + + const CUDA_LONG row = id % rows; + const CUDA_LONG rowB = row / rowsA; + const CUDA_LONG rowA = row % rowsA; + + us[id] = a[rowA + col * rowsA] * b[rowB + col * rowsB]; +} + +template +__global__ void _addColumnReshapeProductOf( + ElemType* us, + const ElemType* a, + const ElemType* b, + const CUDA_LONG rowsB, + const CUDA_LONG rowsC, + const CUDA_LONG cols, + const bool transposeAColumn) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + + const CUDA_LONG col = id / rowsC; + if (col >= cols) + return; + + const CUDA_LONG row = id % rowsC; + CUDA_LONG bBase = col * rowsB; + CUDA_LONG aBase = bBase * rowsC; + ElemType v = 0; + + if (transposeAColumn) + { + aBase += row * rowsB; + for (CUDA_LONG i=0; i +__global__ void _assignElementDivisionOf( + ElemType* us, + const ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + ElemType smallValue = EPS_IN_INVERSE; + + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + ElemType v = b[id]; + + if (v <0 && v > -smallValue) + us[id] = a[id]/(-smallValue); + else if (v >=0 && v < smallValue) + us[id] = a[id]/smallValue; + else + us[id]=a[id]/v; +} + +template +__global__ void _elemInverse( + ElemType* us, + const CUDA_LONG N) +{ + ElemType smallValue = EPS_IN_INVERSE; + + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + if (us[id] <0 && us[id] > -smallValue) + us[id] = 1/-smallValue; + else if (us[id] >=0 && us[id] < smallValue) + us[id] = 1/smallValue; + else + us[id]=1/us[id]; +} + +template +__global__ void _logSoftMaxColWise( + ElemType *a, + const CUDA_LONG m_numCols, + const CUDA_LONG m_numRows) //ld +{ + int col_id = blockDim.x * blockIdx.x + threadIdx.x; + if (col_id >= m_numCols) + return; + + __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; + __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; + maxV[threadIdx.x] = a[IDX2C(0, col_id, m_numRows)]; + Sum[threadIdx.x] = 0; + + for (CUDA_LONG i = 0; imaxV[threadIdx.x]) + { + maxV[threadIdx.x] = a[IDX2C(i, col_id, m_numRows)]; + } + } + + for (CUDA_LONG i = 0; i +//__global__ void _assignColumnwiseSoftmaxOf( +// const ElemType *a, +// ElemType* us, +// const CUDA_LONG m_numCols, +// const CUDA_LONG m_numRows) //thead per column +//{ +// int col_id = blockDim.x * blockIdx.x + threadIdx.x; +// if (col_id>=m_numCols) +// return; +// +// __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; +// __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; +// maxV[threadIdx.x]=a[IDX2C(0,col_id,m_numRows)]; +// Sum[threadIdx.x]=0; +// +// for (CUDA_LONG i=0;imaxV[threadIdx.x]) +// { +// maxV[threadIdx.x]=a[IDX2C(i,col_id,m_numRows)]; +// } +// } +// +// for (CUDA_LONG i=0;i +__global__ void _assignColumnwiseLogSoftmaxOf( + const ElemType *a, + ElemType* us, + const CUDA_LONG m_numCols, + const CUDA_LONG m_numRows) +{ + // We first find max per column + __shared__ ElemType colMax[1]; + __shared__ ElemType partials[512]; + colMax[0] = -10000000; + partials[threadIdx.x] = -10000000; + + for (int i = threadIdx.x; i < m_numRows; i += 512) + { + partials[threadIdx.x] = max(partials[threadIdx.x], a[IDX2C(i, blockIdx.x, m_numRows)]); + } + __syncthreads(); + + if (threadIdx.x < 256) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 256], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 128) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 128], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 64) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 64], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 32) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 32], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 16) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 16], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 8) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 8], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x < 4) + { + partials[threadIdx.x] = max(partials[threadIdx.x + 4], partials[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x == 0) + { + colMax[0] = max(max(partials[0], partials[1]), max(partials[2], partials[3])); + } + partials[threadIdx.x] = 0.0f; + __syncthreads(); + + // Now start finding sums + __shared__ ElemType colSum[1]; + colSum[0] = 0.0f; + for (int i = threadIdx.x; i < m_numRows; i += 512) + { + ElemType tmp = a[IDX2C(i, blockIdx.x, m_numRows)] - colMax[0]; + us[IDX2C(i, blockIdx.x, m_numRows)] = tmp; + partials[threadIdx.x] += (sizeof(ElemType) == sizeof(float)) ? expf(tmp) : exp(tmp); + } + __syncthreads(); + + if (threadIdx.x < 256) + { + partials[threadIdx.x] += partials[threadIdx.x + 256]; + } + __syncthreads(); + + if (threadIdx.x < 128) + { + partials[threadIdx.x] += partials[threadIdx.x + 128]; + } + __syncthreads(); + + if (threadIdx.x < 64) + { + partials[threadIdx.x] += partials[threadIdx.x + 64]; + } + __syncthreads(); + + if (threadIdx.x < 32) + { + partials[threadIdx.x] += partials[threadIdx.x + 32]; + } + __syncthreads(); + + if (threadIdx.x < 16) + { + partials[threadIdx.x] += partials[threadIdx.x + 16]; + } + __syncthreads(); + + if (threadIdx.x < 8) + { + partials[threadIdx.x] += partials[threadIdx.x + 8]; + } + __syncthreads(); + + if (threadIdx.x < 4) + { + partials[threadIdx.x] += partials[threadIdx.x + 4]; + } + __syncthreads(); + + if (threadIdx.x == 0) + { + colSum[0] = partials[0] + partials[1] + partials[2] + partials[3]; + colSum[0] = (sizeof(ElemType) == sizeof(float)) ? logf(colSum[0]) : log(colSum[0]); + } + __syncthreads(); + + for (int i = threadIdx.x; i < m_numRows; i += 512) + { + us[IDX2C(i, blockIdx.x, m_numRows)] -= colSum[0]; + } +} + +template +__global__ void _logSoftMaxRowWise( + ElemType *a, + const CUDA_LONG m_numCols, + const CUDA_LONG m_numRows) //ld +{ + int row_id = blockDim.x * blockIdx.x + threadIdx.x; + if (row_id >= m_numRows) + return; + + __shared__ ElemType maxV[GridDim::maxThreadsPerBlock]; + __shared__ ElemType Sum[GridDim::maxThreadsPerBlock]; + maxV[threadIdx.x] = a[IDX2C(row_id, 0, m_numRows)]; + Sum[threadIdx.x] = 0; + + for (CUDA_LONG j = 0; jmaxV[threadIdx.x]) + { + maxV[threadIdx.x] = a[IDX2C(row_id, j, m_numRows)]; + } + } + + for (CUDA_LONG j = 0; j +__global__ void _assignColumnwiseHardmaxOf( + const ElemType *a, + ElemType* us, + const CUDA_LONG m_numCols, + const CUDA_LONG m_numRows) +{ + // We first find max per column + __shared__ ElemType partials[512]; + __shared__ int colMaxI[512]; + int row = threadIdx.x % m_numRows; + colMaxI[threadIdx.x] = row; + partials[threadIdx.x] = a[IDX2C(row, blockIdx.x, m_numRows)]; + + for (int i = threadIdx.x; i < m_numRows; i += 512) + { + if (partials[threadIdx.x] < a[IDX2C(i, blockIdx.x, m_numRows)]) + { + partials[threadIdx.x] = a[IDX2C(i, blockIdx.x, m_numRows)]; + colMaxI[threadIdx.x] = i; + } + } + __syncthreads(); + + if (m_numRows > 256) + { + if (threadIdx.x < 256) + { + int other = threadIdx.x + 256; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 128) + { + if (threadIdx.x < 128) + { + int other = threadIdx.x + 128; + + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 64) + { + if (threadIdx.x < 64) + { + int other = threadIdx.x + 64; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 32) + { + if (threadIdx.x < 32) + { + int other = threadIdx.x + 32; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 16) + { + if (threadIdx.x < 16) + { + int other = threadIdx.x + 16; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 8) + { + if (threadIdx.x < 8) + { + int other = threadIdx.x + 8; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (m_numRows > 4) + { + if (threadIdx.x < 4) + { + int other = threadIdx.x + 4; + if (partials[threadIdx.x] < partials[other]) + { + partials[threadIdx.x] = partials[other]; + colMaxI[threadIdx.x] = colMaxI[other]; + } + } + __syncthreads(); + } + + if (threadIdx.x == 0) + { + for (int i = 1; i < 4 && i < m_numRows; i++) + { + if (partials[0] < partials[i]) + { + partials[0] = partials[i]; + colMaxI[0] = colMaxI[i]; + } + } + } + __syncthreads(); + + for (int i = threadIdx.x; i < m_numRows; i += 512) + { + us[IDX2C(i, blockIdx.x, m_numRows)] = (i == colMaxI[0]) ? 1 : 0; + } +} + +#if 0 +template +__global__ void _inplaceTruncateBottom( + ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + if (a[id] +__global__ void _assignTruncateBottom( + ElemType* us, + const ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + us[id] = a[id] < threshold ? threshold : a[id]; +} + +#if 0 +template +__global__ void _inplaceTruncateTop( + ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + if (a[id]>threshold) + a[id]=threshold; +} +#endif + +template +__global__ void _assignTruncateTop( + ElemType* us, + const ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + us[id] = a[id] > threshold ? threshold : a[id]; +} + +template +__global__ void _setToZeroIfAbsLessThan( + ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + if (sizeof(ElemType)==sizeof(float)) + { + if (fabsf(a[id]) +__global__ void _areEqual( + const ElemType* a, + const ElemType* b, + const CUDA_LONG N, + const ElemType threshold, + long *d_res) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + if (sizeof(ElemType)==sizeof(float)) + { + if (fabsf(a[id]-b[id]) > threshold) + { + d_res[0]=0; + } + } + else + { + if (fabs(1.0*a[id]-1.0*b[id]) > threshold) + { + d_res[0]=0; + } + } +} + +// see Matrix::TensorShuffleScaleAndAdd() for comments +template +__global__ void _tensorShuffleScaleAndAdd( + ElemType keepWeight, const ElemType* pa, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const ElemType* pb, ElemType* pc) +{ + size_t N = D * S * M * K * T; + CUDA_LONG na = blockDim.x * blockIdx.x + threadIdx.x; // input tensor of dimension (D x S x M x K x T) + if (na >= N) + return; + // recover the 5 indices from the loop counter + size_t d = na % D; + size_t s = (na / D ) % S; + size_t m = (na / D / S ) % M; + size_t k = (na / D / S / M ) % K; + size_t t = (na / D / S / M / K) % T; + // compute index for the a and b/c tensors + size_t nb = (((t * S + s) * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S x T): k/K and s/S swapped + // perform the computation + ElemType cval = keepWeight ? keepWeight * pb[nb] : 0; // if weight is 0 then don't bother to read memory (efficiency) or to multiply (NaN-safe) + cval += scaleFactor * pa[na]; + pc[nb] = cval; +} + +// see Matrix::TensorShuffleScaleAndAdd() for comments +template +__global__ void _tensorShuffleScaleAndAddRowSparse( + const ElemType* anzValues, //source nz values + const GPUSPARSE_INDEX_TYPE* aRowIndex, + const GPUSPARSE_INDEX_TYPE* aColCSCIndex, + ElemType* cnzValues, //target nz values + GPUSPARSE_INDEX_TYPE* cRowIndex, + GPUSPARSE_INDEX_TYPE* cColCSCIndex, + size_t D, size_t S, size_t M, size_t K, size_t T, + size_t nz) +{ + CUDA_LONG N = blockDim.x * blockIdx.x + threadIdx.x; // input tensor of dimension (D x S x M x K x T) + if (N < aColCSCIndex[0] || N >= aColCSCIndex[T]) + return; + + size_t col; + for (col = 0; col < T; col++) + { + if (aColCSCIndex[col + 1] > N) + break; + } + + size_t na = aRowIndex[N]; + int start = aColCSCIndex[col]; + int end = aColCSCIndex[col + 1]; + + // recover the 5 indices from the loop counter + size_t d = (na ) % D; + size_t s = (na / D ) % S; + size_t m = (na / D / S ) % M; + size_t k = (na / D / S / M ) % K; + + // compute index for the a and b/c tensors + size_t nc = ((s * M + m) * K + k) * D + d; // output tensor of dimension (D x K x M x S): k/K and s/S swapped + + int rowIdx = start; + for (size_t j = start; j < end; j++) + { + // recover the 5 indices from the loop counter + size_t na_i = aRowIndex[j]; + size_t d_i = (na_i ) % D; + size_t s_i = (na_i / D ) % S; + size_t m_i = (na_i / D / S ) % M; + size_t k_i = (na_i / D / S / M ) % K; + + // compute index for the a and b/c tensors + size_t nc_i = ((s_i * M + m_i) * K + k_i) * D + d_i; // output tensor of dimension (D x K x M x S): k/K and s/S swapped + if (nc_i < nc) + { + rowIdx++; + } + } + + cnzValues[rowIdx] = anzValues[N]; + cRowIndex[rowIdx] = nc; + + if (N == 0) + { + for (int i = 0; i <= T; i++) + { + cColCSCIndex[i] = aColCSCIndex[i]; + } + } +} + +template +__global__ void _hasElement( + const ElemType* a, + const CUDA_LONG N, + ElemType *d_res /// [2x1] vector. The first is the value to be compared and the second is the 0/1 to return + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + if (a[id] == d_res[0]) + { + d_res[1] = 1; + } +} + +template +__global__ void _setDiagonalValue( + ElemType* a, + const ElemType v, + const CUDA_LONG N, + const CUDA_LONG ld) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[IDX2C(id,id,ld)]=v; + +} + +template +__global__ void _setDiagonalValueFromVector( + ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[IDX2C(id,id,N)]=b[id]; +} + +template +__global__ void _adagrad( + ElemType* a, + ElemType* d_v, + const CUDA_LONG N, + ElemType* multipliers) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + const ElemType floor = 1e-16f; + + a[id] += d_v[id] * d_v[id]; + ElemType temp = sqrt(a[id]+floor); + d_v[id] /= temp; + + if (multipliers != nullptr) + multipliers[id] = 1/temp; +} + +template +__global__ void _adagrad4BlockSparse( + ElemType* a, //dense + const size_t numRows, //number of rows in a and in d_v + ElemType* d_v, //block sparse + const GPUSPARSE_INDEX_TYPE* blockId2ColOrRow, + ElemType* multipliers, + const bool colMajor, + const size_t len, //major dim, numRows in colMajor and numcols in rowMajor + const CUDA_LONG N) //total number of non-zero values +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + const ElemType floor = 1e-16f; + CUDA_LONG blockid = id / len; + CUDA_LONG row = colMajor ? id - blockid*len : blockId2ColOrRow[blockid]; + CUDA_LONG col = colMajor ? blockId2ColOrRow[blockid] : id - blockid*len; + + size_t indexInA = row + col*numRows; + a[indexInA] += d_v[id] * d_v[id]; + ElemType temp = sqrt(a[indexInA] + floor); + d_v[id] /= temp; + + if (multipliers != nullptr) + multipliers[id] = 1 / temp; +} + +template +__global__ void _fsadagrad(CUDA_LONG size, ElemType* grad, ElemType* smoothAda, ElemType* smoothMom, ElemType* val, + ElemType lr, ElemType mom, ElemType adaWeight, ElemType adaMul) +{ + CUDA_LONG idx = blockIdx.x * blockDim.x + threadIdx.x; + CUDA_LONG stride = blockDim.x * gridDim.x; + for (; idx < size; idx += stride) + { + ElemType g = grad[idx]; + ElemType adaSqr = adaWeight * smoothAda[idx] + (1.0f - adaWeight) * g * g; + smoothAda[idx] = adaSqr; + if (adaSqr != 0.0f) + { + ElemType w; + if (sizeof(ElemType) == sizeof(double)) + { + w = adaMul * rsqrt(adaSqr); + } + else + { + w = adaMul * rsqrtf(adaSqr); + } + + if (w > 10.0f) + w = 10.0f; + g *= w; + } + + if (mom > 0.0f) + { + g = mom * smoothMom[idx] + (1.0f - mom) * g; + smoothMom[idx] = g; + } + + g *= lr; + val[idx] -= g; + } +} + +template +__global__ void _rmsprop_init( + ElemType* avars, ElemType* signs, ElemType* steps, + ElemType* curr_grad, + const CUDA_LONG N + ) +{ + CUDA_LONG i = blockDim.x * blockIdx.x + threadIdx.x; + if (i >= N) + return; + + ElemType tmp = curr_grad[i]; + avars[i] = tmp * tmp; + signs[i] = ElemType(0.0); + steps[i] = ElemType(0.02); +} + +template +__global__ void _rmsprop( + ElemType* avars, ElemType* signs, ElemType* steps, + ElemType* curr_grad, + const CUDA_LONG N, + ElemType RMS_GAMMA,ElemType RMS_WGT_INC,ElemType RMS_WGT_MAX,ElemType RMS_WGT_DEC,ElemType RMS_WGT_MIN, + ElemType floor, + ElemType *upd_gpu, + ElemType* multipliers + ) +{ + CUDA_LONG i = blockDim.x * blockIdx.x + threadIdx.x; + if (i >= N) + return; + + avars[i] = RMS_GAMMA * avars[i] + (ElemType(1.0)-RMS_GAMMA)* (curr_grad[i] * curr_grad[i]); + + //// grad sign base 3: 0->neg, 1->zero, 2->pos + //const int grad_sign = 1 + (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); + + //// signs[i] contains three consecutive grad_sign + //signs[i] = 3*(int(signs[i]) % 9) + grad_sign; + + //// update according to the following table: + //// (!pos,!pos,!pos) or (!neg,!neg,!neg): RMS_WGT_INC + //// (!neg,!neg,neg) or (!pos,!pos,pos): RMS_WGT_DEC + //// otherwise: no action + + //switch(int(upd_gpu[int(signs[i])])) + //{ + //case 0: + // steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); + // break; + //case 2: + // steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); + // break; + //} + //curr_grad[i] *= steps[i] / sqrt(avars[i] + floor); + + const int grad_sign = (ElemType(0) < curr_grad[i]) - (curr_grad[i] < ElemType(0)); + + if( signs[i] * grad_sign > 0 ) + steps[i] = min(steps[i] * RMS_WGT_INC, RMS_WGT_MAX); + else + steps[i] = max(steps[i] * RMS_WGT_DEC, RMS_WGT_MIN); + + ElemType temp = steps[i] / sqrt(avars[i] + floor); + curr_grad[i] *= temp; + signs[i] = grad_sign; + + if (multipliers != nullptr) + multipliers[i] = temp; +} + +template +__global__ void _rescaleToRange( + ElemType* a, + const CUDA_LONG N, + const ElemType low, + const ElemType high) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]=a[id]*(high-low)+low; +} + +template +__global__ void _setMaskAndScale( + ElemType* a, + const CUDA_LONG N, + const ElemType maskRate, + const ElemType scaleValue) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + a[id]=a[id]<=maskRate? 0 : scaleValue; +} + +template +__global__ void _vectorSum( + ElemType* c, //output + const ElemType* a, //input + const CUDA_LONG n, //a.numRows + const CUDA_LONG m, //a.numCols + const bool isColWise) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if ((isColWise && id >= m) || (!isColWise && id >= n)) + return; + + ElemType sum = 0; + + if (isColWise) + { + for (CUDA_LONG i = 0; i +__global__ void _vectorNorm1( + ElemType* c, //output + const ElemType* a, //input + const CUDA_LONG n, //a.numRows + const CUDA_LONG m, //a.numCols + const bool isColWise) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if ((isColWise && id>=m)||(!isColWise && id>=n)) + return; + + ElemType sum = 0; + + if (isColWise) + { + for (CUDA_LONG i=0;i +__global__ void _vectorNorm2( + ElemType* c, //output + const ElemType* a, //input + const CUDA_LONG N, //a.GetNumRows(); + const CUDA_LONG M, //a.GetNumCols(); + const bool isColWise) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if ((isColWise && id>=M) || (!isColWise && id>=N)) + return; + + ElemType sum = 0; + if (isColWise) + { + for (CUDA_LONG i=0;i +__global__ void _convertInd2ValsAdjustInd( + ElemType* inds, + const ElemType* M, + ElemType* vals, + const CUDA_LONG n, //number of cols + const CUDA_LONG m, //number of rows + const bool isColWise) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if ((isColWise && id>=n)||(!isColWise && id>=m)) + return; + inds[id]--; + if (isColWise) + { + vals[id]=M[IDX2C((int)inds[id],id,m)]; + } + else + { + vals[id]=M[IDX2C(id,(int)inds[id],m)]; + } +} + + + //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) +template +__global__ void _assignPackedConvolutionInput(ElemType * packedMatrix, const ElemType * inputSubBatch, const CUDA_LONG batchSize, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputChannels, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputChannels, + const CUDA_LONG kernelWidth, const CUDA_LONG kernelHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample, const bool zeroPadding) +{ + const CUDA_LONG inputHeightTimesChannel = inputHeight * inputChannels; + const size_t inputDim = inputWidth*inputHeightTimesChannel; + + const CUDA_LONG idall = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = idall / inputDim; + if (sample >= batchSize) + return; + + const CUDA_LONG id = idall % inputDim; + const CUDA_LONG y = id / inputHeightTimesChannel; //inputCol + + const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; + const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * inputChannels) + // IN_ELEM_COLPOS = sample + + const CUDA_LONG nXC = id % inputHeightTimesChannel; //channel + inputRow*inputChannels + const CUDA_LONG x = nXC / inputChannels; //inputRow + const CUDA_LONG c = nXC % inputChannels; //channel + + ElemType currentInputValue = inputSubBatch[id + sample*inputDim]; + + CUDA_LONG x0 = 0, y0 = 0, x1 = 0, y1 = 0; + if (zeroPadding) + { + const CUDA_LONG halfKernelWidth = kernelWidth/2; + const CUDA_LONG halfKernelHeight = kernelHeight/2; + + x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1.0f+halfKernelHeight)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in + x1 = x+halfKernelHeight-x0*verticalSubsample; //first posxInKernel + y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1.0f+halfKernelWidth)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in + y1 = y+halfKernelWidth-y0*horizontalSubsample; //first posyInKernel + } + else + { + x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in + x1 = x-x0*verticalSubsample; //first posxInKernel + y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in + y1 = y-y0*horizontalSubsample; //first posyInKernel + } + + // PACK_ELEM_ROWPOS(channel, posxInKernel, posyInKernel) = (channel * kernelWidth * kernelHeight + posxInKernel + posyInKernel * kernelHeight) + // PACK_ELEM_COLPOS(sample, wrow, wcol) = (sample*packedInputColsPerSample + outputHeight*wcol + wrow + + CUDA_LONG packColBase = sample*packedInputColsPerSample + y0*outputHeight; + for (CUDA_LONG wcol = y0, posyInKernel = y1; wcol < outputWidth && posyInKernel>=0; wcol++, posyInKernel -= horizontalSubsample) + { + CUDA_LONG packRowBase = c * kernelWidth * kernelHeight + posyInKernel * kernelHeight; + for (CUDA_LONG wrow = x0, posxInKernel = x1; wrow < outputHeight && posxInKernel>=0; wrow++, posxInKernel -= verticalSubsample) + { + const CUDA_LONG packRow = packRowBase + posxInKernel; + const CUDA_LONG packCol = packColBase + wrow; + packedMatrix[packRow + packCol*packedInputRows] = currentInputValue; + } + packColBase += outputHeight; + } +} + + //assume each column is an input sample. Each sample is stored in [channel, row, col] (r00, g00, b00, r01, g01, b01, r10, g10, b10, r11, g11, b11) +template +__global__ void _unpackConvolutionInput(const ElemType * packedMatrix, ElemType * inputSubBatch, const CUDA_LONG batchSize, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputChannels, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputChannels, + const CUDA_LONG kernelWidth, const CUDA_LONG kernelHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample, const bool zeroPadding) +{ + const CUDA_LONG inputHeightTimesChannel = inputHeight * inputChannels; + const size_t inputDim = inputWidth*inputHeightTimesChannel; + + const CUDA_LONG idall = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = idall / inputDim; + if (sample >= batchSize) + return; + + const CUDA_LONG id = idall % inputDim; + const CUDA_LONG y = id / inputHeightTimesChannel; //inputCol + + const size_t packedInputRows = kernelWidth * kernelHeight * inputChannels; + const size_t packedInputColsPerSample = outputWidth * outputHeight; //output size per channel + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * inputChannels) + // IN_ELEM_COLPOS = sample + + const CUDA_LONG nXC = id % inputHeightTimesChannel; //channel + inputRow*inputChannels + const CUDA_LONG x = nXC / inputChannels; //inputRow + const CUDA_LONG c = nXC % inputChannels; //channel + + CUDA_LONG x0 = 0, y0 = 0, x1 = 0, y1 = 0; + if (zeroPadding) + { + const CUDA_LONG halfKernelWidth = kernelWidth/2; + const CUDA_LONG halfKernelHeight = kernelHeight/2; + + x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1.0f+halfKernelHeight)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in + x1 = x+halfKernelHeight-x0*verticalSubsample; //first posxInKernel + y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1.0f+halfKernelWidth)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in + y1 = y+halfKernelWidth-y0*horizontalSubsample; //first posyInKernel + } + else + { + x0 = max(0.0f, ceil((x-(ElemType)kernelHeight+1)/ (ElemType)verticalSubsample)); //row : first wrow in which x is in + x1 = x-x0*verticalSubsample; //first posxInKernel + y0 = max(0.0f, ceil((y-(ElemType)kernelWidth+1)/(ElemType)horizontalSubsample)); //col : first wcol in which y is in + y1 = y-y0*horizontalSubsample; //first posyInKernel + } + + // PACK_ELEM_ROWPOS(channel, posxInKernel, posyInKernel) = (channel * kernelWidth * kernelHeight + posxInKernel + posyInKernel * kernelHeight) + // PACK_ELEM_COLPOS(sample, wrow, wcol) = (sample*packedInputColsPerSample + outputHeight*wcol + wrow + + ElemType currentInputValue = inputSubBatch[id + sample*inputDim]; + CUDA_LONG packColBase = sample*packedInputColsPerSample + y0*outputHeight; + for (CUDA_LONG wcol = y0, posyInKernel = y1; wcol < outputWidth && posyInKernel>=0; wcol++, posyInKernel -= horizontalSubsample) + { + CUDA_LONG packRowBase = c * kernelWidth * kernelHeight + posyInKernel * kernelHeight; + for (CUDA_LONG wrow = x0, posxInKernel = x1; wrow < outputHeight && posxInKernel>=0; wrow++, posxInKernel -= verticalSubsample) + { + const CUDA_LONG packRow = packRowBase + posxInKernel; + const CUDA_LONG packCol = packColBase + wrow; + currentInputValue += packedMatrix[packRow + packCol*packedInputRows]; + } + packColBase += outputHeight; + } + + inputSubBatch[id + sample*inputDim] = currentInputValue; +} + +template +__global__ void _assignMaxPoolingResult(ElemType * outputBatch, const ElemType * inputBatch, const CUDA_LONG batchSize, const CUDA_LONG channels, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, + const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) +{ + const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = outputIndex / outputSizePerSample; + if (sample >= batchSize) + return; + + const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; + const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; + const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; + + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + + const CUDA_LONG y = outputIndexWithinSample / outputHeightTimesChannel; //wcol + const CUDA_LONG nXC = outputIndexWithinSample % outputHeightTimesChannel; //channel + wrow*channels + const CUDA_LONG x = nXC / channels; //wrow + const CUDA_LONG c = nXC % channels; //channel + + const ElemType *inputBatchBase4Sample = inputBatch + sample*inputSizePerSample; + register ElemType maxVal = -FLT_MAX; + const CUDA_LONG rowInWindowBase = (x*verticalSubsample + y*horizontalSubsample*inputHeight)*channels+c; + for (CUDA_LONG colInWindow=0; colInWindow +__global__ void _addMaxPoolingGradient(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, const ElemType * inputBatch, const ElemType * outputBatch, + const CUDA_LONG batchSize, const CUDA_LONG channels, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, + const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) +{ + const CUDA_LONG inputIndex = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = inputIndex / inputSizePerSample; + if (sample >= batchSize) + return; + + const CUDA_LONG inputIndexWithinSample = inputIndex % inputSizePerSample; + + const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; + const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + + const CUDA_LONG y = inputIndexWithinSample / inputHeightTimesChannel; //col in input + const CUDA_LONG nXC = inputIndexWithinSample % inputHeightTimesChannel; //channel + row*chanels + const CUDA_LONG x = nXC / channels; //row in input + const CUDA_LONG c = nXC % channels; //channel + + CUDA_LONG startOutX = max(0.0f, ceil((x-(ElemType)windowHeight+1)/ (ElemType)verticalSubsample)); //inclusive start + CUDA_LONG endOutX = (x/verticalSubsample < outputHeight-1)? x/verticalSubsample : outputHeight-1; //inclusive end + CUDA_LONG startOutY = max(0.0f, ceil((y-(ElemType)windowWidth+1)/(ElemType)horizontalSubsample)); //inclusive start + CUDA_LONG endOutY = (y/horizontalSubsample < outputWidth-1)? y/horizontalSubsample : outputWidth-1; //inclusive end + + + ElemType *inputGradientBatchBase4Sample = inputGradientBatch + sample*inputSizePerSample; + const ElemType *outputGradientBatchBase4Sample = outputGradientBatch + sample*outputSizePerSample; + const ElemType * outputBatchBase4Sample = outputBatch + sample*outputSizePerSample; + + ElemType inputValue = inputBatch[inputIndexWithinSample + sample*inputSizePerSample]; + for (CUDA_LONG outY=startOutY; outY<=endOutY; outY++) + { + for (CUDA_LONG outX=startOutX; outX<=endOutX; outX++) + { + CUDA_LONG outputIndex = outY * outputHeightTimesChannel + outX * channels + c; + if (inputValue == outputBatchBase4Sample[outputIndex]) + inputGradientBatchBase4Sample[inputIndexWithinSample] += outputGradientBatchBase4Sample[outputIndex]; + } + } +} +template +__global__ void _assignAveragePoolingResult(ElemType * outputBatch, const ElemType * inputBatch, const CUDA_LONG batchSize, const CUDA_LONG channels, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, + const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) +{ + const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = outputIndex / outputSizePerSample; + if (sample >= batchSize) + return; + + const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; + const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; + const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; + + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + + const CUDA_LONG y = outputIndexWithinSample / outputHeightTimesChannel; //wcol + const CUDA_LONG nXC = outputIndexWithinSample % outputHeightTimesChannel; //channel + wrow*channels + const CUDA_LONG x = nXC / channels; //wrow + const CUDA_LONG c = nXC % channels; //channel + + const ElemType *inputBatchBase4Sample = inputBatch + sample*inputSizePerSample; + + register ElemType average = 0; + const CUDA_LONG rowInWindowBase = (x*verticalSubsample + y*horizontalSubsample*inputHeight)*channels+c; + for (CUDA_LONG colInWindow=0; colInWindow +__global__ void _addAveragePoolingGradient(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, + const CUDA_LONG batchSize, const CUDA_LONG channels, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, + const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) +{ + const CUDA_LONG inputIndex = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = inputIndex / inputSizePerSample; + if (sample >= batchSize) + return; + + const CUDA_LONG inputIndexWithinSample = inputIndex % inputSizePerSample; + + const CUDA_LONG inputHeightTimesChannel = inputHeight * channels; + const CUDA_LONG outputHeightTimesChannel = outputHeight * channels; + const CUDA_LONG windowSize = windowWidth * windowHeight; + + // IN_ELEM_ROWPOS(channel, row, col) = (channel + (row + col * inputHeight) * channels) + // IN_ELEM_COLPOS = sample + + // OUT_ELEM_ROWPOS(channel, wrow, wcol) = (channel + (wrow + wcol * outputHeight) * channels) + // OUT_ELEM_COLPOS = sample + + const CUDA_LONG y = inputIndexWithinSample / inputHeightTimesChannel; //col in input + const CUDA_LONG nXC = inputIndexWithinSample % inputHeightTimesChannel; //channel + row*chanels + const CUDA_LONG x = nXC / channels; //row in input + const CUDA_LONG c = nXC % channels; //channel + + CUDA_LONG startOutX = max(0.0f, ceil((x-(ElemType)windowHeight+1)/ (ElemType)verticalSubsample)); //inclusive start + CUDA_LONG endOutX = (x/verticalSubsample < outputHeight-1)? x/verticalSubsample : outputHeight-1; //inclusive end + CUDA_LONG startOutY = max(0.0f, ceil((y-(ElemType)windowWidth+1)/(ElemType)horizontalSubsample)); //inclusive start + CUDA_LONG endOutY = (y/horizontalSubsample < outputWidth-1)? y/horizontalSubsample : outputWidth-1; //inclusive end + + ElemType *inputGradientBatchBase4Sample = inputGradientBatch + sample*inputSizePerSample; + const ElemType *outputGradientBatchBase4Sample = outputGradientBatch + sample*outputSizePerSample; + + for (CUDA_LONG outY=startOutY; outY<=endOutY; outY++) + { + for (CUDA_LONG outX=startOutX; outX<=endOutX; outX++) + { + CUDA_LONG outputIndex = outY * outputHeightTimesChannel + outX * channels + c; + inputGradientBatchBase4Sample[inputIndexWithinSample] += outputGradientBatchBase4Sample[outputIndex]/windowSize; + } + } +} + +template +__global__ void _addMaxPoolingGradientLoopOut(ElemType * inputGradientBatch, const ElemType * outputGradientBatch, const ElemType * inputBatch, const ElemType * outputBatch, + const CUDA_LONG batchSize, const CUDA_LONG channels, + const CUDA_LONG inputWidth, const CUDA_LONG inputHeight, const CUDA_LONG inputSizePerSample, + const CUDA_LONG outputWidth, const CUDA_LONG outputHeight, const CUDA_LONG outputSizePerSample, + const CUDA_LONG windowWidth, const CUDA_LONG windowHeight, const CUDA_LONG horizontalSubsample, const CUDA_LONG verticalSubsample) +{ + const CUDA_LONG outputIndex = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG sample = outputIndex / outputSizePerSample; + if (sample >= batchSize) + return; + + const CUDA_LONG outputIndexWithinSample = outputIndex % outputSizePerSample; + const CUDA_LONG inputWidthTimesChannel = inputWidth * channels; + const CUDA_LONG outputWidthTimesChannel = outputWidth * channels; + const CUDA_LONG y = outputIndexWithinSample / outputWidthTimesChannel; + const CUDA_LONG nXC = outputIndexWithinSample % outputWidthTimesChannel; + const CUDA_LONG x = nXC / channels; + const CUDA_LONG c = nXC % channels; + + const CUDA_LONG offset0 = sample*inputSizePerSample + y*verticalSubsample*inputWidthTimesChannel + x*horizontalSubsample*channels; + const ElemType *pCurWindow4Input = inputBatch + offset0; // pooling to current window's first input pixel + ElemType *pCurWindow4InGradient = inputGradientBatch + offset0; + for (CUDA_LONG yy=0; yy +__global__ void _addElementProductOf( + ElemType* us, + const ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + us[id]+=(a[id]*b[id]); +} + +template +__global__ void _columnElementMultiplyWith( + ElemType* us, + const ElemType* a, + const CUDA_LONG N, //a.GetNumRows(); + const CUDA_LONG M) //us.GetNumCols(); +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; + //_a[threadIdx.x]=a[id]; + ElemType mul=a[id]; + for (CUDA_LONG j=0;j +__global__ void _rowElementMultiplyWith( + ElemType* us, + const ElemType* a, + const CUDA_LONG N, //us.GetNumRows(); + const CUDA_LONG M) //a.GetNumCols(); +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=M) + return; + + //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; + //_a[threadIdx.x]=a[id]; + ElemType mul=a[id]; + for (CUDA_LONG i=0;i +__global__ void _rowElementDivideBy( + ElemType* us, + const ElemType* a, + const CUDA_LONG N, //us.GetNumRows(); + const CUDA_LONG M) //a.GetNumCols(); +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= M) + return; + + //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; + //_a[threadIdx.x]=a[id]; + ElemType v = a[id]; + if (v >= 0 && v < EPS_IN_INVERSE) + v = EPS_IN_INVERSE; + else if (v < 0 && v > -EPS_IN_INVERSE) + v = (-EPS_IN_INVERSE); + + for (CUDA_LONG i = 0; i +__global__ void _ColumnElementDivideBy( + ElemType* us, + const ElemType* a, + const CUDA_LONG N, //a.GetNumRows(); + const CUDA_LONG M) //us.GetNumCols(); +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + + ElemType smallValue = EPS_IN_INVERSE; + + //__shared__ ElemType _a[GridDim::maxThreadsPerBlock]; + //_a[threadIdx.x]=a[id]; + ElemType v=a[id]; + for (CUDA_LONG j=0;j -smallValue) + us[IDX2C(id,j,N)] /= (-smallValue); + else if (v >=0 && v < smallValue) + us[IDX2C(id,j,N)] /= smallValue; + else + us[IDX2C(id,j,N)] /= v; + } + +} + + +template +__global__ void _innerProduct( + ElemType* c, + const ElemType* a, + const ElemType* b, + const CUDA_LONG N, //a.GetNumRows(); + const CUDA_LONG M, //a.GetNumCols(); + const bool isColWise) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if ((isColWise && id>=M) || (!isColWise && id>=N)) + return; + + ElemType sum = 0; + CUDA_LONG index; + if (isColWise) + { + for (CUDA_LONG i=0; i +__global__ void _assignSignOf( + ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + ElemType v = b[id]; + a[id] = (v == (ElemType)0? (ElemType)0 : (v > 0? (ElemType)1 : (ElemType)(-1))); +} + +template +__global__ void _addSignOf( + ElemType* a, + const ElemType* b, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + ElemType v = b[id]; + a[id] += (v == (ElemType)0? (ElemType)0 : (v > 0? (ElemType)1 : (ElemType)(-1))); +} + +// This function processes 1 column per block. this function needs 512 threads +template +__global__ void _vectorMaxMinReduce( + const ElemType* us, + ElemType* Indexes, + ElemType* Values, + const CUDA_LONG numRows, + const CUDA_LONG numCols) +{ + //we first find max per column + __shared__ ElemType partials[512]; + __shared__ int partialsInd[512]; + if (IsMax) + { + partials[threadIdx.x]=-10000000; + } + else + { + partials[threadIdx.x]=10000000; + } + partialsInd[threadIdx.x]=-1; + + for (int i = threadIdx.x; i < numRows; i += 512) + { + if ((IsMax ? (us[IDX2C(i, blockIdx.x, numRows)] > partials[threadIdx.x]) : (us[IDX2C(i, blockIdx.x, numRows)] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = us[IDX2C(i, blockIdx.x, numRows)]; + partialsInd[threadIdx.x]=i; + } + } + __syncthreads(); + + if (threadIdx.x < 256) + { + if ((IsMax ? (partials[threadIdx.x + 256] > partials[threadIdx.x]) : (partials[threadIdx.x + 256] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 256]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 256]; + } + } + __syncthreads(); + + if (threadIdx.x < 128) + { + if ((IsMax ? (partials[threadIdx.x + 128] > partials[threadIdx.x]) : (partials[threadIdx.x + 128] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 128]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 128]; + } + } + __syncthreads(); + + if (threadIdx.x < 64) + { + if ((IsMax ? (partials[threadIdx.x + 64] > partials[threadIdx.x]) : (partials[threadIdx.x + 64] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 64]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 64]; + } + } + __syncthreads(); + + if (threadIdx.x < 32) + { + if ((IsMax ? (partials[threadIdx.x + 32] > partials[threadIdx.x]) : (partials[threadIdx.x + 32] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 32]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 32]; + } + } + __syncthreads(); + + if (threadIdx.x < 16) + { + if ((IsMax ? (partials[threadIdx.x + 16] > partials[threadIdx.x]) : (partials[threadIdx.x + 16] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 16]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 16]; + } + } + __syncthreads(); + + if (threadIdx.x < 8) + { + if ((IsMax ? (partials[threadIdx.x + 8] > partials[threadIdx.x]) : (partials[threadIdx.x + 8] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 8]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 8]; + } + } + __syncthreads(); + + if (threadIdx.x < 4) + { + if ((IsMax ? (partials[threadIdx.x + 4] > partials[threadIdx.x]) : (partials[threadIdx.x + 4] < partials[threadIdx.x])) || (partialsInd[threadIdx.x] == -1)) + { + partials[threadIdx.x] = partials[threadIdx.x + 4]; + partialsInd[threadIdx.x] = partialsInd[threadIdx.x + 4]; + } + } + __syncthreads(); + + if (threadIdx.x == 0) + { + ElemType mx = partials[0]; + int ind = partialsInd[0]; + if ((IsMax ? (mx < partials[1]) : (mx > partials[1])) || (ind == -1)) + { + mx = partials[1]; + ind = partialsInd[1]; + } + if ((IsMax ? (mx < partials[2]) : (mx > partials[2])) || (ind == -1)) + { + mx = partials[2]; + ind = partialsInd[2]; + } + if ((IsMax ? (mx < partials[3]) : (mx > partials[3])) || (ind == -1)) + { + mx = partials[3]; + ind = partialsInd[3]; + } + Values[blockIdx.x] = mx; + Indexes[blockIdx.x] = ind; + } +} + +template +__global__ void _vectorMax( + const ElemType* us, + ElemType* maxIndexes, + ElemType* maxValues, + const CUDA_LONG m, //number of rows + const CUDA_LONG n, //number of cols + const bool isColWise) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG maxInd = -1; + ElemType maxVal = -100000; + + if (isColWise) + { + if (id>=n) + return; + + for (CUDA_LONG i=0;i=maxVal) + { + maxInd = i; + maxVal = us[IDX2C(i,id,m)]; + } + } + } + else + { + if (id>=m) + return; + + for (CUDA_LONG j=0;j=maxVal) + { + maxInd = j; + maxVal = us[IDX2C(id,j,m)]; + } + } + } + maxIndexes[id]=maxInd; + maxValues[id]=maxVal; +} + +template +__global__ void _vectorMin( + const ElemType* us, + ElemType* minIndexes, + ElemType* minValues, + const CUDA_LONG m, //number of rows + const CUDA_LONG n, //number of cols + const bool isColWise) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG minInd = -1; + ElemType minVal = -100000; + + if (isColWise) + { + if (id>=n) + return; + + for (CUDA_LONG i=0;i=m) + return; + + for (CUDA_LONG j=0;j +__global__ void _matrixMatrixAddOnCuda( + const ElemType alpha, + const ElemType *a, + const ElemType *b, + ElemType *c, + const CUDA_LONG N + ) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + c[id] = alpha * a[id] + b[id]; +} + +template +__global__ void _matrixVectorRowWiseAddWithThreadPerElem( + const ElemType *a, + const ElemType *b, + ElemType* us, + ElemType alpha, + const CUDA_LONG m, //number of rows + const CUDA_LONG n) //number of cols +{ + CUDA_LONG N = m*n; // used in CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) macro + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + + CUDA_LONG col = id / m; + + us[id] = alpha*a[col] + b[id]; +} + +//this implementation uses more threads but also more memory access +template +__global__ void _matrixVectorColumnWiseAddWithThreadPerElem( + const ElemType *a, + const ElemType *b, + ElemType* us, + ElemType alpha, + const CUDA_LONG m, //number of rows + const CUDA_LONG n) //number of cols +{ + CUDA_LONG N = m*n; // used in CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) macro + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N); + + CUDA_LONG col = id / m; + CUDA_LONG row = id - col*m; + + us[id] = alpha*a[row] + b[id]; +} + +template +__global__ void _matrixVectorColumnWiseAddWithThreadPerRow( + const ElemType *a, + ElemType* us, + ElemType alpha, + const CUDA_LONG m, //number of rows + const CUDA_LONG n) //number of cols +{ +#ifdef VALIDATION + if (blockDim.x * blockIdx.x + threadIdx.x == 0) + { + printf("** _matrixVectorColumnWiseAdd on device:\na = %p, us = %p, alpha = %f, m = %ld, n = %ld\n", + a,us,alpha,m,n); + printf("us[0] = %f\n", us[0]); + printf("a[0] = %f\n", a[0]); + } +#endif + int id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=m) + return; + ElemType tmp = a[id]; +#ifdef VALIDATION + printf(" a[%d] = %f\n", id, tmp); +#endif + for (CUDA_LONG j = 0; j < n; ++j ) + { + us[j*m+id] += alpha*tmp; + } + +} + + +template +__global__ void _matrixVectorColumnWiseAddBlockPerRow( + const ElemType* a, + ElemType* us, + ElemType alpha, + const CUDA_LONG m, //number of rows + const CUDA_LONG n) //number of cols +{ + ElemType tmp; + + if (threadIdx.x==0) + { + tmp = a[blockIdx.x]; + } + __syncthreads(); + + int loadPerThread = n/blockDim.x; + + for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? n : (threadIdx.x+1)*loadPerThread);++i) + { + us[m*blockIdx.x + i] += alpha*tmp; + } +} + + + +template +__global__ void _addScaledDifference( + ElemType alpha, + ElemType *a, + ElemType *b, + ElemType *c, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + c[id] = c[id] + (a[id]-b[id]) * (alpha); +} + +template +__global__ void _assignScaledDifference( + ElemType alpha, + ElemType *a, + ElemType *b, + ElemType *c, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + c[id] = (a[id]-b[id]) * (alpha); +} + +template +__global__ void _addScaledDifference( + ElemType *alpha, + ElemType *a, + ElemType *b, + ElemType *c, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + c[id] = c[id] + (a[id]-b[id]) * alpha[0]; +} + +template +__global__ void _assignScaledDifference( + ElemType *alpha, + ElemType *a, + ElemType *b, + ElemType *c, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + c[id] = (a[id]-b[id]) * alpha[0]; +} + +template +__global__ void _addElementToElement( + const ElemType *a, CUDA_LONG indexA, + ElemType *c, CUDA_LONG indexC) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>0) + return; + c[indexC] += a[indexA]; +} + +template +__global__ void _assignNumOfDiff( + const ElemType *a, + const ElemType *b, + ElemType *c, + CUDA_LONG N) +{ + __shared__ ElemType partialSums[1024]; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG loadPerThread = N/blockDim.x; + for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + partialSums[threadIdx.x]+=(a[i] != b[i]); + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + c[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; + } +} + + +/*template +__global__ void _assignNumOfDiff( +ElemType *a, +ElemType *b, +ElemType *c, +CUDA_LONG N) +{ +//TO DO: replace atomic operation with reduction + +__shared__ int totalSum; +if (threadIdx.x == 0) totalSum = 0; +__syncthreads(); + +int id = blockDim.x * blockIdx.x + threadIdx.x; +if (id>=N) +return; + +int localVal = (a[id] != b[id]); +atomicAdd(&totalSum, localVal); +__syncthreads(); + +c[id] = totalSum; +}*/ + +template +__global__ void _scaleArray( + ElemType alpha, + ElemType *us, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=N) + return; + us[id]=us[id]*alpha; +} + + +template +__global__ void _sparseCSRPlusDense( + ElemType alpha, + const ElemType* m_dVal, + const int* m_dRow, + const int* m_dCol, + ElemType* pArrayDev, + CUDA_LONG M) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=M) + return; + int start = m_dRow[id]; + int end = m_dRow[id+1]; + for (int _i=start;_i +__global__ void _sparseCSRElemMulDense( + const ElemType* m_dVal, + const int* m_dRow, + const int* m_dCol, + const ElemType* b, + ElemType* c, + CUDA_LONG M) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id>=M) + return; + int start = m_dRow[id]; + int end = m_dRow[id+1]; + for (int _i=start;_i +__global__ void _isValid( + const GPUSPARSE_INDEX_TYPE* rowIndex, + const GPUSPARSE_INDEX_TYPE* colCSCIndex, + const int rows, + const int cols, + const int nz, + long* d_res + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= cols || d_res[0] <= 0) + return; + + int start = colCSCIndex[id]; + int end = colCSCIndex[id + 1]; + + if (start > end) + { + if (d_res[0] > 0) + { + d_res[0] = -1; + d_res[1] = id; + d_res[2] = start; + d_res[3] = end; + } + } + else if (end > nz) + { + if (d_res[0] > 0) + { + d_res[0] = -2; + d_res[1] = id + 1; + d_res[2] = end; + d_res[3] = nz; + } + } + else + { + for (int j = start; j < end; j++) //j points to the value + { + if (rowIndex[j] >= rows) + { + if (d_res[0] > 0) + { + d_res[0] = -3; + d_res[1] = j; + d_res[2] = rowIndex[j]; + d_res[3] = rows; + break; + } + } + if (j > start && rowIndex[j] < rowIndex[j - 1]) + { + if (d_res[0] > 0) + { + d_res[0] = -4; + d_res[1] = id; + d_res[2] = j; + d_res[3] = rowIndex[j]; + break; + } + } + } + } +} + +template +__global__ void _shiftColCSCIndexFromSliceViewToAbsolute( + GPUSPARSE_INDEX_TYPE* colCSCIndex, + const int cols, + const int nz + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= cols) + return; + + colCSCIndex[id] = colCSCIndex[id] - colCSCIndex[0]; + + if (id == cols - 1) + colCSCIndex[cols] = nz; +} + +//c = alpha * op(a) * op(b) + beta*c +// TODO: This function can be further improved by loading the kernel in shared memory +template +__global__ void _dense1DConvMultSparseCSCAndWeightedAddToDense( + const int m, // rowDense + const int k, // colDense + const int n, // colSparse + const int numChannels, // input num channels + const int numSteps, // convolution num steps + const int horizontalSubsample,// convolution step size + const bool channelwise, // pixelwise for normal multiplication and channelwise for convolution operation + const ElemType alpha, + const ElemType* a, //dense + const bool transposeA, + const ElemType* bnzValues, //sparse nz values + const GPUSPARSE_INDEX_TYPE* rowIndex, + const GPUSPARSE_INDEX_TYPE* colCSCIndex, + const ElemType beta, + ElemType* c //dense target + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= m*numSteps*n) + return; + + int colInC = id / (m * numSteps); + int rowInC = id % (m * numSteps); + int stepIdx = rowInC / m; + + int start = colCSCIndex[colInC]; + int end = colCSCIndex[colInC + 1]; + + ElemType s = 0; + for (int j = start; j < end; j++) //j points to the value + { + int i = rowIndex[j] - (horizontalSubsample * numChannels * stepIdx); // offset row index by the convolution step + + if (i >= 0) + { + if (i >= k) + break; + + // Convert to channelwise index. + // This is similar to rowwise to columnwise conversion + if (channelwise) + { + int pixel = i / numChannels; + int channel = i % numChannels; + int numPixels = k / numChannels; + i = channel * numPixels + pixel; + } + + if (!transposeA) + s += a[IDX2C(rowInC % m, i, m)] * bnzValues[j]; + else + s += a[IDX2C(i, rowInC % m, k)] * bnzValues[j]; + } + } + + c[IDX2C(rowInC, colInC, m * numSteps)] = alpha * s + (beta == 0 ? 0 : beta * c[IDX2C(rowInC, colInC, m * numSteps)]); // If beta is zero then don't lookup c +} + +/// c += alpha * a * b^T +template +__global__ void _dense1DConvMultSparseCSCTransposeAndAddToDense( + int m, // rowDense + int k, // colDense + int n, // colSparse + int numChannels, // input num channels + int numSteps, // convolution num steps + int horizontalSubsample, // convolution step size + bool channelwise, // pixelwise for normal multiplication and channelwise for convolution operation + int rowInB, // row index of the sparse matrix + ElemType alpha, + const ElemType* a, //dense + bool transposeA, + const ElemType* bnzValues, //sparse nz values + const GPUSPARSE_INDEX_TYPE* rowIndex, + const GPUSPARSE_INDEX_TYPE* colCSCIndex, + ElemType* c //dense target + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= m*numSteps) + return; + + int rowInC = id; + int stepIdx = rowInC / m; + int i = rowInB - (horizontalSubsample * numChannels * stepIdx); // offset row index by the convolution step + + if (i < 0 || i >= k) + return; + + // Convert to channelwise index. + // This is similar to rowwise to columnwise conversion + if (channelwise) + { + int pixel = i / numChannels; + int channel = i % numChannels; + int numPixels = k / numChannels; + i = channel * numPixels + pixel; + } + + int start = colCSCIndex[rowInB]; + int end = colCSCIndex[rowInB + 1]; + + ElemType s = 0; + for (int j = start; j +__global__ void _reshape( + const int oldNumRows, // old row count + const int oldNumCols, // old col count + const int newNumRows, // new row count + const int newNumCols, // new col count + const GPUSPARSE_INDEX_TYPE* oldRowIndex, // old row index array + const GPUSPARSE_INDEX_TYPE* oldColumnIndex, // old column index array + GPUSPARSE_INDEX_TYPE* newRowIndex, // new row index array + GPUSPARSE_INDEX_TYPE* newColumnIndex // new column index array + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= newNumCols) + return; + + int currentCol = id; + int oldColLower = (newNumRows * currentCol) / oldNumRows; + + // initialize to the end and then scan in the right direction in the for-loop + int currentColStart = oldColumnIndex[oldNumCols]; + + for (int oldCol = oldColLower; oldCol < oldNumCols; oldCol++) + { + int start = oldColumnIndex[oldCol]; + int end = oldColumnIndex[oldCol + 1]; + bool done = false; + + for (int j = start; j < end; j++) //j points to the value + { + int oldRow = oldRowIndex[j]; + int index = (oldCol * oldNumRows + oldRow); + int newCol = index / newNumRows; + int newRow = index % newNumRows; + + if (newCol == currentCol) + newRowIndex[j] = newRow; + + if (newCol >= currentCol && currentColStart > j) + currentColStart = j; + + if (newCol > currentCol) + { + done = true; + break; + } + } + + if (done) + break; + } + + newColumnIndex[currentCol] = currentColStart; + + if (currentCol == (newNumCols - 1)) + newColumnIndex[newNumCols] = oldColumnIndex[oldNumCols]; // set end pointer +} + +//called before _determineBlockIds and _denseMulSparseCSCTransposeToSparseBlockCol to determine which columns have values and +//what's the mapping from the column id in the resulted SparseBlockCol format to the column id in the dense format +//input: rowIndexes: the row indexes of the CSC sparse matrix to be multiplied with +//blockIDs: the blockID mapping in the resulting matrix; +//nnz: number of nonzero value or the size of rowIndexes; +template +__global__ void _findColsWithValues( + const GPUSPARSE_INDEX_TYPE* rowIndexes, GPUSPARSE_INDEX_TYPE* blockIds, const size_t nnz) +{ + const size_t index = blockIdx.x * blockDim.x + threadIdx.x; + if (index >= nnz) + return; + + blockIds[rowIndexes[index]] = 1; //this row has value. +} + +//called before _denseMulSparseCSCTransposeToSparseBlockCol and after _findColsWithValuesto determine which columns have values and +//what's the mapping from the column id in the resulted SparseBlockCol format to the column id in the dense format +//input: rowIndexes: the row indexes of the CSC sparse matrix to be multiplied with +//blockId2Col: the blockID to colum id mapping in the resulting matrix; +//col2BlockId: the col2BlockId to blockID mapping in the resulting matrix; +//numCols: number of columns in the resulting matrix or the size of blockIDs +//blockSize: return the blockSize with values, *blockSize must be zero before passed in. +template +__global__ void _determineBlockIds( + GPUSPARSE_INDEX_TYPE* blockId2Col, GPUSPARSE_INDEX_TYPE*col2BlockId, const size_t numCols, size_t* blockSize) +{ + const size_t index = blockIdx.x * blockDim.x + threadIdx.x; + if (index >= numCols) + return; + + size_t blockIndex = numCols; + if (blockId2Col[index] > 0) + { + blockIndex = atomicAdd((unsigned int *)blockSize, (unsigned int)1); + col2BlockId[index] = blockIndex; + } + + __syncthreads(); + + if (blockIndex < numCols) + blockId2Col[blockIndex] = index; +} + +// backward pass from hidden layer to feature weight +//result (sparse BlockCol)= alpha * (lhs (dense) X rhs^T (sparse CSC) +//assume resultValues are 0-initialized +template +__global__ void _denseMulSparseCSCTransposeToSparseBlockCol2( + const ElemType alpha, + const ElemType* lhsValues, + const size_t numRowsLhs, + const size_t numColsRhs, + const ElemType* rhsNZValues, + const GPUSPARSE_INDEX_TYPE* rhsRows, + const GPUSPARSE_INDEX_TYPE* rhsCols, + const GPUSPARSE_INDEX_TYPE* col2blockIds, + ElemType* resultValues) +{ + const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG lhsCol = index / numRowsLhs; //rhsCol == lhsCol + if (lhsCol >= numColsRhs) + return; + const CUDA_LONG lhsRow = index - numRowsLhs*lhsCol; //resultRow == lhsRow + + //each thread handles one [row, col] combination + ElemType lhsValue = alpha*lhsValues[IDX2C(lhsRow, lhsCol, numRowsLhs)]; + + CUDA_LONG start = rhsCols[lhsCol]; //rhsCol == lhsCol + CUDA_LONG end = rhsCols[lhsCol + 1]; + + for (CUDA_LONG p = start; p < end; p++) + { + CUDA_LONG rhsRow = rhsRows[p]; + ElemType rhsVal = rhsNZValues[p]; + CUDA_LONG resultCol = col2blockIds[rhsRow]; //resultCol == rhsRow maps to columnid + + //assume resultValues are 0-initialized + atomicAdd(&resultValues[IDX2C(lhsRow, resultCol, numRowsLhs)], lhsValue * rhsVal); + } +} + +// backward pass from hidden layer to feature weight +//result (sparse BlockCol)= alpha * (lhs (dense) X rhs^T (sparse CSC) +//assume resultValues are 0-initialized +template +__global__ void _denseMulSparseCSCTransposeToSparseBlockCol( + const ElemType alpha, + const ElemType* lhsValues, + const size_t numRowsLhs, + const size_t numColsRhs, + const ElemType* rhsNZValues, + const GPUSPARSE_INDEX_TYPE* rhsRows, + const GPUSPARSE_INDEX_TYPE* rhsCols, + const GPUSPARSE_INDEX_TYPE* rhsRowIdx, + ElemType* resultValues, + GPUSPARSE_INDEX_TYPE* resultBlockIds) +{ + const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; + const CUDA_LONG lhsCol = index / numRowsLhs; //rhsCol == lhsCol + if (lhsCol >= numColsRhs) + return; + const CUDA_LONG lhsRow = index - numRowsLhs*lhsCol; //resultRow == lhsRow + + //each thread handles one [row, col] combination + ElemType lhsValue = alpha*lhsValues[IDX2C(lhsRow, lhsCol, numRowsLhs)]; + + CUDA_LONG start = rhsCols[lhsCol]; //rhsCol == lhsCol + CUDA_LONG end = rhsCols[lhsCol + 1]; + + for (CUDA_LONG p = start; p < end; p++) + { + CUDA_LONG rhsRow = rhsRows[p]; + ElemType rhsVal = rhsNZValues[p]; + CUDA_LONG resultCol = rhsRowIdx[p]; //resultCol == rhsRow maps to columnid + resultBlockIds[resultCol] = rhsRow; //indicate which colmn it actually points to + + //assume resultValues are 0-initialized + atomicAdd(&resultValues[IDX2C(lhsRow, resultCol, numRowsLhs)], lhsValue * rhsVal); + } +} + + +// gradients update +template +__global__ void _scaleSparseBlockAndAddToDense( + const ElemType alpha, + const bool blockCol, //true if blockRow + const size_t numRows, + const size_t numCols, + const size_t numBlocks, + const ElemType* lhsValues, //lhs is blockCol or blockRow + const GPUSPARSE_INDEX_TYPE* blockIds, + ElemType* rhs) +{ + const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; + CUDA_LONG row, col; + if (blockCol) + { + const CUDA_LONG blockId = index / numRows; + if (blockId >= numBlocks) + return; + row = index - numRows* blockId; + col = blockIds[blockId]; + } + else + { + const CUDA_LONG blockId = index / numCols; + if (blockId >= numBlocks) + return; + col = index - numCols* blockId; + row = blockIds[blockId]; + } + rhs[IDX2C(row, col, numRows)] += alpha * lhsValues[index]; +} + +// compute predictions in cross entory node +template +__global__ void _computePrediction( + int nv, + const ElemType* a, + int numrows, + const ElemType* weight, + int nrs, + int labelSize, + const GPUSPARSE_INDEX_TYPE* labelRow, + const size_t* block2Id, + const ElemType* cls, + const ElemType* idx2cls, + ElemType* val, + GPUSPARSE_INDEX_TYPE* row, + GPUSPARSE_INDEX_TYPE* pb) +{ + // get label block id + int id = -1; + int offset = -1; + for(int i = 1; i < labelSize; i++) + { + if (blockIdx.x < block2Id[i]) + { + id = i-1; + offset = blockIdx.x - block2Id[i-1]; + break; + } + } + if( id == -1) + { + id = labelSize-1; + offset = blockIdx.x - block2Id[labelSize-1]; + } + + int t = labelRow[id]; + int iStt; + int iEnd; + if(t < nv) + { + int clsid = idx2cls[t]; + iStt = cls[IDX2C(0, clsid, 2)]; + iEnd = cls[IDX2C(1, clsid, 2)]; + } + else + { + iStt = nv; + iEnd = nrs; + } + int i = iStt + offset; + int j = id /2; + + int loadPerThread = (numrows+blockDim.x-1)/blockDim.x; + int tStart = loadPerThread * threadIdx.x; + int tEnd = min((int)numrows, loadPerThread + tStart); + + ElemType v = 0.0; + for (int h = tStart; h < tEnd; h++) + { + v += weight[IDX2C(i,h,nrs)] * a[IDX2C(h,j,numrows)]; + } + atomicAdd(&val[blockIdx.x], v); + row[blockIdx.x] = i; + + if(blockIdx.x == 0 && threadIdx.x == 0) + pb[0] = 0; + + if((threadIdx.x == 0) && (i == iEnd-1) && (i >= nv)) + pb[j+1] = blockIdx.x+1; +} + +// normalize predictions in cross entropy node +template +__global__ void _normalizePrediction( + const size_t labelSize, + const size_t expandedLabelSize, + const GPUSPARSE_INDEX_TYPE* labelRow, + const size_t* block2Id, + const GPUSPARSE_INDEX_TYPE* row, + ElemType* val, + ElemType* entropyScore) +{ + __shared__ ElemType partials[512]; + partials[threadIdx.x] = 0; + + int p = blockIdx.x; + int t = labelRow[p]; + int start = block2Id[p]; + int end; + if(p == labelSize -1) + { + end = expandedLabelSize; + } + else + { + end = block2Id[p+1]; + } + int len = end - start; + + int loadPerThread = (len+blockDim.x-1)/blockDim.x; + int tStart = loadPerThread * threadIdx.x; + int tLen = min((int)len, loadPerThread + tStart); + + for(int i = start + tStart; i < start + tLen; i++) + { + partials[threadIdx.x] += exp(val[i]); + } + + __syncthreads(); + + // now sum up the objective function + int nTotalThreads = blockDim.x; + + while (nTotalThreads >1) + { + int halfPoint = (nTotalThreads >> 1); + + if (threadIdx.x < halfPoint) + partials[threadIdx.x] += partials[threadIdx.x+halfPoint]; + + __syncthreads(); + + nTotalThreads = (nTotalThreads>>1); + } + + for(int i = start + tStart; i < start + tLen; i++) + { + val[i] = log(exp(val[i])/partials[0]); + if(row[i] == t) + { + atomicAdd(entropyScore, -val[i]); + val[i] *= -1; + } + } +} + +// compute prediction error in cross entropy node +template +__global__ void _computePredictionError( + ElemType* val, + int N) +{ + int p = blockDim.x * blockIdx.x + threadIdx.x; + if (p>=N) + return; + + if(val[p] < 0) + val[p] = exp(val[p]); //negative; + else + val[p] = exp(-val[p])-1; //positive +} + +// compute gradients of input in cross entropy node +template +__global__ void _computeGradientOfInput( + const ElemType* val, + const GPUSPARSE_INDEX_TYPE* row, + const GPUSPARSE_INDEX_TYPE* pb, + ElemType* weight, + size_t nrs, + ElemType* grd, + size_t numrows) +{ + int h = blockIdx.x%numrows; + int j = blockIdx.x/numrows; + + int start = pb[j]; + int end = pb[j+1]; + int len = end - start; + + int load = (len+blockDim.x-1)/blockDim.x; + int pStart = start + load * threadIdx.x; + int pEnd = start + min(len, load * (threadIdx.x+1)); + + ElemType sum = 0; + for(int p = pStart; p < pEnd; p++) + { + int i = row[p]; + sum += val[p] * weight[IDX2C(i, h, nrs)]; + } + + atomicAdd(&grd[IDX2C(h,j,numrows)], sum); +} + + +template +__global__ void computeNCEForwardProp( + const ElemType* val, + const int* col, + int numRows, + int sampleCount, + const ElemType* a, + int numCols_a, + const ElemType* b, + ElemType* res) +{ + // val and col are in CSR format + // val is an array contains log_Pn(w). To differentiate positive and negative samples, + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // res is the buffer to store computed output (sparse) + + // follow the convention, this kernel must be run on 512 threads per block + __shared__ ElemType partials[512]; + partials[threadIdx.x] = 0; + + // determine the elements to be handled by this block + int total = numRows * sampleCount; + int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; + + int start = loadPerBlock * blockIdx.x; + int end = min(total, loadPerBlock * (blockIdx.x + 1)); + + for (int i = start; i < end; i++) + { + int colIndex = col[i]; + int rowIndex = i / sampleCount; + + int loadPerThread = (numCols_a + blockDim.x - 1) / blockDim.x; + int tstart = loadPerThread * threadIdx.x; + int tend = min(numCols_a, loadPerThread * (threadIdx.x + 1)); + + for (int j = tstart; j < tend; j++) + partials[threadIdx.x] = a[IDX2C(rowIndex, j, numRows)] * b[IDX2C(j, colIndex, numCols_a)]; + + __syncthreads(); + + // sum up + int nTotalThreads = blockDim.x; + + while (nTotalThreads >1) + { + int halfPoint = (nTotalThreads >> 1); + + if (threadIdx.x < halfPoint) + partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; + + __syncthreads(); + + nTotalThreads = (nTotalThreads >> 1); + } + + if (threadIdx.x == 0) + res[i] = partials[0]; + } +} + +template +__global__ void _computeNceOutput( + const ElemType* col, + int numRows, + int sampleCount, + const ElemType* a, + int numCols_a, + const ElemType* b, + const ElemType* bias, + ElemType* res) +{ + // val and col are in CSR format + // val is an array contains log_Pn(w). To differentiate positive and negative samples, + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // res is the buffer to store computed output (sparse) + + // follow the convention, this kernel must be run on 512 threads per block + __shared__ ElemType partials[512]; + partials[threadIdx.x] = 0; + + //threadIdx.x range from[0 ~ 512) + //blockIdx.x range from[0 ~ nnz) + //blockDim.x equal to 512 + //gridDim.x equal to nnz + + // determine the elements to be handled by this block + int total = numRows * sampleCount; + int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; + + int start = loadPerBlock * blockIdx.x; + int end = min(total, loadPerBlock * (blockIdx.x + 1)); + + for (int i = start; i < end; i++) + { + int wid = (int)col[2 * i]; + int batchid = i / sampleCount; + + int loadPerThread = (numCols_a + blockDim.x - 1) / blockDim.x; + int tstart = loadPerThread * threadIdx.x; + int tend = min(numCols_a, loadPerThread * (threadIdx.x + 1)); + + for (int j = tstart; j < tend; j++) + partials[threadIdx.x] = a[IDX2C(j, batchid, numCols_a)] * b[IDX2C(j, wid, numCols_a)]; + + __syncthreads(); + + // sum up + int nTotalThreads = blockDim.x; + + while (nTotalThreads >1) + { + int halfPoint = (nTotalThreads >> 1); + + if (threadIdx.x < halfPoint) + partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; + + __syncthreads(); + + nTotalThreads = (nTotalThreads >> 1); + } + + if (threadIdx.x == 0) + res[i] = partials[0] + bias[wid]; + } +} + + +template +__global__ void _assignSoftmaxSum( + const ElemType* softmax, + int sampleCount, + const ElemType* a, + ElemType* c) // run on 512 threads per block +{ + // val and col are in CSR format + // val is an array contains log_Pn(w). To differentiate positive and negative samples, + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // tmp is the buffer that stores NCE output calculated from _computeNceOutput + // c is the matrix to store objective + + __shared__ ElemType partials[512]; + partials[threadIdx.x] = 0; + + int total = sampleCount; + int loadPerThread = (total + blockDim.x - 1) / blockDim.x; + + // find out the items this thread is responsible for + int start = loadPerThread * threadIdx.x; + int end = min(total, loadPerThread * (threadIdx.x + 1)); + for (int i = start; i < end; i++) + { + int wid = (int)a[i]; + partials[threadIdx.x] += softmax[IDX2C(i, wid, sampleCount)]; + } + + __syncthreads(); + + // now sum up the objective function + int nTotalThreads = blockDim.x; + + while (nTotalThreads >1) + { + int halfPoint = (nTotalThreads >> 1); + + if (threadIdx.x < halfPoint) + partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; + + __syncthreads(); + + nTotalThreads = (nTotalThreads >> 1); + } + + if (threadIdx.x == 0) + c[0] = -partials[0]; +} + +template +__global__ void _assignNoiseContrastiveEstimation( + const ElemType* val, + int numRows, + int sampleCount, + const ElemType* a, + int width, // number of columns in a + const ElemType* b, + ElemType* tmp, + ElemType* c) // run on 512 threads per block +{ + // val and col are in CSR format + // val is an array contains log_Pn(w). To differentiate positive and negative samples, + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // tmp is the buffer that stores NCE output calculated from _computeNceOutput + // c is the matrix to store objective + + __shared__ ElemType partials[512]; + partials[threadIdx.x] = 0; + + int total = numRows * sampleCount; + int loadPerThread = (total + blockDim.x - 1) / blockDim.x; + + // find out the items this thread is responsible for + int start = loadPerThread * threadIdx.x; + int end = min(total, loadPerThread * (threadIdx.x + 1)); + + ElemType log_num_noise_samples = log((ElemType)(sampleCount - 1)); + for (int i = start; i < end; i++) + { + ElemType prob = -val[2 * i + 1]; + bool positive = (prob > 0); + if (positive) + prob = -prob; + ElemType score_noise = log_num_noise_samples + prob; + ElemType z = logaddk(tmp[i], score_noise); + ElemType logprob = tmp[i] - z; + ElemType logprob_noise = score_noise - z; + tmp[i] = -exp(logprob); + if (positive) + tmp[i] += 1; + if (positive) + partials[threadIdx.x] += logprob; + else + partials[threadIdx.x] += logprob_noise; + } + + __syncthreads(); + + // now sum up the objective function + int nTotalThreads = blockDim.x; + + while (nTotalThreads >1) + { + int halfPoint = (nTotalThreads >> 1); + + if (threadIdx.x < halfPoint) + partials[threadIdx.x] += partials[threadIdx.x + halfPoint]; + + __syncthreads(); + + nTotalThreads = (nTotalThreads >> 1); + } + + if (threadIdx.x == 0) + c[0] = -partials[0]; +} + +template +__global__ void _assignNceDerivative( + const ElemType* val, + int numRows, + int sampleCount, + const ElemType* a, + int width, // number of columns in a + const ElemType* b, + const ElemType* tmp, + ElemType* c, + size_t inputIndex) +{ + // val and col are CSR format sparse matrix for label + // val is an array contains log_Pn(w). To differentiate positive and negative samples + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // tmp is a matrix of precalculated error + // c is the output matrix to store calculated gradients + + int total = numRows * sampleCount; + int loadPerBlock = (total + gridDim.x - 1) / gridDim.x; + + // find out the items this block is responsible for + int start = loadPerBlock * blockIdx.x; + int end = min(total, loadPerBlock * (blockIdx.x + 1)); + + for (int i = start; i < end; i++) + { + int wid = (int)val[2 * i]; + int batchId = i / sampleCount; + + ElemType er = tmp[i]; // precalculated error for this output node + + // calculate gradients + int loadPerThread = (width + blockDim.x - 1) / blockDim.x; + int tstart = loadPerThread * threadIdx.x; + int tend = min(width, loadPerThread*(threadIdx.x + 1)); + + if (inputIndex == 1) // hidden layer output + { + for (int j = tstart; j < tend; j++) + { + ElemType val = -er * b[IDX2C(j, wid, width)]; + atomicAdd(&c[IDX2C(j, batchId, width)], val); + //c[IDX2C(j, batchId, width)] += val; + //c[IDX2C(batchId, j, numRows)] += val; + } + } + else if (inputIndex == 2) // weight + { + for (int j = tstart; j < tend; j++) + { + ElemType val = -er * a[IDX2C(j, batchId, width)]; + atomicAdd(&c[IDX2C(j, wid, width)], val); + //c[IDX2C(j, wid, width)] += val; + } + } + else //bias vector + { + //ElemType val = -er; + atomicAdd(&c[wid], -er); + //c[wid] -= er; + } + } +} + +template +__global__ void _assignNceDerivativeNew( + const ElemType* val, + int numRows, + int sampleCount, + const ElemType* a, + int width, // number of columns in a + const ElemType* b, + const ElemType* tmp, + ElemType* c, + size_t inputIndex) +{ + // val and col are CSR format sparse matrix for label + // val is an array contains log_Pn(w). To differentiate positive and negative samples + // we store log_Pn(w) as it is for positive samples, and -log_Pn(w) for negative samples + // col is an array contains index of the word samples + // a is a matrix in column major format contains output from hidden layer + // b is the weight matrix for output layer + // tmp is a matrix of precalculated error + // c is the output matrix to store calculated gradients + + // logical single index for this thread + int n = threadIdx.x + blockDim.x* blockIdx.x; + + int batchId = n / sampleCount; + int total = numRows * sampleCount; + // is thread in range for the addition + if (n < total) + { + int wid = (int)val[2 * n]; + ElemType er = tmp[n]; + if (inputIndex == 1) + { + for (int i = 0; i < width; i++) + { + int j = (i + n) % width; //introduce randomization to avoid conflicts + ElemType val = -er * b[IDX2C(j, wid, width)]; + atomicAdd(&c[IDX2C(j, batchId, width)], val); + } + } + else if (inputIndex == 2) + { + for (int i = 0; i < width; i++) + { + int j = (i + n) % width; //introduce randomization to avoid conflicts + ElemType val = -er * a[IDX2C(j, batchId, width)]; + atomicAdd(&c[IDX2C(j, wid, width)], val); + } + } + else + atomicAdd(&c[wid], -er); + } +} +// compute gradients of weights in cross entropy node +template +__global__ void _computeGradientOfWeight( + const ElemType* val, + const GPUSPARSE_INDEX_TYPE* row, + const GPUSPARSE_INDEX_TYPE* pb, + size_t mb, + size_t nv, + const GPUSPARSE_INDEX_TYPE* labelRow, + const size_t* labelBlock2UniqId, + const ElemType* cls, + const ElemType* idx2cls, + ElemType* input, + size_t nrs, + ElemType* blockVal, + GPUSPARSE_INDEX_TYPE* blockIds) +{ + int p = blockIdx.x; + ElemType v = val[p]; + int i = row[p]; + int j = -1; + for(int k = 1; k < mb; k++) + { + if( p < pb[k]) + { + j = k-1; + break; + } + } + if( j == -1) + { + j = mb-1; + } + + //figure out blocks + int bId = i < nv ? 2*j : 2*j+1; + int t = labelRow[bId]; + int iStt; + if(t < nv) + { + int clsid = idx2cls[t]; + iStt = cls[IDX2C(0, clsid, 2)]; + } + else + { + iStt = nv; + } + int offset = i - iStt; + int ii = labelBlock2UniqId[bId] + offset; + + int load = (nrs+blockDim.x-1)/blockDim.x; + int pStart = load * threadIdx.x; + int pEnd = min((int)nrs, load + pStart); + + for(int h = pStart; h < pEnd; h++) + { + ElemType temp = v * input[IDX2C(h, j, nrs)]; + atomicAdd(&blockVal[ii*nrs+h], temp); + blockIds[ii] = i; + } +} + +// used in clipping gradients +template +__global__ void _inplaceTruncate( + ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CALCULATE_ELEMENTWISE_INDEX_OR_EXIT(id,N) + ElemType locThresholdPos = abs(threshold); + ElemType locTHresholdNeg = -locThresholdPos; + if (a[id] > locThresholdPos) + { + a[id] = locThresholdPos; + } + else if(a[id] < locTHresholdNeg) + { + a[id] = locTHresholdNeg; + } +} + +template +__global__ void _inplaceSoftThreshold( + ElemType* a, + const ElemType threshold, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + if (a[id] > threshold) + { + a[id] -= threshold; + } + else if (a[id] < -threshold) + { + a[id] += threshold; + } + else + a[id] = 0; +} + + +template +__global__ void _normalGradForSparseBlock( + const ElemType momentum, + const bool blockCol, //true if blockRow + const size_t numRows, + const size_t numCols, + const size_t numBlocks, + ElemType* lhsValues, //lhs is blockCol or blockRow + const GPUSPARSE_INDEX_TYPE* blockIds, + ElemType* rhs) +{ + const CUDA_LONG index = blockIdx.x * blockDim.x + threadIdx.x; + CUDA_LONG row, col; + if (blockCol) + { + const CUDA_LONG blockId = index / numRows; + if (blockId >= numBlocks) + return; + row = index - numRows* blockId; + col = blockIds[blockId]; + } + else + { + const CUDA_LONG blockId = index / numCols; + if (blockId >= numBlocks) + return; + col = index - numCols* blockId; + row = blockIds[blockId]; + } + rhs[IDX2C(row, col, numRows)] = (1 - momentum)*lhsValues[index] + momentum*rhs[IDX2C(row, col, numRows)]; + lhsValues[index] = rhs[IDX2C(row, col, numRows)]; +} + +//This function should be called with 1024 threads per block and 1 block +//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! +template +__global__ void _reductionSum( + const ElemType* data, + ElemType *sum, + CUDA_LONG N) +{ + + __shared__ ElemType partialSums[1024]; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG loadPerThread = N/blockDim.x; + for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + partialSums[threadIdx.x]+=data[i]; + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + sum[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; + } +} + +//This function should be called with 1024 threads per block and 1 block +//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! +template +__global__ void _reductionSumAndAssign( + ElemType* toAssign, + const ElemType* data, + CUDA_LONG N, //length of data + CUDA_LONG M) //length of toAssign +{ + __shared__ ElemType partialSums[1024]; + __shared__ ElemType res; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG loadPerThread = N/blockDim.x; + for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + partialSums[threadIdx.x]+=data[i]; + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + res = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; + for (CUDA_LONG i=0;i +__global__ void _reductionSum2( + const ElemType* data, + ElemType *sum, + CUDA_LONG N, + bool takeSqrt=false) +{ + + __shared__ ElemType partialSums[1024]; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG loadPerThread = N/blockDim.x; + for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + //for (int i= threadIdx.x*loadPerThread; i<(threadIdx.x+1)*loadPerThread;++i) + { + partialSums[threadIdx.x]+=(data[i]*data[i]); + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+512]; + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+256]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+128]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+64]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+32]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+16]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+8]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]+=partialSums[threadIdx.x+4]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + sum[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; + if (takeSqrt) + { + if (sizeof(ElemType)==sizeof(float)) + sum[0] = sqrtf(sum[0]); + else + sum[0] = sqrt(sum[0]); + } + } +} + + +//This function should be called with 1024 threads per block and 1 block +//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! +template +__global__ void _reductionMatrixNormInf( + const ElemType* data, + ElemType *maxAbs, + CUDA_LONG N) +{ + + __shared__ ElemType partialSums[1024]; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + int loadPerThread = N/blockDim.x; + for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + if (sizeof(ElemType)==sizeof(float)) + { + partialSums[threadIdx.x]=max(fabsf(data[i]),partialSums[threadIdx.x]); + } + else + { + partialSums[threadIdx.x]=max(fabs(data[i]),partialSums[threadIdx.x]); + } + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+512],partialSums[threadIdx.x]); + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+256],partialSums[threadIdx.x]); + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+128],partialSums[threadIdx.x]); + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+64],partialSums[threadIdx.x]); + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+32],partialSums[threadIdx.x]); + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+16],partialSums[threadIdx.x]); + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+8],partialSums[threadIdx.x]); + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]=max(partialSums[threadIdx.x+4],partialSums[threadIdx.x]); + } + __syncthreads(); + + if (threadIdx.x==0) + { + maxAbs[0] = max(max(partialSums[0],partialSums[1]),max(partialSums[2],partialSums[3])); + } +} + +//This function should be called with 1024 threads per block and 1 block +//THIS IS NOT THE MOST EFFICIENT IMPLEMENTATION!!! +template +__global__ void _reductionMatrixNorm0( + const ElemType* data, + ElemType *nz, + CUDA_LONG N) +{ + + __shared__ ElemType partialSums[1024]; + partialSums[threadIdx.x]=0; + //int id = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG loadPerThread = N/blockDim.x; + for (CUDA_LONG i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + if (data[i]!=0) + ++partialSums[threadIdx.x]; + } + __syncthreads(); + + //512 + if (threadIdx.x<512) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+512]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //256 + if (threadIdx.x<256) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+256]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+128]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+64]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+32]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+16]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+8]+partialSums[threadIdx.x]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums[threadIdx.x]=partialSums[threadIdx.x+4]+partialSums[threadIdx.x]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + nz[0] = partialSums[0]+partialSums[1]+partialSums[2]+partialSums[3]; + } +} + + +template +__global__ void _getSparseVectorRepresntationForCSCMatrix( + const int* m_dRow, + const int* m_dCol, + int* vectArray, + const CUDA_LONG M, + const CUDA_LONG N) +{ + int i = blockDim.x * blockIdx.x + threadIdx.x; + if (i>=M) + return; + int start = m_dRow[i]; + int end = m_dRow[i+1]; + for (int _i=start;_i +__global__ void _lrHelper( + const ElemType* data1, + const ElemType* data2, + const CUDA_LONG N, + ElemType* d_res) +{ + __shared__ ElemType partialSums1[512]; + __shared__ ElemType partialSums2[512]; + partialSums1[threadIdx.x]=0; + partialSums2[threadIdx.x]=0; + + //int id = blockDim.x * blockIdx.x + threadIdx.x; + int loadPerThread = N/blockDim.x; + for (int i= threadIdx.x*loadPerThread; i< (threadIdx.x == blockDim.x - 1 ? N : (threadIdx.x+1)*loadPerThread);++i) + { + partialSums1[threadIdx.x]+=(data1[i]*data1[i]); + partialSums2[threadIdx.x]+=(data2[i]*data2[i]); + } + __syncthreads(); + + /* + //512 + if (threadIdx.x<512) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+512]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+512]; + } + __syncthreads();*/ + + //256 + if (threadIdx.x<256) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+256]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+256]; + } + __syncthreads(); + + //128 + if (threadIdx.x<128) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+128]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+128]; + } + __syncthreads(); + + //64 + if (threadIdx.x<64) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+64]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+64]; + } + __syncthreads(); + + //32 + if (threadIdx.x<32) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+32]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+32]; + } + __syncthreads(); + + //16 + if (threadIdx.x<16) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+16]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+16]; + } + __syncthreads(); + + //8 + if (threadIdx.x<8) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+8]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+8]; + } + __syncthreads(); + + //4 + if (threadIdx.x<4) + { + partialSums1[threadIdx.x]+=partialSums1[threadIdx.x+4]; + partialSums2[threadIdx.x]+=partialSums2[threadIdx.x+4]; + } + __syncthreads(); + + if (threadIdx.x==0) + { + ElemType fns1 = partialSums1[0]+partialSums1[1]+partialSums1[2]+partialSums1[3]; + ElemType fns2 = partialSums2[0]+partialSums2[1]+partialSums2[2]+partialSums2[3]; + if (sizeof(ElemType)==sizeof(float)) + { + d_res[0] = max((ElemType)0, d_res[0]/max((ElemType)1.0e-10,sqrtf(fns1))/max((ElemType)1.0e-10,sqrtf(fns2))); + } + else + { + d_res[0] = max((ElemType)0, d_res[0]/max((ElemType)1.0e-10,sqrt(fns1))/max((ElemType)1.0e-10,sqrt(fns2))); + } + } +} + +/* +template +__global__ void _lrHelper( +ElemType* d_tmp) +{ +if (sizeof(ElemType)==sizeof(float)) +{ +d_tmp[0] = max((ElemType)0, d_tmp[0]/max((ElemType)1.0e-10,sqrtf(d_tmp[1]))/max((ElemType)1.0e-10,sqrtf(d_tmp[2]))); +} +else +{ +d_tmp[0] = max((ElemType)0, d_tmp[0]/max((ElemType)1.0e-10,sqrt(d_tmp[1]))/max((ElemType)1.0e-10,sqrt(d_tmp[2]))); +} +} +*/ + + +template +__global__ void _assignElementProductOfWithShiftNeg( + ElemType* us, + const ElemType* a, + const ElemType* b, + const int shift, + const int NTPlusOne, + const int BS) +{ + CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; + + if (idx >= NTPlusOne || idy >= BS) + return; + + if (idx == 0) + { + // this is row-0. No need to shift + us[IDX2C(idx, idy, NTPlusOne)] = a[idy] * b[idy]; + } + else + { + int cs = shift + idx - 1; + int tmpidy = (idy + cs) % BS; + us[IDX2C(idx, idy, NTPlusOne)] = a[idy] * b[tmpidy]; + } +} + +template +__global__ void _innerProductWithShiftNeg( + ElemType* c, + const ElemType* a, + const ElemType* b, + const CUDA_LONG N, //a.GetNumRows(); + const CUDA_LONG M, //a.GetNumCols(); + const CUDA_LONG shift, + const CUDA_LONG NTPlusOne + ) +{ + CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; + + if (idx >= NTPlusOne || idy >= M) + return; + + ElemType sum = 0; + CUDA_LONG index_a = 0; + CUDA_LONG index_b = 0; + CUDA_LONG col_a = 0; + CUDA_LONG col_b = 0; + if (idx == 0) + { + // this is row 0. No need to shift + // the product of a(:,idy) dot b(:,idy) + col_a = idy; + for (CUDA_LONG i = 0; i < N; ++i) + { + index_a = IDX2C(i, col_a, N); + sum += a[index_a] * b[index_a]; + } + } + else + { + int cs = shift + idx - 1; + col_a = idy; + col_b = (idy + cs) % M; + for (int i = 0; i < N; ++i) + { + index_a = IDX2C(i, col_a, N); + index_b = IDX2C(i, col_b, N); + sum += a[index_a] * b[index_b]; + } + } + c[IDX2C(idx, idy, NTPlusOne)] = sum; + +} + +template +__global__ void _getARowByIndex( + ElemType* us, + const ElemType* a, + const int O, // a's rows + const int P, // a's cols + const int m // the m-th row of a + ) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= P) + return; + // us[id] = a[id] * b[id]; + us[id] = a[IDX2C(m, id, O)]; +} + + +template +__global__ void _conductRowElementMultiplyWithShift( + ElemType* us, + const ElemType* a, + const ElemType* b, + const int O, // b's rows + const int P, // b's cols + const int shift, + const bool isafixed) +{ + CUDA_LONG idx = blockDim.x * blockIdx.x + threadIdx.x; + CUDA_LONG idy = blockDim.y * blockIdx.y + threadIdx.y; + + if (idx >= O || idy >= P) + return; + + int tmpidy = (idy + shift) % P; + if (isafixed) + { + // we fix a, and shift b + us[IDX2C(idx, idy, O)] = a[idy] * b[IDX2C(idx, tmpidy, O)]; + } + else + { + // we fix b, but shift a + us[IDX2C(idx, idy, O)] = a[tmpidy] * b[IDX2C(idx, idy, O)]; + } + +} + +template +__global__ void _assignElementProductOfWithShift( + ElemType* us, + const ElemType* a, + const ElemType* b, + const int shift, + const CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + + int tmpidb = (id + shift) % N; + us[id] = a[id] * b[tmpidb]; +} + + +/// minus 1 at a specific position +template +__global__ void _minusOneAt( + ElemType *c, + CUDA_LONG position, + CUDA_LONG N) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + if (id == position) + c[id] = c[id] - 1.0; +} + + +/// the kernel function for RCRF backward computation +/// assume a column slice of input and output +template +__global__ void _rcrfBackwardCompute( + const size_t iNumPos, + const ElemType* galpha, /// column slice at current time t + ElemType* gbeta, /// column slices with [row, 2] at current time t for [ + const ElemType* gpair_scores, + const size_t iNumLab, const int shift) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + + extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * + /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) + + ElemType * alpha = (ElemType*)(sh_alpha_and_beta); + ElemType * pair_scores = alpha + iNumPos * iNumLab; + ElemType * beta = alpha + iNumPos * iNumLab + iNumLab * iNumLab; + + if (id < 0 || id >= iNumLab) + return; + + /// copy global memory to shared memory to save time + for (int t = iNumPos - 1; t >= 0; t--) + { + alpha[IDX2C(id, t, iNumLab)] = galpha[IDX2C(id, t, iNumLab)]; + } + + for (int j = 0; j < iNumLab; j++) + pair_scores[IDX2C(id, j, iNumLab)] = gpair_scores[IDX2C(id, j, iNumLab)]; + + __syncthreads(); + + for (int t = iNumPos - 1; t >= 0; t--) + { + ElemType fSum; + ElemType fTmp = LZERO; + if (t == iNumPos - 1) + { + fSum = LZERO; + for (int j = 0; j < iNumLab; j++) + { + fSum = logaddk(fSum, alpha[IDX2C(j, t, iNumLab)]); + } + + fTmp = alpha[IDX2C(id, t, iNumLab)] - fSum; + } + else + { + for (int j = 0; j < iNumLab; j++) + { + fSum = LZERO; + for (int m = 0; m < iNumLab; m++) + { + fSum = logaddk(fSum, alpha[IDX2C(m, t, iNumLab)] + pair_scores[IDX2C(j, m, iNumLab)]); + } + + fTmp = logaddk(fTmp, beta[IDX2C(j, t + 1, iNumLab)] + alpha[IDX2C(id, t, iNumLab)] + pair_scores[IDX2C(j, id, iNumLab)] - fSum); + } + } + + beta[IDX2C(id, t, iNumLab)] = fTmp; + __syncthreads(); + } + + /// copy from shared memory to global memory to pass values + for (int t = iNumPos - 1; t >= 0; t--) + { + gbeta[IDX2C(id, t, iNumLab)] = beta[IDX2C(id, t, iNumLab)]; + } + // __syncthreads(); +} + +/// the kernel function for RCRF backward computation +/// assume a column slice of input and output +template +__global__ void _rcrfBackwardCompute( + const size_t t, /// time position + const size_t iNumPos, + const ElemType* galpha, /// column slice at current time t + ElemType* gbeta, /// column slices with [row, 2] at current time t for [ + const ElemType* gzeta, /// column slices with [row, 2] at current time t for [ + const ElemType* gpair_scores, /// column slice at current time t + const size_t iNumLab, const int shift) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + + extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * + /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) + + ElemType * alpha = (ElemType*)(sh_alpha_and_beta); + ElemType * beta_t1 = (ElemType*)(alpha + iNumLab); + ElemType * zeta = (ElemType*)(beta_t1 + iNumLab); + ElemType pair_scores[1024]; + + if (id < 0 || id >= iNumLab) + return; + + /// copy global memory to shared memory to save time + alpha[id] = galpha[IDX2C(id, t, iNumLab)]; + if (t < iNumPos - 1) + beta_t1[id] = gbeta[IDX2C(id, t + 1, iNumLab)]; + zeta[id] = gzeta[id]; + + __syncthreads(); + + for (int j = 0; j < iNumLab; j++) + pair_scores[j] = gpair_scores[IDX2C(j, id, iNumLab)]; + + ElemType fTmp = LZERO; + if (t == iNumPos - 1) + { + fTmp = alpha[id] - zeta[id]; + } + else + { + for (int j = 0; j < iNumLab; j++) + { + fTmp = logaddk(fTmp, beta_t1[j] + alpha[id] + pair_scores[j] - zeta[j]); + } + } + + gbeta[IDX2C(id, t, iNumLab)] = fTmp; + +} + +/// $\zeta_t(j) = {\sum_k exp(\delta_{t-1}(k) + a_{kj}(t))}$. +template +__global__ void _rcrfBackwardComputeZeta( + const size_t t, /// time position + const size_t iNumPos, + const ElemType* galpha, /// column slice at current time t + ElemType* gzeta, /// column slices with [row, 2] at current time t for [ + const ElemType* gpair_scores, + const size_t iNumLab, const int shift) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + + extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * + /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) + + ElemType * alpha = (ElemType*)(sh_alpha_and_beta); + ElemType pair_scores[1024]; + + if (id < 0 || id >= iNumLab) + return; + + /// copy global memory to shared memory to save time + alpha[id] = galpha[IDX2C(id, t, iNumLab)]; + + __syncthreads(); + + for (int j = 0; j < iNumLab; j++) + pair_scores[j] = gpair_scores[IDX2C(id, j, iNumLab)]; + + ElemType fSum = LZERO; + for (int m = 0; m < iNumLab; m++) + { + if (t == iNumPos - 1) + fSum = logaddk(fSum, alpha[IDX2C(m, 0, iNumLab)]); + else + fSum = logaddk(fSum, alpha[IDX2C(m, 0, iNumLab)] + pair_scores[m]); + } + + gzeta[id] = fSum; + +} + +/// $\zeta_t(j) = {\sum_k exp(\delta_{t-1}(k) + a_{kj}(t))}$. +template +__global__ void _rcrfTransGrdComputeZeta( + const int t, /// time position + const size_t iNumPos, + const ElemType* galpha, /// column slice at current time t + ElemType* gzeta, /// column slices with [row, 2] at current time t for [ + const ElemType* gpair_scores, + const size_t iNumLab, + const size_t start_lbl, + const int shift) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + + extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * + /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) + + ElemType * alpha = (ElemType*)(sh_alpha_and_beta); + ElemType pair_scores[1024]; + + if (id < 0 || id >= iNumLab) + return; + + /// copy global memory to shared memory to save time + if (t >= 0) + alpha[id] = galpha[IDX2C(id, t, iNumLab)]; + + __syncthreads(); + + for (int j = 0; j < iNumLab; j++) + pair_scores[j] = gpair_scores[IDX2C(id, j, iNumLab)]; + + ElemType fSum = LZERO; + ElemType fTmp; + for (int m = 0; m < iNumLab; m++) + { + if (t < 0) + { + if (m == start_lbl) + fTmp = 0; + else fTmp = LZERO; + } + else + fTmp = alpha[m]; + + fSum = logaddk(fSum, pair_scores[m] + fTmp); + } + + gzeta[id] = fSum; + +} + +template +__global__ void _rcrfTransGrdCompute( + int t, + const size_t start_lbl, + const ElemType* galpha, + const ElemType* gbeta, + const ElemType* gzeta, + const ElemType* gpair_scores, + const ElemType * lbls, + ElemType* grd, + const size_t iNumPos, + const size_t iNumLab, + const int shift) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + + extern __shared__ double sh_alpha_and_beta[]; /// intersting, has to use [], instead of * + /// need bye size = (iNumPos * iNumLab * 2 + iNumLab * iNumLab) * sizeof(ElemType) + + ElemType * alpha = (ElemType*)(sh_alpha_and_beta); + ElemType * beta = (ElemType*)(alpha + iNumLab); + ElemType * zeta = (ElemType*)(beta + iNumLab); + ElemType pair_scores[1024]; + + if (id < 0 || id >= iNumLab) + return; + + /// copy global memory to shared memory to save time + if (t > 0) + alpha[id] = galpha[IDX2C(id, t - 1, iNumLab)]; + beta[id] = gbeta[IDX2C(id, t, iNumLab)]; + zeta[id] = gzeta[id]; + + __syncthreads(); + + for (int j = 0; j < iNumLab; j++) + pair_scores[j] = gpair_scores[IDX2C(j, id, iNumLab)]; + + ElemType fTmp; + ElemType fTmp2; + for (int j = 0; j < iNumLab; j++){ + if (t == 0) + { + if (id == start_lbl) + fTmp = 0; + else + fTmp = LZERO; + } + else + fTmp = alpha[id]; + + fTmp2 = fTmp + pair_scores[j] - zeta[j]; + assert(fTmp2 <= 0.0); + fTmp2 += beta[j]; + + fTmp = exp(fTmp2); + grd[IDX2C(j, id, iNumLab)] += fTmp; + } + + if ((t == 0 && id == start_lbl) || (t > 0 && t < iNumPos && lbls[IDX2C(id, t - 1, iNumLab)] != 0)) + { + for (int ik = 0; ik < iNumLab; ik++) + { + if (lbls[IDX2C(ik, t, iNumLab)] != 0) + grd[IDX2C(ik, id, iNumLab)] -= 1.0; + } + } + +}; + +template +__global__ void _reductionLogAddSum( + const ElemType* data, + ElemType *sum, + const size_t sum_size, + CUDA_LONG N) +{ + + __shared__ ElemType partialLogAddSum[GridDim::maxThreadsPerBlock]; + + int id = blockDim.x * blockIdx.x + threadIdx.x; + int tid = threadIdx.x; + + if (id < N) + partialLogAddSum[tid] = data[id]; + else + partialLogAddSum[tid] = LZERO; + + __syncthreads(); + + /// do reduction on the shared memory + size_t start_width = ceil((N + 0.0) / 2.0); + for (size_t s = start_width; s > 0; s >>= 1) + { + ElemType lSum = LZERO; + if (tid < s){ + lSum = logaddk(partialLogAddSum[tid], partialLogAddSum[tid + s]); + partialLogAddSum[tid] = lSum; + } + } + __syncthreads(); + + + if (tid == 0) + sum[0] = partialLogAddSum[0]; +} + +// set the value of certain columns to be zero +// the column is decided by threshhold value +// TODO: This kernel has very poor performace and needs to +// be optimized +template +__global__ void _DropFrame( + ElemType *a, + const ElemType *label, + const ElemType *gamma, + const ElemType framedropthreshhold, + const long m_numCols, + const long m_numRows) //ld +{ + int col_id = blockDim.x * blockIdx.x + threadIdx.x; + if (col_id >= m_numCols) + return; + bool dropframe = false; + // find the 1 in the one-hot representation of the labels + // This is a linear scan--bad perf! + for (long i = 0; i +__global__ void _AssignSequenceError(const ElemType hsmoothingWeight, ElemType *error, const ElemType *label, + const ElemType *dnnoutput, const ElemType *gamma, ElemType alpha, const long N) +{ + int id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= N) + return; + error[id] -= alpha * (label[id] - (1.0 - hsmoothingWeight)*dnnoutput[id] - hsmoothingWeight * gamma[id]); + //change to ce + //error[id] -= alpha * (label[id] - dnnoutput[id] ); + +} + +template +__global__ void _copyTopKResults(const uint64_t* indexes, const ElemType* values, ElemType* maxIndexes, ElemType* maxValues, + CUDA_LONG crow, CUDA_LONG ccol, int topK) +{ + CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x; + if (id >= topK * ccol) + return; + CUDA_LONG irow = id % topK; + CUDA_LONG icol = id / topK; + maxIndexes[id] = static_cast(indexes[icol * crow + irow] >> 32); + maxValues[id] = values[icol * crow + irow]; +} + +template +__global__ void _assignNumOfDiffCol(const ElemType *a, const ElemType *b, ElemType *c, CUDA_LONG crowB, CUDA_LONG ccol) +{ + assert(gridDim.x == 1 && gridDim.y == 1 && gridDim.z == 1); + + int cur = 0; + CUDA_LONG icol = threadIdx.x; + for (; icol < ccol; icol += blockDim.x) + { + ElemType key = a[icol]; + CUDA_LONG idxB = icol * crowB; + CUDA_LONG irow = 0; + for (; irow < crowB; irow++, idxB++) + { + if (b[idxB] == key) + break; + } + + cur += (irow == crowB); + } + + using BlockReduceT = cub::BlockReduce; + __shared__ typename BlockReduceT::TempStorage tmp; + + int res = BlockReduceT(tmp).Sum(cur); + if (threadIdx.x == 0) + *c = res; +} + +template +__global__ void _maskColumnsValue(ElemType *a, const char *columnsMask, CUDA_LONG numCols, CUDA_LONG numRows, ElemType val) +{ + CUDA_LONG colIdx = blockIdx.x; + if (colIdx > numCols) + return; + + if (columnsMask[IDX2C(0, colIdx, 1)] == 1) + return; + + CUDA_LONG rowIdx = threadIdx.x; + for (; rowIdx < numRows; rowIdx += blockDim.x) + { + a[IDX2C(rowIdx, colIdx, numRows)] = val; + } +} + +}}} + +#endif // !CPUONLY diff --git a/Source/Math/Math.vcxproj b/Source/Math/Math.vcxproj index d79abfb2889a..1ad021d30873 100644 --- a/Source/Math/Math.vcxproj +++ b/Source/Math/Math.vcxproj @@ -1,222 +1,222 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} - Win32Proj - Math - - - - - - - - - Math - - - - DynamicLibrary - true - v120 - Unicode - No - - - DynamicLibrary - false - v120 - true - Unicode - No - false - - - - - - - - - - - - false - ..\Common\include;$(ACML_PATH)\include;$(CUDA_PATH)\include;$(IncludePath) - $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CUDA_PATH)\lib\$(Platform);$(LibraryPath) - $(Platform)\$(Configuration)\$(ProjectName)\ - Math - - - false - ..\Common\include;$(ACML_PATH)\include;$(CUDA_PATH)\include;$(IncludePath) - $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CUDA_PATH)\lib\$(Platform);$(LibraryPath) - $(Platform)\$(Configuration)\$(ProjectName)\ - Math - - - - NotUsing - Level4 - NO_SYNC; WIN32; _DEBUG; _WINDOWS; _USRDLL; MATH_EXPORTS; %(PreprocessorDefinitions) - true - ..\Common\include\;%(AdditionalIncludeDirectories) - true - Disabled - Fast - true - false - true - - - Console - true - libacml_mp_dll.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\ - cublas64_70.dll; cusparse64_70.dll; curand64_70.dll; cudart64_70.dll; libacml_mp_dll.dll; %(DelayLoadDLLs) - true - - - xcopy /D /I /Y "$(ACML_PATH)\lib\*.dll" $(OutputPath) - - - Copying ACML DLLs - - - 64 - compute_20,sm_20;compute_30,sm_30;compute_35,sm_35;compute_50,sm_50; - None - compile - true - - - false - - - - - - - - - Level4 - Use - MaxSpeed - true - true - NO_SYNC; WIN32; NDEBUG; _WINDOWS; _USRDLL; MATH_EXPORTS; %(PreprocessorDefinitions) - true - ..\Common\include\;%(AdditionalIncludeDirectories) - true - Fast - true - true - false - true - /d2Zi+ %(AdditionalOptions) - MultiThreadedDLL - - - Console - true - true - true - $(SolutionDir)$(Platform)\$(Configuration)\ - libacml_mp_dll.lib;%(AdditionalDependencies) - true - cublas64_70.dll; cusparse64_70.dll; curand64_70.dll; cudart64_70.dll; libacml_dll.dll; libacml_mp_dll.dll; %(DelayLoadDLLs) - - - xcopy /D /I /Y "$(ACML_PATH)\lib\*.dll" $(OutputPath) - Copying ACML DLLs - - - true - 64 - compute_20,sm_20;compute_30,sm_30;compute_35,sm_35 - true - true - true - - - O3 - - - true - - - - - - - - - - - - - - - - CppHeader - - - CppHeader - - - - - - - - - - - - - - - NotUsing - - - NotUsing - - - - - - false - - - - - - - - - - - Create - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} + Win32Proj + Math + + + + + + + + + Math + + + + DynamicLibrary + true + v120 + Unicode + No + + + DynamicLibrary + false + v120 + true + Unicode + No + false + + + + + + + + + + + + false + ..\Common\include;$(ACML_PATH)\include;$(CUDA_PATH)\include;$(IncludePath) + $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CUDA_PATH)\lib\$(Platform);$(LibraryPath) + $(Platform)\$(Configuration)\$(ProjectName)\ + Math + + + false + ..\Common\include;$(ACML_PATH)\include;$(CUDA_PATH)\include;$(IncludePath) + $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CUDA_PATH)\lib\$(Platform);$(LibraryPath) + $(Platform)\$(Configuration)\$(ProjectName)\ + Math + + + + NotUsing + Level4 + NO_SYNC; WIN32; _DEBUG; _WINDOWS; _USRDLL; MATH_EXPORTS; %(PreprocessorDefinitions) + true + ..\Common\include\;%(AdditionalIncludeDirectories) + true + Disabled + Fast + true + false + true + + + Console + true + libacml_mp_dll.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\ + cublas64_70.dll; cusparse64_70.dll; curand64_70.dll; cudart64_70.dll; libacml_mp_dll.dll; %(DelayLoadDLLs) + true + + + xcopy /D /I /Y "$(ACML_PATH)\lib\*.dll" $(OutputPath) + + + Copying ACML DLLs + + + 64 + compute_20,sm_20;compute_30,sm_30;compute_35,sm_35;compute_50,sm_50; + None + compile + true + + + false + + + + + + + + + Level4 + Use + MaxSpeed + true + true + NO_SYNC; WIN32; NDEBUG; _WINDOWS; _USRDLL; MATH_EXPORTS; %(PreprocessorDefinitions) + true + ..\Common\include\;%(AdditionalIncludeDirectories) + true + Fast + true + true + false + true + /d2Zi+ %(AdditionalOptions) + MultiThreadedDLL + + + Console + true + true + true + $(SolutionDir)$(Platform)\$(Configuration)\ + libacml_mp_dll.lib;%(AdditionalDependencies) + true + cublas64_70.dll; cusparse64_70.dll; curand64_70.dll; cudart64_70.dll; libacml_dll.dll; libacml_mp_dll.dll; %(DelayLoadDLLs) + + + xcopy /D /I /Y "$(ACML_PATH)\lib\*.dll" $(OutputPath) + Copying ACML DLLs + + + true + 64 + compute_20,sm_20;compute_30,sm_30;compute_35,sm_35 + true + true + true + + + O3 + + + true + + + + + + + + + + + + + + + + CppHeader + + + CppHeader + + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + + + + false + + + + + + + + + + + Create + + + + + + + + + + + + \ No newline at end of file diff --git a/Source/Math/Math.vcxproj.filters b/Source/Math/Math.vcxproj.filters index 9ca2631b7153..bb5e1996453a 100644 --- a/Source/Math/Math.vcxproj.filters +++ b/Source/Math/Math.vcxproj.filters @@ -1,150 +1,150 @@ - - - - - - Common - - - Common - - - Common - - - CPU - - - CPU - - - GPU - - - CPU\1bitSGD - - - GPU\1bitSGD - - - Tensors - - - Misc - - - Convolution - - - Misc - - - 1bitSGD - - - 1bitSGD - - - - - - - Common\Include - - - Common\Include - - - CPU - - - CPU - - - GPU\1bitSGD - - - CPU\1bitSGD - - - - GPU\1bitSGD - - - Tensors - - - Tensors - - - Common\Include - - - Misc - - - Common\Include - - - Convolution - - - Misc - - - Misc - - - 1bitSGD - - - 1bitSGD - - - - - GPU - - - GPU - - - GPU - - - GPU - - - - - {4d07e945-74fb-48fa-aa63-23f3a7763789} - - - {51b468dd-7e8a-4be8-ae6f-5e3f3d752b88} - - - {94878fe0-ecce-4868-b3be-2199f4ee7507} - - - {cc9a219d-d8ab-484a-b253-fd2a29ad7c7c} - - - {cc9ad68a-5ca1-40ad-9a60-4e55ef3d802b} - - - {af1f6489-f531-4338-a4c5-ebe52b884e5c} - - - {70fb07cf-603e-4444-bc10-f0add4920fd2} - - - {62b92193-92d0-4e5b-8c3e-67ffd01a98c0} - - - {3a49e94d-14ee-4ca1-a56e-a1472206a076} - - - {546cacbd-253e-485b-8c8c-8b9ee0e2f631} - - + + + + + + Common + + + Common + + + Common + + + CPU + + + CPU + + + GPU + + + CPU\1bitSGD + + + GPU\1bitSGD + + + Tensors + + + Misc + + + Convolution + + + Misc + + + 1bitSGD + + + 1bitSGD + + + + + + + Common\Include + + + Common\Include + + + CPU + + + CPU + + + GPU\1bitSGD + + + CPU\1bitSGD + + + + GPU\1bitSGD + + + Tensors + + + Tensors + + + Common\Include + + + Misc + + + Common\Include + + + Convolution + + + Misc + + + Misc + + + 1bitSGD + + + 1bitSGD + + + + + GPU + + + GPU + + + GPU + + + GPU + + + + + {4d07e945-74fb-48fa-aa63-23f3a7763789} + + + {51b468dd-7e8a-4be8-ae6f-5e3f3d752b88} + + + {94878fe0-ecce-4868-b3be-2199f4ee7507} + + + {cc9a219d-d8ab-484a-b253-fd2a29ad7c7c} + + + {cc9ad68a-5ca1-40ad-9a60-4e55ef3d802b} + + + {af1f6489-f531-4338-a4c5-ebe52b884e5c} + + + {70fb07cf-603e-4444-bc10-f0add4920fd2} + + + {62b92193-92d0-4e5b-8c3e-67ffd01a98c0} + + + {3a49e94d-14ee-4ca1-a56e-a1472206a076} + + + {546cacbd-253e-485b-8c8c-8b9ee0e2f631} + + \ No newline at end of file diff --git a/Source/Math/MathCUDA.vcxproj b/Source/Math/MathCUDA.vcxproj index ad29f39a4e05..a9f6c713e1f3 100644 --- a/Source/Math/MathCUDA.vcxproj +++ b/Source/Math/MathCUDA.vcxproj @@ -1,231 +1,231 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {B3DD765E-694E-4494-BAD7-37BBF2942517} - Win32Proj - Math - - - - - - - - - MathCUDA - $(CUDA_PATH_V7_0) - $(CudaPath) - - - - StaticLibrary - v120 - Unicode - - - true - - - false - - - - - - - - - - - - true - USE_CUDNN - $(CUDNN_PATH)\include - $(CUDNN_PATH)\lib\x64 - cudnn.lib - $(CUDNN_PATH)\bin\cudnn64_4.dll - - - - - false - - - - - - ..\Common\include;$(ACML_PATH)\include;$(CudaPath)\include;$(CUB_PATH);$(CuDnnIncPath);$(IncludePath) - $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CudaPath)\lib\$(Platform);$(CuDnnLibPath);$(LibraryPath) - $(Platform)\$(Configuration)\$(ProjectName)\ - - - true - $(CNTK_CUDA_CODEGEN_DEBUG) - compute_20,compute_20 - - - false - $(CNTK_CUDA_CODEGEN_RELEASE) - compute_20,sm_20;compute_30,sm_30;compute_35,sm_35;compute_50,sm_50; - - - - NotUsing - Level4 - NO_SYNC; WIN32; _WINDOWS; _USRDLL; MATH_EXPORTS; $(CuDnnDefine); %(PreprocessorDefinitions) - true - ..\Common\include\;%(AdditionalIncludeDirectories) - true - Fast - true - true - - - Console - true - cudart.lib;cublas.lib;cusparse.lib;curand.lib;libacml_mp_dll.lib;%(AdditionalDependencies) - true - cublas64_70.dll;cusparse64_70.dll;curand64_70.dll;cudart64_70.dll;%(DelayLoadDLLs) - - - $(CuDnnLibPath);%(AdditionalLibraryDirectories) - $(CuDnnLib) - - - 64 - true - /WX - $(CudaCodeGen) - true - false - true - - - -xcopy /D /I /Y "$(CudaPath)\bin\cudart64_*.dll" $(OutputPath) -if exist "$(CuDnnDll)" (xcopy /Y "$(CuDnnDll)" $(OutputPath)) - - - - - - _DEBUG; %(PreprocessorDefinitions) - Disabled - false - - - - - - - MaxSpeed - true - true - NDEBUG; %(PreprocessorDefinitions) - true - false - /d2Zi+ %(AdditionalOptions) - - - true - true - - - false - - - - - - - - - - - - - - - - - - - - - - CppHeader - - - CppHeader - - - - - - - - - true - true - - - CppCode - - - CppCode - - - CppCode - - - - - - - Create - - - - - - - CppCode - - - true - CppCode - - - - - CppCode - true - false - - - true - CppCode - - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {B3DD765E-694E-4494-BAD7-37BBF2942517} + Win32Proj + Math + + + + + + + + + MathCUDA + $(CUDA_PATH_V7_0) + $(CudaPath) + + + + StaticLibrary + v120 + Unicode + + + true + + + false + + + + + + + + + + + + true + USE_CUDNN + $(CUDNN_PATH)\include + $(CUDNN_PATH)\lib\x64 + cudnn.lib + $(CUDNN_PATH)\bin\cudnn64_4.dll + + + + + false + + + + + + ..\Common\include;$(ACML_PATH)\include;$(CudaPath)\include;$(CUB_PATH);$(CuDnnIncPath);$(IncludePath) + $(SolutionDir)$(Platform)\$(Configuration);$(ACML_PATH)\lib;$(CudaPath)\lib\$(Platform);$(CuDnnLibPath);$(LibraryPath) + $(Platform)\$(Configuration)\$(ProjectName)\ + + + true + $(CNTK_CUDA_CODEGEN_DEBUG) + compute_20,compute_20 + + + false + $(CNTK_CUDA_CODEGEN_RELEASE) + compute_20,sm_20;compute_30,sm_30;compute_35,sm_35;compute_50,sm_50; + + + + NotUsing + Level4 + NO_SYNC; WIN32; _WINDOWS; _USRDLL; MATH_EXPORTS; $(CuDnnDefine); %(PreprocessorDefinitions) + true + ..\Common\include\;%(AdditionalIncludeDirectories) + true + Fast + true + true + + + Console + true + cudart.lib;cublas.lib;cusparse.lib;curand.lib;libacml_mp_dll.lib;%(AdditionalDependencies) + true + cublas64_70.dll;cusparse64_70.dll;curand64_70.dll;cudart64_70.dll;%(DelayLoadDLLs) + + + $(CuDnnLibPath);%(AdditionalLibraryDirectories) + $(CuDnnLib) + + + 64 + true + /WX + $(CudaCodeGen) + true + false + true + + + +xcopy /D /I /Y "$(CudaPath)\bin\cudart64_*.dll" $(OutputPath) +if exist "$(CuDnnDll)" (xcopy /Y "$(CuDnnDll)" $(OutputPath)) + + + + + + _DEBUG; %(PreprocessorDefinitions) + Disabled + false + + + + + + + MaxSpeed + true + true + NDEBUG; %(PreprocessorDefinitions) + true + false + /d2Zi+ %(AdditionalOptions) + + + true + true + + + false + + + + + + + + + + + + + + + + + + + + + + CppHeader + + + CppHeader + + + + + + + + + true + true + + + CppCode + + + CppCode + + + CppCode + + + + + + + Create + + + + + + + CppCode + + + true + CppCode + + + + + CppCode + true + false + + + true + CppCode + + + + + + + + + + + + + \ No newline at end of file diff --git a/Source/Math/MathCUDA.vcxproj.filters b/Source/Math/MathCUDA.vcxproj.filters index 366600547350..5706fe4ab5ab 100644 --- a/Source/Math/MathCUDA.vcxproj.filters +++ b/Source/Math/MathCUDA.vcxproj.filters @@ -1,154 +1,154 @@ - - - - - GPU - - - GPU - - - GPU - - - GPU\1bitSGD - - - GPU\1bitSGD - - - GPU\SequenceTraining - - - GPU - - - GPU\Tensors - - - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - Misc - - - Misc - - - GPU\Convolution - - - GPU - - - - - Common\Include - - - Common\Include - - - Common\Include - - - GPU\1bitSGD - - - GPU\1bitSGD - - - GPU\1bitSGD - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\SequenceTraining - - - GPU\Tensors - - - Misc - - - Misc - - - Misc - - - from Math - - - GPU\Convolution - - - from Math - - - GPU - - - - - GPU - - - GPU - - - GPU - - - - - {4d07e945-74fb-48fa-aa63-23f3a7763789} - - - {51b468dd-7e8a-4be8-ae6f-5e3f3d752b88} - - - {cc9a219d-d8ab-484a-b253-fd2a29ad7c7c} - - - {3415233d-9ef7-41c6-abbb-cec1b4f8d14c} - - - {6a3569b1-6c9e-47b3-870f-bb581349e75e} - - - {3c982109-64b1-469a-8d85-2abdf12d636a} - - - {16214e65-2d24-4e4c-a0dd-c37e505bda32} - - - {b1b59e2e-5c54-4e40-ad0a-1523ddeb63ba} - - - {3155488f-128f-494e-858d-459b4cc9fab7} - - + + + + + GPU + + + GPU + + + GPU + + + GPU\1bitSGD + + + GPU\1bitSGD + + + GPU\SequenceTraining + + + GPU + + + GPU\Tensors + + + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + Misc + + + Misc + + + GPU\Convolution + + + GPU + + + + + Common\Include + + + Common\Include + + + Common\Include + + + GPU\1bitSGD + + + GPU\1bitSGD + + + GPU\1bitSGD + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\SequenceTraining + + + GPU\Tensors + + + Misc + + + Misc + + + Misc + + + from Math + + + GPU\Convolution + + + from Math + + + GPU + + + + + GPU + + + GPU + + + GPU + + + + + {4d07e945-74fb-48fa-aa63-23f3a7763789} + + + {51b468dd-7e8a-4be8-ae6f-5e3f3d752b88} + + + {cc9a219d-d8ab-484a-b253-fd2a29ad7c7c} + + + {3415233d-9ef7-41c6-abbb-cec1b4f8d14c} + + + {6a3569b1-6c9e-47b3-870f-bb581349e75e} + + + {3c982109-64b1-469a-8d85-2abdf12d636a} + + + {16214e65-2d24-4e4c-a0dd-c37e505bda32} + + + {b1b59e2e-5c54-4e40-ad0a-1523ddeb63ba} + + + {3155488f-128f-494e-858d-459b4cc9fab7} + + \ No newline at end of file diff --git a/Source/Math/Matrix.cpp b/Source/Math/Matrix.cpp index b7a20e730381..1a27890bb290 100644 --- a/Source/Math/Matrix.cpp +++ b/Source/Math/Matrix.cpp @@ -1,5311 +1,5311 @@ -// Matrix.cpp -- main CPP file that contains all Matrix functions exported by the CNTKMath.dll -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#include "stdafx.h" -#include "Basics.h" -#include "Matrix.h" -#include "CPUMatrix.h" -#include "CPUSparseMatrix.h" -#include "GPUMatrix.h" -#include "GPUSparseMatrix.h" -#include "File.h" -#include -#include -#include "GPUWatcher.h" // bring in this class as well so that it gets exported from this DLL -#ifndef CPUONLY -#pragma comment (lib, "MathCUDA.lib") // built by CNTKMathCUDA project -#endif - -#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this -#pragma warning (disable: 4239) // nonstandard extension; triggered by this pattern: "auto& second = transposeB ? b.m_GPUMatrix->Transpose() : *b.m_GPUMatrix;" -#pragma warning (disable: 4702) // unreachable code; triggered for unknown reasons - -#ifndef min -#define min(a,b) (((a) < (b)) ? (a) : (b)) -#endif - -//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly -#define DISPATCH_MATRIX_ON_FLAG(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ - { \ - CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ - if (curLocation==CurrentDataLocation::GPU || curLocation==CurrentDataLocation::BOTH) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - GPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ - } \ - else \ - { \ - GPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ - } \ - } \ - else if (curLocation==CurrentDataLocation::CPU) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - CPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ - } \ - else \ - { \ - CPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ - } \ - } \ - else \ - { \ - RuntimeError("Matrices do not exist in either CPU or GPU."); \ - } \ - } - -//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly -#define DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ - { \ - CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ - if (curLocation==CurrentDataLocation::GPU) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - GPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ - } \ - else \ - { \ - GPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ - } \ - } \ - else if (curLocation==CurrentDataLocation::CPU || curLocation==CurrentDataLocation::BOTH) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - CPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ - } \ - else \ - { \ - CPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ - } \ - } \ - else \ - { \ - RuntimeError("Matrices do not exist in either CPU or GPU."); \ - } \ - } - -//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly -#define DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ - { \ - CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ - if (curLocation == CurrentDataLocation::BOTH) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - CPUDense; \ - GPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::BOTH, MatrixType::DENSE); \ - } \ - else \ - { \ - CPUSparse; \ - GPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::BOTH, MatrixType::SPARSE); \ - } \ - } \ - else if (curLocation == CurrentDataLocation::GPU) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - GPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ - } \ - else \ - { \ - GPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ - } \ - } \ - else if (curLocation == CurrentDataLocation::CPU) \ - { \ - if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ - { \ - CPUDense; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ - } \ - else \ - { \ - CPUSparse; \ - if (MatrixPointerToSetFlag != nullptr) \ - ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ - } \ - } \ - else \ - { \ - RuntimeError("Matrices do not exist in either CPU or GPU."); \ - } \ - } - -// EnforceOneGPUOnly - enforce that we only use one GPU (because we don't really support more than one at this point in time) -// BUGBUG workaround. -// Call this after every place a device is deviced. -// We have multiple independent mechanisms to pick a device. -// After selecting a device id, always run the result through this function, which will cache the first choice. -// TODO: This is a stop-gap. It will be cleaned up once we also fix the GPU late-locking bug. -// The correct fix is to always route GPU selection through a single function in the first place. -DEVICEID_TYPE EnforceOneGPUOnly(DEVICEID_TYPE requestedDeviceId) -{ - if (requestedDeviceId < 0) // only apply this to GPU ids - return requestedDeviceId; - static DEVICEID_TYPE theGPUId = DEVICEID_NOTYETDETERMINED; - if (theGPUId == DEVICEID_NOTYETDETERMINED) - theGPUId = requestedDeviceId; - else if (theGPUId != requestedDeviceId) - { - static bool shown = false; - if (!shown) - { - fprintf(stderr, "EnforceOneGPUOnly: WARNING: Ignored attempt to change GPU choice from %d to %d. This message will be shown only once.\n", theGPUId, requestedDeviceId); - shown = true; - } - } - return theGPUId; -} - -namespace Microsoft { namespace MSR { namespace CNTK { -#pragma region Constructors, destructors and other static matrix builders - - //This function will only initialize default bland matrix. The actual matrices need to allocated - //after calling this function and flags need to set correctly by calling SetDataLocation. - template - void Matrix::Init(DEVICEID_TYPE deviceId) - { - m_baseMatrix=NULL; - m_GPUMatrix=NULL; - m_CPUMatrix=NULL; - m_GPUSparseMatrix=NULL; - m_CPUSparseMatrix=NULL; - m_currentDataLocation = CurrentDataLocation::NONE; - m_matrixType = MatrixType::UNDETERMINED; - - int _devId = deviceId!=AUTOPLACEMATRIX ? deviceId : GetBestGPUDeviceId(); - _devId = EnforceOneGPUOnly(_devId); // see EnforceOneGPUOnly() for comment on what this is - m_preferredDeviceId=_devId; - m_numTimesDeviceChanged = 0; - m_numTimesMatrixTypeChanged = 0; - m_devicesTransferedTo[1] = m_devicesTransferedTo[0] = CPUDEVICE - 1; - } - - //this function is used to indicate where (CPUDense, CPUSparse, GPUDense, GPUSparse) the most updated results are in - //after the actual matrix is updated. - template - void Matrix::SetDataLocation(CurrentDataLocation location, MatrixType type) const - { - m_currentDataLocation = location; - - // set the matrix type if passed in - if (type != MatrixType::UNDETERMINED) - { - m_matrixType = type; - } - - if (m_matrixType == MatrixType::DENSE) - { - m_baseMatrix = ((m_currentDataLocation == CurrentDataLocation::CPU)?(BaseMatrix*)m_CPUMatrix:(BaseMatrix*)m_GPUMatrix); - } - else if (m_matrixType == MatrixType::SPARSE) - { - m_baseMatrix = ((m_currentDataLocation == CurrentDataLocation::CPU)?(BaseMatrix*)m_CPUSparseMatrix:(BaseMatrix*)m_GPUSparseMatrix); - } - } - - //this is a private constructor only used internally to initialize a blank matrix - template - Matrix::Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, const MatrixFormat matrixFormat, DEVICEID_TYPE deviceID) - { - Init(deviceID); - - if (!(matrixFlags & matrixFlagDontOwnBuffer)) - SwitchToMatrixType(matrixType, matrixFormat, false); - } - - - //this is a private constructor only used internally to initialize a blank matrix - template - Matrix::Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, DEVICEID_TYPE deviceID) - { - Init(deviceID); - - if (!(matrixFlags & matrixFlagDontOwnBuffer)) - SwitchToMatrixType(matrixType, matrixType == MatrixType::DENSE? MatrixFormat::matrixFormatDense : MatrixFormat::matrixFormatSparseCSC, false); - } - - //this is a private constructor only used internally to initialize a blank matrix - template - Matrix::Matrix(const MatrixFlags matrixFlags, DEVICEID_TYPE deviceID) - { - Init(deviceID); - - if (!(matrixFlags & matrixFlagDontOwnBuffer)) - SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, false); - } - - template - Matrix::Matrix(DEVICEID_TYPE deviceID) - { - Init(deviceID); - - SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, false); - } - - // constructor for Matrix class to wrap an externally managed BaseMatrix - // baseMatrix - base matrix for this element - // pArray - pointer to current data array, will replace existing pointer in baseMatrix if != NULL - // deviceId - deviceId where the pArray exists - template - Matrix::Matrix(BaseMatrix* baseMatrix, ElemType *pArray, DEVICEID_TYPE deviceId) // constructor for setting Matrix from a base matrix - { - Init(deviceId); - - if (baseMatrix->GetFormat() & matrixFormatSparse) - { - if (m_preferredDeviceId == CPUDEVICE) - { - m_CPUSparseMatrix = (CPUSparseMatrix*)baseMatrix; - SetDataLocation(CPU, SPARSE); - } - else - { - m_GPUSparseMatrix = (GPUSparseMatrix*)baseMatrix; - SetDataLocation(GPU, SPARSE); - } - } - else - { - if (m_preferredDeviceId == CPUDEVICE) - { - m_CPUMatrix = (CPUMatrix*)baseMatrix; - SetDataLocation(CPU, DENSE); - } - else - { - m_GPUMatrix = (GPUMatrix*)baseMatrix; - SetDataLocation(GPU, DENSE); - } - } - m_baseMatrix = baseMatrix; - m_baseMatrix->SetArray(pArray); - } - - //matrixName is used to verify that correct matrix is read. - template - Matrix::Matrix(FILE* f, const char * matrixName, DEVICEID_TYPE deviceId, const MatrixType matrixType) - { - Init(deviceId); - - if (matrixType == MatrixType::SPARSE) - { - if (m_preferredDeviceId == CPUDEVICE) - { - NOT_IMPLEMENTED; - //m_CPUSparseMatrix = new CPUSparseMatrix(f,matrixName); - SetDataLocation(CPU, SPARSE); - } - else - { - NOT_IMPLEMENTED; - //m_GPUSparseMatrix = new GPUSparseMatrix(f,matrixName, m_preferredDeviceId); - SetDataLocation(GPU, SPARSE); - } - } - else - { - if (m_preferredDeviceId == CPUDEVICE) - { - m_CPUMatrix = new CPUMatrix(f,matrixName); - SetDataLocation(CPU, DENSE); - } - else - { - m_GPUMatrix = new GPUMatrix(f,matrixName, m_preferredDeviceId); - SetDataLocation(GPU, DENSE); - } - } - } - - template - Matrix::Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId, const MatrixType matrixType, const MatrixFormat matrixFormat) - { - Init(deviceId); - - if (matrixType == MatrixType::SPARSE) - { - if (m_preferredDeviceId == CPUDEVICE) - { - m_CPUSparseMatrix = new CPUSparseMatrix(matrixFormat, numRows, numCols, 0); - SetDataLocation(CPU, SPARSE); - } - else - { - m_GPUSparseMatrix = new GPUSparseMatrix(numRows, numCols, 0, matrixFormat, m_preferredDeviceId); - SetDataLocation(GPU, SPARSE); - } - } - else - { - if (matrixFormat != matrixFormatDense) - { - NOT_IMPLEMENTED; - } - - if (m_preferredDeviceId == CPUDEVICE) - { - m_CPUMatrix = new CPUMatrix(numRows, numCols); - SetDataLocation(CPU, DENSE); - } - else - { - m_GPUMatrix = new GPUMatrix(numRows, numCols, m_preferredDeviceId); - SetDataLocation(GPU, DENSE); - } - - SetValue(0); - } - } - - template - Matrix::Matrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags, DEVICEID_TYPE deviceId, const size_t nnz) - { - Init(deviceId); - - if (m_preferredDeviceId == CPUDEVICE) - { - if (matrixFlags&matrixFormatSparse) - { - //WARNING: matrixFlag is not passed in and externally managed array cannot be passed in - m_CPUSparseMatrix = new CPUSparseMatrix(matrixFormatSparseCSC, numRows,numCols, nnz); - SetDataLocation(CPU, SPARSE); - } - else - { - m_CPUMatrix = new CPUMatrix(numRows,numCols,pArray,matrixFlags); - SetDataLocation(CPU, DENSE); - } - } - else - { - if (matrixFlags&matrixFormatSparse) - { - //m_GPUSparseMatrix = new GPUSparseMatrix(numRows,numCols,nnz, pArray,matrixFlags,m_preferredDeviceId); - m_GPUSparseMatrix = new GPUSparseMatrix(MatrixFormat(matrixFlags & MatrixFormat::matrixFormatMask), m_preferredDeviceId); - m_GPUSparseMatrix->Resize(numRows, numCols, nnz, true, false); - SetDataLocation(GPU, SPARSE); - } - else - { - m_GPUMatrix = new GPUMatrix(numRows, numCols, m_preferredDeviceId, pArray, matrixFlags); - SetDataLocation(GPU, DENSE); - } - } - - if (matrixFlagDontOwnBuffer & matrixFlags) - m_baseMatrix->SetOwnBuffer(false); - } - - //copy constructor, deep copy - template - Matrix::Matrix(const Matrix& deepCopyFrom, DEVICEID_TYPE deviceId) - { - int origCopyFromDeviceId = deepCopyFrom.GetDeviceId(); - - if (deviceId == AUTOPLACEMATRIX) //use copyFrom's device if we have choice - deviceId = (DEVICEID_TYPE)origCopyFromDeviceId; - - Init(deviceId); //will set m_preferredDeviceId - - deepCopyFrom._transferToDevice(m_preferredDeviceId, true); - - DISPATCH_MATRIX_ON_FLAG(&deepCopyFrom, - this, - m_CPUMatrix = new CPUMatrix(*(deepCopyFrom.m_CPUMatrix)), - m_GPUMatrix = new GPUMatrix(*(deepCopyFrom.m_GPUMatrix)), - m_CPUSparseMatrix = new CPUSparseMatrix(*(deepCopyFrom.m_CPUSparseMatrix)), - m_GPUSparseMatrix = new GPUSparseMatrix(*(deepCopyFrom.m_GPUSparseMatrix)) - ); - - //should we move back? - deepCopyFrom._transferToDevice(origCopyFromDeviceId, true); - - m_preferredDeviceId = deepCopyFrom.m_preferredDeviceId; - } - - //assignment operator, deep copy - template - Matrix& Matrix::operator=(const Matrix& deepCopyFrom) - { - if (this != &deepCopyFrom) - { - SetValue(deepCopyFrom); - } - return *this; - } - - //move constructor, shallow copy - template - Matrix::Matrix(Matrix&& moveFrom) - { - Init((DEVICEID_TYPE)moveFrom.GetDeviceId()); - - DISPATCH_MATRIX_ON_FLAG(&moveFrom, - this, - m_CPUMatrix = new CPUMatrix(static_cast&&>(*(moveFrom.m_CPUMatrix))), - m_GPUMatrix = new GPUMatrix(static_cast&&>(*(moveFrom.m_GPUMatrix))), - m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))), - m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))) - ); - - m_preferredDeviceId = moveFrom.m_preferredDeviceId; - } - - //move assignment operator, shallow copy - template - Matrix& Matrix::operator=(Matrix&& moveFrom) - { - Clear(); - m_preferredDeviceId = moveFrom.m_preferredDeviceId; - - DISPATCH_MATRIX_ON_FLAG(&moveFrom, - this, - if (m_CPUMatrix != nullptr) m_CPUMatrix->operator=(static_cast&&>(*(moveFrom.m_CPUMatrix))); - else m_CPUMatrix = new CPUMatrix(static_cast&&>(*(moveFrom.m_CPUMatrix))), - - if (m_GPUMatrix != nullptr) m_GPUMatrix->operator=(static_cast&&>(*(moveFrom.m_GPUMatrix))); - else m_GPUMatrix = new GPUMatrix(static_cast&&>(*(moveFrom.m_GPUMatrix))), - - if (m_CPUSparseMatrix != nullptr) m_CPUSparseMatrix->operator=(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))); - else m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))), - - if (m_GPUSparseMatrix != nullptr) m_GPUSparseMatrix->operator=(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))); - else m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))) - ); - - return *this; - } - - template - void Matrix::Clear() - { - delete m_CPUMatrix; - m_CPUMatrix = nullptr; - delete m_GPUMatrix; - m_GPUMatrix = nullptr; - delete m_GPUSparseMatrix; - m_GPUSparseMatrix = nullptr; - delete m_CPUSparseMatrix; - m_CPUSparseMatrix = nullptr; - - m_matrixType=MatrixType::UNDETERMINED; - m_currentDataLocation = CurrentDataLocation::NONE; - } - - template - Matrix::~Matrix(void) - { - Clear(); - } - - template - Matrix Matrix::Ones(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId) - { - Matrix c(rows, cols, deviceId); //will initialize to 0 - c.SetValue(1); - return c; - } - - template - Matrix Matrix::Zeros(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId) - { - Matrix c(rows, cols, deviceId); //will initialize to 0 - c.SetValue(0); - return c; - } - - template - Matrix Matrix::Eye(const size_t rows, DEVICEID_TYPE deviceId) - { - Matrix c(rows, rows, deviceId); //will initialize to 0 - c.SetDiagonalValue(1); - return c; - } - - template - Matrix Matrix::RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed, DEVICEID_TYPE deviceId) - { - Matrix c(rows, cols, deviceId); //will initialize to 0 - c.SetUniformRandomValue(low, high, seed); - return c; - } - - template - Matrix Matrix::RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed, DEVICEID_TYPE deviceId) - { - Matrix c(rows, cols, deviceId); //will initialize to 0 - c.SetGaussianRandomValue(mean, sigma, seed); - return c; - } - - template - void Matrix::SetDevice(DEVICEID_TYPE deviceId) - { - if (deviceId >= 0) - GPUMatrix::SetDevice(deviceId); - } - - template - void Matrix::Read(File& stream) - { - Matrix& M = *this; - char type; - stream >> type; - if (type == 'd') - { - if (M.GetDeviceId()<0) - { - if (M.m_CPUMatrix == NULL) M.m_CPUMatrix = new CPUMatrix(); - stream >> (*M.m_CPUMatrix); - M.SetDataLocation(CPU, DENSE); - } - else - { - if (M.m_GPUMatrix == NULL) M.m_GPUMatrix = new GPUMatrix(M.GetDeviceId()); - stream >> (*M.m_GPUMatrix); - M.SetDataLocation(GPU, DENSE); - } - } - else if (type == 's') - { - if (M.GetDeviceId()<0) - { - NOT_IMPLEMENTED;//You might want to tranfer your matrix to GPU - } - else - { - if (M.m_GPUSparseMatrix == NULL) M.m_GPUSparseMatrix = new GPUSparseMatrix(); - stream >> (*M.m_GPUSparseMatrix); - M.SetDataLocation(GPU, SPARSE); - } - } - else - LogicError("wrong matrix type!"); - } - - template - void Matrix::Write(File& stream) const - { - const Matrix& M = *this; - if (M.GetMatrixType() == MatrixType::DENSE) - { - stream << 'd'; - if (M.GetDeviceId() < 0) - stream << (*M.m_CPUMatrix); - else - stream << (*M.m_GPUMatrix); - } - else - { - stream << 's'; - if (M.GetDeviceId() < 0) - NOT_IMPLEMENTED //stream<<(*M.m_CPUMatrix); - else - stream << (*M.m_GPUSparseMatrix); - } - } - -#pragma endregion Constructors, destructors and other static matrix builders - -#pragma region Basic Operators - - template - void Matrix::ShiftBy(int numShift) - { - assert (numShift > 0); - - int devId = GetDeviceId(); - - if (GetMatrixType() == MatrixType::DENSE) - { - for (size_t i = GetNumCols() - 1; i >= -numShift; i--) - { - Matrix inp = ColumnSlice(i + numShift, 1); - Matrix out = ColumnSlice(i, 1); - out = inp; - } - for (size_t i = 0; i < min(GetNumCols(), -numShift); i++) - ColumnSlice(i, 1).SetValue(0); - } - else if (GetMatrixType() == MatrixType::SPARSE) - { - if (devId == CPUDEVICE) - { - m_CPUSparseMatrix->ShiftBy(numShift); - } - else - NOT_IMPLEMENTED; - } - else - { - RuntimeError("Unknown matrix type"); - } - } - - template - size_t Matrix::BufferSize() const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_baseMatrix->GetSizeAllocated()*sizeof(ElemType), - return m_baseMatrix->GetSizeAllocated()*sizeof(ElemType), - return m_CPUSparseMatrix->BufferSize(), - return m_GPUSparseMatrix->BufferSizeAllocated() - ); - } - - template - ElemType* Matrix::BufferPointer() const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_baseMatrix->GetArray(), - return m_baseMatrix->GetArray(), - return m_CPUSparseMatrix->BufferPointer(), - return (ElemType*)m_GPUSparseMatrix->BufferPointer() - ); - } - - template - size_t Matrix::NzCount() const {return m_baseMatrix->NzCount();} - - template - ElemType* Matrix::CopyToArray() const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->CopyToArray(), - return m_GPUMatrix->CopyToArray(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done - //return number of elements copied - template - size_t Matrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->CopyToArray(arrayCopyTo, currentArraySize), - return m_GPUMatrix->CopyToArray(arrayCopyTo, currentArraySize), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - m_CPUMatrix->CopySection(numRows, numCols, dst, colStride), - m_GPUMatrix->CopySection(numRows, numCols, dst, colStride), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - // BUGBUG: Some code checks before calling here whether one of the dimensions is 0. - // This function must handle that case properly, that is, preserving the non-zero dimension. - template - Matrix Matrix::ColumnSlice(size_t startColumn, size_t numCols) const - { - int devId = GetDeviceId(); - - Matrix slice(matrixFlagDontOwnBuffer, (DEVICEID_TYPE)devId); // - - slice.m_preferredDeviceId = m_preferredDeviceId; - - if (GetMatrixType() == MatrixType::DENSE) - { - if (devId == CPUDEVICE) - { - if (slice.m_CPUMatrix != nullptr) - slice.m_CPUMatrix->operator=(static_cast&&>(m_CPUMatrix->ColumnSlice(startColumn, numCols))); - else - slice.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUMatrix->ColumnSlice(startColumn, numCols))); - slice.SetDataLocation(CPU, DENSE); - } - else - { - if (slice.m_GPUMatrix != nullptr) - slice.m_GPUMatrix->operator=(static_cast&&>(m_GPUMatrix->ColumnSlice(startColumn, numCols))); - else - slice.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUMatrix->ColumnSlice(startColumn, numCols))); - slice.SetDataLocation(GPU, DENSE); - } - } - else if (GetMatrixType() == MatrixType::SPARSE) - { - if (devId == CPUDEVICE) - { - if (slice.m_CPUSparseMatrix != nullptr) - slice.m_CPUSparseMatrix->operator=(static_cast&&>(m_CPUSparseMatrix->ColumnSlice(startColumn, numCols))); - else - slice.m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(m_CPUSparseMatrix->ColumnSlice(startColumn, numCols))); - slice.SetDataLocation(CPU, SPARSE); - } - else - { - if (slice.m_GPUSparseMatrix != nullptr) - slice.m_GPUSparseMatrix->operator=(static_cast&&>(m_GPUSparseMatrix->ColumnSlice(startColumn, numCols))); - else - slice.m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(m_GPUSparseMatrix->ColumnSlice(startColumn, numCols))); - slice.SetDataLocation(GPU, SPARSE); - } - } - else - { - RuntimeError("Unknown matrix type"); - } - - return slice; - } - - template - Matrix& Matrix::AssignColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols) - { - Clear(); - m_preferredDeviceId = fromMatrix.m_preferredDeviceId; - - DISPATCH_MATRIX_ON_FLAG(&fromMatrix, - this, - if (m_CPUMatrix != nullptr) m_CPUMatrix->AssignColumnSlice(*fromMatrix.m_CPUMatrix, startColumn, numCols); - else m_CPUMatrix = new CPUMatrix(static_cast&&>(fromMatrix.m_CPUMatrix->ColumnSlice(startColumn, numCols))), - - if (m_GPUMatrix != nullptr) m_GPUMatrix->AssignColumnSlice(*fromMatrix.m_GPUMatrix, startColumn, numCols); - else m_GPUMatrix = new GPUMatrix(static_cast&&>(fromMatrix.m_GPUMatrix->ColumnSlice(startColumn, numCols))), - - NOT_IMPLEMENTED, - - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::SetColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols) - { - assert(m_CPUMatrix != nullptr || m_GPUMatrix != nullptr); - // must already been allocated - - DISPATCH_MATRIX_ON_FLAG(&fromMatrix, - this, - m_CPUMatrix->SetColumnSlice(*fromMatrix.m_CPUMatrix, startColumn, numCols), - m_GPUMatrix->SetColumnSlice(*fromMatrix.m_GPUMatrix, startColumn, numCols), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - void Matrix::CopyColumnsStrided(const Matrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) - { - assert(m_CPUMatrix != nullptr || m_GPUMatrix != nullptr); - - DISPATCH_MATRIX_ON_FLAG(&fromMatrix, - this, - m_CPUMatrix->CopyColumnsStrided(*fromMatrix.m_CPUMatrix, numCols, srcNumColsStride, destNumColsStride), - m_GPUMatrix->CopyColumnsStrided(*fromMatrix.m_GPUMatrix, numCols, srcNumColsStride, destNumColsStride), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix Matrix::Diagonal() const - { - int devId = GetDeviceId(); - - Matrix diag(matrixFlagDontOwnBuffer, (DEVICEID_TYPE)devId); - diag.m_preferredDeviceId = m_preferredDeviceId; - - AssignDiagonalValuesTo(diag); - - return diag; - } - - template - Matrix Matrix::AssignDiagonalValuesTo(Matrix& diag) const - { - int devId = GetDeviceId(); - DecideAndMoveToRightDevice(*this, diag); - - if (GetMatrixType() == MatrixType::DENSE) - { - if (devId == CPUDEVICE) - { - if (diag.m_CPUMatrix != nullptr) - diag.m_CPUMatrix->operator=(static_cast&&>(m_CPUMatrix->Diagonal())); - else - diag.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUMatrix->Diagonal())); - diag.SetDataLocation(CPU, DENSE); - } - else - { - if (diag.m_GPUMatrix != nullptr) - diag.m_GPUMatrix->operator=(static_cast&&>(m_GPUMatrix->Diagonal())); - else - diag.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUMatrix->Diagonal())); - diag.SetDataLocation(GPU, DENSE); - } - } - else if (GetMatrixType() == MatrixType::SPARSE) - { - // TODO: Implement optimized diagonal functions for sparse matrices. For now use the DiagonalToDense instead. - if (devId == CPUDEVICE) - { - if (diag.m_CPUMatrix != nullptr) - diag.m_CPUMatrix->operator=(static_cast&&>(m_CPUSparseMatrix->DiagonalToDense())); - else - diag.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUSparseMatrix->DiagonalToDense())); - diag.SetDataLocation(CPU, DENSE); - } - else - { - if (diag.m_GPUMatrix != nullptr) - diag.m_GPUMatrix->operator=(static_cast&&>(m_GPUSparseMatrix->DiagonalToDense())); - else - diag.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUSparseMatrix->DiagonalToDense())); - diag.SetDataLocation(GPU, DENSE); - } - } - else - { - RuntimeError("Unknown matrix type"); - } - - return diag; - } - - //this function will change the matrix type between DENSE and SPARSE. - //WARNING: The correct implementation is to copy the matrix between DENSE and SPARSE - // However, the conversion functions are not implemented yet and so it will always create - // a new blank matrix and destroy all info in the original matrix if different matrix type is asked. - // In case of !keepValues, the matrix content will be undefined. - template - void Matrix::SwitchToMatrixType(MatrixType newMatrixType, MatrixFormat newMatrixFormat, bool keepValues) - { - // This check should be uncommented but unfortunately there are still places - // this function is being called with incorrect "default" format value - /*if (m_matrixType == newMatrixType && GetFormat() != newMatrixFormat) - NOT_IMPLEMENTED;*/ - - if (m_matrixType == newMatrixType) - return; - - if (m_baseMatrix == nullptr) - keepValues = false; - -#define NUM_MATRIXTYPE_CHANGED_WARN 20 - m_numTimesMatrixTypeChanged++; - - if (m_numTimesMatrixTypeChanged == NUM_MATRIXTYPE_CHANGED_WARN) - fprintf(stderr, "WARNING: The same matrix with dim [%lu, %lu] has been transferred between different devices for %d times.\n", (unsigned long)GetNumRows(), (unsigned long)GetNumCols(), NUM_MATRIXTYPE_CHANGED_WARN); - - if (GetDeviceId()<0) //CPU - { - if (newMatrixType==MatrixType::SPARSE) - { - if (m_baseMatrix == nullptr) - m_CPUSparseMatrix = new CPUSparseMatrix(newMatrixFormat); - else - m_CPUSparseMatrix = new CPUSparseMatrix(newMatrixFormat, GetNumRows(), GetNumCols(), 1); - - if (keepValues) - CopyElementsFromDenseToSparse(*m_CPUMatrix, *m_CPUSparseMatrix); - - delete m_CPUMatrix; - m_CPUMatrix = nullptr; - - SetDataLocation(CPU, SPARSE); - } - else if (newMatrixType==MatrixType::DENSE) - { - if (m_baseMatrix == nullptr) - m_CPUMatrix = new CPUMatrix(); - else - m_CPUMatrix = new CPUMatrix(GetNumRows(), GetNumCols()); - - if (keepValues) - m_CPUMatrix->SetValue(m_CPUSparseMatrix->CopyColumnSliceToDense(0, GetNumCols())); - - delete m_CPUSparseMatrix; - m_CPUSparseMatrix = nullptr; - - SetDataLocation(CPU, DENSE); - } - else - LogicError("SwitchToMatrixType: Unexpected/invalid new matrix type"); - } - else //GPU - { - if (newMatrixType==MatrixType::SPARSE) - { - if (m_baseMatrix == nullptr) - m_GPUSparseMatrix = new GPUSparseMatrix(newMatrixFormat, GetDeviceId()); - else - m_GPUSparseMatrix = new GPUSparseMatrix(GetNumRows(), GetNumCols(), 0, newMatrixFormat, GetDeviceId()); - - if (keepValues) - m_GPUSparseMatrix->SetValue(*m_GPUMatrix); - - delete m_GPUMatrix; - m_GPUMatrix = nullptr; - - SetDataLocation(GPU, SPARSE); - } - else if (newMatrixType==MatrixType::DENSE) - { - if (m_baseMatrix == nullptr) - m_GPUMatrix = new GPUMatrix(GetDeviceId()); - else - m_GPUMatrix = new GPUMatrix(GetNumRows(), GetNumCols(), GetDeviceId()); - - if (keepValues) - m_GPUSparseMatrix->CopyToDenseMatrix(*m_GPUMatrix); - - delete m_GPUSparseMatrix; - m_GPUSparseMatrix = nullptr; - - SetDataLocation(GPU, DENSE); - } - else - LogicError("SwitchToMatrixType: Unexpected/invalid new matrix type"); - } - } - - template - void Matrix::CopyElementsFromDenseToSparse(CPUMatrix& from, CPUSparseMatrix& dest) - { - foreach_coord(row, col, from) - { - auto val = from(row, col); - dest.SetValue(row, col, val); - } - } - - template - ElemType Matrix::Get00Element() const - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->Get00Element(), - return m_GPUMatrix->Get00Element(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - const ElemType Matrix::operator() (const size_t row, const size_t col) const - { - DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, - nullptr, - return m_CPUMatrix->operator()(row,col), - _transferFromDeviceToDevice(GetDeviceId(), CPUDEVICE, false); return m_CPUMatrix->operator()(row,col), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - - //WARNING: This function is very slow for GPUs since it requires copying values between CPUs and GPUs. - //In addition, if ColumnSlice is used after this function but before the values are copied back to GPU - //the operation will fail since the memory is not managed by the slice. - //If you don't need to modify the values, please make sure to call the const version above. - template - ElemType& Matrix::operator() (const size_t row, const size_t col) - { - DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, - nullptr, - return m_CPUMatrix->operator()(row,col), - _transferFromDeviceToDevice(GetDeviceId(), CPUDEVICE, false); SetDataLocation(CPU, DENSE); return m_CPUMatrix->operator()(row,col), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix Matrix::Transpose() - { - if (IsEmpty()) - LogicError("Transpose: Matrix is empty."); - - Matrix c(GetNumCols(), GetNumRows(), (DEVICEID_TYPE)GetDeviceId(), this->GetMatrixType(), this->GetFormat()); - c.AssignTransposeOf(*this); - return c; - } - - template - Matrix& Matrix::AssignTransposeOf (const Matrix& a) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignTransposeOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignTransposeOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignTransposeOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - // set all elements of a matrix to a scalar value - // For sparse matrices, the only allowed value is 0. - template - void Matrix::SetValue(const ElemType v) - { - if (IsEmpty()) // if empty then we are done - return; - - if (v == 0 && GetMatrixType() == MatrixType::SPARSE) // if sparse, setting it to 0 is special - { - Reset(); - return; - } - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetValue(v), - m_GPUMatrix->SetValue(v), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetValue(const DeviceBoundNumber& db_number) - { - if (IsEmpty()) // if empty then we are done - return; - //LogicError("SetValue: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetValue(*db_number.ExposePointer2Value()), - if (GetDeviceId()!=db_number.GetDeviceId()) - RuntimeError("Matrix and device bound number must be on the same device"); - m_GPUMatrix->SetValue(db_number.ExposePointer2Value()), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template<> /*static*/ float Matrix::MakeNan(size_t /*payload*/) { return nanf(""); } - template<> /*static*/ double Matrix::MakeNan(size_t /*payload*/) { return nan(""); } - template<> /*static*/ char Matrix::MakeNan(size_t) { return 0; } // (needed for completeness) - - template - void Matrix::MaskColumnsValue(const Matrix& columnsMask, ElemType val) - { - if (GetNumCols() != columnsMask.GetNumCols()) - RuntimeError("Matrix and column mask must have equal number of columns"); - - if (GetDeviceId() != columnsMask.GetDeviceId()) - RuntimeError("Matrix and column mask must be on the same device"); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->MaskColumnsValue(*columnsMask.m_CPUMatrix, val), - m_GPUMatrix->MaskColumnsValue(*columnsMask.m_GPUMatrix, val), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetColumn(const ElemType* colPointer, size_t colInd) - { - if (colPointer == nullptr) - InvalidArgument("SetColumn: colPointer is null."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetColumn(colPointer,colInd), - m_GPUMatrix->SetColumn(colPointer,colInd), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetColumn(const ElemType val, size_t colInd) - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetColumn(val,colInd), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED); - } - - template - void Matrix::SetColumn(const Matrix& colMat, size_t colInd) - { - DecideAndMoveToRightDevice(*this, colMat); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetColumn(*colMat.m_CPUMatrix,colInd), - m_GPUMatrix->SetColumn(*colMat.m_GPUMatrix, colInd), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED); - } - - - template - void Matrix::SetValue(const Matrix& deepCopyFrom, const MatrixFormat format /*= matrixFormatSparseCSR*/) - { - if (this == &deepCopyFrom) - return; - - m_preferredDeviceId = deepCopyFrom.m_preferredDeviceId; - DecideAndMoveToRightDevice(deepCopyFrom, *this); - SwitchToMatrixType(deepCopyFrom.GetMatrixType(), format, false); - - DISPATCH_MATRIX_ON_FLAG(&deepCopyFrom, - this, - m_CPUMatrix->SetValue(*deepCopyFrom.m_CPUMatrix), - m_GPUMatrix->SetValue(*deepCopyFrom.m_GPUMatrix), - m_CPUSparseMatrix->SetValue(*deepCopyFrom.m_CPUSparseMatrix), - m_GPUSparseMatrix->SetValue(*deepCopyFrom.m_GPUSparseMatrix) - ); - } - - - template - void Matrix::SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags) - { - if (((numRows * numCols) > 0) && (pArray == nullptr)) - InvalidArgument("Invalid pArray."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetValue(numRows,numCols,pArray,matrixFlags), - m_GPUMatrix->SetValue(numRows,numCols,deviceId,pArray, matrixFlags), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetValue(const size_t rIdx, const size_t cIdx, ElemType val) - { - DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, - this, - (*m_CPUMatrix)(rIdx, cIdx) = val, - NOT_IMPLEMENTED, - m_CPUSparseMatrix->SetValue(rIdx,cIdx,val), - NOT_IMPLEMENTED - ); - } - - // read features - template - void Matrix::SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, - const size_t nz, const size_t numRows, const size_t numCols) - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - m_CPUSparseMatrix->SetMatrixFromCSCFormat(h_CSCCol, h_Row, h_Val, nz, numRows, numCols), - m_GPUSparseMatrix->SetMatrixFromCSCFormat(h_CSCCol, h_Row, h_Val, nz, numRows, numCols) - ); - } - - template - void Matrix::SetDiagonalValue(const ElemType v) - { - if (IsEmpty()) - LogicError("SetDiagonalValue: Matrix is empty."); - - if (GetNumRows() != GetNumCols()) - LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetDiagonalValue(v), - m_GPUMatrix->SetDiagonalValue(v), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetDiagonalValue(const Matrix& vector) - { - if (IsEmpty() || vector.IsEmpty()) - LogicError("SetDiagonalValue: Matrix is empty."); - - if (GetNumRows() != GetNumCols()) - LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); - - if (vector.GetNumRows() != 1 && vector.GetNumCols() != 1) - LogicError("SetDiagonalValue: input vector must be a vector."); - - DecideAndMoveToRightDevice(*this, vector); - - if (vector.GetNumElements() == 1) //reduce to simple form - { - DISPATCH_MATRIX_ON_FLAG(&vector, - nullptr, - SetDiagonalValue(vector(0,0)), - SetDiagonalValue(vector.m_GPUMatrix->Get00Element()), // BUGBUG: efficiency - SetDiagonalValue(vector(0,0)), - SetDiagonalValue(vector.m_GPUMatrix->Get00Element()) // BUGBUG: efficiency - ); - } - else if (vector.GetNumRows() != GetNumRows()) - LogicError("SetDiagonalValue: input vector's dimension does not agree with [this]."); - else - { - //WARNING: we use this pointer to decide which function to call. However, vector may be stored in a different matrix type (DENSE, SPARSE) - DISPATCH_MATRIX_ON_FLAG(this, - this, - assert(vector.m_CPUMatrix != nullptr); m_CPUMatrix->SetDiagonalValue(*vector.m_CPUMatrix), - assert(vector.m_GPUMatrix != nullptr); m_GPUMatrix->SetDiagonalValue(*vector.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - } - - template - void Matrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) - { - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetUniformRandomValue(low,high,seed), - m_GPUMatrix->SetUniformRandomValue(low,high,seed), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) - { - if (sigma <= 0) - InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); - - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetGaussianRandomValue(mean, sigma, seed), - m_GPUMatrix->SetGaussianRandomValue(mean, sigma, seed), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) - { - if (sigma <= 0) - InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); - - if (IsEmpty()) - LogicError("SetUniformRandomValue: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddGaussianRandomValue(mean, sigma, seed), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - //maskRate: percentage of values masked out (similar to dropout rate) - //scaleValue: which scale value to set to the left ones (unmasked items). - template - void Matrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) - { - if (IsEmpty()) - LogicError("SetUniformRandomMask: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetUniformRandomMask(maskRate,scaleValue,seed), - m_GPUMatrix->SetUniformRandomMask(maskRate,scaleValue,seed), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::NormalGrad(Matrix& gradients, - Matrix& functionValues, - const ElemType learnRatePerSample, - const ElemType momentum, - const bool useNesterovMomentum - ) - { - DecideAndMoveToRightDevice(*this, gradients, functionValues); - - if (!useNesterovMomentum) - { - DISPATCH_MATRIX_ON_FLAG(&gradients, - nullptr, - ScaleAndAdd((1-momentum) * learnRatePerSample, gradients, momentum, *this); functionValues -= *this, - ScaleAndAdd((1-momentum) * learnRatePerSample, gradients, momentum, *this); functionValues -= *this, - if (momentum != 0) gradients.m_CPUSparseMatrix->NormalGrad(*m_CPUMatrix, momentum); ScaleAndAdd(-learnRatePerSample, gradients, functionValues), - if (momentum != 0) gradients.m_GPUSparseMatrix->NormalGrad(*m_GPUMatrix, momentum); ScaleAndAdd(-learnRatePerSample, gradients, functionValues) - ); - } - else - { - DISPATCH_MATRIX_ON_FLAG(&gradients, - nullptr, - {/* CPU dense */ - ScaleAndAdd((1 - momentum) * learnRatePerSample, gradients, momentum, *this); - ScaleAndAdd(-momentum, *this, functionValues); - ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradients, functionValues); - // w_t = w_{t-1} - momentum * v_ {t-1} - (1-momentum)*learnRatePerSampele*gardient, - }, - {/* GPU dense */ - ScaleAndAdd((1 - momentum) * learnRatePerSample, gradients, momentum, *this); - ScaleAndAdd(-momentum, *this, functionValues); - ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradients, functionValues); - }, - { /* CPU sparse */ - if (momentum != 0) - { - Matrix gradientCache(gradients.GetDeviceId()); - gradientCache.SetValue(gradients); - gradients.m_CPUSparseMatrix->NormalGrad(*m_CPUMatrix, momentum); - ScaleAndAdd(-momentum, *this, functionValues); - ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradientCache, functionValues); - } - }, - { /* GPU sparse */ - if (momentum != 0) - { - Matrix gradientCache(gradients.GetDeviceId()); - gradientCache.SetValue(gradients); - gradients.m_GPUSparseMatrix->NormalGrad(*m_GPUMatrix, momentum); - ScaleAndAdd(-momentum, *this, functionValues); - ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradientCache, functionValues); - } - } - ); - } - } - - //both this and gradients will be changed - template - ElemType Matrix::Adagrad(Matrix& gradients, const bool needAveMultiplier) - { - DecideAndMoveToRightDevice(*this, gradients); - - DISPATCH_MATRIX_ON_FLAG(&gradients, - &gradients, - return m_CPUMatrix->Adagrad(*gradients.m_CPUMatrix, needAveMultiplier); SetDataLocation(CPU), - return m_GPUMatrix->Adagrad(*gradients.m_GPUMatrix, needAveMultiplier); SetDataLocation(GPU), - return gradients.m_CPUSparseMatrix->Adagrad(*m_CPUMatrix, needAveMultiplier); SetDataLocation(CPU), - return gradients.m_GPUSparseMatrix->Adagrad(*m_GPUMatrix, needAveMultiplier); SetDataLocation(GPU) - ); - } - - template - void Matrix::FSAdagrad(size_t mbSize, Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum) - { - // TODO: The values of 'adagradT' and 'targetadagradavdenom' are currently hardcoded constants taken from DBN (empirically determined). - // These should be made configurable if needed - const size_t adagradT = 2 * 3600 * 100; - const ElemType targetadagradavdenom = 0.0025; // 1/400 magic constant - const ElemType adagradkeepweight = static_cast(exp(-1.0 * mbSize / adagradT)); - - static ElemType aggadagradsqrframes = 0; - aggadagradsqrframes = adagradkeepweight * aggadagradsqrframes + (1.0f - adagradkeepweight) * mbSize; - const ElemType targetadagradavdenom_x_sqrtadagradsqrframes = static_cast(targetadagradavdenom * sqrt(aggadagradsqrframes)); - - DISPATCH_MATRIX_ON_FLAG(&gradients, - &gradients, - m_CPUMatrix->FSAdagrad(*gradients.m_CPUMatrix, *functionValues.m_CPUMatrix, learnRatePerSample, momentum, adagradkeepweight, targetadagradavdenom_x_sqrtadagradsqrframes); SetDataLocation(CPU), - m_GPUMatrix->FSAdagrad(*gradients.m_GPUMatrix, *functionValues.m_GPUMatrix, learnRatePerSample, momentum, adagradkeepweight, targetadagradavdenom_x_sqrtadagradsqrframes); SetDataLocation(GPU), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - ElemType Matrix::RmsProp(Matrix& gradients, - ElemType RMS_GAMMA, - ElemType RMS_WGT_INC, - ElemType RMS_WGT_MAX, - ElemType RMS_WGT_DEC, - ElemType RMS_WGT_MIN, - const bool needAveMultiplier - ) - { - DecideAndMoveToRightDevice(*this, gradients); - - DISPATCH_MATRIX_ON_FLAG(this, - &gradients, - return m_CPUMatrix->RmsProp(*gradients.m_CPUMatrix, RMS_GAMMA, RMS_WGT_INC, RMS_WGT_MAX, RMS_WGT_DEC, RMS_WGT_MIN, needAveMultiplier); SetDataLocation(CPU), - return m_GPUMatrix->RmsProp(*gradients.m_GPUMatrix, RMS_GAMMA, RMS_WGT_INC, RMS_WGT_MAX, RMS_WGT_DEC, RMS_WGT_MIN, needAveMultiplier); SetDataLocation(GPU), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::Reshape(const size_t numRows, const size_t numCols) - { - if (numRows != GetNumRows() || numCols != GetNumCols()) - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->Reshape(numRows, numCols), - m_GPUMatrix->Reshape(numRows, numCols), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->Reshape(numRows, numCols) - ); - } - } - - // Note: Resize() will leave the matrix content undefined. - template - void Matrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve /*=0*/, bool growOnly /*=true*/) - { - // TODO: should this function test whether the size is changing, and skip if it isn't? We have at least one explicit test for this code calling this (recurrent node) - DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(this, - this, - m_CPUMatrix->Resize(numRows, numCols, growOnly), - m_GPUMatrix->Resize(numRows, numCols, growOnly), - m_CPUSparseMatrix->Resize(numRows, numCols, numNZElemToReserve, growOnly, false), - m_GPUSparseMatrix->Resize(numRows, numCols, numNZElemToReserve, growOnly, false) - ); -#ifdef _DEBUG - if (GetMatrixType() != MatrixType::SPARSE) - Invalidate(); // Fill the matrix with NaNs to detect using the content which is undefined. Unfortunately this won't work for sparse matrices. -#endif - } - - template - Matrix Matrix::RepMat(const Matrix& frmMat, const size_t rowRatio, const size_t colRatio) - { - size_t nCols = frmMat.GetNumCols(); - size_t nRows = frmMat.GetNumRows(); - - if (rowRatio > 1) - RuntimeError("RepMat not yet supporting raw ratio larger than 1"); - size_t newCols = colRatio * nCols; - - Matrix c(nRows, newCols, frmMat.GetDeviceId()); - for (size_t i = 0; i < colRatio; i++) - { - c.ColumnSlice(i * nCols, nCols) = frmMat; - } - - return c; - } - - template - size_t Matrix::GetAllocatedSize() const - { - return m_baseMatrix->GetSizeAllocated(); - } - - // reset for sparse matrix. Semantically the same as setting all values to 0. - template - void Matrix::Reset() - { - DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(this, - this, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - m_CPUSparseMatrix->Reset(), - m_GPUSparseMatrix->Reset() - ); - } - - template - size_t Matrix::GetNumRows() const - { - return m_baseMatrix->GetNumRows(); - } - - template - size_t Matrix::GetNumCols() const - { - return m_baseMatrix->GetNumCols(); - } - - template - size_t Matrix::GetNumElements() const - { - return GetNumRows()*GetNumCols(); - } - - template - bool Matrix::IsEmpty() const - { - return m_baseMatrix->IsEmpty(); - } - -#pragma endregion Basic Operators - -#pragma region Member BLAS Functions - - template - Matrix& Matrix::operator+= (ElemType alpha) - { - return AssignSumOf(alpha, *this); - } - - template - Matrix Matrix::operator+ (ElemType alpha) const - { - Matrix c(GetNumRows(), GetNumCols()); - c.AssignSumOf(alpha, *this); - return c; - } - - template - Matrix& Matrix::AssignSumOf(const ElemType alpha, const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSumOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSumOf(alpha,*a.m_CPUMatrix), - m_GPUMatrix->AssignSumOf(alpha,*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - - //if [this] and a have same dimension then [this]=[this]+a - //if a is a column vector, add to all columns of [this] - //if a is a row vector, add to all rows of [this] - //if a is a scalar, add it to all elements. - template - Matrix& Matrix::operator+= (const Matrix& a) - { - DecideAndMoveToRightDevice(*this, a); - - if(!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->operator+=(*a.m_CPUMatrix), - m_GPUMatrix->operator+=(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED); - - return *this; - } - - //if [this] and a have same dimension then OUTPUT=[this]+a - //if a is a column vector, add to all columns of [this] - //if a is a row vector, add to all rows of [this] - template - Matrix Matrix::operator+ (const Matrix& a) const - { - if (GetNumElements() == 1) - { - Matrix c(a); - - DISPATCH_MATRIX_ON_FLAG(this, - &c, - c += (*this)(0,0), - c += (m_GPUMatrix->Get00Element()), // BUGBUG: efficiency - c += (*this)(0,0), - NOT_IMPLEMENTED - ); - return c; - } - else if (a.GetNumElements() == 1) - { - Matrix c(*this); - - DISPATCH_MATRIX_ON_FLAG(&a, - &c, - c += a(0,0), - c += (a.m_GPUMatrix->Get00Element()), // BUGBUG: efficiency - c += a(0,0), - NOT_IMPLEMENTED - ); - return c; - } - else - { - Matrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code - c += a; - return c; - } - } - - template - Matrix& Matrix::AssignSumOf(const Matrix& a, const Matrix& b) - { - if (a.GetNumElements() == 1) - { - SetValue(b); - (*this) += a; - } - else - { - SetValue(a); - (*this) += b; - } - return *this; - } - - template - Matrix& Matrix::operator-= (ElemType alpha) - { - return AssignDifferenceOf(*this, alpha); - } - - template - Matrix Matrix::operator- (ElemType alpha) const - { - Matrix c(GetNumRows(), GetNumCols()); - c.AssignDifferenceOf(*this, alpha); - return c; - } - - //for each column of a, we assign numRows starting from startIndex to this - template - Matrix& Matrix::AssignRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), - m_GPUMatrix->AssignRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - return *this; - } - - //for each column of a, we assign all rows of a to this starting from startIndex - template - Matrix& Matrix::AssignToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignToRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), - m_GPUMatrix->AssignToRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //for the row slice of this starting from startIndex we add a to it. - template - Matrix& Matrix::AddToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddToRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), - m_GPUMatrix->AddToRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //for each column of this, we add row slice of a starting from startIndex - template - Matrix& Matrix::AddWithRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (! (GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddWithRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), - m_GPUMatrix->AddWithRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - -#if 0 // no longer needed, and overkill anyway as it can be implemented as a bunch of calls to AssignRowSliceValuesOf() - //stack the columns in inputMatrices (starting from sliceStartCol for sliceNumCols columns) and assign it to [this] object. - template - Matrix& Matrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) - { - for (int i = 0; i < inputMatrices.size(); i++) - { - const Matrix& a = *inputMatrices[i]; - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - } - - CurrentDataLocation curLocation = GetCurrentMatrixLocation(); - if (curLocation == CurrentDataLocation::GPU || curLocation == CurrentDataLocation::BOTH) - { - if (GetMatrixType() != MatrixType::SPARSE) - { - //GPUDense; - std::vector*> gpuInputMatrices; - gpuInputMatrices.resize(inputMatrices.size()); - for (int i = 0; i < inputMatrices.size(); i++) - gpuInputMatrices[i] = inputMatrices[i]->m_GPUMatrix; - - m_GPUMatrix->AssignRowStackValuesOf(gpuInputMatrices, sliceStartCol, sliceNumCols); - - SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); - } - else - { - NOT_IMPLEMENTED; - } - } - else if (curLocation == CurrentDataLocation::CPU) - { - if (GetMatrixType() != MatrixType::SPARSE) - { - //CPUDense; - std::vector*> cpuInputMatrices; - cpuInputMatrices.resize(inputMatrices.size()); - for (int i = 0; i < inputMatrices.size(); i++) - cpuInputMatrices[i] = inputMatrices[i]->m_CPUMatrix; - - m_CPUMatrix->AssignRowStackValuesOf(cpuInputMatrices, sliceStartCol, sliceNumCols); - - SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); - } - else - { - NOT_IMPLEMENTED; - } - } - else - { - RuntimeError("Matrices do not exist in either CPU or GPU."); - } - - return *this; - } -#endif - - template - Matrix& Matrix::AssignRepeatOf(const Matrix& a, const size_t numRowRepeats, const size_t numColRepeats) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignRepeatOf(*a.m_CPUMatrix, numRowRepeats, numColRepeats), - m_GPUMatrix->AssignRepeatOf(*a.m_GPUMatrix, numRowRepeats, numColRepeats), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AddToRowRepeatValuesOf(const Matrix& a, const size_t numRepeats) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddToRowRepeatValuesOf(*a.m_CPUMatrix, numRepeats), - m_GPUMatrix->AddToRowRepeatValuesOf(*a.m_GPUMatrix, numRepeats), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //used in the DSSM model. The resulted *this is a [a.GetRows()*(negNumber+1), a.GetCols()] matrix - //each column contains posNumber of positive samples (original) and negNumber negative samples generated by copying - //sample shifted by shiftNumber columns - template - Matrix& Matrix::AssignPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignPositiveAndShiftedNegSample(*a.m_CPUMatrix, posNumber, negNumber, shiftNumber), - m_GPUMatrix->AssignPositiveAndShiftedNegSample(*a.m_GPUMatrix, posNumber, negNumber, shiftNumber), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //used in the DSSM model. *this = *this + positive and negative samples folded back to the right place - //each column of a contains posNumber of positive samples (original) and negNumber negative samples generated by copying - //sample shifted by shiftNumber columns - template - Matrix& Matrix::AddFoldedPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) - { - DecideAndMoveToRightDevice(*this, a); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddFoldedPositiveAndShiftedNegSample(*a.m_CPUMatrix, posNumber, negNumber, shiftNumber), - m_GPUMatrix->AddFoldedPositiveAndShiftedNegSample(*a.m_GPUMatrix, posNumber, negNumber, shiftNumber), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignDifferenceOf(const ElemType alpha, const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignDifferenceOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignDifferenceOf(alpha,*a.m_CPUMatrix), - m_GPUMatrix->AssignDifferenceOf(alpha,*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignDifferenceOf(const Matrix& a, const ElemType alpha) - { - if (a.IsEmpty()) - LogicError("AssignDifferenceOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignDifferenceOf(*a.m_CPUMatrix, alpha), - m_GPUMatrix->AssignDifferenceOf(*a.m_GPUMatrix, alpha), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //if [this] and a have same dimension then [this]=[this]-a - //if a is a column vector, minus it from all columns of [this] - //if a is a row vector, minus it from all rows of [this] - template - Matrix& Matrix::operator-= (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("Minus Operation: Matrix a is empty."); - DecideAndMoveToRightDevice(*this, a); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - *m_CPUMatrix -= *a.m_CPUMatrix, - *m_GPUMatrix -= *a.m_GPUMatrix, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //if [this] and a have same dimension then output=[this]-a - //if a is a column vector, minus it from all columns of [this] - //if a is a row vector, minus it from all rows of [this] - template - Matrix Matrix::operator- (const Matrix& a) const - { - Matrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code - ScaleAndAdd(-1,a,c); - return c; - } - - template - Matrix& Matrix::AssignDifferenceOf(const Matrix& a, const Matrix& b) - { - // if first arg broadcasts, we swap first and the flip the sign - // This is because there is no equivalent to operator-=() that works the other way round. - // TODO: We need ternary ops where the output storage is separate. - if (a.GetNumRows() < b.GetNumRows() || a.GetNumCols() < b.GetNumCols()) - { - if (a.GetNumRows() > b.GetNumRows() || a.GetNumCols() > b.GetNumCols()) - LogicError("AssignDifferenceOf: Invalid dimensions."); - AssignDifferenceOf(b, a); - *this *= -1; - return *this; - } - if (this != &a) - SetValue(a); - (*this) -= b; - return *this; - } - - template - Matrix& Matrix::operator*= (ElemType alpha) - { - Scale(alpha, *this); - return *this; - } - - template - Matrix Matrix::operator* (ElemType alpha) const - { - Matrix c(GetNumRows(), GetNumCols(), (DEVICEID_TYPE)m_preferredDeviceId); - Scale(alpha, *this, c); - return c; - } - - template - Matrix& Matrix::AssignProductOf(const ElemType alpha, const Matrix& a) - { - Scale(alpha, a, *this); - return *this; - } - - // [this]=a*b - template - Matrix& Matrix::AssignProductOf (const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB) - { - if (a.GetNumElements() == 1) - { - if (transposeB) - (*this)=AssignTransposeOf(b); - else - (*this)=b; - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - (*this) *= a(0,0), - (*this) *= a.m_GPUMatrix->Get00Element(), - (*this) *= a(0,0), - NOT_IMPLEMENTED - ); - } - else if (b.GetNumElements() == 1) - { - if (transposeA) - (*this)=AssignTransposeOf(a); - else - (*this)=a; - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - (*this) *= b(0,0), - (*this) *= b.m_GPUMatrix->Get00Element(), - (*this) *= b(0,0) , - NOT_IMPLEMENTED - ); - } - else - Multiply(a, transposeA, b, transposeB, *this); - - return *this; - } - - template - Matrix Matrix::operator* (const Matrix& a) const - { - if (GetNumElements() == 1) - { - Matrix c((DEVICEID_TYPE)a.GetPreferredDeviceId()); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - c.AssignProductOf((*this)(0,0), a), - c.AssignProductOf(m_GPUMatrix->Get00Element(), a), // BUGBUG: efficiency - c.AssignProductOf((*this)(0,0), a), - NOT_IMPLEMENTED - ); - - return c; - } - else if (a.GetNumElements() == 1) - { - Matrix c((DEVICEID_TYPE)GetPreferredDeviceId()); - - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - c.AssignProductOf(a(0,0), (*this)), - c.AssignProductOf(a.m_GPUMatrix->Get00Element(), (*this)), // BUGBUG: efficiency - c.AssignProductOf(a(0,0), (*this)), - NOT_IMPLEMENTED - ); - - return c; - } - else - { - Matrix c(GetNumRows(), a.GetNumCols(), (DEVICEID_TYPE)GetPreferredDeviceId()); - Multiply(*this, a, c); - return c; - } - } - - // [this]=a*b where a is a 1x1 scalar - template - Matrix& Matrix::Assign1x1ProductOf (const Matrix& a, const Matrix& b) - { - Multiply1x1AndWeightedAdd(+1, a, b, 0.0f, *this); - return *this; - } - - template - Matrix& Matrix::operator/= (ElemType alpha) - { - (*this) *= 1/alpha; - return (*this); - } - - template - Matrix Matrix::operator/ (ElemType alpha) const - { - return ((*this) * (1/alpha)); - } - - //element-wise power - template - Matrix& Matrix::operator^= (ElemType alpha) - { - auto& us = *this; - ElementWisePower(alpha, us, us); - return us; - } - - //element-wise power - template - Matrix Matrix::operator^ (ElemType alpha) const - { - Matrix c(GetNumRows(), GetNumCols(), (DEVICEID_TYPE)GetDeviceId()); - ElementWisePower(alpha, *this, c); - return c; - } - - - template - Matrix& Matrix::AssignElementPowerOf(const Matrix& a, const ElemType power) - { - ElementWisePower(power, a, *this); - return *this; - } - - //[this]=[this] .* a (we cannot override operator .* in c++) - template - Matrix& Matrix::ElementMultiplyWith (const Matrix& a) - { - return AssignElementProductOf(*this, a); - } - - template - Matrix& Matrix::ElementDivideBy(const Matrix& a) - { - return AssignElementDivisionOf(*this, a); - } - - //[this]=a .* b - template - Matrix& Matrix::AssignElementProductOf (const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("The input matrix dimensions do not match."); - - DecideAndMoveToRightDevice(a, b, *this); - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignElementProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), - m_GPUMatrix->AssignElementProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AddElementProductOf (const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AddElementProductOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("The input matrix dimensions do not match."); - - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == GetNumCols())) - InvalidArgument("The input matrix dimensions do not match [this]."); - - DecideAndMoveToRightDevice(*this, a, b); - - if (! (a.GetMatrixType() == b.GetMatrixType() && GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - m_CPUMatrix->AddElementProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), - m_GPUMatrix->AddElementProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //[this]=a ./ b - template - Matrix& Matrix::AssignElementDivisionOf (const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementDivisionOf: Matrix is empty."); - - assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("The input matrix dimensions do not match."); - - DecideAndMoveToRightDevice(a, b, *this); - //WARNING: a and b must have same type - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignElementDivisionOf(*a.m_CPUMatrix,*b.m_CPUMatrix), - m_GPUMatrix->AssignElementDivisionOf(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - - template - Matrix& Matrix::ColumnElementMultiplyWith(const Matrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("ColumnElementMultiplyWith: Matrix is empty."); - - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) - InvalidArgument("ColumnElementMultiplyWith: The input matrix should be a col vector and match [this]'s rows."); - - DecideAndMoveToRightDevice(*this, a); - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->ColumnElementMultiplyWith(*a.m_CPUMatrix), - m_GPUMatrix->ColumnElementMultiplyWith(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::RowElementMultiplyWith(const Matrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("RowElementMultiplyWith: Matrix is empty."); - - if (!(a.GetNumCols() == GetNumCols() && a.GetNumRows() == 1)) - InvalidArgument("RowElementMultiplyWith: The input matrix should be a row vector and match [this]'s columns."); - - //WARNING: a and this must have same type - if (! (GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->RowElementMultiplyWith(*a.m_CPUMatrix), - m_GPUMatrix->RowElementMultiplyWith(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::RowElementDivideBy(const Matrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("RowElementDivideBy: Matrix is empty."); - - if (!(a.GetNumCols() == GetNumCols() && a.GetNumRows() == 1)) - InvalidArgument("RowElementDivideBy: The input matrix should be a row vector and match [this]'s columns."); - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->RowElementDivideBy(*a.m_CPUMatrix), - m_GPUMatrix->RowElementDivideBy(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - - template - Matrix& Matrix::ColumnElementDivideBy(const Matrix& a) - { - if (a.IsEmpty() || IsEmpty()) - LogicError("ColumnElementDivideBy: Matrix is empty."); - - if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) - InvalidArgument("ColumnElementDivideBy: The input matrix should be a col vector and match [this]'s rows."); - - DecideAndMoveToRightDevice(*this, a); - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->ColumnElementDivideBy(*a.m_CPUMatrix), - m_GPUMatrix->ColumnElementDivideBy(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - - //[this]=1 ./ a - template - Matrix& Matrix::ElementInverse () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->ElementInverse(), - m_GPUMatrix->ElementInverse(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->ElementInverse() - ); - - return (*this); - } - - template - Matrix& Matrix::AssignElementInverseOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignElementInverseOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignElementInverseOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignElementInverseOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignElementInverseOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - template - Matrix& Matrix::InplaceSigmoid () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceSigmoid(), - m_GPUMatrix->InplaceSigmoid(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceSigmoid() - ); - - return (*this); - } - - template - Matrix& Matrix::AssignSigmoidOf (const Matrix& a) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSigmoidOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignSigmoidOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignSigmoidOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=sigmoid([this]) element wise - template - Matrix& Matrix::InplaceLinearRectifierDerivative () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceLinearRectifierDerivative(), - m_GPUMatrix->InplaceLinearRectifierDerivative(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceLinearRectifierDerivative() - ); - - return (*this); - } - - template - Matrix& Matrix::AssignLinearRectifierDerivativeOf (const Matrix& a) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignLinearRectifierDerivativeOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignLinearRectifierDerivativeOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignLinearRectifierDerivativeOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=sigmoid([this]) element wise - template - Matrix& Matrix::InplaceSigmoidDerivative () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceSigmoidDerivative(), - m_GPUMatrix->InplaceSigmoidDerivative(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return (*this); - } - - template - Matrix& Matrix::AssignSigmoidDerivativeOf (const Matrix& a) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSigmoidDerivativeOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignSigmoidDerivativeOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignNumOfDiff (const Matrix& a, const Matrix& b, bool searchInCol) - { - DecideAndMoveToRightDevice(a, b, *this); - //WARNING: a and b must have same type - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignNumOfDiff(*a.m_CPUMatrix, *b.m_CPUMatrix, searchInCol), - m_GPUMatrix->AssignNumOfDiff(*a.m_GPUMatrix, *b.m_GPUMatrix, searchInCol), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - //[this]=tanh([this]) element wise - template - Matrix& Matrix::InplaceTanh () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceTanh(), - m_GPUMatrix->InplaceTanh(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceTanh() - ); - - return (*this); - } - - template - Matrix& Matrix::AssignTanhOf (const Matrix& a) - { - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignTanhOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignTanhOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignTanhOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=softmax([this]) element wise - template - Matrix& Matrix::InplaceLogSoftmax (const bool isColWise) - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceLogSoftmax(isColWise), - m_GPUMatrix->InplaceLogSoftmax(isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignLogSoftmaxOf (const Matrix& a, const bool isColWise) - { - if (a.IsEmpty()) - LogicError("AssignLogSoftmaxOf: Matrix a is empty."); - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignLogSoftmaxOf(*a.m_CPUMatrix,isColWise), - m_GPUMatrix->AssignLogSoftmaxOf(*a.m_GPUMatrix,isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //[this]=softmax([this]) element wise - template - Matrix& Matrix::InplaceHardmax(const bool isColWise) - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceHardmax(isColWise), - m_GPUMatrix->InplaceHardmax(isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignHardmaxOf(const Matrix& a, const bool isColWise) - { - if (a.IsEmpty()) - LogicError("AssignHardmaxOf: Matrix a is empty."); - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignHardmaxOf(*a.m_CPUMatrix, isColWise), - m_GPUMatrix->AssignHardmaxOf(*a.m_GPUMatrix, isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::InplaceSqrt () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceSqrt(), - m_GPUMatrix->InplaceSqrt(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceSqrt() - ); - - return *this; - } - - template - Matrix& Matrix::AssignSqrtOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSqrtOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSqrtOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignSqrtOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignSqrtOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=exp([this]) element wise - template - Matrix& Matrix::InplaceExp () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceExp(), - m_GPUMatrix->InplaceExp(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceExp() - ); - - return *this; - } - - - template - Matrix& Matrix::AssignExpOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignExpOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignExpOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignExpOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignExpOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=exp([this]) element wise - template - Matrix& Matrix::InplaceAbs () - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - m_CPUMatrix->InplaceAbs(), - m_GPUMatrix->InplaceAbs(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceAbs() - ); - - return *this; - } - - template - Matrix& Matrix::AssignAbsOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignAbsOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignAbsOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignAbsOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignAbsOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=log([this]) element wise - template - Matrix& Matrix::InplaceLog () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceLog(), - m_GPUMatrix->InplaceLog(), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceLog() - ); - - return *this; - } - - //[this]=log([this]) element wise - template - Matrix& Matrix::InplaceLog10 () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceLog10(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignLogOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignLogOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignLogOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignLogOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignLogOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - template - Matrix& Matrix::AssignLog10Of (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignLogOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignLog10Of(*a.m_CPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignLogOf(*a.m_GPUSparseMatrix) - ); - - return *this; - } - - //[this]=cos([this]) element wise - template - Matrix& Matrix::InplaceCosine () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceCosine(), - m_GPUMatrix->InplaceCosine(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignCosineOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignCosineOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignCosineOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignCosineOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //[this]= -sin([this]) element wise - template - Matrix& Matrix::InplaceNegativeSine () - { - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceNegativeSine(), - m_GPUMatrix->InplaceNegativeSine(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignNegativeSineOf (const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignNegativeSineOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignNegativeSineOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignNegativeSineOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::InplaceTruncate(const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncate: Matrix is empty."); - - if (sizeof(ElemType)==sizeof(float)) - { - if (!isfinite((float)threshold)) - return *this; - } - else - { - if (!isfinite(threshold)) - return *this; - } - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceTruncate(threshold), - m_GPUMatrix->InplaceTruncate(threshold), - m_CPUSparseMatrix->InplaceTruncate(threshold), - m_GPUSparseMatrix->InplaceTruncate(threshold) - ); - - return *this; - } - - template - void Matrix::InplaceTranspose() - { - if (IsEmpty()) - return; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - m_GPUSparseMatrix->InplaceTranspose() - ); - } - - template - Matrix& Matrix::InplaceSoftThreshold(const ElemType threshold) - { - assert(threshold >= 0); - - if (IsEmpty()) - LogicError("InplaceSoftThreshold: Matrix is empty."); - - if (threshold == 0) - return *this; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceSoftThreshold(threshold), - m_GPUMatrix->InplaceSoftThreshold(threshold), - m_CPUSparseMatrix->InplaceSoftThreshold(threshold), - m_GPUSparseMatrix->InplaceSoftThreshold(threshold) - ); - - return *this; - } - //Threshold truncating: this[i] = max( this[i], threshold ) - template - Matrix& Matrix::InplaceTruncateBottom (const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncateBottom: Matrix is empty."); - - if (sizeof(ElemType)==sizeof(float)) - { - if (!isfinite((float)threshold)) - return *this; - } - else - { - if (!isfinite(threshold)) - return *this; - } - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceTruncateBottom(threshold), - m_GPUMatrix->InplaceTruncateBottom(threshold), - m_CPUSparseMatrix->InplaceTruncateBottom(threshold), - m_GPUSparseMatrix->InplaceTruncateBottom(threshold) - ); - - return *this; - } - - //Threshold truncating: this[i] = max( a[i], threshold ) - template - Matrix& Matrix::AssignTruncateBottomOf (const Matrix& a, const ElemType threshold) - { - if (a.IsEmpty()) - LogicError("AssignTruncateBottomOf: Matrix a is empty."); - - if (sizeof(ElemType)==sizeof(float)) - { - if (!isfinite((float)threshold)) - { - (*this) = a; - return *this; - } - } - else - { - if (!isfinite(threshold)) - { - (*this) = a; - return *this; - } - } - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignTruncateBottomOf(*a.m_CPUMatrix, threshold), - m_GPUMatrix->AssignTruncateBottomOf(*a.m_GPUMatrix, threshold), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignTruncateBottomOf(*a.m_GPUSparseMatrix, threshold) - ); - - return *this; - } - - //Threshold truncating: this[i] = min( this[i], threshold ) - template - Matrix& Matrix::InplaceTruncateTop (const ElemType threshold) - { - if (IsEmpty()) - LogicError("InplaceTruncateTop: Matrix is empty."); - - if (sizeof(ElemType)==sizeof(float)) - { - if (!isfinite((float)threshold)) - return *this; - } - else - { - if (!isfinite(threshold)) - return *this; - } - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->InplaceTruncateTop(threshold), - m_GPUMatrix->InplaceTruncateTop(threshold), - m_CPUSparseMatrix->InplaceTruncateTop(threshold), - m_GPUSparseMatrix->InplaceTruncateTop(threshold) - ); - - return *this; - } - //Threshold truncating: this[i] = min( a[i], threshold ) - template - Matrix& Matrix::AssignTruncateTopOf (const Matrix& a, const ElemType threshold) - { - if (a.IsEmpty()) - LogicError("AssignTruncateTopOf: Matrix a is empty."); - - if (sizeof(ElemType)==sizeof(float)) - { - if (!isfinite((float)threshold)) - { - (*this) = a; - return *this; - } - } - else - { - if (!isfinite(threshold)) - { - (*this) = a; - return *this; - } - } - - DecideAndMoveToRightDevice(a, *this); - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignTruncateTopOf(*a.m_CPUMatrix, threshold), - m_GPUMatrix->AssignTruncateTopOf(*a.m_GPUMatrix, threshold), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->AssignTruncateTopOf(*a.m_GPUSparseMatrix, threshold) - ); - - return *this; - } - - //Threshold truncating: this[i] = 0 if abs(this[i] - Matrix& Matrix::SetToZeroIfAbsLessThan (const ElemType threshold) - { - if (IsEmpty()) - LogicError("SetToZeroIfAbsLessThan: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->SetToZeroIfAbsLessThan(threshold), - m_GPUMatrix->SetToZeroIfAbsLessThan(threshold), - NOT_IMPLEMENTED, - m_GPUSparseMatrix->SetToZeroIfAbsLessThan(threshold) - ); - - return *this; - } - - //sum of all elements - template - ElemType Matrix::SumOfElements () const - { - if (IsEmpty()) - LogicError("SumOfElements: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->SumOfElements(), - return m_GPUMatrix->SumOfElements(), - return m_CPUSparseMatrix->SumOfElements(), - return m_GPUSparseMatrix->SumOfElements() - ); - } - - template - Matrix& Matrix::AssignSumOfElements(const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSumOfElements: Matrix a is empty."); - - // WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSumOfElements(*a.m_CPUMatrix), - m_GPUMatrix->AssignSumOfElements(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - DeviceBoundNumber Matrix::Sum_AsDeviceBoundNum() const - { - DeviceBoundNumber result; - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - ElemType* val = new ElemType; *val = m_CPUMatrix->SumOfElements(); result.ShallowCopyFrom(val,-1); return result, - return m_GPUMatrix->Sum_AsDeviceBoundNum(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - //sum of all elements - template - ElemType Matrix::SumOfAbsElements () const - { - if (IsEmpty()) - LogicError("SumOfAbsElements: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->SumOfAbsElements(), - return m_GPUMatrix->SumOfAbsElements(), - NOT_IMPLEMENTED, - return m_GPUSparseMatrix->SumOfAbsElements() - ); - } - - //sum of all elements - template - ElemType Matrix::LogAddSumOfElements() const - { - if (IsEmpty()) - LogicError("LogAddSumOfElements: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->LogAddSumOfElements(), - return m_GPUMatrix->LogAddSumOfElements(), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - bool Matrix::IsValid() const - { - if (m_currentDataLocation == CurrentDataLocation::GPU && GetMatrixType() == MatrixType::SPARSE) - { - return this->m_GPUSparseMatrix->IsValid(); - } - else - { - NOT_IMPLEMENTED; - } - - return false; - } - - template - bool Matrix::IsEqualTo(const Matrix& a, const ElemType threshold /*= 1e-8*/) const - { - return AreEqual(*this, a, threshold); - } - - template - void Matrix::VectorSum(const Matrix& a, Matrix& c, const bool isColWise) - { - DecideAndMoveToRightDevice(c, a); - if (!(a.GetMatrixType() == c.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::VectorSum(*a.m_CPUMatrix, *c.m_CPUMatrix, isColWise), - GPUMatrix::VectorSum(*a.m_GPUMatrix, *c.m_GPUMatrix, isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::VectorNorm1(Matrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNormInf: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, c); - c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &c, - m_CPUMatrix->VectorNorm1(*c.m_CPUMatrix,isColWise), - m_GPUMatrix->VectorNorm1(*c.m_GPUMatrix,isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::AssignVectorNorm1Of(Matrix& a, const bool isColWise) - { - a.VectorNorm1(*this, isColWise); - return *this; - } - - template - void Matrix::VectorNorm2(Matrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNorm2: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, c); - c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &c, - m_CPUMatrix->VectorNorm2(*c.m_CPUMatrix,isColWise), - m_GPUMatrix->VectorNorm2(*c.m_GPUMatrix,isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::AssignVectorNorm2Of(Matrix& a, const bool isColWise) - { - a.VectorNorm2(*this, isColWise); - return *this; - } - - template - void Matrix::VectorNormInf(Matrix& c, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorNormInf: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, c); - c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &c, - m_CPUMatrix->VectorNormInf(*c.m_CPUMatrix,isColWise), - m_GPUMatrix->VectorNormInf(*c.m_GPUMatrix,isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::AssignVectorNormInfOf(Matrix& a, const bool isColWise) - { - a.VectorNormInf(*this, isColWise); - return *this; - } - - template - Matrix& Matrix::AssignInnerProductOf(const Matrix& a, const Matrix& b, const bool isColWise) - { - InnerProduct (a, b, *this,isColWise); - return *this; - } - - //column-wise crossproduct - template - Matrix& Matrix::AssignKhatriRaoProductOf(const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignKhatriRaoProductOf: Matrix is empty."); - - assert (a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AssignKhatriRaoProductOf: The input matrix dimensions do not match."); - - DecideAndMoveToRightDevice(a, b, *this); - //WARNING: a and b must have same type - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignKhatriRaoProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), - m_GPUMatrix->AssignKhatriRaoProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //column-wise reshaped product. Used to compute KhatriRaoProduct Gradient - // this = reshape each column of a from (K1xK2,1) to (K1, K2) - // if each column of a is not transposed, each (K1, K2) times each column of b (K2, frames). - // the output is a (K1, frames) matrix - // if each column of a is tranposed, each (K1, K2)^T times each column of b(K1, frames) and output is (K2, frames) - //column-wise crossproduct - template - Matrix& Matrix::AddColumnReshapeProductOf(const Matrix& a, const Matrix& b, const bool transposeAColumn) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AddColumnReshapeProductOf: Matrix is empty."); - - assert (a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumCols() == b.GetNumCols())) - InvalidArgument("AddColumnReshapeProductOf: The input matrix dimensions do not match."); - - DecideAndMoveToRightDevice(*this, a, b); - //WARNING: a and b must have same type - if (!(a.GetMatrixType() == b.GetMatrixType() && GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddColumnReshapeProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix, transposeAColumn), - m_GPUMatrix->AddColumnReshapeProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix, transposeAColumn), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AddWithScaleOf(ElemType alpha, const Matrix& a) - { - ScaleAndAdd(alpha, a, *this); - return *this; - } - - template - ElemType Matrix::FrobeniusNorm() const - { - if (IsEmpty()) - LogicError("FrobeniusNorm: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->FrobeniusNorm(), - return m_GPUMatrix->FrobeniusNorm(), - return m_CPUSparseMatrix->FrobeniusNorm(), - return m_GPUSparseMatrix->FrobeniusNorm() - ); - } - - template - Matrix& Matrix::AssignFrobeniusNormOf(const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignFrobeniusNormOf: Matrix a is empty."); - - Resize(1,1); - - //WARNING: a and this must have same type - if (! (GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignFrobeniusNormOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignFrobeniusNormOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - ElemType Matrix::MatrixNormInf() const - { - if (IsEmpty()) - LogicError("MatrixNormInf: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->MatrixNormInf(), - return m_GPUMatrix->MatrixNormInf(), - NOT_IMPLEMENTED, - return m_GPUSparseMatrix->MatrixNormInf() - ); - } - - template - ElemType Matrix::MatrixNorm1() const - { - if (IsEmpty()) - LogicError("MatrixNorm1: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->MatrixNorm1(), - return m_GPUMatrix->MatrixNorm1(), - NOT_IMPLEMENTED, - return m_GPUSparseMatrix->MatrixNorm1() - ); - } - - template - ElemType Matrix::MatrixNorm0() const - { - if (IsEmpty()) - LogicError("MatrixNorm0: Matrix is empty."); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return m_CPUMatrix->MatrixNorm0(), - return m_GPUMatrix->MatrixNorm0(), - NOT_IMPLEMENTED, - return m_GPUSparseMatrix->MatrixNorm0() - ); - } - - template - Matrix& Matrix::AssignSignOf(const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AssignSignOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AssignSignOf(*a.m_CPUMatrix), - m_GPUMatrix->AssignSignOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AddSignOf(const Matrix& a) - { - if (a.IsEmpty()) - LogicError("AddSignOf: Matrix a is empty."); - - DecideAndMoveToRightDevice(a, *this); - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->AddSignOf(*a.m_CPUMatrix), - m_GPUMatrix->AddSignOf(*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //I decided to use Matrix& maxIndexes instead of integer vector because the result may be used to do additional calculation - template - void Matrix::VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorMax: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, maxIndexes, maxValues); - maxIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - maxValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &maxValues, - m_CPUMatrix->VectorMax(*maxIndexes.m_CPUMatrix, *maxValues.m_CPUMatrix, isColWise); maxIndexes.SetDataLocation(CPU, DENSE), - m_GPUMatrix->VectorMax(*maxIndexes.m_GPUMatrix, *maxValues.m_GPUMatrix, isColWise); maxIndexes.SetDataLocation(GPU, DENSE), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise, int topK) const - { - if (IsEmpty()) - LogicError("VectorMax: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, maxIndexes, maxValues); - maxIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - maxValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &maxValues, - m_CPUMatrix->VectorMax(*maxIndexes.m_CPUMatrix, *maxValues.m_CPUMatrix, isColWise, topK); maxIndexes.SetDataLocation(CPU, DENSE), - m_GPUMatrix->VectorMax(*maxIndexes.m_GPUMatrix, *maxValues.m_GPUMatrix, isColWise, topK); maxIndexes.SetDataLocation(GPU, DENSE), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::VectorMin(Matrix& minIndexes, Matrix& minValues, const bool isColWise) const - { - if (IsEmpty()) - LogicError("VectorMin: Matrix is empty."); - - DecideAndMoveToRightDevice(*this, minIndexes, minValues); - minIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - minValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &minValues, - m_CPUMatrix->VectorMin(*minIndexes.m_CPUMatrix,*minValues.m_CPUMatrix,isColWise); minIndexes.SetDataLocation(CPU, DENSE), - m_GPUMatrix->VectorMin(*minIndexes.m_GPUMatrix,*minValues.m_GPUMatrix,isColWise); minIndexes.SetDataLocation(GPU, DENSE), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - -#pragma endregion Member BLAS Functions - -#pragma region Other helper Functions - - template - wchar_t* Matrix::GetMatrixName() const - { - return m_baseMatrix->GetMatrixName(); - } - - template - void Matrix::SetMatrixName(const wchar_t* s) - { - if (m_currentDataLocation==CurrentDataLocation::BOTH) - { - if (GetMatrixType() == MatrixType::DENSE) - { - m_CPUMatrix->SetMatrixName(s); - m_GPUMatrix->SetMatrixName(s); - } - else if (GetMatrixType() == MatrixType::SPARSE) - { - m_CPUSparseMatrix->SetMatrixName(s); - m_GPUSparseMatrix->SetMatrixName(s); - } - } - else - { - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - m_CPUMatrix->SetMatrixName(s), - m_GPUMatrix->SetMatrixName(s), - m_CPUSparseMatrix->SetMatrixName(s), - m_GPUSparseMatrix->SetMatrixName(s) - ); - } - } - - template - int Matrix::GetDeviceId() const - { - if (m_currentDataLocation==CurrentDataLocation::NONE) - return m_preferredDeviceId; - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - return CPUDEVICE, - return m_GPUMatrix->GetComputeDeviceId(), - return CPUDEVICE, - return m_GPUSparseMatrix->GetComputeDeviceId() - ); - } - - // bring two matrices onto the same device - // If different and prefered devices are the same, move to preferred device. - // Otherwise GPU takes precedence over CPU, and if both are GPU move to a's device. - // The inputs are only distinguished in that a's GPU takes precedence over b's in case they differ. - // TODO: This is called somewhat inconsistently, sometimes with a=*this, sometimes with b=*this. - template - void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b) - { - int deviceIdA = a.GetDeviceId(), deviceIdB = b.GetDeviceId(); - if (deviceIdA == deviceIdB) - return; - - int preferredDeviceIdA = a.GetPreferredDeviceId(), preferredDeviceIdB = b.GetPreferredDeviceId(); - - if (preferredDeviceIdA == preferredDeviceIdB) // both prefer the same device: move to preferred - { - a._transferToDevice(preferredDeviceIdA); - b._transferToDevice(preferredDeviceIdA); - } - else if (deviceIdA != CPUDEVICE) // one of them lives on GPU: use that - { - b._transferToDevice(deviceIdA); - } - else - { - a._transferToDevice(deviceIdB); - } - } - - // same but for 3 matrices - // If b and c are both on the same GPU then a will be forced to go there; otherwise a's GPU takes precedence, then b's. - template - void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b, const Matrix &c) - { - int deviceIdA = a.GetDeviceId(), deviceIdB = b.GetDeviceId(), deviceIdC = c.GetDeviceId(); - if (deviceIdA == deviceIdB && deviceIdA == deviceIdC) - return; - - int preferredDeviceIdA = a.GetPreferredDeviceId(), preferredDeviceIdB = b.GetPreferredDeviceId(), preferredDeviceIdC = c.GetPreferredDeviceId(); - - if (preferredDeviceIdA == preferredDeviceIdB && preferredDeviceIdA == preferredDeviceIdC) - { - a._transferToDevice(preferredDeviceIdA); - b._transferToDevice(preferredDeviceIdA); - c._transferToDevice(preferredDeviceIdA); - } - else if (deviceIdB == deviceIdC && deviceIdB != CPUDEVICE) // TODO: why not the other two combinations? - { - a._transferToDevice(deviceIdB); // 'a' is outvoted - } - else if (deviceIdA != CPUDEVICE) // one of them lives on GPU: use that - { - b._transferToDevice(deviceIdA); - c._transferToDevice(deviceIdA); - } - else if(deviceIdB != CPUDEVICE) - { - a._transferToDevice(deviceIdB); - c._transferToDevice(deviceIdB); - } - else - { - a._transferToDevice(deviceIdC); - b._transferToDevice(deviceIdC); - } - } - - // same but for 4 matrices - template - void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b, const Matrix &c, const Matrix &d) - { - // this function is only called for one operator, so for now we keep it imple - DecideAndMoveToRightDevice(a, b, c); - d._transferToDevice(a.GetDeviceId()); // BUGBUG: Is this correct in case a,b,c share the same preferredDevice? - } - - template - void Matrix::_transferToDevice(int to_id, bool ismoved,bool emptyTransfer) const - { - int from_id = GetDeviceId(); - if (to_id == from_id) //nothing to do - return; - - if (OwnBuffer()) - _transferFromDeviceToDevice(from_id, to_id, ismoved, emptyTransfer); - else - RuntimeError("Cannot move externally owned matrices to the preferred device."); - } - - template - void Matrix::_transferFromDeviceToDevice(int from_id, int to_id, bool ismoved,bool emptyTransfer) const - { - if (from_id < 0) - from_id = CPUDEVICE; - if (to_id < 0) - to_id = CPUDEVICE; - - if (from_id == to_id) - { - if (from_id != GetDeviceId()) - RuntimeError("Trying to transfer matrix from device to the same device while the matrix does not live in the from device."); - - return; - } - -#define NUM_DEVICE_CHANGED_WARN 20 - if (m_numTimesDeviceChanged <= NUM_DEVICE_CHANGED_WARN && - (!emptyTransfer || (from_id >= 0 && to_id >= 0))) - { - m_numTimesDeviceChanged++; - if (m_devicesTransferedTo[0] < CPUDEVICE) - { - m_devicesTransferedTo[0] = to_id; - } - else if (m_devicesTransferedTo[0] != to_id) - { - m_devicesTransferedTo[1] = to_id; - } - } - if (m_numTimesDeviceChanged == NUM_DEVICE_CHANGED_WARN && m_devicesTransferedTo[1] >= CPUDEVICE) - { - fprintf(stderr, "WARNING: The same matrix with dim [%lu, %lu] has been transferred between different devices for %d times.\n", (unsigned long)GetNumRows(), (unsigned long)GetNumCols(), NUM_DEVICE_CHANGED_WARN); - } - - if (m_matrixType == MatrixType::SPARSE) - { - if (from_id == CPUDEVICE) //from CPU to GPU - { - if (m_CPUSparseMatrix == NULL) - LogicError("Can't move from CPU because I'm not there!"); - - if (m_GPUSparseMatrix == NULL) - m_GPUSparseMatrix = new GPUSparseMatrix(m_CPUSparseMatrix->GetFormat(), to_id); - else - m_GPUSparseMatrix->ChangeDeviceTo(to_id); - - if (m_CPUSparseMatrix->GetNumElements() != 0 && !emptyTransfer) - { - m_GPUSparseMatrix->SetValue(*m_CPUSparseMatrix); - } - - if (ismoved) - { - delete m_CPUSparseMatrix; - m_CPUSparseMatrix = NULL; - SetDataLocation(GPU, SPARSE); - } - else - { - SetDataLocation(BOTH, SPARSE); - } - } - else //from GPU - { - if (m_GPUSparseMatrix == NULL || m_GPUSparseMatrix->GetComputeDeviceId() != from_id) - LogicError("This matrix isn't on this (or any?) GPU"); - - if (to_id < 0) //to CPU - { - if (m_CPUSparseMatrix == NULL) - m_CPUSparseMatrix = new CPUSparseMatrix(m_GPUSparseMatrix->GetFormat()); - - if (m_GPUSparseMatrix->GetNumElements() != 0 && !emptyTransfer) - { - m_GPUSparseMatrix->CopyToCPUSparseMatrix(*m_CPUSparseMatrix); - } - - if (ismoved) - { - delete m_GPUSparseMatrix; - m_GPUSparseMatrix = NULL; - SetDataLocation(CPU, SPARSE); - } - else - { - SetDataLocation(BOTH, SPARSE); - } - } - else //to another GPU - { - m_GPUSparseMatrix->ChangeDeviceTo(to_id); - } - } - } - else -#pragma omp critical - { - if (from_id == CPUDEVICE) //from CPU to GPU - { - if (m_CPUMatrix==NULL) - LogicError("Can't move from CPU because I'm not there!"); - if (m_GPUMatrix!=NULL) - delete m_GPUMatrix; - if (m_CPUMatrix->GetNumElements() !=0 && !emptyTransfer) - { - m_GPUMatrix = new GPUMatrix(m_CPUMatrix->GetNumRows(), m_CPUMatrix->GetNumCols(), to_id, m_CPUMatrix->GetArray(), matrixFlagNormal); - } - else - { - m_GPUMatrix = new GPUMatrix(to_id); - } - if (ismoved) - { - delete m_CPUMatrix; - m_CPUMatrix=NULL; - SetDataLocation(GPU, DENSE); - } - else - { - SetDataLocation(BOTH, DENSE); - } - } - else //from GPU - { - if (m_GPUMatrix==NULL || m_GPUMatrix->GetComputeDeviceId()!=from_id) - LogicError("This matrix isn't on this (or any?) GPU"); - - if (to_id < 0) //to CPU - { - if (m_CPUMatrix!=NULL) - delete m_CPUMatrix; - - if (m_GPUMatrix->GetNumElements() !=0 && !emptyTransfer) - { - ElemType *arr = m_GPUMatrix->CopyToArray(); // TODO: unnecessary allocation/copy; why not make this a vector that we move over as an rvalue ref? - m_CPUMatrix = new CPUMatrix(m_GPUMatrix->GetNumRows(), m_GPUMatrix->GetNumCols(), arr, matrixFlagNormal); - delete[] arr; - } - else - { - m_CPUMatrix = new CPUMatrix(); - } - - if (ismoved) - { - delete m_GPUMatrix; - m_GPUMatrix = NULL; - SetDataLocation(CPU, DENSE); - } - else - { - SetDataLocation(BOTH, DENSE); - } - } - else //to another GPU - { - m_GPUMatrix->ChangeDeviceTo(to_id); - } - } - }// and of omp critical section - } - - template - void Matrix::TransferFromDeviceToDevice(int from_id, int to_id, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const - { - _transferFromDeviceToDevice(from_id,to_id,ismoved,emptyTransfer); - if (updatePreferredDevice) - m_preferredDeviceId=GetDeviceId(); - } - template - void Matrix::TransferToDeviceIfNotThere(int id_to, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const - { - if (GetDeviceId() != id_to) - TransferFromDeviceToDevice(GetDeviceId(), id_to, ismoved, emptyTransfer, updatePreferredDevice); - } - // TODO: This function vv is now (after memshare update) only used by LearnableParameter::InitRandom(). Maybe it is time to get rid of it. - template - void Matrix::TransferToDeviceIfNotThereAndNotAutoPlace(int id_to, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const - { - if (id_to != AUTOPLACEMATRIX) - TransferToDeviceIfNotThere(id_to, ismoved, emptyTransfer, updatePreferredDevice); - } - - template - void Matrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const - { - if (IsEmpty()) - LogicError("Print: Matrix is empty."); - DEVICEID_TYPE orgdevice = GetDeviceId(); - - DISPATCH_MATRIX_ON_FLAG(this, - nullptr, - m_CPUMatrix->Print(matrixName, rowStart, rowEnd, colStart, colEnd), - _transferToDevice(CPUDEVICE, false, false); m_CPUMatrix->Print(matrixName, rowStart, rowEnd, colStart, colEnd); _transferToDevice(orgdevice, false, false), - m_CPUSparseMatrix->Print(matrixName), - _transferToDevice(CPUDEVICE, false, false); m_CPUSparseMatrix->Print(matrixName); _transferToDevice(orgdevice, false, false) - ); - } - - template - void Matrix::Print(const char* matrixName /*=nullptr*/) const - { - Print(matrixName, 0, GetNumRows()-1, 0, GetNumCols()-1); - } - - //helpfer function used for convolution neural network - template - Matrix& Matrix::AssignPackedConvolutionInput(const Matrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) - { - DecideAndMoveToRightDevice(inputSubBatch, *this); - SwitchToMatrixType(inputSubBatch.GetMatrixType(), inputSubBatch.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&inputSubBatch, - this, - m_CPUMatrix->AssignPackedConvolutionInput(*(inputSubBatch.m_CPUMatrix), - inputWidth, inputHeight, inputChannels, - outputWidth, outputHeight, outputChannels, - kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, - zeroPadding), - m_GPUMatrix->AssignPackedConvolutionInput(*(inputSubBatch.m_GPUMatrix), - inputWidth, inputHeight, inputChannels, - outputWidth, outputHeight, outputChannels, - kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, - zeroPadding), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - //helpfer function used for convolution neural network - template - Matrix& Matrix::UnpackConvolutionInput(Matrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) const - { - DecideAndMoveToRightDevice(*this, inputSubBatch); - inputSubBatch.SwitchToMatrixType(GetMatrixType(), inputSubBatch.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - &inputSubBatch, - m_CPUMatrix->UnpackConvolutionInput(*(inputSubBatch.m_CPUMatrix), - inputWidth, inputHeight, inputChannels, - outputWidth, outputHeight, outputChannels, - kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, - zeroPadding), - m_GPUMatrix->UnpackConvolutionInput(*(inputSubBatch.m_GPUMatrix), - inputWidth, inputHeight, inputChannels, - outputWidth, outputHeight, outputChannels, - kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, - zeroPadding), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return inputSubBatch; - } - - template - Matrix& Matrix::AssignMaxPoolingResult(const Matrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - DecideAndMoveToRightDevice(inputBatch, *this); - SwitchToMatrixType(inputBatch.GetMatrixType(), inputBatch.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&inputBatch, - this, - m_CPUMatrix->AssignMaxPoolingResult(*(inputBatch.m_CPUMatrix), channels, - inputWidth, inputHeight,inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - m_GPUMatrix->AssignMaxPoolingResult(*(inputBatch.m_GPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AddMaxPoolingGradient(const Matrix& outputGradientBatch, const Matrix& inputBatch, const Matrix& outputBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - DecideAndMoveToRightDevice(*this, outputGradientBatch, inputBatch); - outputBatch._transferToDevice(GetDeviceId()); - - if (!(GetMatrixType() == outputGradientBatch.GetMatrixType() - && GetMatrixType() == inputBatch.GetMatrixType() - && GetMatrixType() == outputBatch.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddMaxPoolingGradient(*(outputGradientBatch.m_CPUMatrix), *(inputBatch.m_CPUMatrix), *(outputBatch.m_CPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - m_GPUMatrix->AddMaxPoolingGradient(*(outputGradientBatch.m_GPUMatrix), *(inputBatch.m_GPUMatrix), *(outputBatch.m_GPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample);, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignAveragePoolingResult(const Matrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - DecideAndMoveToRightDevice(inputBatch, *this); - SwitchToMatrixType(inputBatch.GetMatrixType(), inputBatch.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&inputBatch, - this, - m_CPUMatrix->AssignAveragePoolingResult(*(inputBatch.m_CPUMatrix), channels, - inputWidth, inputHeight,inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - m_GPUMatrix->AssignAveragePoolingResult(*(inputBatch.m_GPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - Matrix& Matrix::AssignSoftmaxSum(const Matrix& a, const Matrix& softmax) - { - Resize(1, 1); - if (GetDeviceId() < 0) - a.m_CPUMatrix->AssignSoftmaxSum(*softmax.m_CPUMatrix, *m_CPUMatrix); - else - a.m_GPUMatrix->AssignSoftmaxSum(*softmax.m_GPUMatrix, *m_GPUMatrix); - return *this; - } - - template - Matrix& Matrix::AssignNceUnnormalizedEval(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias) - { - //if (a.GetMatrixType() != MatrixType::SPARSE) - // NOT_IMPLEMENTED; - - Resize(1, 1); - if (GetDeviceId() < 0) - a.m_CPUMatrix->AssignNCEUnnormalizedEval(*b.m_CPUMatrix, *c.m_CPUMatrix, *bias.m_CPUMatrix, *m_CPUMatrix); - else - a.m_GPUMatrix->AssignNCEUnnormalizedEval(*b.m_GPUMatrix, *c.m_GPUMatrix, *m_GPUMatrix); - return *this; - } - - template - Matrix& Matrix::AssignNoiseContrastiveEstimation(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias, Matrix& tmp) - { - if (a.IsEmpty() || b.IsEmpty() || c.IsEmpty()) - LogicError("AssignNoiseContrastiveEstimation: one of the input matrices is empty."); - - if (a.GetDeviceId() != b.GetDeviceId() || b.GetDeviceId() != c.GetDeviceId() || c.GetDeviceId() != GetDeviceId()) - NOT_IMPLEMENTED; - - Resize(1, 1); - - if (GetDeviceId() < 0) - { - size_t sampleCount = a.m_CPUMatrix->GetNumElements() / a.m_CPUMatrix->GetNumRows(); - tmp.Resize(a.GetNumRows() / 2, sampleCount); - a.m_CPUMatrix->AssignNoiseContrastiveEstimation(*b.m_CPUMatrix, *c.m_CPUMatrix, - *bias.m_CPUMatrix, *tmp.m_CPUMatrix, *m_CPUMatrix); - } - else - { - size_t sampleCount = a.m_GPUMatrix->GetNumElements() / a.m_GPUMatrix->GetNumRows(); - tmp.Resize(a.GetNumRows() / 2, sampleCount); - a.m_GPUMatrix->AssignNoiseContrastiveEstimation(*b.m_GPUMatrix, *c.m_GPUMatrix, - *bias.m_GPUMatrix, sampleCount, *tmp.m_GPUMatrix, *m_GPUMatrix); - } - return *this; - } - - template - Matrix& Matrix::AssignNCEDerivative(const Matrix& tmp, const Matrix& a, const Matrix& b, const Matrix& c, size_t inputIndex) - { - if (a.IsEmpty() || b.IsEmpty() || c.IsEmpty()) - LogicError("AssignNoiseContrastiveEstimation: one of the input matrices is empty."); - - if (a.GetDeviceId() != b.GetDeviceId() || b.GetDeviceId() != c.GetDeviceId() || c.GetDeviceId() != GetDeviceId()) - NOT_IMPLEMENTED; - - assert(tmp.GetNumRows() == a.GetNumRows() / 2); - if (GetDeviceId() < 0) - { - // samples gradient hidden embedding embedding/hidden - a.m_CPUMatrix->AssignNCEDerivative(*tmp.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, inputIndex, *m_CPUMatrix); - } - else - { - a.m_GPUMatrix->AssignNCEDerivative(*tmp.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, inputIndex, *m_GPUMatrix); - } - return *this; - } - - template - Matrix& Matrix::AddAveragePoolingGradient(const Matrix& outputGradientBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) - { - DecideAndMoveToRightDevice(*this, outputGradientBatch); - if (!(GetMatrixType() == outputGradientBatch.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AddAveragePoolingGradient(*(outputGradientBatch.m_CPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - m_GPUMatrix->AddAveragePoolingGradient(*(outputGradientBatch.m_GPUMatrix), channels, - inputWidth, inputHeight, inputSizePerSample, - outputWidth, outputHeight, outputSizePerSample, - windowWidth, windowHeight, horizontalSubsample, verticalSubsample), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - -#pragma endregion Other Helper Functions - -#pragma region Static BLAS Functions - - template - void Matrix::SVD(const Matrix& A, Matrix& SIGMA, Matrix& U, Matrix& VT, Matrix& W) - { - if (A.IsEmpty() ) - LogicError("SVD: the input matrix is empty."); - - DecideAndMoveToRightDevice(A, SIGMA, U); - VT._transferToDevice(A.GetDeviceId()); - W._transferToDevice(A.GetDeviceId()); - - SIGMA.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); - U.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); - VT.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); - W.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&A, - nullptr, - Matrix tA = A; - CPUMatrix::SVD(*tA.m_CPUMatrix, *SIGMA.m_CPUMatrix, *U.m_CPUMatrix, *VT.m_CPUMatrix, *W.m_CPUMatrix); - SIGMA.SetDataLocation(CPU); - U.SetDataLocation(CPU); - VT.SetDataLocation(CPU); - W.SetDataLocation(CPU), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - } - - /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c - /// Scalar - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Scalar - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::MultiplyAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, - ElemType beta, Matrix& c) - { - DecideAndMoveToRightDevice(a,b,c); - - if (c.GetDeviceId()<0) //CPU - { - if (a.GetMatrixType()==MatrixType::SPARSE) - NOT_IMPLEMENTED; - if ( b.GetMatrixType()==MatrixType::SPARSE) - { - if (c.GetMatrixType() == MatrixType::DENSE) - { - CPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_CPUMatrix, transposeA, *b.m_CPUSparseMatrix, transposeB, beta, *c.m_CPUMatrix); - c.SetDataLocation(CPU, DENSE); - } - else if (c.GetMatrixType() == MatrixType::SPARSE) - { - CPUSparseMatrix::MultiplyAndAdd(alpha, *a.m_CPUMatrix, transposeA, *b.m_CPUSparseMatrix, transposeB, *c.m_CPUSparseMatrix); - c.SetDataLocation(CPU, SPARSE); - } - else - NOT_IMPLEMENTED; - } - else - { - c.SwitchToMatrixType(MatrixType::DENSE, matrixFormatDense, false); - CPUMatrix::MultiplyAndWeightedAdd(alpha,*a.m_CPUMatrix,transposeA,*b.m_CPUMatrix,transposeB,beta,*c.m_CPUMatrix); - c.SetDataLocation(CPU, DENSE); - } - - } - else //GPU operations - { - if (a.m_matrixType==b.m_matrixType && b.m_matrixType==c.m_matrixType && a.m_matrixType==MatrixType::DENSE) //All dense - { - GPUMatrix::MultiplyAndWeightedAdd(alpha,*a.m_GPUMatrix,transposeA,*b.m_GPUMatrix,transposeB,beta,*c.m_GPUMatrix); - c.SetDataLocation(GPU, DENSE); - } - else if (a.m_matrixType==MatrixType::SPARSE && b.m_matrixType==c.m_matrixType && b.m_matrixType==MatrixType::DENSE) //Sparse*Dense+Dense - { - GPUMatrix second = transposeB ? b.m_GPUMatrix->Transpose() : *b.m_GPUMatrix; - GPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUSparseMatrix, transposeA, second, false, beta, *c.m_GPUMatrix); - c.SetDataLocation(GPU, DENSE); - } - else if (a.m_matrixType==MatrixType::DENSE && b.m_matrixType==MatrixType::SPARSE && c.m_matrixType==MatrixType::DENSE) //Dense*Sparse + Dense - { - //if (b.m_GPUSparseMatrix->GetFormat() == MatrixFormat::matrixFormatSparseCSR) - //{ - GPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, beta, *c.m_GPUMatrix); - //} - //else - //{ - // GPUMatrix firstDummy = transposeA ? a.m_GPUMatrix->Transpose()*alpha : (*a.m_GPUMatrix)*alpha; - // GPUMatrix & first= firstDummy; // GCC does not support mixing refs and non-refs - // GPUSparseMatrix secondDummy = transposeB ? b.m_GPUSparseMatrix->Transpose() : *b.m_GPUSparseMatrix; - // GPUSparseMatrix & second = secondDummy; - // if (beta==0) - // { - // GPUSparseMatrix::Multiply(first,second,*c.m_GPUMatrix); - // } - // else - // { - // Matrix tmp(c.GetNumRows(),c.GetNumCols(),(DEVICEID_TYPE)c.GetDeviceId()); - // GPUSparseMatrix::Multiply(first,second,*tmp.m_GPUMatrix); - // c=tmp+c*beta; - // } - //} - c.SetDataLocation(GPU, DENSE); - } - else if (a.m_matrixType==MatrixType::DENSE && b.m_matrixType==MatrixType::SPARSE && c.m_matrixType==MatrixType::SPARSE) // h -> u0 - { - // new GPU sparse matrix code - GPUSparseMatrix::MultiplyAndAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, *c.m_GPUSparseMatrix); - c.SetDataLocation(GPU, SPARSE); - } - else if (a.m_matrixType==b.m_matrixType && b.m_matrixType==c.m_matrixType && a.m_matrixType==MatrixType::SPARSE) - { - GPUSparseMatrix firstDummy = alpha==1 ? *a.m_GPUSparseMatrix : (*a.m_GPUSparseMatrix)*alpha; - GPUSparseMatrix & first = firstDummy; // By Malcolm.. gcc doesn't support auto - if (beta==0) - { - GPUSparseMatrix::Multiply(first,transposeA,*b.m_GPUSparseMatrix,transposeB,*c.m_GPUSparseMatrix); - c.SetDataLocation(GPU, SPARSE); - } - else - { - GPUSparseMatrix tmp; - GPUSparseMatrix::Multiply(first,transposeA,*b.m_GPUSparseMatrix,transposeB,tmp); - *c.m_GPUSparseMatrix = tmp + (*c.m_GPUSparseMatrix)*beta; - c.SetDataLocation(GPU, SPARSE); - } - } - else if (a.m_matrixType == b.m_matrixType && a.m_matrixType == MatrixType::DENSE && c.m_matrixType == MatrixType::SPARSE) - { - GPUMatrix tmp(a.m_GPUMatrix->GetComputeDeviceId()); - GPUSparseMatrix tmpSparse; - GPUMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUMatrix, transposeB, beta, tmp); - tmpSparse.SetValue(tmp); - *c.m_GPUSparseMatrix = tmpSparse + (*c.m_GPUSparseMatrix)*beta; - c.SetDataLocation(GPU, SPARSE); - } - else - NOT_IMPLEMENTED; - } - } - - template - /*static*/ void Matrix::Multiply1x1AndWeightedAdd(ElemType alpha, const Matrix& a, const Matrix& b, ElemType beta, Matrix& c) - { - // special case: a is a 1x1 matrix - // The only alternative is to Get00Elements(), which makes things inefficient. - if (a.GetNumElements() != 1) - InvalidArgument("Multiply1x1AndWeightedAdd: first arg must be a scalar."); - - DISPATCH_MATRIX_ON_FLAG(&c, - nullptr, - CPUMatrix::Multiply1x1AndWeightedAdd(alpha, *a.m_CPUMatrix, *b.m_CPUMatrix, beta, *c.m_CPUMatrix), - GPUMatrix::Multiply1x1AndWeightedAdd(alpha, *a.m_GPUMatrix, *b.m_GPUMatrix, beta, *c.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + c - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::MultiplyAndAdd(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, - Matrix& c) - { - return Matrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 1.0, c); - } - - /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::Multiply(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, - Matrix& c) - { - return Matrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 0.0, c); - } - - /// Matrix-matrix multiply with col-major matrices (a and b are not transposed): c = a * b - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::Multiply(const Matrix& a, const Matrix& b, Matrix& c) - { - return Matrix::MultiplyAndWeightedAdd(1.0, a, false, b, false, 0.0, c); - } - - /// 1-D Convolution with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c. MultiplyAndWeightedAdd is just a special case of this. - /// Scalar - /// Input matrix - /// Whether matrix a is transposed - /// Input matrix - /// Whether matrix b is transposed - /// Scalar - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::ConvolveAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, - ElemType beta, Matrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise) - { - DecideAndMoveToRightDevice(a, b, c); - - if (c.GetDeviceId() >= 0 /*GPU*/ && a.GetMatrixType() == MatrixType::DENSE - && b.GetMatrixType() == MatrixType::SPARSE && c.GetMatrixType() == MatrixType::DENSE) - { - GPUSparseMatrix::ConvolveAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, beta, *c.m_GPUMatrix, numChannels, horizontalSubsample, padding, channelwise); - } - else - { - NOT_IMPLEMENTED; - } - } - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c - /// if a is a column vector, add to all columns of c - /// if a is a row vector, add to all rows of c - /// Scalar - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - /*static*/void Matrix::ScaleAndAdd(ElemType alpha, const Matrix& a, Matrix& c) - { - if (a.IsEmpty() || c.IsEmpty()) - LogicError("ScaleAndAdd: one of the input matrices is empty."); - - DecideAndMoveToRightDevice(c, a); - - if (a.GetMatrixType() == c.GetMatrixType()) - { - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::ScaleAndAdd(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), - GPUMatrix::ScaleAndAdd(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - GPUSparseMatrix b = move(*c.m_GPUSparseMatrix); GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,1,b,*c.m_GPUSparseMatrix) - ); - } - else - { - DISPATCH_MATRIX_ON_FLAG(&c, - nullptr, - CPUSparseMatrix::ScaleAndAdd(alpha,*a.m_CPUSparseMatrix,*c.m_CPUMatrix); c.SetDataLocation(CPU), - if (a.m_GPUSparseMatrix->GetFormat() == MatrixFormat::matrixFormatSparseCSC) - { - GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,1,*c.m_GPUMatrix,*c.m_GPUMatrix); - } - else // new GPU sparse matrix code - { - GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,*c.m_GPUMatrix); - } - c.SetDataLocation(GPU), - NOT_IMPLEMENTED, - { - c.m_GPUMatrix = new GPUMatrix(c.m_GPUSparseMatrix->CopyToDenseMatrix()); - GPUSparseMatrix::ScaleAndAdd(alpha, *a.m_GPUMatrix, 1, *c.m_GPUSparseMatrix, *c.m_GPUMatrix); - delete c.m_GPUSparseMatrix; c.m_GPUSparseMatrix = NULL; - c.SetDataLocation(GPU, DENSE); - } - ); - } - } - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a + beta * c - /// if a is a column vector, add to all columns of c - /// if a is a row vector, add to all rows of c - /// Scalar - /// Input matrix - /// Scalar - /// Resulting matrix, caller is responsible for allocating this - template - /*static*/ void Matrix::ScaleAndAdd(ElemType alpha, const Matrix& a, ElemType beta, Matrix& c) - { - if (beta==1) - ScaleAndAdd(alpha,a,c); - else if (beta==0) - { - Scale(alpha,a,c); - } - else - { - ScaleAndAdd(alpha/beta,a,c); // c1=alpha/beta * a + c - Scale(beta,c); // c/beta * beta - } - } - - // tensor swapping and addition: c <- keepWeight * b + scaleFactor * swap_dimensions(a, S, K) - // where - // - a is interpreted as a tensor of dimension (D x S x M x K x T) // column-major, as usual - // - b and c as a tensor of dimension (D x K x M x S x T) // note: K and S swapped - // The main point of this function is to reshuffle a tensor w.r.t. two dimensions that get swapped in memory, - // but for gradients, we will need to add, hence the keepWeight. - // Notes: - // - c and b may be the same (in-place operation is expressly allowed). - // - D, M, and/or T may be 1. For example, D == M == T == 1 implements a 2D matrix transpose from (S x K) to (K x S). - // - If keepWeight == 0, then b will just get overwritten (straight assignment, b may be uninitialized or contain NaNs). - // - The original matrix dimensions are ignored except that sizes must match (rows x cols == D x S x M x K x T). - // For diagnostics purposes, this function also enforces the rows % D == 0 and cols % T == 0, but this is not a functional requirement and can be removed if that helps. - // - Dense matrices only. - // TODO: Handle these cases: - // - no swapping happening --just do a block copy - // - swapping can be implemented by cuDNN --do so - template - /*static*/ void Matrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const Matrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const Matrix& b, Matrix& c) - { - if (a.GetNumElements() != c.GetNumElements() || b.GetNumElements() != c.GetNumElements()) // allocations must match (but not dimensions, since we reinterpret the dimensions anyway) - InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c must have same number of elements."); - if (c.IsEmpty()) // operating on empty minibatch slices is perfectly cromulent - return; - - // sanity checks for current use cases--these are not strictly necessary and can be deleted - if (a.GetNumRows() % D != 0 || b.GetNumRows() % D != 0 || c.GetNumRows() % D != 0) - InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c are meant to have a row dimension that is a multiple of D."); - if (a.GetNumCols() % T != 0 || b.GetNumCols() % T != 0 || c.GetNumCols() % T != 0) - InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c are meant to have a column dimension that is a multiple of T."); - - DecideAndMoveToRightDevice(a, b, c); - - DISPATCH_MATRIX_ON_FLAG(&c, - nullptr, - CPUMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_CPUMatrix, D, S, M, K, T, scaleFactor, *b.m_CPUMatrix, *c.m_CPUMatrix), - GPUMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_GPUMatrix, D, S, M, K, T, scaleFactor, *b.m_GPUMatrix, *c.m_GPUMatrix), - NOT_IMPLEMENTED, - GPUSparseMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_GPUSparseMatrix, D, S, M, K, T, scaleFactor, *b.m_GPUSparseMatrix, *c.m_GPUSparseMatrix) - ); - } - - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::AddScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c) - { - DecideAndMoveToRightDevice(c, a, b); - if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == c.GetMatrixType() )) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::AddScaledDifference(alpha,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), - GPUMatrix::AddScaledDifference(alpha,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::AssignScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c) - { - DecideAndMoveToRightDevice(a, b, c); - - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::AssignScaledDifference(alpha,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), - GPUMatrix::AssignScaledDifference(alpha,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::AddScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c) - { - DecideAndMoveToRightDevice(c, a, b); - alpha._transferToDevice(c.GetDeviceId()); - - if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == c.GetMatrixType() && a.GetMatrixType() == alpha.GetMatrixType())) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::AddScaledDifference(*alpha.m_CPUMatrix,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), - GPUMatrix::AddScaledDifference(*alpha.m_GPUMatrix,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::AssignScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c) - { - DecideAndMoveToRightDevice(a, b, alpha); - c._transferToDevice(a.GetDeviceId()); - - if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == alpha.GetMatrixType())) - NOT_IMPLEMENTED; - - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - nullptr, - CPUMatrix::AssignScaledDifference(*alpha.m_CPUMatrix,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), - GPUMatrix::AssignScaledDifference(*alpha.m_GPUMatrix,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - } - - //c[ci,cj] += a[ai,aj] - template - void Matrix::AddElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj) - { - DecideAndMoveToRightDevice(c, a); - - if (c.GetMatrixType() != a.GetMatrixType()) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::AddElementToElement(*a.m_CPUMatrix, ai, aj, *c.m_CPUMatrix, ci, cj), - GPUMatrix::AddElementToElement(*a.m_GPUMatrix, ai, aj, *c.m_GPUMatrix, ci, cj), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - //c[ci,cj] = a[ai,aj] - template - void Matrix::AssignElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj) - { - DecideAndMoveToRightDevice(c, a); - - if (c.GetMatrixType() != a.GetMatrixType()) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::AssignElementToElement(*a.m_CPUMatrix, ai, aj, *c.m_CPUMatrix, ci, cj), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - } - - //for each column of this, we add row slice of a starting from startIndex - template - void Matrix::MinusOneAt(Matrix& a, const size_t position) - { - DISPATCH_MATRIX_ON_FLAG(&a, - &a, - CPUMatrix::MinusOneAt(*a.m_CPUMatrix, position), - GPUMatrix::MinusOneAt(*a.m_GPUMatrix, position), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a - /// Scalar - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void Matrix::Scale(ElemType alpha, const Matrix& a, Matrix& c) - { - DecideAndMoveToRightDevice(c, a); - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::Scale(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), - GPUMatrix::Scale(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - *c.m_GPUSparseMatrix = (*a.m_GPUSparseMatrix)*alpha - ); - } - - /// Matrix-scalar multiply with col-major matrices: a = alpha * a - /// Scalar - /// Input matrix - template - void Matrix::Scale(ElemType alpha, Matrix& a) - { - if (a.IsEmpty()) - return; - - DISPATCH_MATRIX_ON_FLAG(&a, - &a, - CPUMatrix::Scale(alpha,*a.m_CPUMatrix), - GPUMatrix::Scale(alpha,*a.m_GPUMatrix), - NOT_IMPLEMENTED, - GPUSparseMatrix::Scale(alpha,*a.m_GPUSparseMatrix) - ); - } - - /// Matrix scalar matrix multiply with col-major matrices: a = alpha[0,0] * a - /// 1x1 matrix - /// Input matrix - template - void Matrix::Scale(const Matrix& alpha, Matrix& a) - { - if (a.IsEmpty()) - return; - - DecideAndMoveToRightDevice(a,alpha); - - if (a.GetMatrixType() != alpha.GetMatrixType()) - NOT_IMPLEMENTED; - - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - CPUMatrix::Scale(*alpha.m_CPUMatrix,*a.m_CPUMatrix), - GPUMatrix::Scale(*alpha.m_GPUMatrix,*a.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::InnerProduct (const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrix is empty."); - - DecideAndMoveToRightDevice(a, b, c); - - if (a.GetMatrixType() != b.GetMatrixType()) - NOT_IMPLEMENTED; - - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::InnerProduct(*a.m_CPUMatrix,*b.m_CPUMatrix,*c.m_CPUMatrix,isColWise), - GPUMatrix::InnerProduct(*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix,isColWise), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - ElemType Matrix::InnerProductOfMatrices(const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProductOfMatrices: one of the input matrices is empty."); - - DecideAndMoveToRightDevice(a,b); - - if (a.GetMatrixType() == b.GetMatrixType()) - { - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - return CPUMatrix::InnerProductOfMatrices(*a.m_CPUMatrix,*b.m_CPUMatrix), - return GPUMatrix::InnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - else - { - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - NOT_IMPLEMENTED, - return GPUSparseMatrix::InnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUSparseMatrix), - NOT_IMPLEMENTED, - return GPUSparseMatrix::InnerProductOfMatrices(*a.m_GPUSparseMatrix,*b.m_GPUMatrix) - ); - } - } - - template - Matrix& Matrix::AssignInnerProductOfMatrices(const Matrix& a, const Matrix& b) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProductOfMatrices: one of the input matrices is empty."); - - Resize(1,1); - - DecideAndMoveToRightDevice(a, b, *this); - - if (a.GetMatrixType() == b.GetMatrixType()) - { - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&a, - this, - m_CPUMatrix->SetValue(CPUMatrix::InnerProductOfMatrices(*a.m_CPUMatrix,*b.m_CPUMatrix)), - m_GPUMatrix->AssignInnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUMatrix), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - else - { - NOT_IMPLEMENTED; - } - - return *this; - } - - template - void Matrix::ElementWisePower (ElemType alpha, const Matrix& a, Matrix& c) - { - if (a.IsEmpty()) - return; - - DecideAndMoveToRightDevice(a, c); - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - nullptr, - CPUMatrix::ElementWisePower(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), - GPUMatrix::ElementWisePower(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), - NOT_IMPLEMENTED, - GPUSparseMatrix::ElementWisePower(alpha,*a.m_GPUSparseMatrix,*c.m_GPUSparseMatrix) - ); - } - - template - bool Matrix::AreEqual(const Matrix& a, const Matrix& b, const ElemType threshold /*= 1e-8*/) - { - if (a.GetNumRows() != b.GetNumRows() || a.GetNumCols() != b.GetNumCols()) - return false; - - DecideAndMoveToRightDevice(a,b); - - if (a.GetMatrixType() == b.GetMatrixType()) - { - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - return CPUMatrix::AreEqual(*a.m_CPUMatrix, *b.m_CPUMatrix, threshold), - return GPUMatrix::AreEqual(*a.m_GPUMatrix, *b.m_GPUMatrix, threshold), - return CPUSparseMatrix::AreEqual(*a.m_CPUSparseMatrix, *b.m_CPUSparseMatrix, threshold), - return GPUSparseMatrix::AreEqual(*a.m_GPUSparseMatrix, *b.m_GPUSparseMatrix, threshold) - ); - } - else - { - DISPATCH_MATRIX_ON_FLAG(&a, - nullptr, - NOT_IMPLEMENTED; return false, - return GPUSparseMatrix::AreEqual(*a.m_GPUMatrix, *b.m_GPUSparseMatrix, threshold), - NOT_IMPLEMENTED; return false, - return GPUSparseMatrix::AreEqual(*a.m_GPUSparseMatrix, *b.m_GPUMatrix, threshold) - ); - } - - } - - template - bool Matrix::HasElement(const Matrix& a, const ElemType value) - { - if (a.IsEmpty()) - return false; - - DISPATCH_MATRIX_ON_FLAG(&a, - &a, - return CPUMatrix::HasElement(*a.m_CPUMatrix, value), - return GPUMatrix::HasElement(*a.m_GPUMatrix, value), - NOT_IMPLEMENTED; return false, - NOT_IMPLEMENTED; return false - ); - } - - // diagnostics helper to check if matrix has a NaN - // This is very slow. - template - bool Matrix::HasNan (const char * name) const - { - // Not implemented for sparse matrices. - // Return false as a workaround to at - // least evaluate the dense matrices. - if (m_matrixType == MatrixType::SPARSE) - return false; - - if (IsEmpty()) - return false; - - // if GPU then first detect NaN there, will be faster - if (GetDeviceId() != CPUDEVICE) - { - Matrix sum; - sum.AssignSumOfElements(*this); - auto x = sum.Get00Element(); - if (!std::isnan(x)) - return false; - } - - // const auto & us = *this; - const Matrix & us = *this; - - foreach_coord (i, j, us) - if (std::isnan(us(i, j))) - { - fprintf(stderr, "HasNan: NaN detected at %s (%ld,%ld) in (%d,%d) matrix\n", name, i, j, (int)GetNumRows(), (int)GetNumCols()); - return true; - } - return false; - } -#define CheckNan(m) m.HasNan (#m) - - // another diagnostics helper to check if matrix has a NaN - // This is used at load and save time. This test is slow. - - template - size_t Matrix::CountNanInf() const - { - const auto & us = *this; - size_t n = 0; // number of NaNs/INF found - foreach_coord (i, j, us) - { - auto val = us(i,j); - if (std::isnan (val) || !std::isfinite (val)) - n++; - } - return n; - } - - template - DEVICEID_TYPE Matrix::GetBestGPUDeviceId() - { - return EnforceOneGPUOnly(GPUMatrix::GetBestGPUDeviceId()); // see EnforceOneGPUOnly() for comment on what this is - } - - // TODO: these are scalar operations--why are they in Matrix? - template - ElemType Matrix::Exp10(ElemType num) - { - return (ElemType)exp(num*2.302585093); - } - - template - ElemType Matrix::Mod(ElemType x, ElemType y) - { - assert(y > 0); - if (y <= 0) - LogicError("y is smaller than zero"); - - return x - y * floor(x / y); - } - - // TODO: use static LogAdd() as defined in TensorOps.h - // Not doing this currently because that one uses ElemType for all ops, while this one uses double inside. Must compare before making this change. - template - ElemType Matrix::LogAdd(ElemType x, ElemType y) - { - ElemType temp, diff, z; - - if (x < y) - { - temp = x; x = y; y = temp; // TODO: ::swap(x,y)? - } - diff = y - x; - if (diff < MINLOGEXP) - { - return (ElemType) ((x < LSMALL) ? LZERO : x); - } - else - { - z = exp(diff); - return (ElemType)(x + log(1.0 + z)); - } - } - - //Matrix& Matrix::Shift(const Matrix& a, size_t shift) - //[this]= (a right shift by n), padded with zeros - // shift left, shift needs to be negative value - // shift right, shift needs to be positive value - // BUGBUG: Leaves uninitialized values in the opened-up columns. - template - Matrix& Matrix::Shift(const Matrix& a, int shift) - { - if (a.IsEmpty()) - LogicError("Shift: Matrix is empty."); - else - LogicError("Shift: BUGBUG This function currently leaves uninitialized values. Fix the code or contact fseide@microsoft.com."); - - auto& us = *this; - if (this != &a) - { - Resize(a.GetNumRows(), a.GetNumCols()); - } - - long n = (long)GetNumCols(); - - if (shift >= 0 && shift < n) - us.ColumnSlice(shift, n - shift).SetValue(a.ColumnSlice(0, n - shift)); - if (shift < 0 && shift > -n) - us.ColumnSlice(0, n + shift).SetValue(a.ColumnSlice(-shift, n + shift)); - return *this; - } - - template - Matrix& Matrix::AssignElementProductOfWithShiftNeg(const Matrix& a, const Matrix& b, size_t shift, size_t negnumber) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); - - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("The input matrix dimensions do not match."); - - if (a.GetNumRows() != 1) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); - - DecideAndMoveToRightDevice(a, b, *this); - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignElementProductOfWithShiftNeg(*a.m_CPUMatrix, *b.m_CPUMatrix, shift, negnumber), - m_GPUMatrix->AssignElementProductOfWithShiftNeg(*a.m_GPUMatrix, *b.m_GPUMatrix, shift, negnumber), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - return *this; - } - - template - Matrix& Matrix::AssignInnerProductOfWithShiftNeg(const Matrix& a, const Matrix& b, const bool isColWise, size_t shift, size_t negnumber) - { - InnerProductWithShiftNeg(a, b, *this, isColWise, shift, negnumber); - return *this; - } - - template - void Matrix::InnerProductWithShiftNeg(const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise, size_t shift, size_t negnumber) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrix is empty."); - - DecideAndMoveToRightDevice(a, b, c); - - if (a.GetMatrixType() != b.GetMatrixType()) - NOT_IMPLEMENTED; - - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::InnerProductWithShiftNeg(*a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, isColWise, shift, negnumber), - GPUMatrix::InnerProductWithShiftNeg(*a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, shift, negnumber), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::GetARowByIndex(const Matrix& a, size_t index) - { - if (a.IsEmpty()) - LogicError("GetARowByIndex: Matrix is empty."); - - - //WARNING: a and this must have same type - if (!(GetMatrixType() == a.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->GetARowByIndex(*a.m_CPUMatrix, index), - m_GPUMatrix->GetARowByIndex(*a.m_GPUMatrix, index), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - template - void Matrix::ConductRowElementMultiplyWithShift(const Matrix& a, const Matrix& b, Matrix& c, size_t shift, bool bFirstmatrixfixed) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("InnerProduct: one of the input matrix is empty."); - - DecideAndMoveToRightDevice(a, b, c); - - if (a.GetMatrixType() != b.GetMatrixType()) - NOT_IMPLEMENTED; - - c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(&c, - &c, - CPUMatrix::ConductRowElementMultiplyWithShift(*a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, shift, bFirstmatrixfixed), - GPUMatrix::ConductRowElementMultiplyWithShift(*a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, shift, bFirstmatrixfixed), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::AssignElementProductOfWithShift(const Matrix& a, const Matrix& b, size_t shift) - { - if (a.IsEmpty() || b.IsEmpty()) - LogicError("AssignElementProductOfWithShift: Matrix is empty."); - - assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); - if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) - InvalidArgument("The input matrix dimensions do not match."); - - if (a.GetNumRows() != 1) - InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); - - DecideAndMoveToRightDevice(a, b, *this); - if (!(a.GetMatrixType() == b.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignElementProductOfWithShift(*a.m_CPUMatrix, *b.m_CPUMatrix, shift), - m_GPUMatrix->AssignElementProductOfWithShift(*a.m_GPUMatrix, *b.m_GPUMatrix, shift), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - return *this; - } - - template - void Matrix::RCRFBackwardCompute(const Matrix& alpha, Matrix& beta, - Matrix& functionValues, const Matrix& lbls, - const Matrix& pos_scores, const Matrix& pair_scores, const int shift) - { - DecideAndMoveToRightDevice(alpha, beta); - functionValues._transferToDevice(alpha.GetDeviceId()); - beta._transferToDevice(alpha.GetDeviceId()); - - DISPATCH_MATRIX_ON_FLAG(&alpha, - &beta, - CPUMatrix::RCRFBackwardCompute( - *alpha.m_CPUMatrix, - *beta.m_CPUMatrix, - *lbls.m_CPUMatrix, - *pair_scores.m_CPUMatrix), - GPUMatrix::RCRFBackwardCompute( - *alpha.m_GPUMatrix, - *beta.m_GPUMatrix, - *lbls.m_GPUMatrix, - *pos_scores.m_GPUMatrix, - *pair_scores.m_GPUMatrix, shift), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::RCRFTransGrdCompute(const Matrix& lbls, - const Matrix& alpha, - const Matrix& beta, - const Matrix& pair_scores, - Matrix& grd, - const int startLbl, - const int shift) - { - DecideAndMoveToRightDevice(alpha, grd); - grd._transferToDevice(alpha.GetDeviceId()); - - DISPATCH_MATRIX_ON_FLAG(&alpha, - &grd, - CPUMatrix::RCRFTransGrdCompute( - *lbls.m_CPUMatrix, - *alpha.m_CPUMatrix, - *beta.m_CPUMatrix, - *pair_scores.m_CPUMatrix, - *grd.m_CPUMatrix), - GPUMatrix::RCRFTransGrdCompute( - *lbls.m_GPUMatrix, - *alpha.m_GPUMatrix, - *beta.m_GPUMatrix, - *pair_scores.m_GPUMatrix, - *grd.m_GPUMatrix, - startLbl, - shift), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - Matrix& Matrix::DropFrame(const Matrix& label, const Matrix& gamma, const ElemType & threshhold) - { - DecideAndMoveToRightDevice(*this, label, gamma); - - if (label.GetNumCols() != gamma.GetNumCols() || label.GetNumRows() != gamma.GetNumRows()) - LogicError("DropFrame: label matrix is not in the same size as gamm matrix."); - SwitchToMatrixType(label.GetMatrixType(), label.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->DropFrame(*label.m_CPUMatrix, *gamma.m_CPUMatrix, threshhold), - m_GPUMatrix->DropFrame(*label.m_GPUMatrix, *gamma.m_GPUMatrix, threshhold), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - - return *this; - } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - Matrix& Matrix::AssignSequenceError(const ElemType hsmoothingWeight, const Matrix& label, - const Matrix& dnnoutput, const Matrix& gamma, ElemType alpha) - { - DecideAndMoveToRightDevice(label, dnnoutput, gamma); - - if (!(label.GetMatrixType() == gamma.GetMatrixType())) - NOT_IMPLEMENTED; - - SwitchToMatrixType(label.GetMatrixType(), label.GetFormat(), false); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->AssignSequenceError(hsmoothingWeight, *label.m_CPUMatrix, *dnnoutput.m_CPUMatrix, *gamma.m_CPUMatrix, alpha), - m_GPUMatrix->AssignSequenceError(hsmoothingWeight, *label.m_GPUMatrix, *dnnoutput.m_GPUMatrix, *gamma.m_GPUMatrix, alpha), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - return *this; - } -#pragma endregion Static BLAS Functions - - template - void Matrix::TensorOp(ElemType beta, const Matrix& a, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 2> & regularStrides, - const SmallVector & reducingOpDims, const array, 2> & reducingStrides) - { - DecideAndMoveToRightDevice(*this, a); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::TensorOp(ElemType beta, const Matrix& a, const Matrix& b, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 3> & regularStrides, - const SmallVector & reducingOpDims, const array, 3> & reducingStrides) - { - DecideAndMoveToRightDevice(*this, a, b); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, *b.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, *b.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template - void Matrix::TensorOp(ElemType beta, const Matrix& a, const Matrix& b, const Matrix& c, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 4> & regularStrides, - const SmallVector & reducingOpDims, const array, 4> & reducingStrides) - { - DecideAndMoveToRightDevice(*this, a, b, c); - - DISPATCH_MATRIX_ON_FLAG(this, - this, - m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), - NOT_IMPLEMENTED, - NOT_IMPLEMENTED - ); - } - - template class Matrix; - template class Matrix; - - // We use Matrix as the backing store for QuantizedMatrix, and also as a flag matrix. - // Let's explicitly instantiate the methods we need for that purpose - template Matrix::Matrix(DEVICEID_TYPE); - template Matrix::Matrix(Matrix&&); - template Matrix::Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId, const MatrixType matrixType, const MatrixFormat matrixFormat); - template Matrix::Matrix(const size_t numRows, const size_t numCols, char *pArray, const size_t matrixFlags, DEVICEID_TYPE deviceId, const size_t nnz); - template Matrix::~Matrix(); - template Matrix& Matrix::operator=(Matrix&& moveFrom); - template Matrix& Matrix::operator=(const Matrix& deepCopyFrom); - template char* Matrix::BufferPointer() const; - template int Matrix::GetDeviceId() const; - template size_t Matrix::GetNumElements() const; - template Matrix Matrix::ColumnSlice(size_t startColumn, size_t numCols) const; - template void Matrix::_transferToDevice(int id_to, bool ismoved, bool emptyTransfer) const; - template size_t Matrix::GetNumRows() const; - template size_t Matrix::GetNumCols() const; - template void Matrix::SetValue(const char); - template void Matrix::SetValue(size_t numRows, const size_t numCols, int deviceId, char *pArray, size_t matrixFlags); - template bool Matrix::IsEmpty() const; - template void Matrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, bool growOnly); - -}}} +// Matrix.cpp -- main CPP file that contains all Matrix functions exported by the CNTKMath.dll +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#include "stdafx.h" +#include "Basics.h" +#include "Matrix.h" +#include "CPUMatrix.h" +#include "CPUSparseMatrix.h" +#include "GPUMatrix.h" +#include "GPUSparseMatrix.h" +#include "File.h" +#include +#include +#include "GPUWatcher.h" // bring in this class as well so that it gets exported from this DLL +#ifndef CPUONLY +#pragma comment (lib, "MathCUDA.lib") // built by CNTKMathCUDA project +#endif + +#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this +#pragma warning (disable: 4239) // nonstandard extension; triggered by this pattern: "auto& second = transposeB ? b.m_GPUMatrix->Transpose() : *b.m_GPUMatrix;" +#pragma warning (disable: 4702) // unreachable code; triggered for unknown reasons + +#ifndef min +#define min(a,b) (((a) < (b)) ? (a) : (b)) +#endif + +//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly +#define DISPATCH_MATRIX_ON_FLAG(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ + { \ + CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ + if (curLocation==CurrentDataLocation::GPU || curLocation==CurrentDataLocation::BOTH) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + GPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ + } \ + else \ + { \ + GPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ + } \ + } \ + else if (curLocation==CurrentDataLocation::CPU) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + CPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ + } \ + else \ + { \ + CPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ + } \ + } \ + else \ + { \ + RuntimeError("Matrices do not exist in either CPU or GPU."); \ + } \ + } + +//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly +#define DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ + { \ + CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ + if (curLocation==CurrentDataLocation::GPU) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + GPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ + } \ + else \ + { \ + GPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ + } \ + } \ + else if (curLocation==CurrentDataLocation::CPU || curLocation==CurrentDataLocation::BOTH) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + CPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ + } \ + else \ + { \ + CPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ + } \ + } \ + else \ + { \ + RuntimeError("Matrices do not exist in either CPU or GPU."); \ + } \ + } + +//before calling the following macro the current matrix location and matrix type on MatrixPointerToCheck must have been set correctly +#define DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(MatrixPointerToCheck, MatrixPointerToSetFlag, CPUDense, GPUDense, CPUSparse, GPUSparse) \ + { \ + CurrentDataLocation curLocation = (MatrixPointerToCheck)->GetCurrentMatrixLocation(); \ + if (curLocation == CurrentDataLocation::BOTH) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + CPUDense; \ + GPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::BOTH, MatrixType::DENSE); \ + } \ + else \ + { \ + CPUSparse; \ + GPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::BOTH, MatrixType::SPARSE); \ + } \ + } \ + else if (curLocation == CurrentDataLocation::GPU) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + GPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); \ + } \ + else \ + { \ + GPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::GPU, MatrixType::SPARSE); \ + } \ + } \ + else if (curLocation == CurrentDataLocation::CPU) \ + { \ + if ((MatrixPointerToCheck)->GetMatrixType() != MatrixType::SPARSE) \ + { \ + CPUDense; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); \ + } \ + else \ + { \ + CPUSparse; \ + if (MatrixPointerToSetFlag != nullptr) \ + ((Matrix*)MatrixPointerToSetFlag)->SetDataLocation(CurrentDataLocation::CPU, MatrixType::SPARSE); \ + } \ + } \ + else \ + { \ + RuntimeError("Matrices do not exist in either CPU or GPU."); \ + } \ + } + +// EnforceOneGPUOnly - enforce that we only use one GPU (because we don't really support more than one at this point in time) +// BUGBUG workaround. +// Call this after every place a device is deviced. +// We have multiple independent mechanisms to pick a device. +// After selecting a device id, always run the result through this function, which will cache the first choice. +// TODO: This is a stop-gap. It will be cleaned up once we also fix the GPU late-locking bug. +// The correct fix is to always route GPU selection through a single function in the first place. +DEVICEID_TYPE EnforceOneGPUOnly(DEVICEID_TYPE requestedDeviceId) +{ + if (requestedDeviceId < 0) // only apply this to GPU ids + return requestedDeviceId; + static DEVICEID_TYPE theGPUId = DEVICEID_NOTYETDETERMINED; + if (theGPUId == DEVICEID_NOTYETDETERMINED) + theGPUId = requestedDeviceId; + else if (theGPUId != requestedDeviceId) + { + static bool shown = false; + if (!shown) + { + fprintf(stderr, "EnforceOneGPUOnly: WARNING: Ignored attempt to change GPU choice from %d to %d. This message will be shown only once.\n", theGPUId, requestedDeviceId); + shown = true; + } + } + return theGPUId; +} + +namespace Microsoft { namespace MSR { namespace CNTK { +#pragma region Constructors, destructors and other static matrix builders + + //This function will only initialize default bland matrix. The actual matrices need to allocated + //after calling this function and flags need to set correctly by calling SetDataLocation. + template + void Matrix::Init(DEVICEID_TYPE deviceId) + { + m_baseMatrix=NULL; + m_GPUMatrix=NULL; + m_CPUMatrix=NULL; + m_GPUSparseMatrix=NULL; + m_CPUSparseMatrix=NULL; + m_currentDataLocation = CurrentDataLocation::NONE; + m_matrixType = MatrixType::UNDETERMINED; + + int _devId = deviceId!=AUTOPLACEMATRIX ? deviceId : GetBestGPUDeviceId(); + _devId = EnforceOneGPUOnly(_devId); // see EnforceOneGPUOnly() for comment on what this is + m_preferredDeviceId=_devId; + m_numTimesDeviceChanged = 0; + m_numTimesMatrixTypeChanged = 0; + m_devicesTransferedTo[1] = m_devicesTransferedTo[0] = CPUDEVICE - 1; + } + + //this function is used to indicate where (CPUDense, CPUSparse, GPUDense, GPUSparse) the most updated results are in + //after the actual matrix is updated. + template + void Matrix::SetDataLocation(CurrentDataLocation location, MatrixType type) const + { + m_currentDataLocation = location; + + // set the matrix type if passed in + if (type != MatrixType::UNDETERMINED) + { + m_matrixType = type; + } + + if (m_matrixType == MatrixType::DENSE) + { + m_baseMatrix = ((m_currentDataLocation == CurrentDataLocation::CPU)?(BaseMatrix*)m_CPUMatrix:(BaseMatrix*)m_GPUMatrix); + } + else if (m_matrixType == MatrixType::SPARSE) + { + m_baseMatrix = ((m_currentDataLocation == CurrentDataLocation::CPU)?(BaseMatrix*)m_CPUSparseMatrix:(BaseMatrix*)m_GPUSparseMatrix); + } + } + + //this is a private constructor only used internally to initialize a blank matrix + template + Matrix::Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, const MatrixFormat matrixFormat, DEVICEID_TYPE deviceID) + { + Init(deviceID); + + if (!(matrixFlags & matrixFlagDontOwnBuffer)) + SwitchToMatrixType(matrixType, matrixFormat, false); + } + + + //this is a private constructor only used internally to initialize a blank matrix + template + Matrix::Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, DEVICEID_TYPE deviceID) + { + Init(deviceID); + + if (!(matrixFlags & matrixFlagDontOwnBuffer)) + SwitchToMatrixType(matrixType, matrixType == MatrixType::DENSE? MatrixFormat::matrixFormatDense : MatrixFormat::matrixFormatSparseCSC, false); + } + + //this is a private constructor only used internally to initialize a blank matrix + template + Matrix::Matrix(const MatrixFlags matrixFlags, DEVICEID_TYPE deviceID) + { + Init(deviceID); + + if (!(matrixFlags & matrixFlagDontOwnBuffer)) + SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, false); + } + + template + Matrix::Matrix(DEVICEID_TYPE deviceID) + { + Init(deviceID); + + SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, false); + } + + // constructor for Matrix class to wrap an externally managed BaseMatrix + // baseMatrix - base matrix for this element + // pArray - pointer to current data array, will replace existing pointer in baseMatrix if != NULL + // deviceId - deviceId where the pArray exists + template + Matrix::Matrix(BaseMatrix* baseMatrix, ElemType *pArray, DEVICEID_TYPE deviceId) // constructor for setting Matrix from a base matrix + { + Init(deviceId); + + if (baseMatrix->GetFormat() & matrixFormatSparse) + { + if (m_preferredDeviceId == CPUDEVICE) + { + m_CPUSparseMatrix = (CPUSparseMatrix*)baseMatrix; + SetDataLocation(CPU, SPARSE); + } + else + { + m_GPUSparseMatrix = (GPUSparseMatrix*)baseMatrix; + SetDataLocation(GPU, SPARSE); + } + } + else + { + if (m_preferredDeviceId == CPUDEVICE) + { + m_CPUMatrix = (CPUMatrix*)baseMatrix; + SetDataLocation(CPU, DENSE); + } + else + { + m_GPUMatrix = (GPUMatrix*)baseMatrix; + SetDataLocation(GPU, DENSE); + } + } + m_baseMatrix = baseMatrix; + m_baseMatrix->SetArray(pArray); + } + + //matrixName is used to verify that correct matrix is read. + template + Matrix::Matrix(FILE* f, const char * matrixName, DEVICEID_TYPE deviceId, const MatrixType matrixType) + { + Init(deviceId); + + if (matrixType == MatrixType::SPARSE) + { + if (m_preferredDeviceId == CPUDEVICE) + { + NOT_IMPLEMENTED; + //m_CPUSparseMatrix = new CPUSparseMatrix(f,matrixName); + SetDataLocation(CPU, SPARSE); + } + else + { + NOT_IMPLEMENTED; + //m_GPUSparseMatrix = new GPUSparseMatrix(f,matrixName, m_preferredDeviceId); + SetDataLocation(GPU, SPARSE); + } + } + else + { + if (m_preferredDeviceId == CPUDEVICE) + { + m_CPUMatrix = new CPUMatrix(f,matrixName); + SetDataLocation(CPU, DENSE); + } + else + { + m_GPUMatrix = new GPUMatrix(f,matrixName, m_preferredDeviceId); + SetDataLocation(GPU, DENSE); + } + } + } + + template + Matrix::Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId, const MatrixType matrixType, const MatrixFormat matrixFormat) + { + Init(deviceId); + + if (matrixType == MatrixType::SPARSE) + { + if (m_preferredDeviceId == CPUDEVICE) + { + m_CPUSparseMatrix = new CPUSparseMatrix(matrixFormat, numRows, numCols, 0); + SetDataLocation(CPU, SPARSE); + } + else + { + m_GPUSparseMatrix = new GPUSparseMatrix(numRows, numCols, 0, matrixFormat, m_preferredDeviceId); + SetDataLocation(GPU, SPARSE); + } + } + else + { + if (matrixFormat != matrixFormatDense) + { + NOT_IMPLEMENTED; + } + + if (m_preferredDeviceId == CPUDEVICE) + { + m_CPUMatrix = new CPUMatrix(numRows, numCols); + SetDataLocation(CPU, DENSE); + } + else + { + m_GPUMatrix = new GPUMatrix(numRows, numCols, m_preferredDeviceId); + SetDataLocation(GPU, DENSE); + } + + SetValue(0); + } + } + + template + Matrix::Matrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags, DEVICEID_TYPE deviceId, const size_t nnz) + { + Init(deviceId); + + if (m_preferredDeviceId == CPUDEVICE) + { + if (matrixFlags&matrixFormatSparse) + { + //WARNING: matrixFlag is not passed in and externally managed array cannot be passed in + m_CPUSparseMatrix = new CPUSparseMatrix(matrixFormatSparseCSC, numRows,numCols, nnz); + SetDataLocation(CPU, SPARSE); + } + else + { + m_CPUMatrix = new CPUMatrix(numRows,numCols,pArray,matrixFlags); + SetDataLocation(CPU, DENSE); + } + } + else + { + if (matrixFlags&matrixFormatSparse) + { + //m_GPUSparseMatrix = new GPUSparseMatrix(numRows,numCols,nnz, pArray,matrixFlags,m_preferredDeviceId); + m_GPUSparseMatrix = new GPUSparseMatrix(MatrixFormat(matrixFlags & MatrixFormat::matrixFormatMask), m_preferredDeviceId); + m_GPUSparseMatrix->Resize(numRows, numCols, nnz, true, false); + SetDataLocation(GPU, SPARSE); + } + else + { + m_GPUMatrix = new GPUMatrix(numRows, numCols, m_preferredDeviceId, pArray, matrixFlags); + SetDataLocation(GPU, DENSE); + } + } + + if (matrixFlagDontOwnBuffer & matrixFlags) + m_baseMatrix->SetOwnBuffer(false); + } + + //copy constructor, deep copy + template + Matrix::Matrix(const Matrix& deepCopyFrom, DEVICEID_TYPE deviceId) + { + int origCopyFromDeviceId = deepCopyFrom.GetDeviceId(); + + if (deviceId == AUTOPLACEMATRIX) //use copyFrom's device if we have choice + deviceId = (DEVICEID_TYPE)origCopyFromDeviceId; + + Init(deviceId); //will set m_preferredDeviceId + + deepCopyFrom._transferToDevice(m_preferredDeviceId, true); + + DISPATCH_MATRIX_ON_FLAG(&deepCopyFrom, + this, + m_CPUMatrix = new CPUMatrix(*(deepCopyFrom.m_CPUMatrix)), + m_GPUMatrix = new GPUMatrix(*(deepCopyFrom.m_GPUMatrix)), + m_CPUSparseMatrix = new CPUSparseMatrix(*(deepCopyFrom.m_CPUSparseMatrix)), + m_GPUSparseMatrix = new GPUSparseMatrix(*(deepCopyFrom.m_GPUSparseMatrix)) + ); + + //should we move back? + deepCopyFrom._transferToDevice(origCopyFromDeviceId, true); + + m_preferredDeviceId = deepCopyFrom.m_preferredDeviceId; + } + + //assignment operator, deep copy + template + Matrix& Matrix::operator=(const Matrix& deepCopyFrom) + { + if (this != &deepCopyFrom) + { + SetValue(deepCopyFrom); + } + return *this; + } + + //move constructor, shallow copy + template + Matrix::Matrix(Matrix&& moveFrom) + { + Init((DEVICEID_TYPE)moveFrom.GetDeviceId()); + + DISPATCH_MATRIX_ON_FLAG(&moveFrom, + this, + m_CPUMatrix = new CPUMatrix(static_cast&&>(*(moveFrom.m_CPUMatrix))), + m_GPUMatrix = new GPUMatrix(static_cast&&>(*(moveFrom.m_GPUMatrix))), + m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))), + m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))) + ); + + m_preferredDeviceId = moveFrom.m_preferredDeviceId; + } + + //move assignment operator, shallow copy + template + Matrix& Matrix::operator=(Matrix&& moveFrom) + { + Clear(); + m_preferredDeviceId = moveFrom.m_preferredDeviceId; + + DISPATCH_MATRIX_ON_FLAG(&moveFrom, + this, + if (m_CPUMatrix != nullptr) m_CPUMatrix->operator=(static_cast&&>(*(moveFrom.m_CPUMatrix))); + else m_CPUMatrix = new CPUMatrix(static_cast&&>(*(moveFrom.m_CPUMatrix))), + + if (m_GPUMatrix != nullptr) m_GPUMatrix->operator=(static_cast&&>(*(moveFrom.m_GPUMatrix))); + else m_GPUMatrix = new GPUMatrix(static_cast&&>(*(moveFrom.m_GPUMatrix))), + + if (m_CPUSparseMatrix != nullptr) m_CPUSparseMatrix->operator=(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))); + else m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(*(moveFrom.m_CPUSparseMatrix))), + + if (m_GPUSparseMatrix != nullptr) m_GPUSparseMatrix->operator=(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))); + else m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(*(moveFrom.m_GPUSparseMatrix))) + ); + + return *this; + } + + template + void Matrix::Clear() + { + delete m_CPUMatrix; + m_CPUMatrix = nullptr; + delete m_GPUMatrix; + m_GPUMatrix = nullptr; + delete m_GPUSparseMatrix; + m_GPUSparseMatrix = nullptr; + delete m_CPUSparseMatrix; + m_CPUSparseMatrix = nullptr; + + m_matrixType=MatrixType::UNDETERMINED; + m_currentDataLocation = CurrentDataLocation::NONE; + } + + template + Matrix::~Matrix(void) + { + Clear(); + } + + template + Matrix Matrix::Ones(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId) + { + Matrix c(rows, cols, deviceId); //will initialize to 0 + c.SetValue(1); + return c; + } + + template + Matrix Matrix::Zeros(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId) + { + Matrix c(rows, cols, deviceId); //will initialize to 0 + c.SetValue(0); + return c; + } + + template + Matrix Matrix::Eye(const size_t rows, DEVICEID_TYPE deviceId) + { + Matrix c(rows, rows, deviceId); //will initialize to 0 + c.SetDiagonalValue(1); + return c; + } + + template + Matrix Matrix::RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed, DEVICEID_TYPE deviceId) + { + Matrix c(rows, cols, deviceId); //will initialize to 0 + c.SetUniformRandomValue(low, high, seed); + return c; + } + + template + Matrix Matrix::RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed, DEVICEID_TYPE deviceId) + { + Matrix c(rows, cols, deviceId); //will initialize to 0 + c.SetGaussianRandomValue(mean, sigma, seed); + return c; + } + + template + void Matrix::SetDevice(DEVICEID_TYPE deviceId) + { + if (deviceId >= 0) + GPUMatrix::SetDevice(deviceId); + } + + template + void Matrix::Read(File& stream) + { + Matrix& M = *this; + char type; + stream >> type; + if (type == 'd') + { + if (M.GetDeviceId()<0) + { + if (M.m_CPUMatrix == NULL) M.m_CPUMatrix = new CPUMatrix(); + stream >> (*M.m_CPUMatrix); + M.SetDataLocation(CPU, DENSE); + } + else + { + if (M.m_GPUMatrix == NULL) M.m_GPUMatrix = new GPUMatrix(M.GetDeviceId()); + stream >> (*M.m_GPUMatrix); + M.SetDataLocation(GPU, DENSE); + } + } + else if (type == 's') + { + if (M.GetDeviceId()<0) + { + NOT_IMPLEMENTED;//You might want to tranfer your matrix to GPU + } + else + { + if (M.m_GPUSparseMatrix == NULL) M.m_GPUSparseMatrix = new GPUSparseMatrix(); + stream >> (*M.m_GPUSparseMatrix); + M.SetDataLocation(GPU, SPARSE); + } + } + else + LogicError("wrong matrix type!"); + } + + template + void Matrix::Write(File& stream) const + { + const Matrix& M = *this; + if (M.GetMatrixType() == MatrixType::DENSE) + { + stream << 'd'; + if (M.GetDeviceId() < 0) + stream << (*M.m_CPUMatrix); + else + stream << (*M.m_GPUMatrix); + } + else + { + stream << 's'; + if (M.GetDeviceId() < 0) + NOT_IMPLEMENTED //stream<<(*M.m_CPUMatrix); + else + stream << (*M.m_GPUSparseMatrix); + } + } + +#pragma endregion Constructors, destructors and other static matrix builders + +#pragma region Basic Operators + + template + void Matrix::ShiftBy(int numShift) + { + assert (numShift > 0); + + int devId = GetDeviceId(); + + if (GetMatrixType() == MatrixType::DENSE) + { + for (size_t i = GetNumCols() - 1; i >= -numShift; i--) + { + Matrix inp = ColumnSlice(i + numShift, 1); + Matrix out = ColumnSlice(i, 1); + out = inp; + } + for (size_t i = 0; i < min(GetNumCols(), -numShift); i++) + ColumnSlice(i, 1).SetValue(0); + } + else if (GetMatrixType() == MatrixType::SPARSE) + { + if (devId == CPUDEVICE) + { + m_CPUSparseMatrix->ShiftBy(numShift); + } + else + NOT_IMPLEMENTED; + } + else + { + RuntimeError("Unknown matrix type"); + } + } + + template + size_t Matrix::BufferSize() const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_baseMatrix->GetSizeAllocated()*sizeof(ElemType), + return m_baseMatrix->GetSizeAllocated()*sizeof(ElemType), + return m_CPUSparseMatrix->BufferSize(), + return m_GPUSparseMatrix->BufferSizeAllocated() + ); + } + + template + ElemType* Matrix::BufferPointer() const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_baseMatrix->GetArray(), + return m_baseMatrix->GetArray(), + return m_CPUSparseMatrix->BufferPointer(), + return (ElemType*)m_GPUSparseMatrix->BufferPointer() + ); + } + + template + size_t Matrix::NzCount() const {return m_baseMatrix->NzCount();} + + template + ElemType* Matrix::CopyToArray() const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->CopyToArray(), + return m_GPUMatrix->CopyToArray(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done + //return number of elements copied + template + size_t Matrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->CopyToArray(arrayCopyTo, currentArraySize), + return m_GPUMatrix->CopyToArray(arrayCopyTo, currentArraySize), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + m_CPUMatrix->CopySection(numRows, numCols, dst, colStride), + m_GPUMatrix->CopySection(numRows, numCols, dst, colStride), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + // BUGBUG: Some code checks before calling here whether one of the dimensions is 0. + // This function must handle that case properly, that is, preserving the non-zero dimension. + template + Matrix Matrix::ColumnSlice(size_t startColumn, size_t numCols) const + { + int devId = GetDeviceId(); + + Matrix slice(matrixFlagDontOwnBuffer, (DEVICEID_TYPE)devId); // + + slice.m_preferredDeviceId = m_preferredDeviceId; + + if (GetMatrixType() == MatrixType::DENSE) + { + if (devId == CPUDEVICE) + { + if (slice.m_CPUMatrix != nullptr) + slice.m_CPUMatrix->operator=(static_cast&&>(m_CPUMatrix->ColumnSlice(startColumn, numCols))); + else + slice.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUMatrix->ColumnSlice(startColumn, numCols))); + slice.SetDataLocation(CPU, DENSE); + } + else + { + if (slice.m_GPUMatrix != nullptr) + slice.m_GPUMatrix->operator=(static_cast&&>(m_GPUMatrix->ColumnSlice(startColumn, numCols))); + else + slice.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUMatrix->ColumnSlice(startColumn, numCols))); + slice.SetDataLocation(GPU, DENSE); + } + } + else if (GetMatrixType() == MatrixType::SPARSE) + { + if (devId == CPUDEVICE) + { + if (slice.m_CPUSparseMatrix != nullptr) + slice.m_CPUSparseMatrix->operator=(static_cast&&>(m_CPUSparseMatrix->ColumnSlice(startColumn, numCols))); + else + slice.m_CPUSparseMatrix = new CPUSparseMatrix(static_cast&&>(m_CPUSparseMatrix->ColumnSlice(startColumn, numCols))); + slice.SetDataLocation(CPU, SPARSE); + } + else + { + if (slice.m_GPUSparseMatrix != nullptr) + slice.m_GPUSparseMatrix->operator=(static_cast&&>(m_GPUSparseMatrix->ColumnSlice(startColumn, numCols))); + else + slice.m_GPUSparseMatrix = new GPUSparseMatrix(static_cast&&>(m_GPUSparseMatrix->ColumnSlice(startColumn, numCols))); + slice.SetDataLocation(GPU, SPARSE); + } + } + else + { + RuntimeError("Unknown matrix type"); + } + + return slice; + } + + template + Matrix& Matrix::AssignColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols) + { + Clear(); + m_preferredDeviceId = fromMatrix.m_preferredDeviceId; + + DISPATCH_MATRIX_ON_FLAG(&fromMatrix, + this, + if (m_CPUMatrix != nullptr) m_CPUMatrix->AssignColumnSlice(*fromMatrix.m_CPUMatrix, startColumn, numCols); + else m_CPUMatrix = new CPUMatrix(static_cast&&>(fromMatrix.m_CPUMatrix->ColumnSlice(startColumn, numCols))), + + if (m_GPUMatrix != nullptr) m_GPUMatrix->AssignColumnSlice(*fromMatrix.m_GPUMatrix, startColumn, numCols); + else m_GPUMatrix = new GPUMatrix(static_cast&&>(fromMatrix.m_GPUMatrix->ColumnSlice(startColumn, numCols))), + + NOT_IMPLEMENTED, + + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::SetColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols) + { + assert(m_CPUMatrix != nullptr || m_GPUMatrix != nullptr); + // must already been allocated + + DISPATCH_MATRIX_ON_FLAG(&fromMatrix, + this, + m_CPUMatrix->SetColumnSlice(*fromMatrix.m_CPUMatrix, startColumn, numCols), + m_GPUMatrix->SetColumnSlice(*fromMatrix.m_GPUMatrix, startColumn, numCols), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + void Matrix::CopyColumnsStrided(const Matrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) + { + assert(m_CPUMatrix != nullptr || m_GPUMatrix != nullptr); + + DISPATCH_MATRIX_ON_FLAG(&fromMatrix, + this, + m_CPUMatrix->CopyColumnsStrided(*fromMatrix.m_CPUMatrix, numCols, srcNumColsStride, destNumColsStride), + m_GPUMatrix->CopyColumnsStrided(*fromMatrix.m_GPUMatrix, numCols, srcNumColsStride, destNumColsStride), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix Matrix::Diagonal() const + { + int devId = GetDeviceId(); + + Matrix diag(matrixFlagDontOwnBuffer, (DEVICEID_TYPE)devId); + diag.m_preferredDeviceId = m_preferredDeviceId; + + AssignDiagonalValuesTo(diag); + + return diag; + } + + template + Matrix Matrix::AssignDiagonalValuesTo(Matrix& diag) const + { + int devId = GetDeviceId(); + DecideAndMoveToRightDevice(*this, diag); + + if (GetMatrixType() == MatrixType::DENSE) + { + if (devId == CPUDEVICE) + { + if (diag.m_CPUMatrix != nullptr) + diag.m_CPUMatrix->operator=(static_cast&&>(m_CPUMatrix->Diagonal())); + else + diag.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUMatrix->Diagonal())); + diag.SetDataLocation(CPU, DENSE); + } + else + { + if (diag.m_GPUMatrix != nullptr) + diag.m_GPUMatrix->operator=(static_cast&&>(m_GPUMatrix->Diagonal())); + else + diag.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUMatrix->Diagonal())); + diag.SetDataLocation(GPU, DENSE); + } + } + else if (GetMatrixType() == MatrixType::SPARSE) + { + // TODO: Implement optimized diagonal functions for sparse matrices. For now use the DiagonalToDense instead. + if (devId == CPUDEVICE) + { + if (diag.m_CPUMatrix != nullptr) + diag.m_CPUMatrix->operator=(static_cast&&>(m_CPUSparseMatrix->DiagonalToDense())); + else + diag.m_CPUMatrix = new CPUMatrix(static_cast&&>(m_CPUSparseMatrix->DiagonalToDense())); + diag.SetDataLocation(CPU, DENSE); + } + else + { + if (diag.m_GPUMatrix != nullptr) + diag.m_GPUMatrix->operator=(static_cast&&>(m_GPUSparseMatrix->DiagonalToDense())); + else + diag.m_GPUMatrix = new GPUMatrix(static_cast&&>(m_GPUSparseMatrix->DiagonalToDense())); + diag.SetDataLocation(GPU, DENSE); + } + } + else + { + RuntimeError("Unknown matrix type"); + } + + return diag; + } + + //this function will change the matrix type between DENSE and SPARSE. + //WARNING: The correct implementation is to copy the matrix between DENSE and SPARSE + // However, the conversion functions are not implemented yet and so it will always create + // a new blank matrix and destroy all info in the original matrix if different matrix type is asked. + // In case of !keepValues, the matrix content will be undefined. + template + void Matrix::SwitchToMatrixType(MatrixType newMatrixType, MatrixFormat newMatrixFormat, bool keepValues) + { + // This check should be uncommented but unfortunately there are still places + // this function is being called with incorrect "default" format value + /*if (m_matrixType == newMatrixType && GetFormat() != newMatrixFormat) + NOT_IMPLEMENTED;*/ + + if (m_matrixType == newMatrixType) + return; + + if (m_baseMatrix == nullptr) + keepValues = false; + +#define NUM_MATRIXTYPE_CHANGED_WARN 20 + m_numTimesMatrixTypeChanged++; + + if (m_numTimesMatrixTypeChanged == NUM_MATRIXTYPE_CHANGED_WARN) + fprintf(stderr, "WARNING: The same matrix with dim [%lu, %lu] has been transferred between different devices for %d times.\n", (unsigned long)GetNumRows(), (unsigned long)GetNumCols(), NUM_MATRIXTYPE_CHANGED_WARN); + + if (GetDeviceId()<0) //CPU + { + if (newMatrixType==MatrixType::SPARSE) + { + if (m_baseMatrix == nullptr) + m_CPUSparseMatrix = new CPUSparseMatrix(newMatrixFormat); + else + m_CPUSparseMatrix = new CPUSparseMatrix(newMatrixFormat, GetNumRows(), GetNumCols(), 1); + + if (keepValues) + CopyElementsFromDenseToSparse(*m_CPUMatrix, *m_CPUSparseMatrix); + + delete m_CPUMatrix; + m_CPUMatrix = nullptr; + + SetDataLocation(CPU, SPARSE); + } + else if (newMatrixType==MatrixType::DENSE) + { + if (m_baseMatrix == nullptr) + m_CPUMatrix = new CPUMatrix(); + else + m_CPUMatrix = new CPUMatrix(GetNumRows(), GetNumCols()); + + if (keepValues) + m_CPUMatrix->SetValue(m_CPUSparseMatrix->CopyColumnSliceToDense(0, GetNumCols())); + + delete m_CPUSparseMatrix; + m_CPUSparseMatrix = nullptr; + + SetDataLocation(CPU, DENSE); + } + else + LogicError("SwitchToMatrixType: Unexpected/invalid new matrix type"); + } + else //GPU + { + if (newMatrixType==MatrixType::SPARSE) + { + if (m_baseMatrix == nullptr) + m_GPUSparseMatrix = new GPUSparseMatrix(newMatrixFormat, GetDeviceId()); + else + m_GPUSparseMatrix = new GPUSparseMatrix(GetNumRows(), GetNumCols(), 0, newMatrixFormat, GetDeviceId()); + + if (keepValues) + m_GPUSparseMatrix->SetValue(*m_GPUMatrix); + + delete m_GPUMatrix; + m_GPUMatrix = nullptr; + + SetDataLocation(GPU, SPARSE); + } + else if (newMatrixType==MatrixType::DENSE) + { + if (m_baseMatrix == nullptr) + m_GPUMatrix = new GPUMatrix(GetDeviceId()); + else + m_GPUMatrix = new GPUMatrix(GetNumRows(), GetNumCols(), GetDeviceId()); + + if (keepValues) + m_GPUSparseMatrix->CopyToDenseMatrix(*m_GPUMatrix); + + delete m_GPUSparseMatrix; + m_GPUSparseMatrix = nullptr; + + SetDataLocation(GPU, DENSE); + } + else + LogicError("SwitchToMatrixType: Unexpected/invalid new matrix type"); + } + } + + template + void Matrix::CopyElementsFromDenseToSparse(CPUMatrix& from, CPUSparseMatrix& dest) + { + foreach_coord(row, col, from) + { + auto val = from(row, col); + dest.SetValue(row, col, val); + } + } + + template + ElemType Matrix::Get00Element() const + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->Get00Element(), + return m_GPUMatrix->Get00Element(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + const ElemType Matrix::operator() (const size_t row, const size_t col) const + { + DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, + nullptr, + return m_CPUMatrix->operator()(row,col), + _transferFromDeviceToDevice(GetDeviceId(), CPUDEVICE, false); return m_CPUMatrix->operator()(row,col), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + + //WARNING: This function is very slow for GPUs since it requires copying values between CPUs and GPUs. + //In addition, if ColumnSlice is used after this function but before the values are copied back to GPU + //the operation will fail since the memory is not managed by the slice. + //If you don't need to modify the values, please make sure to call the const version above. + template + ElemType& Matrix::operator() (const size_t row, const size_t col) + { + DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, + nullptr, + return m_CPUMatrix->operator()(row,col), + _transferFromDeviceToDevice(GetDeviceId(), CPUDEVICE, false); SetDataLocation(CPU, DENSE); return m_CPUMatrix->operator()(row,col), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix Matrix::Transpose() + { + if (IsEmpty()) + LogicError("Transpose: Matrix is empty."); + + Matrix c(GetNumCols(), GetNumRows(), (DEVICEID_TYPE)GetDeviceId(), this->GetMatrixType(), this->GetFormat()); + c.AssignTransposeOf(*this); + return c; + } + + template + Matrix& Matrix::AssignTransposeOf (const Matrix& a) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignTransposeOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignTransposeOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignTransposeOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + // set all elements of a matrix to a scalar value + // For sparse matrices, the only allowed value is 0. + template + void Matrix::SetValue(const ElemType v) + { + if (IsEmpty()) // if empty then we are done + return; + + if (v == 0 && GetMatrixType() == MatrixType::SPARSE) // if sparse, setting it to 0 is special + { + Reset(); + return; + } + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetValue(v), + m_GPUMatrix->SetValue(v), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetValue(const DeviceBoundNumber& db_number) + { + if (IsEmpty()) // if empty then we are done + return; + //LogicError("SetValue: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetValue(*db_number.ExposePointer2Value()), + if (GetDeviceId()!=db_number.GetDeviceId()) + RuntimeError("Matrix and device bound number must be on the same device"); + m_GPUMatrix->SetValue(db_number.ExposePointer2Value()), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template<> /*static*/ float Matrix::MakeNan(size_t /*payload*/) { return nanf(""); } + template<> /*static*/ double Matrix::MakeNan(size_t /*payload*/) { return nan(""); } + template<> /*static*/ char Matrix::MakeNan(size_t) { return 0; } // (needed for completeness) + + template + void Matrix::MaskColumnsValue(const Matrix& columnsMask, ElemType val) + { + if (GetNumCols() != columnsMask.GetNumCols()) + RuntimeError("Matrix and column mask must have equal number of columns"); + + if (GetDeviceId() != columnsMask.GetDeviceId()) + RuntimeError("Matrix and column mask must be on the same device"); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->MaskColumnsValue(*columnsMask.m_CPUMatrix, val), + m_GPUMatrix->MaskColumnsValue(*columnsMask.m_GPUMatrix, val), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetColumn(const ElemType* colPointer, size_t colInd) + { + if (colPointer == nullptr) + InvalidArgument("SetColumn: colPointer is null."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetColumn(colPointer,colInd), + m_GPUMatrix->SetColumn(colPointer,colInd), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetColumn(const ElemType val, size_t colInd) + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetColumn(val,colInd), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED); + } + + template + void Matrix::SetColumn(const Matrix& colMat, size_t colInd) + { + DecideAndMoveToRightDevice(*this, colMat); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetColumn(*colMat.m_CPUMatrix,colInd), + m_GPUMatrix->SetColumn(*colMat.m_GPUMatrix, colInd), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED); + } + + + template + void Matrix::SetValue(const Matrix& deepCopyFrom, const MatrixFormat format /*= matrixFormatSparseCSR*/) + { + if (this == &deepCopyFrom) + return; + + m_preferredDeviceId = deepCopyFrom.m_preferredDeviceId; + DecideAndMoveToRightDevice(deepCopyFrom, *this); + SwitchToMatrixType(deepCopyFrom.GetMatrixType(), format, false); + + DISPATCH_MATRIX_ON_FLAG(&deepCopyFrom, + this, + m_CPUMatrix->SetValue(*deepCopyFrom.m_CPUMatrix), + m_GPUMatrix->SetValue(*deepCopyFrom.m_GPUMatrix), + m_CPUSparseMatrix->SetValue(*deepCopyFrom.m_CPUSparseMatrix), + m_GPUSparseMatrix->SetValue(*deepCopyFrom.m_GPUSparseMatrix) + ); + } + + + template + void Matrix::SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags) + { + if (((numRows * numCols) > 0) && (pArray == nullptr)) + InvalidArgument("Invalid pArray."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetValue(numRows,numCols,pArray,matrixFlags), + m_GPUMatrix->SetValue(numRows,numCols,deviceId,pArray, matrixFlags), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetValue(const size_t rIdx, const size_t cIdx, ElemType val) + { + DISPATCH_MATRIX_ON_FLAG_USECPU_4BOTH(this, + this, + (*m_CPUMatrix)(rIdx, cIdx) = val, + NOT_IMPLEMENTED, + m_CPUSparseMatrix->SetValue(rIdx,cIdx,val), + NOT_IMPLEMENTED + ); + } + + // read features + template + void Matrix::SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, + const size_t nz, const size_t numRows, const size_t numCols) + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + m_CPUSparseMatrix->SetMatrixFromCSCFormat(h_CSCCol, h_Row, h_Val, nz, numRows, numCols), + m_GPUSparseMatrix->SetMatrixFromCSCFormat(h_CSCCol, h_Row, h_Val, nz, numRows, numCols) + ); + } + + template + void Matrix::SetDiagonalValue(const ElemType v) + { + if (IsEmpty()) + LogicError("SetDiagonalValue: Matrix is empty."); + + if (GetNumRows() != GetNumCols()) + LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetDiagonalValue(v), + m_GPUMatrix->SetDiagonalValue(v), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetDiagonalValue(const Matrix& vector) + { + if (IsEmpty() || vector.IsEmpty()) + LogicError("SetDiagonalValue: Matrix is empty."); + + if (GetNumRows() != GetNumCols()) + LogicError("SetDiagonalValue: NumRows and NumCols do not agree."); + + if (vector.GetNumRows() != 1 && vector.GetNumCols() != 1) + LogicError("SetDiagonalValue: input vector must be a vector."); + + DecideAndMoveToRightDevice(*this, vector); + + if (vector.GetNumElements() == 1) //reduce to simple form + { + DISPATCH_MATRIX_ON_FLAG(&vector, + nullptr, + SetDiagonalValue(vector(0,0)), + SetDiagonalValue(vector.m_GPUMatrix->Get00Element()), // BUGBUG: efficiency + SetDiagonalValue(vector(0,0)), + SetDiagonalValue(vector.m_GPUMatrix->Get00Element()) // BUGBUG: efficiency + ); + } + else if (vector.GetNumRows() != GetNumRows()) + LogicError("SetDiagonalValue: input vector's dimension does not agree with [this]."); + else + { + //WARNING: we use this pointer to decide which function to call. However, vector may be stored in a different matrix type (DENSE, SPARSE) + DISPATCH_MATRIX_ON_FLAG(this, + this, + assert(vector.m_CPUMatrix != nullptr); m_CPUMatrix->SetDiagonalValue(*vector.m_CPUMatrix), + assert(vector.m_GPUMatrix != nullptr); m_GPUMatrix->SetDiagonalValue(*vector.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + } + + template + void Matrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) + { + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetUniformRandomValue(low,high,seed), + m_GPUMatrix->SetUniformRandomValue(low,high,seed), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) + { + if (sigma <= 0) + InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); + + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetGaussianRandomValue(mean, sigma, seed), + m_GPUMatrix->SetGaussianRandomValue(mean, sigma, seed), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) + { + if (sigma <= 0) + InvalidArgument("SetUniformRandomValue: sigma must be a positive value."); + + if (IsEmpty()) + LogicError("SetUniformRandomValue: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddGaussianRandomValue(mean, sigma, seed), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + //maskRate: percentage of values masked out (similar to dropout rate) + //scaleValue: which scale value to set to the left ones (unmasked items). + template + void Matrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) + { + if (IsEmpty()) + LogicError("SetUniformRandomMask: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetUniformRandomMask(maskRate,scaleValue,seed), + m_GPUMatrix->SetUniformRandomMask(maskRate,scaleValue,seed), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::NormalGrad(Matrix& gradients, + Matrix& functionValues, + const ElemType learnRatePerSample, + const ElemType momentum, + const bool useNesterovMomentum + ) + { + DecideAndMoveToRightDevice(*this, gradients, functionValues); + + if (!useNesterovMomentum) + { + DISPATCH_MATRIX_ON_FLAG(&gradients, + nullptr, + ScaleAndAdd((1-momentum) * learnRatePerSample, gradients, momentum, *this); functionValues -= *this, + ScaleAndAdd((1-momentum) * learnRatePerSample, gradients, momentum, *this); functionValues -= *this, + if (momentum != 0) gradients.m_CPUSparseMatrix->NormalGrad(*m_CPUMatrix, momentum); ScaleAndAdd(-learnRatePerSample, gradients, functionValues), + if (momentum != 0) gradients.m_GPUSparseMatrix->NormalGrad(*m_GPUMatrix, momentum); ScaleAndAdd(-learnRatePerSample, gradients, functionValues) + ); + } + else + { + DISPATCH_MATRIX_ON_FLAG(&gradients, + nullptr, + {/* CPU dense */ + ScaleAndAdd((1 - momentum) * learnRatePerSample, gradients, momentum, *this); + ScaleAndAdd(-momentum, *this, functionValues); + ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradients, functionValues); + // w_t = w_{t-1} - momentum * v_ {t-1} - (1-momentum)*learnRatePerSampele*gardient, + }, + {/* GPU dense */ + ScaleAndAdd((1 - momentum) * learnRatePerSample, gradients, momentum, *this); + ScaleAndAdd(-momentum, *this, functionValues); + ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradients, functionValues); + }, + { /* CPU sparse */ + if (momentum != 0) + { + Matrix gradientCache(gradients.GetDeviceId()); + gradientCache.SetValue(gradients); + gradients.m_CPUSparseMatrix->NormalGrad(*m_CPUMatrix, momentum); + ScaleAndAdd(-momentum, *this, functionValues); + ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradientCache, functionValues); + } + }, + { /* GPU sparse */ + if (momentum != 0) + { + Matrix gradientCache(gradients.GetDeviceId()); + gradientCache.SetValue(gradients); + gradients.m_GPUSparseMatrix->NormalGrad(*m_GPUMatrix, momentum); + ScaleAndAdd(-momentum, *this, functionValues); + ScaleAndAdd(-(1 - momentum)*learnRatePerSample, gradientCache, functionValues); + } + } + ); + } + } + + //both this and gradients will be changed + template + ElemType Matrix::Adagrad(Matrix& gradients, const bool needAveMultiplier) + { + DecideAndMoveToRightDevice(*this, gradients); + + DISPATCH_MATRIX_ON_FLAG(&gradients, + &gradients, + return m_CPUMatrix->Adagrad(*gradients.m_CPUMatrix, needAveMultiplier); SetDataLocation(CPU), + return m_GPUMatrix->Adagrad(*gradients.m_GPUMatrix, needAveMultiplier); SetDataLocation(GPU), + return gradients.m_CPUSparseMatrix->Adagrad(*m_CPUMatrix, needAveMultiplier); SetDataLocation(CPU), + return gradients.m_GPUSparseMatrix->Adagrad(*m_GPUMatrix, needAveMultiplier); SetDataLocation(GPU) + ); + } + + template + void Matrix::FSAdagrad(size_t mbSize, Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum) + { + // TODO: The values of 'adagradT' and 'targetadagradavdenom' are currently hardcoded constants taken from DBN (empirically determined). + // These should be made configurable if needed + const size_t adagradT = 2 * 3600 * 100; + const ElemType targetadagradavdenom = 0.0025; // 1/400 magic constant + const ElemType adagradkeepweight = static_cast(exp(-1.0 * mbSize / adagradT)); + + static ElemType aggadagradsqrframes = 0; + aggadagradsqrframes = adagradkeepweight * aggadagradsqrframes + (1.0f - adagradkeepweight) * mbSize; + const ElemType targetadagradavdenom_x_sqrtadagradsqrframes = static_cast(targetadagradavdenom * sqrt(aggadagradsqrframes)); + + DISPATCH_MATRIX_ON_FLAG(&gradients, + &gradients, + m_CPUMatrix->FSAdagrad(*gradients.m_CPUMatrix, *functionValues.m_CPUMatrix, learnRatePerSample, momentum, adagradkeepweight, targetadagradavdenom_x_sqrtadagradsqrframes); SetDataLocation(CPU), + m_GPUMatrix->FSAdagrad(*gradients.m_GPUMatrix, *functionValues.m_GPUMatrix, learnRatePerSample, momentum, adagradkeepweight, targetadagradavdenom_x_sqrtadagradsqrframes); SetDataLocation(GPU), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + ElemType Matrix::RmsProp(Matrix& gradients, + ElemType RMS_GAMMA, + ElemType RMS_WGT_INC, + ElemType RMS_WGT_MAX, + ElemType RMS_WGT_DEC, + ElemType RMS_WGT_MIN, + const bool needAveMultiplier + ) + { + DecideAndMoveToRightDevice(*this, gradients); + + DISPATCH_MATRIX_ON_FLAG(this, + &gradients, + return m_CPUMatrix->RmsProp(*gradients.m_CPUMatrix, RMS_GAMMA, RMS_WGT_INC, RMS_WGT_MAX, RMS_WGT_DEC, RMS_WGT_MIN, needAveMultiplier); SetDataLocation(CPU), + return m_GPUMatrix->RmsProp(*gradients.m_GPUMatrix, RMS_GAMMA, RMS_WGT_INC, RMS_WGT_MAX, RMS_WGT_DEC, RMS_WGT_MIN, needAveMultiplier); SetDataLocation(GPU), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::Reshape(const size_t numRows, const size_t numCols) + { + if (numRows != GetNumRows() || numCols != GetNumCols()) + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->Reshape(numRows, numCols), + m_GPUMatrix->Reshape(numRows, numCols), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->Reshape(numRows, numCols) + ); + } + } + + // Note: Resize() will leave the matrix content undefined. + template + void Matrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve /*=0*/, bool growOnly /*=true*/) + { + // TODO: should this function test whether the size is changing, and skip if it isn't? We have at least one explicit test for this code calling this (recurrent node) + DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(this, + this, + m_CPUMatrix->Resize(numRows, numCols, growOnly), + m_GPUMatrix->Resize(numRows, numCols, growOnly), + m_CPUSparseMatrix->Resize(numRows, numCols, numNZElemToReserve, growOnly, false), + m_GPUSparseMatrix->Resize(numRows, numCols, numNZElemToReserve, growOnly, false) + ); +#ifdef _DEBUG + if (GetMatrixType() != MatrixType::SPARSE) + Invalidate(); // Fill the matrix with NaNs to detect using the content which is undefined. Unfortunately this won't work for sparse matrices. +#endif + } + + template + Matrix Matrix::RepMat(const Matrix& frmMat, const size_t rowRatio, const size_t colRatio) + { + size_t nCols = frmMat.GetNumCols(); + size_t nRows = frmMat.GetNumRows(); + + if (rowRatio > 1) + RuntimeError("RepMat not yet supporting raw ratio larger than 1"); + size_t newCols = colRatio * nCols; + + Matrix c(nRows, newCols, frmMat.GetDeviceId()); + for (size_t i = 0; i < colRatio; i++) + { + c.ColumnSlice(i * nCols, nCols) = frmMat; + } + + return c; + } + + template + size_t Matrix::GetAllocatedSize() const + { + return m_baseMatrix->GetSizeAllocated(); + } + + // reset for sparse matrix. Semantically the same as setting all values to 0. + template + void Matrix::Reset() + { + DISPATCH_MATRIX_ON_FLAG_USEBOTH_4BOTH(this, + this, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + m_CPUSparseMatrix->Reset(), + m_GPUSparseMatrix->Reset() + ); + } + + template + size_t Matrix::GetNumRows() const + { + return m_baseMatrix->GetNumRows(); + } + + template + size_t Matrix::GetNumCols() const + { + return m_baseMatrix->GetNumCols(); + } + + template + size_t Matrix::GetNumElements() const + { + return GetNumRows()*GetNumCols(); + } + + template + bool Matrix::IsEmpty() const + { + return m_baseMatrix->IsEmpty(); + } + +#pragma endregion Basic Operators + +#pragma region Member BLAS Functions + + template + Matrix& Matrix::operator+= (ElemType alpha) + { + return AssignSumOf(alpha, *this); + } + + template + Matrix Matrix::operator+ (ElemType alpha) const + { + Matrix c(GetNumRows(), GetNumCols()); + c.AssignSumOf(alpha, *this); + return c; + } + + template + Matrix& Matrix::AssignSumOf(const ElemType alpha, const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSumOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSumOf(alpha,*a.m_CPUMatrix), + m_GPUMatrix->AssignSumOf(alpha,*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + + //if [this] and a have same dimension then [this]=[this]+a + //if a is a column vector, add to all columns of [this] + //if a is a row vector, add to all rows of [this] + //if a is a scalar, add it to all elements. + template + Matrix& Matrix::operator+= (const Matrix& a) + { + DecideAndMoveToRightDevice(*this, a); + + if(!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->operator+=(*a.m_CPUMatrix), + m_GPUMatrix->operator+=(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED); + + return *this; + } + + //if [this] and a have same dimension then OUTPUT=[this]+a + //if a is a column vector, add to all columns of [this] + //if a is a row vector, add to all rows of [this] + template + Matrix Matrix::operator+ (const Matrix& a) const + { + if (GetNumElements() == 1) + { + Matrix c(a); + + DISPATCH_MATRIX_ON_FLAG(this, + &c, + c += (*this)(0,0), + c += (m_GPUMatrix->Get00Element()), // BUGBUG: efficiency + c += (*this)(0,0), + NOT_IMPLEMENTED + ); + return c; + } + else if (a.GetNumElements() == 1) + { + Matrix c(*this); + + DISPATCH_MATRIX_ON_FLAG(&a, + &c, + c += a(0,0), + c += (a.m_GPUMatrix->Get00Element()), // BUGBUG: efficiency + c += a(0,0), + NOT_IMPLEMENTED + ); + return c; + } + else + { + Matrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code + c += a; + return c; + } + } + + template + Matrix& Matrix::AssignSumOf(const Matrix& a, const Matrix& b) + { + if (a.GetNumElements() == 1) + { + SetValue(b); + (*this) += a; + } + else + { + SetValue(a); + (*this) += b; + } + return *this; + } + + template + Matrix& Matrix::operator-= (ElemType alpha) + { + return AssignDifferenceOf(*this, alpha); + } + + template + Matrix Matrix::operator- (ElemType alpha) const + { + Matrix c(GetNumRows(), GetNumCols()); + c.AssignDifferenceOf(*this, alpha); + return c; + } + + //for each column of a, we assign numRows starting from startIndex to this + template + Matrix& Matrix::AssignRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), + m_GPUMatrix->AssignRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + return *this; + } + + //for each column of a, we assign all rows of a to this starting from startIndex + template + Matrix& Matrix::AssignToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignToRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), + m_GPUMatrix->AssignToRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //for the row slice of this starting from startIndex we add a to it. + template + Matrix& Matrix::AddToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddToRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), + m_GPUMatrix->AddToRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //for each column of this, we add row slice of a starting from startIndex + template + Matrix& Matrix::AddWithRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (! (GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddWithRowSliceValuesOf(*a.m_CPUMatrix, startIndex, numRows), + m_GPUMatrix->AddWithRowSliceValuesOf(*a.m_GPUMatrix, startIndex, numRows), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + +#if 0 // no longer needed, and overkill anyway as it can be implemented as a bunch of calls to AssignRowSliceValuesOf() + //stack the columns in inputMatrices (starting from sliceStartCol for sliceNumCols columns) and assign it to [this] object. + template + Matrix& Matrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) + { + for (int i = 0; i < inputMatrices.size(); i++) + { + const Matrix& a = *inputMatrices[i]; + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + } + + CurrentDataLocation curLocation = GetCurrentMatrixLocation(); + if (curLocation == CurrentDataLocation::GPU || curLocation == CurrentDataLocation::BOTH) + { + if (GetMatrixType() != MatrixType::SPARSE) + { + //GPUDense; + std::vector*> gpuInputMatrices; + gpuInputMatrices.resize(inputMatrices.size()); + for (int i = 0; i < inputMatrices.size(); i++) + gpuInputMatrices[i] = inputMatrices[i]->m_GPUMatrix; + + m_GPUMatrix->AssignRowStackValuesOf(gpuInputMatrices, sliceStartCol, sliceNumCols); + + SetDataLocation(CurrentDataLocation::GPU, MatrixType::DENSE); + } + else + { + NOT_IMPLEMENTED; + } + } + else if (curLocation == CurrentDataLocation::CPU) + { + if (GetMatrixType() != MatrixType::SPARSE) + { + //CPUDense; + std::vector*> cpuInputMatrices; + cpuInputMatrices.resize(inputMatrices.size()); + for (int i = 0; i < inputMatrices.size(); i++) + cpuInputMatrices[i] = inputMatrices[i]->m_CPUMatrix; + + m_CPUMatrix->AssignRowStackValuesOf(cpuInputMatrices, sliceStartCol, sliceNumCols); + + SetDataLocation(CurrentDataLocation::CPU, MatrixType::DENSE); + } + else + { + NOT_IMPLEMENTED; + } + } + else + { + RuntimeError("Matrices do not exist in either CPU or GPU."); + } + + return *this; + } +#endif + + template + Matrix& Matrix::AssignRepeatOf(const Matrix& a, const size_t numRowRepeats, const size_t numColRepeats) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignRepeatOf(*a.m_CPUMatrix, numRowRepeats, numColRepeats), + m_GPUMatrix->AssignRepeatOf(*a.m_GPUMatrix, numRowRepeats, numColRepeats), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AddToRowRepeatValuesOf(const Matrix& a, const size_t numRepeats) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddToRowRepeatValuesOf(*a.m_CPUMatrix, numRepeats), + m_GPUMatrix->AddToRowRepeatValuesOf(*a.m_GPUMatrix, numRepeats), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //used in the DSSM model. The resulted *this is a [a.GetRows()*(negNumber+1), a.GetCols()] matrix + //each column contains posNumber of positive samples (original) and negNumber negative samples generated by copying + //sample shifted by shiftNumber columns + template + Matrix& Matrix::AssignPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignPositiveAndShiftedNegSample(*a.m_CPUMatrix, posNumber, negNumber, shiftNumber), + m_GPUMatrix->AssignPositiveAndShiftedNegSample(*a.m_GPUMatrix, posNumber, negNumber, shiftNumber), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //used in the DSSM model. *this = *this + positive and negative samples folded back to the right place + //each column of a contains posNumber of positive samples (original) and negNumber negative samples generated by copying + //sample shifted by shiftNumber columns + template + Matrix& Matrix::AddFoldedPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) + { + DecideAndMoveToRightDevice(*this, a); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddFoldedPositiveAndShiftedNegSample(*a.m_CPUMatrix, posNumber, negNumber, shiftNumber), + m_GPUMatrix->AddFoldedPositiveAndShiftedNegSample(*a.m_GPUMatrix, posNumber, negNumber, shiftNumber), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignDifferenceOf(const ElemType alpha, const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignDifferenceOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignDifferenceOf(alpha,*a.m_CPUMatrix), + m_GPUMatrix->AssignDifferenceOf(alpha,*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignDifferenceOf(const Matrix& a, const ElemType alpha) + { + if (a.IsEmpty()) + LogicError("AssignDifferenceOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignDifferenceOf(*a.m_CPUMatrix, alpha), + m_GPUMatrix->AssignDifferenceOf(*a.m_GPUMatrix, alpha), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //if [this] and a have same dimension then [this]=[this]-a + //if a is a column vector, minus it from all columns of [this] + //if a is a row vector, minus it from all rows of [this] + template + Matrix& Matrix::operator-= (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("Minus Operation: Matrix a is empty."); + DecideAndMoveToRightDevice(*this, a); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + *m_CPUMatrix -= *a.m_CPUMatrix, + *m_GPUMatrix -= *a.m_GPUMatrix, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //if [this] and a have same dimension then output=[this]-a + //if a is a column vector, minus it from all columns of [this] + //if a is a row vector, minus it from all rows of [this] + template + Matrix Matrix::operator- (const Matrix& a) const + { + Matrix c(*this); //this implementation will introduce a copy overhead. but make resue of the code + ScaleAndAdd(-1,a,c); + return c; + } + + template + Matrix& Matrix::AssignDifferenceOf(const Matrix& a, const Matrix& b) + { + // if first arg broadcasts, we swap first and the flip the sign + // This is because there is no equivalent to operator-=() that works the other way round. + // TODO: We need ternary ops where the output storage is separate. + if (a.GetNumRows() < b.GetNumRows() || a.GetNumCols() < b.GetNumCols()) + { + if (a.GetNumRows() > b.GetNumRows() || a.GetNumCols() > b.GetNumCols()) + LogicError("AssignDifferenceOf: Invalid dimensions."); + AssignDifferenceOf(b, a); + *this *= -1; + return *this; + } + if (this != &a) + SetValue(a); + (*this) -= b; + return *this; + } + + template + Matrix& Matrix::operator*= (ElemType alpha) + { + Scale(alpha, *this); + return *this; + } + + template + Matrix Matrix::operator* (ElemType alpha) const + { + Matrix c(GetNumRows(), GetNumCols(), (DEVICEID_TYPE)m_preferredDeviceId); + Scale(alpha, *this, c); + return c; + } + + template + Matrix& Matrix::AssignProductOf(const ElemType alpha, const Matrix& a) + { + Scale(alpha, a, *this); + return *this; + } + + // [this]=a*b + template + Matrix& Matrix::AssignProductOf (const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB) + { + if (a.GetNumElements() == 1) + { + if (transposeB) + (*this)=AssignTransposeOf(b); + else + (*this)=b; + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + (*this) *= a(0,0), + (*this) *= a.m_GPUMatrix->Get00Element(), + (*this) *= a(0,0), + NOT_IMPLEMENTED + ); + } + else if (b.GetNumElements() == 1) + { + if (transposeA) + (*this)=AssignTransposeOf(a); + else + (*this)=a; + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + (*this) *= b(0,0), + (*this) *= b.m_GPUMatrix->Get00Element(), + (*this) *= b(0,0) , + NOT_IMPLEMENTED + ); + } + else + Multiply(a, transposeA, b, transposeB, *this); + + return *this; + } + + template + Matrix Matrix::operator* (const Matrix& a) const + { + if (GetNumElements() == 1) + { + Matrix c((DEVICEID_TYPE)a.GetPreferredDeviceId()); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + c.AssignProductOf((*this)(0,0), a), + c.AssignProductOf(m_GPUMatrix->Get00Element(), a), // BUGBUG: efficiency + c.AssignProductOf((*this)(0,0), a), + NOT_IMPLEMENTED + ); + + return c; + } + else if (a.GetNumElements() == 1) + { + Matrix c((DEVICEID_TYPE)GetPreferredDeviceId()); + + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + c.AssignProductOf(a(0,0), (*this)), + c.AssignProductOf(a.m_GPUMatrix->Get00Element(), (*this)), // BUGBUG: efficiency + c.AssignProductOf(a(0,0), (*this)), + NOT_IMPLEMENTED + ); + + return c; + } + else + { + Matrix c(GetNumRows(), a.GetNumCols(), (DEVICEID_TYPE)GetPreferredDeviceId()); + Multiply(*this, a, c); + return c; + } + } + + // [this]=a*b where a is a 1x1 scalar + template + Matrix& Matrix::Assign1x1ProductOf (const Matrix& a, const Matrix& b) + { + Multiply1x1AndWeightedAdd(+1, a, b, 0.0f, *this); + return *this; + } + + template + Matrix& Matrix::operator/= (ElemType alpha) + { + (*this) *= 1/alpha; + return (*this); + } + + template + Matrix Matrix::operator/ (ElemType alpha) const + { + return ((*this) * (1/alpha)); + } + + //element-wise power + template + Matrix& Matrix::operator^= (ElemType alpha) + { + auto& us = *this; + ElementWisePower(alpha, us, us); + return us; + } + + //element-wise power + template + Matrix Matrix::operator^ (ElemType alpha) const + { + Matrix c(GetNumRows(), GetNumCols(), (DEVICEID_TYPE)GetDeviceId()); + ElementWisePower(alpha, *this, c); + return c; + } + + + template + Matrix& Matrix::AssignElementPowerOf(const Matrix& a, const ElemType power) + { + ElementWisePower(power, a, *this); + return *this; + } + + //[this]=[this] .* a (we cannot override operator .* in c++) + template + Matrix& Matrix::ElementMultiplyWith (const Matrix& a) + { + return AssignElementProductOf(*this, a); + } + + template + Matrix& Matrix::ElementDivideBy(const Matrix& a) + { + return AssignElementDivisionOf(*this, a); + } + + //[this]=a .* b + template + Matrix& Matrix::AssignElementProductOf (const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("The input matrix dimensions do not match."); + + DecideAndMoveToRightDevice(a, b, *this); + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignElementProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), + m_GPUMatrix->AssignElementProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AddElementProductOf (const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AddElementProductOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("The input matrix dimensions do not match."); + + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == GetNumCols())) + InvalidArgument("The input matrix dimensions do not match [this]."); + + DecideAndMoveToRightDevice(*this, a, b); + + if (! (a.GetMatrixType() == b.GetMatrixType() && GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + m_CPUMatrix->AddElementProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), + m_GPUMatrix->AddElementProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //[this]=a ./ b + template + Matrix& Matrix::AssignElementDivisionOf (const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementDivisionOf: Matrix is empty."); + + assert (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("The input matrix dimensions do not match."); + + DecideAndMoveToRightDevice(a, b, *this); + //WARNING: a and b must have same type + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignElementDivisionOf(*a.m_CPUMatrix,*b.m_CPUMatrix), + m_GPUMatrix->AssignElementDivisionOf(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + + template + Matrix& Matrix::ColumnElementMultiplyWith(const Matrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("ColumnElementMultiplyWith: Matrix is empty."); + + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) + InvalidArgument("ColumnElementMultiplyWith: The input matrix should be a col vector and match [this]'s rows."); + + DecideAndMoveToRightDevice(*this, a); + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->ColumnElementMultiplyWith(*a.m_CPUMatrix), + m_GPUMatrix->ColumnElementMultiplyWith(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::RowElementMultiplyWith(const Matrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("RowElementMultiplyWith: Matrix is empty."); + + if (!(a.GetNumCols() == GetNumCols() && a.GetNumRows() == 1)) + InvalidArgument("RowElementMultiplyWith: The input matrix should be a row vector and match [this]'s columns."); + + //WARNING: a and this must have same type + if (! (GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->RowElementMultiplyWith(*a.m_CPUMatrix), + m_GPUMatrix->RowElementMultiplyWith(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::RowElementDivideBy(const Matrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("RowElementDivideBy: Matrix is empty."); + + if (!(a.GetNumCols() == GetNumCols() && a.GetNumRows() == 1)) + InvalidArgument("RowElementDivideBy: The input matrix should be a row vector and match [this]'s columns."); + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->RowElementDivideBy(*a.m_CPUMatrix), + m_GPUMatrix->RowElementDivideBy(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + + template + Matrix& Matrix::ColumnElementDivideBy(const Matrix& a) + { + if (a.IsEmpty() || IsEmpty()) + LogicError("ColumnElementDivideBy: Matrix is empty."); + + if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1)) + InvalidArgument("ColumnElementDivideBy: The input matrix should be a col vector and match [this]'s rows."); + + DecideAndMoveToRightDevice(*this, a); + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->ColumnElementDivideBy(*a.m_CPUMatrix), + m_GPUMatrix->ColumnElementDivideBy(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + + //[this]=1 ./ a + template + Matrix& Matrix::ElementInverse () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->ElementInverse(), + m_GPUMatrix->ElementInverse(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->ElementInverse() + ); + + return (*this); + } + + template + Matrix& Matrix::AssignElementInverseOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignElementInverseOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignElementInverseOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignElementInverseOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignElementInverseOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + template + Matrix& Matrix::InplaceSigmoid () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceSigmoid(), + m_GPUMatrix->InplaceSigmoid(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceSigmoid() + ); + + return (*this); + } + + template + Matrix& Matrix::AssignSigmoidOf (const Matrix& a) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSigmoidOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignSigmoidOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignSigmoidOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=sigmoid([this]) element wise + template + Matrix& Matrix::InplaceLinearRectifierDerivative () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceLinearRectifierDerivative(), + m_GPUMatrix->InplaceLinearRectifierDerivative(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceLinearRectifierDerivative() + ); + + return (*this); + } + + template + Matrix& Matrix::AssignLinearRectifierDerivativeOf (const Matrix& a) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignLinearRectifierDerivativeOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignLinearRectifierDerivativeOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignLinearRectifierDerivativeOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=sigmoid([this]) element wise + template + Matrix& Matrix::InplaceSigmoidDerivative () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceSigmoidDerivative(), + m_GPUMatrix->InplaceSigmoidDerivative(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return (*this); + } + + template + Matrix& Matrix::AssignSigmoidDerivativeOf (const Matrix& a) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSigmoidDerivativeOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignSigmoidDerivativeOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignNumOfDiff (const Matrix& a, const Matrix& b, bool searchInCol) + { + DecideAndMoveToRightDevice(a, b, *this); + //WARNING: a and b must have same type + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignNumOfDiff(*a.m_CPUMatrix, *b.m_CPUMatrix, searchInCol), + m_GPUMatrix->AssignNumOfDiff(*a.m_GPUMatrix, *b.m_GPUMatrix, searchInCol), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + //[this]=tanh([this]) element wise + template + Matrix& Matrix::InplaceTanh () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceTanh(), + m_GPUMatrix->InplaceTanh(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceTanh() + ); + + return (*this); + } + + template + Matrix& Matrix::AssignTanhOf (const Matrix& a) + { + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignTanhOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignTanhOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignTanhOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=softmax([this]) element wise + template + Matrix& Matrix::InplaceLogSoftmax (const bool isColWise) + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceLogSoftmax(isColWise), + m_GPUMatrix->InplaceLogSoftmax(isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignLogSoftmaxOf (const Matrix& a, const bool isColWise) + { + if (a.IsEmpty()) + LogicError("AssignLogSoftmaxOf: Matrix a is empty."); + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignLogSoftmaxOf(*a.m_CPUMatrix,isColWise), + m_GPUMatrix->AssignLogSoftmaxOf(*a.m_GPUMatrix,isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //[this]=softmax([this]) element wise + template + Matrix& Matrix::InplaceHardmax(const bool isColWise) + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceHardmax(isColWise), + m_GPUMatrix->InplaceHardmax(isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignHardmaxOf(const Matrix& a, const bool isColWise) + { + if (a.IsEmpty()) + LogicError("AssignHardmaxOf: Matrix a is empty."); + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignHardmaxOf(*a.m_CPUMatrix, isColWise), + m_GPUMatrix->AssignHardmaxOf(*a.m_GPUMatrix, isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::InplaceSqrt () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceSqrt(), + m_GPUMatrix->InplaceSqrt(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceSqrt() + ); + + return *this; + } + + template + Matrix& Matrix::AssignSqrtOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSqrtOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSqrtOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignSqrtOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignSqrtOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=exp([this]) element wise + template + Matrix& Matrix::InplaceExp () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceExp(), + m_GPUMatrix->InplaceExp(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceExp() + ); + + return *this; + } + + + template + Matrix& Matrix::AssignExpOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignExpOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignExpOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignExpOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignExpOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=exp([this]) element wise + template + Matrix& Matrix::InplaceAbs () + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + m_CPUMatrix->InplaceAbs(), + m_GPUMatrix->InplaceAbs(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceAbs() + ); + + return *this; + } + + template + Matrix& Matrix::AssignAbsOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignAbsOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignAbsOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignAbsOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignAbsOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=log([this]) element wise + template + Matrix& Matrix::InplaceLog () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceLog(), + m_GPUMatrix->InplaceLog(), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceLog() + ); + + return *this; + } + + //[this]=log([this]) element wise + template + Matrix& Matrix::InplaceLog10 () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceLog10(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignLogOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignLogOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignLogOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignLogOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignLogOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + template + Matrix& Matrix::AssignLog10Of (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignLogOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignLog10Of(*a.m_CPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignLogOf(*a.m_GPUSparseMatrix) + ); + + return *this; + } + + //[this]=cos([this]) element wise + template + Matrix& Matrix::InplaceCosine () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceCosine(), + m_GPUMatrix->InplaceCosine(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignCosineOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignCosineOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignCosineOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignCosineOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //[this]= -sin([this]) element wise + template + Matrix& Matrix::InplaceNegativeSine () + { + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceNegativeSine(), + m_GPUMatrix->InplaceNegativeSine(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignNegativeSineOf (const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignNegativeSineOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignNegativeSineOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignNegativeSineOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::InplaceTruncate(const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncate: Matrix is empty."); + + if (sizeof(ElemType)==sizeof(float)) + { + if (!isfinite((float)threshold)) + return *this; + } + else + { + if (!isfinite(threshold)) + return *this; + } + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceTruncate(threshold), + m_GPUMatrix->InplaceTruncate(threshold), + m_CPUSparseMatrix->InplaceTruncate(threshold), + m_GPUSparseMatrix->InplaceTruncate(threshold) + ); + + return *this; + } + + template + void Matrix::InplaceTranspose() + { + if (IsEmpty()) + return; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + m_GPUSparseMatrix->InplaceTranspose() + ); + } + + template + Matrix& Matrix::InplaceSoftThreshold(const ElemType threshold) + { + assert(threshold >= 0); + + if (IsEmpty()) + LogicError("InplaceSoftThreshold: Matrix is empty."); + + if (threshold == 0) + return *this; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceSoftThreshold(threshold), + m_GPUMatrix->InplaceSoftThreshold(threshold), + m_CPUSparseMatrix->InplaceSoftThreshold(threshold), + m_GPUSparseMatrix->InplaceSoftThreshold(threshold) + ); + + return *this; + } + //Threshold truncating: this[i] = max( this[i], threshold ) + template + Matrix& Matrix::InplaceTruncateBottom (const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncateBottom: Matrix is empty."); + + if (sizeof(ElemType)==sizeof(float)) + { + if (!isfinite((float)threshold)) + return *this; + } + else + { + if (!isfinite(threshold)) + return *this; + } + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceTruncateBottom(threshold), + m_GPUMatrix->InplaceTruncateBottom(threshold), + m_CPUSparseMatrix->InplaceTruncateBottom(threshold), + m_GPUSparseMatrix->InplaceTruncateBottom(threshold) + ); + + return *this; + } + + //Threshold truncating: this[i] = max( a[i], threshold ) + template + Matrix& Matrix::AssignTruncateBottomOf (const Matrix& a, const ElemType threshold) + { + if (a.IsEmpty()) + LogicError("AssignTruncateBottomOf: Matrix a is empty."); + + if (sizeof(ElemType)==sizeof(float)) + { + if (!isfinite((float)threshold)) + { + (*this) = a; + return *this; + } + } + else + { + if (!isfinite(threshold)) + { + (*this) = a; + return *this; + } + } + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignTruncateBottomOf(*a.m_CPUMatrix, threshold), + m_GPUMatrix->AssignTruncateBottomOf(*a.m_GPUMatrix, threshold), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignTruncateBottomOf(*a.m_GPUSparseMatrix, threshold) + ); + + return *this; + } + + //Threshold truncating: this[i] = min( this[i], threshold ) + template + Matrix& Matrix::InplaceTruncateTop (const ElemType threshold) + { + if (IsEmpty()) + LogicError("InplaceTruncateTop: Matrix is empty."); + + if (sizeof(ElemType)==sizeof(float)) + { + if (!isfinite((float)threshold)) + return *this; + } + else + { + if (!isfinite(threshold)) + return *this; + } + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->InplaceTruncateTop(threshold), + m_GPUMatrix->InplaceTruncateTop(threshold), + m_CPUSparseMatrix->InplaceTruncateTop(threshold), + m_GPUSparseMatrix->InplaceTruncateTop(threshold) + ); + + return *this; + } + //Threshold truncating: this[i] = min( a[i], threshold ) + template + Matrix& Matrix::AssignTruncateTopOf (const Matrix& a, const ElemType threshold) + { + if (a.IsEmpty()) + LogicError("AssignTruncateTopOf: Matrix a is empty."); + + if (sizeof(ElemType)==sizeof(float)) + { + if (!isfinite((float)threshold)) + { + (*this) = a; + return *this; + } + } + else + { + if (!isfinite(threshold)) + { + (*this) = a; + return *this; + } + } + + DecideAndMoveToRightDevice(a, *this); + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignTruncateTopOf(*a.m_CPUMatrix, threshold), + m_GPUMatrix->AssignTruncateTopOf(*a.m_GPUMatrix, threshold), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->AssignTruncateTopOf(*a.m_GPUSparseMatrix, threshold) + ); + + return *this; + } + + //Threshold truncating: this[i] = 0 if abs(this[i] + Matrix& Matrix::SetToZeroIfAbsLessThan (const ElemType threshold) + { + if (IsEmpty()) + LogicError("SetToZeroIfAbsLessThan: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->SetToZeroIfAbsLessThan(threshold), + m_GPUMatrix->SetToZeroIfAbsLessThan(threshold), + NOT_IMPLEMENTED, + m_GPUSparseMatrix->SetToZeroIfAbsLessThan(threshold) + ); + + return *this; + } + + //sum of all elements + template + ElemType Matrix::SumOfElements () const + { + if (IsEmpty()) + LogicError("SumOfElements: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->SumOfElements(), + return m_GPUMatrix->SumOfElements(), + return m_CPUSparseMatrix->SumOfElements(), + return m_GPUSparseMatrix->SumOfElements() + ); + } + + template + Matrix& Matrix::AssignSumOfElements(const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSumOfElements: Matrix a is empty."); + + // WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSumOfElements(*a.m_CPUMatrix), + m_GPUMatrix->AssignSumOfElements(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + DeviceBoundNumber Matrix::Sum_AsDeviceBoundNum() const + { + DeviceBoundNumber result; + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + ElemType* val = new ElemType; *val = m_CPUMatrix->SumOfElements(); result.ShallowCopyFrom(val,-1); return result, + return m_GPUMatrix->Sum_AsDeviceBoundNum(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + //sum of all elements + template + ElemType Matrix::SumOfAbsElements () const + { + if (IsEmpty()) + LogicError("SumOfAbsElements: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->SumOfAbsElements(), + return m_GPUMatrix->SumOfAbsElements(), + NOT_IMPLEMENTED, + return m_GPUSparseMatrix->SumOfAbsElements() + ); + } + + //sum of all elements + template + ElemType Matrix::LogAddSumOfElements() const + { + if (IsEmpty()) + LogicError("LogAddSumOfElements: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->LogAddSumOfElements(), + return m_GPUMatrix->LogAddSumOfElements(), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + bool Matrix::IsValid() const + { + if (m_currentDataLocation == CurrentDataLocation::GPU && GetMatrixType() == MatrixType::SPARSE) + { + return this->m_GPUSparseMatrix->IsValid(); + } + else + { + NOT_IMPLEMENTED; + } + + return false; + } + + template + bool Matrix::IsEqualTo(const Matrix& a, const ElemType threshold /*= 1e-8*/) const + { + return AreEqual(*this, a, threshold); + } + + template + void Matrix::VectorSum(const Matrix& a, Matrix& c, const bool isColWise) + { + DecideAndMoveToRightDevice(c, a); + if (!(a.GetMatrixType() == c.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::VectorSum(*a.m_CPUMatrix, *c.m_CPUMatrix, isColWise), + GPUMatrix::VectorSum(*a.m_GPUMatrix, *c.m_GPUMatrix, isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::VectorNorm1(Matrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNormInf: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, c); + c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &c, + m_CPUMatrix->VectorNorm1(*c.m_CPUMatrix,isColWise), + m_GPUMatrix->VectorNorm1(*c.m_GPUMatrix,isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::AssignVectorNorm1Of(Matrix& a, const bool isColWise) + { + a.VectorNorm1(*this, isColWise); + return *this; + } + + template + void Matrix::VectorNorm2(Matrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNorm2: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, c); + c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &c, + m_CPUMatrix->VectorNorm2(*c.m_CPUMatrix,isColWise), + m_GPUMatrix->VectorNorm2(*c.m_GPUMatrix,isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::AssignVectorNorm2Of(Matrix& a, const bool isColWise) + { + a.VectorNorm2(*this, isColWise); + return *this; + } + + template + void Matrix::VectorNormInf(Matrix& c, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorNormInf: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, c); + c.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &c, + m_CPUMatrix->VectorNormInf(*c.m_CPUMatrix,isColWise), + m_GPUMatrix->VectorNormInf(*c.m_GPUMatrix,isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::AssignVectorNormInfOf(Matrix& a, const bool isColWise) + { + a.VectorNormInf(*this, isColWise); + return *this; + } + + template + Matrix& Matrix::AssignInnerProductOf(const Matrix& a, const Matrix& b, const bool isColWise) + { + InnerProduct (a, b, *this,isColWise); + return *this; + } + + //column-wise crossproduct + template + Matrix& Matrix::AssignKhatriRaoProductOf(const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignKhatriRaoProductOf: Matrix is empty."); + + assert (a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AssignKhatriRaoProductOf: The input matrix dimensions do not match."); + + DecideAndMoveToRightDevice(a, b, *this); + //WARNING: a and b must have same type + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignKhatriRaoProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix), + m_GPUMatrix->AssignKhatriRaoProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //column-wise reshaped product. Used to compute KhatriRaoProduct Gradient + // this = reshape each column of a from (K1xK2,1) to (K1, K2) + // if each column of a is not transposed, each (K1, K2) times each column of b (K2, frames). + // the output is a (K1, frames) matrix + // if each column of a is tranposed, each (K1, K2)^T times each column of b(K1, frames) and output is (K2, frames) + //column-wise crossproduct + template + Matrix& Matrix::AddColumnReshapeProductOf(const Matrix& a, const Matrix& b, const bool transposeAColumn) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AddColumnReshapeProductOf: Matrix is empty."); + + assert (a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumCols() == b.GetNumCols())) + InvalidArgument("AddColumnReshapeProductOf: The input matrix dimensions do not match."); + + DecideAndMoveToRightDevice(*this, a, b); + //WARNING: a and b must have same type + if (!(a.GetMatrixType() == b.GetMatrixType() && GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddColumnReshapeProductOf(*a.m_CPUMatrix,*b.m_CPUMatrix, transposeAColumn), + m_GPUMatrix->AddColumnReshapeProductOf(*a.m_GPUMatrix,*b.m_GPUMatrix, transposeAColumn), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AddWithScaleOf(ElemType alpha, const Matrix& a) + { + ScaleAndAdd(alpha, a, *this); + return *this; + } + + template + ElemType Matrix::FrobeniusNorm() const + { + if (IsEmpty()) + LogicError("FrobeniusNorm: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->FrobeniusNorm(), + return m_GPUMatrix->FrobeniusNorm(), + return m_CPUSparseMatrix->FrobeniusNorm(), + return m_GPUSparseMatrix->FrobeniusNorm() + ); + } + + template + Matrix& Matrix::AssignFrobeniusNormOf(const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignFrobeniusNormOf: Matrix a is empty."); + + Resize(1,1); + + //WARNING: a and this must have same type + if (! (GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignFrobeniusNormOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignFrobeniusNormOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + ElemType Matrix::MatrixNormInf() const + { + if (IsEmpty()) + LogicError("MatrixNormInf: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->MatrixNormInf(), + return m_GPUMatrix->MatrixNormInf(), + NOT_IMPLEMENTED, + return m_GPUSparseMatrix->MatrixNormInf() + ); + } + + template + ElemType Matrix::MatrixNorm1() const + { + if (IsEmpty()) + LogicError("MatrixNorm1: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->MatrixNorm1(), + return m_GPUMatrix->MatrixNorm1(), + NOT_IMPLEMENTED, + return m_GPUSparseMatrix->MatrixNorm1() + ); + } + + template + ElemType Matrix::MatrixNorm0() const + { + if (IsEmpty()) + LogicError("MatrixNorm0: Matrix is empty."); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return m_CPUMatrix->MatrixNorm0(), + return m_GPUMatrix->MatrixNorm0(), + NOT_IMPLEMENTED, + return m_GPUSparseMatrix->MatrixNorm0() + ); + } + + template + Matrix& Matrix::AssignSignOf(const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AssignSignOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AssignSignOf(*a.m_CPUMatrix), + m_GPUMatrix->AssignSignOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AddSignOf(const Matrix& a) + { + if (a.IsEmpty()) + LogicError("AddSignOf: Matrix a is empty."); + + DecideAndMoveToRightDevice(a, *this); + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->AddSignOf(*a.m_CPUMatrix), + m_GPUMatrix->AddSignOf(*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //I decided to use Matrix& maxIndexes instead of integer vector because the result may be used to do additional calculation + template + void Matrix::VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorMax: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, maxIndexes, maxValues); + maxIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + maxValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &maxValues, + m_CPUMatrix->VectorMax(*maxIndexes.m_CPUMatrix, *maxValues.m_CPUMatrix, isColWise); maxIndexes.SetDataLocation(CPU, DENSE), + m_GPUMatrix->VectorMax(*maxIndexes.m_GPUMatrix, *maxValues.m_GPUMatrix, isColWise); maxIndexes.SetDataLocation(GPU, DENSE), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise, int topK) const + { + if (IsEmpty()) + LogicError("VectorMax: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, maxIndexes, maxValues); + maxIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + maxValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &maxValues, + m_CPUMatrix->VectorMax(*maxIndexes.m_CPUMatrix, *maxValues.m_CPUMatrix, isColWise, topK); maxIndexes.SetDataLocation(CPU, DENSE), + m_GPUMatrix->VectorMax(*maxIndexes.m_GPUMatrix, *maxValues.m_GPUMatrix, isColWise, topK); maxIndexes.SetDataLocation(GPU, DENSE), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::VectorMin(Matrix& minIndexes, Matrix& minValues, const bool isColWise) const + { + if (IsEmpty()) + LogicError("VectorMin: Matrix is empty."); + + DecideAndMoveToRightDevice(*this, minIndexes, minValues); + minIndexes.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + minValues.SwitchToMatrixType(GetMatrixType(), GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &minValues, + m_CPUMatrix->VectorMin(*minIndexes.m_CPUMatrix,*minValues.m_CPUMatrix,isColWise); minIndexes.SetDataLocation(CPU, DENSE), + m_GPUMatrix->VectorMin(*minIndexes.m_GPUMatrix,*minValues.m_GPUMatrix,isColWise); minIndexes.SetDataLocation(GPU, DENSE), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + +#pragma endregion Member BLAS Functions + +#pragma region Other helper Functions + + template + wchar_t* Matrix::GetMatrixName() const + { + return m_baseMatrix->GetMatrixName(); + } + + template + void Matrix::SetMatrixName(const wchar_t* s) + { + if (m_currentDataLocation==CurrentDataLocation::BOTH) + { + if (GetMatrixType() == MatrixType::DENSE) + { + m_CPUMatrix->SetMatrixName(s); + m_GPUMatrix->SetMatrixName(s); + } + else if (GetMatrixType() == MatrixType::SPARSE) + { + m_CPUSparseMatrix->SetMatrixName(s); + m_GPUSparseMatrix->SetMatrixName(s); + } + } + else + { + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + m_CPUMatrix->SetMatrixName(s), + m_GPUMatrix->SetMatrixName(s), + m_CPUSparseMatrix->SetMatrixName(s), + m_GPUSparseMatrix->SetMatrixName(s) + ); + } + } + + template + int Matrix::GetDeviceId() const + { + if (m_currentDataLocation==CurrentDataLocation::NONE) + return m_preferredDeviceId; + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + return CPUDEVICE, + return m_GPUMatrix->GetComputeDeviceId(), + return CPUDEVICE, + return m_GPUSparseMatrix->GetComputeDeviceId() + ); + } + + // bring two matrices onto the same device + // If different and prefered devices are the same, move to preferred device. + // Otherwise GPU takes precedence over CPU, and if both are GPU move to a's device. + // The inputs are only distinguished in that a's GPU takes precedence over b's in case they differ. + // TODO: This is called somewhat inconsistently, sometimes with a=*this, sometimes with b=*this. + template + void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b) + { + int deviceIdA = a.GetDeviceId(), deviceIdB = b.GetDeviceId(); + if (deviceIdA == deviceIdB) + return; + + int preferredDeviceIdA = a.GetPreferredDeviceId(), preferredDeviceIdB = b.GetPreferredDeviceId(); + + if (preferredDeviceIdA == preferredDeviceIdB) // both prefer the same device: move to preferred + { + a._transferToDevice(preferredDeviceIdA); + b._transferToDevice(preferredDeviceIdA); + } + else if (deviceIdA != CPUDEVICE) // one of them lives on GPU: use that + { + b._transferToDevice(deviceIdA); + } + else + { + a._transferToDevice(deviceIdB); + } + } + + // same but for 3 matrices + // If b and c are both on the same GPU then a will be forced to go there; otherwise a's GPU takes precedence, then b's. + template + void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b, const Matrix &c) + { + int deviceIdA = a.GetDeviceId(), deviceIdB = b.GetDeviceId(), deviceIdC = c.GetDeviceId(); + if (deviceIdA == deviceIdB && deviceIdA == deviceIdC) + return; + + int preferredDeviceIdA = a.GetPreferredDeviceId(), preferredDeviceIdB = b.GetPreferredDeviceId(), preferredDeviceIdC = c.GetPreferredDeviceId(); + + if (preferredDeviceIdA == preferredDeviceIdB && preferredDeviceIdA == preferredDeviceIdC) + { + a._transferToDevice(preferredDeviceIdA); + b._transferToDevice(preferredDeviceIdA); + c._transferToDevice(preferredDeviceIdA); + } + else if (deviceIdB == deviceIdC && deviceIdB != CPUDEVICE) // TODO: why not the other two combinations? + { + a._transferToDevice(deviceIdB); // 'a' is outvoted + } + else if (deviceIdA != CPUDEVICE) // one of them lives on GPU: use that + { + b._transferToDevice(deviceIdA); + c._transferToDevice(deviceIdA); + } + else if(deviceIdB != CPUDEVICE) + { + a._transferToDevice(deviceIdB); + c._transferToDevice(deviceIdB); + } + else + { + a._transferToDevice(deviceIdC); + b._transferToDevice(deviceIdC); + } + } + + // same but for 4 matrices + template + void Matrix::DecideAndMoveToRightDevice(const Matrix &a, const Matrix &b, const Matrix &c, const Matrix &d) + { + // this function is only called for one operator, so for now we keep it imple + DecideAndMoveToRightDevice(a, b, c); + d._transferToDevice(a.GetDeviceId()); // BUGBUG: Is this correct in case a,b,c share the same preferredDevice? + } + + template + void Matrix::_transferToDevice(int to_id, bool ismoved,bool emptyTransfer) const + { + int from_id = GetDeviceId(); + if (to_id == from_id) //nothing to do + return; + + if (OwnBuffer()) + _transferFromDeviceToDevice(from_id, to_id, ismoved, emptyTransfer); + else + RuntimeError("Cannot move externally owned matrices to the preferred device."); + } + + template + void Matrix::_transferFromDeviceToDevice(int from_id, int to_id, bool ismoved,bool emptyTransfer) const + { + if (from_id < 0) + from_id = CPUDEVICE; + if (to_id < 0) + to_id = CPUDEVICE; + + if (from_id == to_id) + { + if (from_id != GetDeviceId()) + RuntimeError("Trying to transfer matrix from device to the same device while the matrix does not live in the from device."); + + return; + } + +#define NUM_DEVICE_CHANGED_WARN 20 + if (m_numTimesDeviceChanged <= NUM_DEVICE_CHANGED_WARN && + (!emptyTransfer || (from_id >= 0 && to_id >= 0))) + { + m_numTimesDeviceChanged++; + if (m_devicesTransferedTo[0] < CPUDEVICE) + { + m_devicesTransferedTo[0] = to_id; + } + else if (m_devicesTransferedTo[0] != to_id) + { + m_devicesTransferedTo[1] = to_id; + } + } + if (m_numTimesDeviceChanged == NUM_DEVICE_CHANGED_WARN && m_devicesTransferedTo[1] >= CPUDEVICE) + { + fprintf(stderr, "WARNING: The same matrix with dim [%lu, %lu] has been transferred between different devices for %d times.\n", (unsigned long)GetNumRows(), (unsigned long)GetNumCols(), NUM_DEVICE_CHANGED_WARN); + } + + if (m_matrixType == MatrixType::SPARSE) + { + if (from_id == CPUDEVICE) //from CPU to GPU + { + if (m_CPUSparseMatrix == NULL) + LogicError("Can't move from CPU because I'm not there!"); + + if (m_GPUSparseMatrix == NULL) + m_GPUSparseMatrix = new GPUSparseMatrix(m_CPUSparseMatrix->GetFormat(), to_id); + else + m_GPUSparseMatrix->ChangeDeviceTo(to_id); + + if (m_CPUSparseMatrix->GetNumElements() != 0 && !emptyTransfer) + { + m_GPUSparseMatrix->SetValue(*m_CPUSparseMatrix); + } + + if (ismoved) + { + delete m_CPUSparseMatrix; + m_CPUSparseMatrix = NULL; + SetDataLocation(GPU, SPARSE); + } + else + { + SetDataLocation(BOTH, SPARSE); + } + } + else //from GPU + { + if (m_GPUSparseMatrix == NULL || m_GPUSparseMatrix->GetComputeDeviceId() != from_id) + LogicError("This matrix isn't on this (or any?) GPU"); + + if (to_id < 0) //to CPU + { + if (m_CPUSparseMatrix == NULL) + m_CPUSparseMatrix = new CPUSparseMatrix(m_GPUSparseMatrix->GetFormat()); + + if (m_GPUSparseMatrix->GetNumElements() != 0 && !emptyTransfer) + { + m_GPUSparseMatrix->CopyToCPUSparseMatrix(*m_CPUSparseMatrix); + } + + if (ismoved) + { + delete m_GPUSparseMatrix; + m_GPUSparseMatrix = NULL; + SetDataLocation(CPU, SPARSE); + } + else + { + SetDataLocation(BOTH, SPARSE); + } + } + else //to another GPU + { + m_GPUSparseMatrix->ChangeDeviceTo(to_id); + } + } + } + else +#pragma omp critical + { + if (from_id == CPUDEVICE) //from CPU to GPU + { + if (m_CPUMatrix==NULL) + LogicError("Can't move from CPU because I'm not there!"); + if (m_GPUMatrix!=NULL) + delete m_GPUMatrix; + if (m_CPUMatrix->GetNumElements() !=0 && !emptyTransfer) + { + m_GPUMatrix = new GPUMatrix(m_CPUMatrix->GetNumRows(), m_CPUMatrix->GetNumCols(), to_id, m_CPUMatrix->GetArray(), matrixFlagNormal); + } + else + { + m_GPUMatrix = new GPUMatrix(to_id); + } + if (ismoved) + { + delete m_CPUMatrix; + m_CPUMatrix=NULL; + SetDataLocation(GPU, DENSE); + } + else + { + SetDataLocation(BOTH, DENSE); + } + } + else //from GPU + { + if (m_GPUMatrix==NULL || m_GPUMatrix->GetComputeDeviceId()!=from_id) + LogicError("This matrix isn't on this (or any?) GPU"); + + if (to_id < 0) //to CPU + { + if (m_CPUMatrix!=NULL) + delete m_CPUMatrix; + + if (m_GPUMatrix->GetNumElements() !=0 && !emptyTransfer) + { + ElemType *arr = m_GPUMatrix->CopyToArray(); // TODO: unnecessary allocation/copy; why not make this a vector that we move over as an rvalue ref? + m_CPUMatrix = new CPUMatrix(m_GPUMatrix->GetNumRows(), m_GPUMatrix->GetNumCols(), arr, matrixFlagNormal); + delete[] arr; + } + else + { + m_CPUMatrix = new CPUMatrix(); + } + + if (ismoved) + { + delete m_GPUMatrix; + m_GPUMatrix = NULL; + SetDataLocation(CPU, DENSE); + } + else + { + SetDataLocation(BOTH, DENSE); + } + } + else //to another GPU + { + m_GPUMatrix->ChangeDeviceTo(to_id); + } + } + }// and of omp critical section + } + + template + void Matrix::TransferFromDeviceToDevice(int from_id, int to_id, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const + { + _transferFromDeviceToDevice(from_id,to_id,ismoved,emptyTransfer); + if (updatePreferredDevice) + m_preferredDeviceId=GetDeviceId(); + } + template + void Matrix::TransferToDeviceIfNotThere(int id_to, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const + { + if (GetDeviceId() != id_to) + TransferFromDeviceToDevice(GetDeviceId(), id_to, ismoved, emptyTransfer, updatePreferredDevice); + } + // TODO: This function vv is now (after memshare update) only used by LearnableParameter::InitRandom(). Maybe it is time to get rid of it. + template + void Matrix::TransferToDeviceIfNotThereAndNotAutoPlace(int id_to, bool ismoved, bool emptyTransfer, bool updatePreferredDevice) const + { + if (id_to != AUTOPLACEMATRIX) + TransferToDeviceIfNotThere(id_to, ismoved, emptyTransfer, updatePreferredDevice); + } + + template + void Matrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const + { + if (IsEmpty()) + LogicError("Print: Matrix is empty."); + DEVICEID_TYPE orgdevice = GetDeviceId(); + + DISPATCH_MATRIX_ON_FLAG(this, + nullptr, + m_CPUMatrix->Print(matrixName, rowStart, rowEnd, colStart, colEnd), + _transferToDevice(CPUDEVICE, false, false); m_CPUMatrix->Print(matrixName, rowStart, rowEnd, colStart, colEnd); _transferToDevice(orgdevice, false, false), + m_CPUSparseMatrix->Print(matrixName), + _transferToDevice(CPUDEVICE, false, false); m_CPUSparseMatrix->Print(matrixName); _transferToDevice(orgdevice, false, false) + ); + } + + template + void Matrix::Print(const char* matrixName /*=nullptr*/) const + { + Print(matrixName, 0, GetNumRows()-1, 0, GetNumCols()-1); + } + + //helpfer function used for convolution neural network + template + Matrix& Matrix::AssignPackedConvolutionInput(const Matrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) + { + DecideAndMoveToRightDevice(inputSubBatch, *this); + SwitchToMatrixType(inputSubBatch.GetMatrixType(), inputSubBatch.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&inputSubBatch, + this, + m_CPUMatrix->AssignPackedConvolutionInput(*(inputSubBatch.m_CPUMatrix), + inputWidth, inputHeight, inputChannels, + outputWidth, outputHeight, outputChannels, + kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, + zeroPadding), + m_GPUMatrix->AssignPackedConvolutionInput(*(inputSubBatch.m_GPUMatrix), + inputWidth, inputHeight, inputChannels, + outputWidth, outputHeight, outputChannels, + kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, + zeroPadding), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + //helpfer function used for convolution neural network + template + Matrix& Matrix::UnpackConvolutionInput(Matrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) const + { + DecideAndMoveToRightDevice(*this, inputSubBatch); + inputSubBatch.SwitchToMatrixType(GetMatrixType(), inputSubBatch.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + &inputSubBatch, + m_CPUMatrix->UnpackConvolutionInput(*(inputSubBatch.m_CPUMatrix), + inputWidth, inputHeight, inputChannels, + outputWidth, outputHeight, outputChannels, + kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, + zeroPadding), + m_GPUMatrix->UnpackConvolutionInput(*(inputSubBatch.m_GPUMatrix), + inputWidth, inputHeight, inputChannels, + outputWidth, outputHeight, outputChannels, + kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, + zeroPadding), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return inputSubBatch; + } + + template + Matrix& Matrix::AssignMaxPoolingResult(const Matrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + DecideAndMoveToRightDevice(inputBatch, *this); + SwitchToMatrixType(inputBatch.GetMatrixType(), inputBatch.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&inputBatch, + this, + m_CPUMatrix->AssignMaxPoolingResult(*(inputBatch.m_CPUMatrix), channels, + inputWidth, inputHeight,inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + m_GPUMatrix->AssignMaxPoolingResult(*(inputBatch.m_GPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AddMaxPoolingGradient(const Matrix& outputGradientBatch, const Matrix& inputBatch, const Matrix& outputBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + DecideAndMoveToRightDevice(*this, outputGradientBatch, inputBatch); + outputBatch._transferToDevice(GetDeviceId()); + + if (!(GetMatrixType() == outputGradientBatch.GetMatrixType() + && GetMatrixType() == inputBatch.GetMatrixType() + && GetMatrixType() == outputBatch.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddMaxPoolingGradient(*(outputGradientBatch.m_CPUMatrix), *(inputBatch.m_CPUMatrix), *(outputBatch.m_CPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + m_GPUMatrix->AddMaxPoolingGradient(*(outputGradientBatch.m_GPUMatrix), *(inputBatch.m_GPUMatrix), *(outputBatch.m_GPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample);, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignAveragePoolingResult(const Matrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + DecideAndMoveToRightDevice(inputBatch, *this); + SwitchToMatrixType(inputBatch.GetMatrixType(), inputBatch.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&inputBatch, + this, + m_CPUMatrix->AssignAveragePoolingResult(*(inputBatch.m_CPUMatrix), channels, + inputWidth, inputHeight,inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + m_GPUMatrix->AssignAveragePoolingResult(*(inputBatch.m_GPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + Matrix& Matrix::AssignSoftmaxSum(const Matrix& a, const Matrix& softmax) + { + Resize(1, 1); + if (GetDeviceId() < 0) + a.m_CPUMatrix->AssignSoftmaxSum(*softmax.m_CPUMatrix, *m_CPUMatrix); + else + a.m_GPUMatrix->AssignSoftmaxSum(*softmax.m_GPUMatrix, *m_GPUMatrix); + return *this; + } + + template + Matrix& Matrix::AssignNceUnnormalizedEval(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias) + { + //if (a.GetMatrixType() != MatrixType::SPARSE) + // NOT_IMPLEMENTED; + + Resize(1, 1); + if (GetDeviceId() < 0) + a.m_CPUMatrix->AssignNCEUnnormalizedEval(*b.m_CPUMatrix, *c.m_CPUMatrix, *bias.m_CPUMatrix, *m_CPUMatrix); + else + a.m_GPUMatrix->AssignNCEUnnormalizedEval(*b.m_GPUMatrix, *c.m_GPUMatrix, *m_GPUMatrix); + return *this; + } + + template + Matrix& Matrix::AssignNoiseContrastiveEstimation(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias, Matrix& tmp) + { + if (a.IsEmpty() || b.IsEmpty() || c.IsEmpty()) + LogicError("AssignNoiseContrastiveEstimation: one of the input matrices is empty."); + + if (a.GetDeviceId() != b.GetDeviceId() || b.GetDeviceId() != c.GetDeviceId() || c.GetDeviceId() != GetDeviceId()) + NOT_IMPLEMENTED; + + Resize(1, 1); + + if (GetDeviceId() < 0) + { + size_t sampleCount = a.m_CPUMatrix->GetNumElements() / a.m_CPUMatrix->GetNumRows(); + tmp.Resize(a.GetNumRows() / 2, sampleCount); + a.m_CPUMatrix->AssignNoiseContrastiveEstimation(*b.m_CPUMatrix, *c.m_CPUMatrix, + *bias.m_CPUMatrix, *tmp.m_CPUMatrix, *m_CPUMatrix); + } + else + { + size_t sampleCount = a.m_GPUMatrix->GetNumElements() / a.m_GPUMatrix->GetNumRows(); + tmp.Resize(a.GetNumRows() / 2, sampleCount); + a.m_GPUMatrix->AssignNoiseContrastiveEstimation(*b.m_GPUMatrix, *c.m_GPUMatrix, + *bias.m_GPUMatrix, sampleCount, *tmp.m_GPUMatrix, *m_GPUMatrix); + } + return *this; + } + + template + Matrix& Matrix::AssignNCEDerivative(const Matrix& tmp, const Matrix& a, const Matrix& b, const Matrix& c, size_t inputIndex) + { + if (a.IsEmpty() || b.IsEmpty() || c.IsEmpty()) + LogicError("AssignNoiseContrastiveEstimation: one of the input matrices is empty."); + + if (a.GetDeviceId() != b.GetDeviceId() || b.GetDeviceId() != c.GetDeviceId() || c.GetDeviceId() != GetDeviceId()) + NOT_IMPLEMENTED; + + assert(tmp.GetNumRows() == a.GetNumRows() / 2); + if (GetDeviceId() < 0) + { + // samples gradient hidden embedding embedding/hidden + a.m_CPUMatrix->AssignNCEDerivative(*tmp.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, inputIndex, *m_CPUMatrix); + } + else + { + a.m_GPUMatrix->AssignNCEDerivative(*tmp.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, inputIndex, *m_GPUMatrix); + } + return *this; + } + + template + Matrix& Matrix::AddAveragePoolingGradient(const Matrix& outputGradientBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) + { + DecideAndMoveToRightDevice(*this, outputGradientBatch); + if (!(GetMatrixType() == outputGradientBatch.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AddAveragePoolingGradient(*(outputGradientBatch.m_CPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + m_GPUMatrix->AddAveragePoolingGradient(*(outputGradientBatch.m_GPUMatrix), channels, + inputWidth, inputHeight, inputSizePerSample, + outputWidth, outputHeight, outputSizePerSample, + windowWidth, windowHeight, horizontalSubsample, verticalSubsample), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + +#pragma endregion Other Helper Functions + +#pragma region Static BLAS Functions + + template + void Matrix::SVD(const Matrix& A, Matrix& SIGMA, Matrix& U, Matrix& VT, Matrix& W) + { + if (A.IsEmpty() ) + LogicError("SVD: the input matrix is empty."); + + DecideAndMoveToRightDevice(A, SIGMA, U); + VT._transferToDevice(A.GetDeviceId()); + W._transferToDevice(A.GetDeviceId()); + + SIGMA.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); + U.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); + VT.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); + W.SwitchToMatrixType(A.GetMatrixType(), A.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&A, + nullptr, + Matrix tA = A; + CPUMatrix::SVD(*tA.m_CPUMatrix, *SIGMA.m_CPUMatrix, *U.m_CPUMatrix, *VT.m_CPUMatrix, *W.m_CPUMatrix); + SIGMA.SetDataLocation(CPU); + U.SetDataLocation(CPU); + VT.SetDataLocation(CPU); + W.SetDataLocation(CPU), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + } + + /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c + /// Scalar + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Scalar + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::MultiplyAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, + ElemType beta, Matrix& c) + { + DecideAndMoveToRightDevice(a,b,c); + + if (c.GetDeviceId()<0) //CPU + { + if (a.GetMatrixType()==MatrixType::SPARSE) + NOT_IMPLEMENTED; + if ( b.GetMatrixType()==MatrixType::SPARSE) + { + if (c.GetMatrixType() == MatrixType::DENSE) + { + CPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_CPUMatrix, transposeA, *b.m_CPUSparseMatrix, transposeB, beta, *c.m_CPUMatrix); + c.SetDataLocation(CPU, DENSE); + } + else if (c.GetMatrixType() == MatrixType::SPARSE) + { + CPUSparseMatrix::MultiplyAndAdd(alpha, *a.m_CPUMatrix, transposeA, *b.m_CPUSparseMatrix, transposeB, *c.m_CPUSparseMatrix); + c.SetDataLocation(CPU, SPARSE); + } + else + NOT_IMPLEMENTED; + } + else + { + c.SwitchToMatrixType(MatrixType::DENSE, matrixFormatDense, false); + CPUMatrix::MultiplyAndWeightedAdd(alpha,*a.m_CPUMatrix,transposeA,*b.m_CPUMatrix,transposeB,beta,*c.m_CPUMatrix); + c.SetDataLocation(CPU, DENSE); + } + + } + else //GPU operations + { + if (a.m_matrixType==b.m_matrixType && b.m_matrixType==c.m_matrixType && a.m_matrixType==MatrixType::DENSE) //All dense + { + GPUMatrix::MultiplyAndWeightedAdd(alpha,*a.m_GPUMatrix,transposeA,*b.m_GPUMatrix,transposeB,beta,*c.m_GPUMatrix); + c.SetDataLocation(GPU, DENSE); + } + else if (a.m_matrixType==MatrixType::SPARSE && b.m_matrixType==c.m_matrixType && b.m_matrixType==MatrixType::DENSE) //Sparse*Dense+Dense + { + GPUMatrix second = transposeB ? b.m_GPUMatrix->Transpose() : *b.m_GPUMatrix; + GPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUSparseMatrix, transposeA, second, false, beta, *c.m_GPUMatrix); + c.SetDataLocation(GPU, DENSE); + } + else if (a.m_matrixType==MatrixType::DENSE && b.m_matrixType==MatrixType::SPARSE && c.m_matrixType==MatrixType::DENSE) //Dense*Sparse + Dense + { + //if (b.m_GPUSparseMatrix->GetFormat() == MatrixFormat::matrixFormatSparseCSR) + //{ + GPUSparseMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, beta, *c.m_GPUMatrix); + //} + //else + //{ + // GPUMatrix firstDummy = transposeA ? a.m_GPUMatrix->Transpose()*alpha : (*a.m_GPUMatrix)*alpha; + // GPUMatrix & first= firstDummy; // GCC does not support mixing refs and non-refs + // GPUSparseMatrix secondDummy = transposeB ? b.m_GPUSparseMatrix->Transpose() : *b.m_GPUSparseMatrix; + // GPUSparseMatrix & second = secondDummy; + // if (beta==0) + // { + // GPUSparseMatrix::Multiply(first,second,*c.m_GPUMatrix); + // } + // else + // { + // Matrix tmp(c.GetNumRows(),c.GetNumCols(),(DEVICEID_TYPE)c.GetDeviceId()); + // GPUSparseMatrix::Multiply(first,second,*tmp.m_GPUMatrix); + // c=tmp+c*beta; + // } + //} + c.SetDataLocation(GPU, DENSE); + } + else if (a.m_matrixType==MatrixType::DENSE && b.m_matrixType==MatrixType::SPARSE && c.m_matrixType==MatrixType::SPARSE) // h -> u0 + { + // new GPU sparse matrix code + GPUSparseMatrix::MultiplyAndAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, *c.m_GPUSparseMatrix); + c.SetDataLocation(GPU, SPARSE); + } + else if (a.m_matrixType==b.m_matrixType && b.m_matrixType==c.m_matrixType && a.m_matrixType==MatrixType::SPARSE) + { + GPUSparseMatrix firstDummy = alpha==1 ? *a.m_GPUSparseMatrix : (*a.m_GPUSparseMatrix)*alpha; + GPUSparseMatrix & first = firstDummy; // By Malcolm.. gcc doesn't support auto + if (beta==0) + { + GPUSparseMatrix::Multiply(first,transposeA,*b.m_GPUSparseMatrix,transposeB,*c.m_GPUSparseMatrix); + c.SetDataLocation(GPU, SPARSE); + } + else + { + GPUSparseMatrix tmp; + GPUSparseMatrix::Multiply(first,transposeA,*b.m_GPUSparseMatrix,transposeB,tmp); + *c.m_GPUSparseMatrix = tmp + (*c.m_GPUSparseMatrix)*beta; + c.SetDataLocation(GPU, SPARSE); + } + } + else if (a.m_matrixType == b.m_matrixType && a.m_matrixType == MatrixType::DENSE && c.m_matrixType == MatrixType::SPARSE) + { + GPUMatrix tmp(a.m_GPUMatrix->GetComputeDeviceId()); + GPUSparseMatrix tmpSparse; + GPUMatrix::MultiplyAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUMatrix, transposeB, beta, tmp); + tmpSparse.SetValue(tmp); + *c.m_GPUSparseMatrix = tmpSparse + (*c.m_GPUSparseMatrix)*beta; + c.SetDataLocation(GPU, SPARSE); + } + else + NOT_IMPLEMENTED; + } + } + + template + /*static*/ void Matrix::Multiply1x1AndWeightedAdd(ElemType alpha, const Matrix& a, const Matrix& b, ElemType beta, Matrix& c) + { + // special case: a is a 1x1 matrix + // The only alternative is to Get00Elements(), which makes things inefficient. + if (a.GetNumElements() != 1) + InvalidArgument("Multiply1x1AndWeightedAdd: first arg must be a scalar."); + + DISPATCH_MATRIX_ON_FLAG(&c, + nullptr, + CPUMatrix::Multiply1x1AndWeightedAdd(alpha, *a.m_CPUMatrix, *b.m_CPUMatrix, beta, *c.m_CPUMatrix), + GPUMatrix::Multiply1x1AndWeightedAdd(alpha, *a.m_GPUMatrix, *b.m_GPUMatrix, beta, *c.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + c + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::MultiplyAndAdd(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, + Matrix& c) + { + return Matrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 1.0, c); + } + + /// Matrix-matrix multiply with col-major matrices (a and b may be transposed): c = op(a) * op(b) + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::Multiply(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, + Matrix& c) + { + return Matrix::MultiplyAndWeightedAdd(1.0, a, transposeA, b, transposeB, 0.0, c); + } + + /// Matrix-matrix multiply with col-major matrices (a and b are not transposed): c = a * b + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::Multiply(const Matrix& a, const Matrix& b, Matrix& c) + { + return Matrix::MultiplyAndWeightedAdd(1.0, a, false, b, false, 0.0, c); + } + + /// 1-D Convolution with col-major matrices (a and b may be transposed): c = alpha * op(a) * op(b) + beta*c. MultiplyAndWeightedAdd is just a special case of this. + /// Scalar + /// Input matrix + /// Whether matrix a is transposed + /// Input matrix + /// Whether matrix b is transposed + /// Scalar + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::ConvolveAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, + ElemType beta, Matrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise) + { + DecideAndMoveToRightDevice(a, b, c); + + if (c.GetDeviceId() >= 0 /*GPU*/ && a.GetMatrixType() == MatrixType::DENSE + && b.GetMatrixType() == MatrixType::SPARSE && c.GetMatrixType() == MatrixType::DENSE) + { + GPUSparseMatrix::ConvolveAndWeightedAdd(alpha, *a.m_GPUMatrix, transposeA, *b.m_GPUSparseMatrix, transposeB, beta, *c.m_GPUMatrix, numChannels, horizontalSubsample, padding, channelwise); + } + else + { + NOT_IMPLEMENTED; + } + } + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c + /// if a is a column vector, add to all columns of c + /// if a is a row vector, add to all rows of c + /// Scalar + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + /*static*/void Matrix::ScaleAndAdd(ElemType alpha, const Matrix& a, Matrix& c) + { + if (a.IsEmpty() || c.IsEmpty()) + LogicError("ScaleAndAdd: one of the input matrices is empty."); + + DecideAndMoveToRightDevice(c, a); + + if (a.GetMatrixType() == c.GetMatrixType()) + { + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::ScaleAndAdd(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), + GPUMatrix::ScaleAndAdd(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + GPUSparseMatrix b = move(*c.m_GPUSparseMatrix); GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,1,b,*c.m_GPUSparseMatrix) + ); + } + else + { + DISPATCH_MATRIX_ON_FLAG(&c, + nullptr, + CPUSparseMatrix::ScaleAndAdd(alpha,*a.m_CPUSparseMatrix,*c.m_CPUMatrix); c.SetDataLocation(CPU), + if (a.m_GPUSparseMatrix->GetFormat() == MatrixFormat::matrixFormatSparseCSC) + { + GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,1,*c.m_GPUMatrix,*c.m_GPUMatrix); + } + else // new GPU sparse matrix code + { + GPUSparseMatrix::ScaleAndAdd(alpha,*a.m_GPUSparseMatrix,*c.m_GPUMatrix); + } + c.SetDataLocation(GPU), + NOT_IMPLEMENTED, + { + c.m_GPUMatrix = new GPUMatrix(c.m_GPUSparseMatrix->CopyToDenseMatrix()); + GPUSparseMatrix::ScaleAndAdd(alpha, *a.m_GPUMatrix, 1, *c.m_GPUSparseMatrix, *c.m_GPUMatrix); + delete c.m_GPUSparseMatrix; c.m_GPUSparseMatrix = NULL; + c.SetDataLocation(GPU, DENSE); + } + ); + } + } + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + beta * c + /// if a is a column vector, add to all columns of c + /// if a is a row vector, add to all rows of c + /// Scalar + /// Input matrix + /// Scalar + /// Resulting matrix, caller is responsible for allocating this + template + /*static*/ void Matrix::ScaleAndAdd(ElemType alpha, const Matrix& a, ElemType beta, Matrix& c) + { + if (beta==1) + ScaleAndAdd(alpha,a,c); + else if (beta==0) + { + Scale(alpha,a,c); + } + else + { + ScaleAndAdd(alpha/beta,a,c); // c1=alpha/beta * a + c + Scale(beta,c); // c/beta * beta + } + } + + // tensor swapping and addition: c <- keepWeight * b + scaleFactor * swap_dimensions(a, S, K) + // where + // - a is interpreted as a tensor of dimension (D x S x M x K x T) // column-major, as usual + // - b and c as a tensor of dimension (D x K x M x S x T) // note: K and S swapped + // The main point of this function is to reshuffle a tensor w.r.t. two dimensions that get swapped in memory, + // but for gradients, we will need to add, hence the keepWeight. + // Notes: + // - c and b may be the same (in-place operation is expressly allowed). + // - D, M, and/or T may be 1. For example, D == M == T == 1 implements a 2D matrix transpose from (S x K) to (K x S). + // - If keepWeight == 0, then b will just get overwritten (straight assignment, b may be uninitialized or contain NaNs). + // - The original matrix dimensions are ignored except that sizes must match (rows x cols == D x S x M x K x T). + // For diagnostics purposes, this function also enforces the rows % D == 0 and cols % T == 0, but this is not a functional requirement and can be removed if that helps. + // - Dense matrices only. + // TODO: Handle these cases: + // - no swapping happening --just do a block copy + // - swapping can be implemented by cuDNN --do so + template + /*static*/ void Matrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const Matrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const Matrix& b, Matrix& c) + { + if (a.GetNumElements() != c.GetNumElements() || b.GetNumElements() != c.GetNumElements()) // allocations must match (but not dimensions, since we reinterpret the dimensions anyway) + InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c must have same number of elements."); + if (c.IsEmpty()) // operating on empty minibatch slices is perfectly cromulent + return; + + // sanity checks for current use cases--these are not strictly necessary and can be deleted + if (a.GetNumRows() % D != 0 || b.GetNumRows() % D != 0 || c.GetNumRows() % D != 0) + InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c are meant to have a row dimension that is a multiple of D."); + if (a.GetNumCols() % T != 0 || b.GetNumCols() % T != 0 || c.GetNumCols() % T != 0) + InvalidArgument("TensorShuffleScaleAndAdd: a, b, and c are meant to have a column dimension that is a multiple of T."); + + DecideAndMoveToRightDevice(a, b, c); + + DISPATCH_MATRIX_ON_FLAG(&c, + nullptr, + CPUMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_CPUMatrix, D, S, M, K, T, scaleFactor, *b.m_CPUMatrix, *c.m_CPUMatrix), + GPUMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_GPUMatrix, D, S, M, K, T, scaleFactor, *b.m_GPUMatrix, *c.m_GPUMatrix), + NOT_IMPLEMENTED, + GPUSparseMatrix::TensorShuffleScaleAndAdd(keepWeight, *a.m_GPUSparseMatrix, D, S, M, K, T, scaleFactor, *b.m_GPUSparseMatrix, *c.m_GPUSparseMatrix) + ); + } + + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::AddScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c) + { + DecideAndMoveToRightDevice(c, a, b); + if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == c.GetMatrixType() )) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::AddScaledDifference(alpha,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), + GPUMatrix::AddScaledDifference(alpha,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::AssignScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c) + { + DecideAndMoveToRightDevice(a, b, c); + + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::AssignScaledDifference(alpha,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), + GPUMatrix::AssignScaledDifference(alpha,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::AddScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c) + { + DecideAndMoveToRightDevice(c, a, b); + alpha._transferToDevice(c.GetDeviceId()); + + if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == c.GetMatrixType() && a.GetMatrixType() == alpha.GetMatrixType())) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::AddScaledDifference(*alpha.m_CPUMatrix,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), + GPUMatrix::AddScaledDifference(*alpha.m_GPUMatrix,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::AssignScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c) + { + DecideAndMoveToRightDevice(a, b, alpha); + c._transferToDevice(a.GetDeviceId()); + + if (!(a.GetMatrixType() == b.GetMatrixType() && a.GetMatrixType() == alpha.GetMatrixType())) + NOT_IMPLEMENTED; + + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + nullptr, + CPUMatrix::AssignScaledDifference(*alpha.m_CPUMatrix,*a.m_CPUMatrix,*b.m_CPUMatrix, *c.m_CPUMatrix), + GPUMatrix::AssignScaledDifference(*alpha.m_GPUMatrix,*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + } + + //c[ci,cj] += a[ai,aj] + template + void Matrix::AddElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj) + { + DecideAndMoveToRightDevice(c, a); + + if (c.GetMatrixType() != a.GetMatrixType()) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::AddElementToElement(*a.m_CPUMatrix, ai, aj, *c.m_CPUMatrix, ci, cj), + GPUMatrix::AddElementToElement(*a.m_GPUMatrix, ai, aj, *c.m_GPUMatrix, ci, cj), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + //c[ci,cj] = a[ai,aj] + template + void Matrix::AssignElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj) + { + DecideAndMoveToRightDevice(c, a); + + if (c.GetMatrixType() != a.GetMatrixType()) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::AssignElementToElement(*a.m_CPUMatrix, ai, aj, *c.m_CPUMatrix, ci, cj), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + } + + //for each column of this, we add row slice of a starting from startIndex + template + void Matrix::MinusOneAt(Matrix& a, const size_t position) + { + DISPATCH_MATRIX_ON_FLAG(&a, + &a, + CPUMatrix::MinusOneAt(*a.m_CPUMatrix, position), + GPUMatrix::MinusOneAt(*a.m_GPUMatrix, position), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + /// Scalar + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void Matrix::Scale(ElemType alpha, const Matrix& a, Matrix& c) + { + DecideAndMoveToRightDevice(c, a); + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::Scale(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), + GPUMatrix::Scale(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + *c.m_GPUSparseMatrix = (*a.m_GPUSparseMatrix)*alpha + ); + } + + /// Matrix-scalar multiply with col-major matrices: a = alpha * a + /// Scalar + /// Input matrix + template + void Matrix::Scale(ElemType alpha, Matrix& a) + { + if (a.IsEmpty()) + return; + + DISPATCH_MATRIX_ON_FLAG(&a, + &a, + CPUMatrix::Scale(alpha,*a.m_CPUMatrix), + GPUMatrix::Scale(alpha,*a.m_GPUMatrix), + NOT_IMPLEMENTED, + GPUSparseMatrix::Scale(alpha,*a.m_GPUSparseMatrix) + ); + } + + /// Matrix scalar matrix multiply with col-major matrices: a = alpha[0,0] * a + /// 1x1 matrix + /// Input matrix + template + void Matrix::Scale(const Matrix& alpha, Matrix& a) + { + if (a.IsEmpty()) + return; + + DecideAndMoveToRightDevice(a,alpha); + + if (a.GetMatrixType() != alpha.GetMatrixType()) + NOT_IMPLEMENTED; + + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + CPUMatrix::Scale(*alpha.m_CPUMatrix,*a.m_CPUMatrix), + GPUMatrix::Scale(*alpha.m_GPUMatrix,*a.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::InnerProduct (const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrix is empty."); + + DecideAndMoveToRightDevice(a, b, c); + + if (a.GetMatrixType() != b.GetMatrixType()) + NOT_IMPLEMENTED; + + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::InnerProduct(*a.m_CPUMatrix,*b.m_CPUMatrix,*c.m_CPUMatrix,isColWise), + GPUMatrix::InnerProduct(*a.m_GPUMatrix,*b.m_GPUMatrix,*c.m_GPUMatrix,isColWise), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + ElemType Matrix::InnerProductOfMatrices(const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProductOfMatrices: one of the input matrices is empty."); + + DecideAndMoveToRightDevice(a,b); + + if (a.GetMatrixType() == b.GetMatrixType()) + { + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + return CPUMatrix::InnerProductOfMatrices(*a.m_CPUMatrix,*b.m_CPUMatrix), + return GPUMatrix::InnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + else + { + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + NOT_IMPLEMENTED, + return GPUSparseMatrix::InnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUSparseMatrix), + NOT_IMPLEMENTED, + return GPUSparseMatrix::InnerProductOfMatrices(*a.m_GPUSparseMatrix,*b.m_GPUMatrix) + ); + } + } + + template + Matrix& Matrix::AssignInnerProductOfMatrices(const Matrix& a, const Matrix& b) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProductOfMatrices: one of the input matrices is empty."); + + Resize(1,1); + + DecideAndMoveToRightDevice(a, b, *this); + + if (a.GetMatrixType() == b.GetMatrixType()) + { + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&a, + this, + m_CPUMatrix->SetValue(CPUMatrix::InnerProductOfMatrices(*a.m_CPUMatrix,*b.m_CPUMatrix)), + m_GPUMatrix->AssignInnerProductOfMatrices(*a.m_GPUMatrix,*b.m_GPUMatrix), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + else + { + NOT_IMPLEMENTED; + } + + return *this; + } + + template + void Matrix::ElementWisePower (ElemType alpha, const Matrix& a, Matrix& c) + { + if (a.IsEmpty()) + return; + + DecideAndMoveToRightDevice(a, c); + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + nullptr, + CPUMatrix::ElementWisePower(alpha,*a.m_CPUMatrix,*c.m_CPUMatrix), + GPUMatrix::ElementWisePower(alpha,*a.m_GPUMatrix,*c.m_GPUMatrix), + NOT_IMPLEMENTED, + GPUSparseMatrix::ElementWisePower(alpha,*a.m_GPUSparseMatrix,*c.m_GPUSparseMatrix) + ); + } + + template + bool Matrix::AreEqual(const Matrix& a, const Matrix& b, const ElemType threshold /*= 1e-8*/) + { + if (a.GetNumRows() != b.GetNumRows() || a.GetNumCols() != b.GetNumCols()) + return false; + + DecideAndMoveToRightDevice(a,b); + + if (a.GetMatrixType() == b.GetMatrixType()) + { + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + return CPUMatrix::AreEqual(*a.m_CPUMatrix, *b.m_CPUMatrix, threshold), + return GPUMatrix::AreEqual(*a.m_GPUMatrix, *b.m_GPUMatrix, threshold), + return CPUSparseMatrix::AreEqual(*a.m_CPUSparseMatrix, *b.m_CPUSparseMatrix, threshold), + return GPUSparseMatrix::AreEqual(*a.m_GPUSparseMatrix, *b.m_GPUSparseMatrix, threshold) + ); + } + else + { + DISPATCH_MATRIX_ON_FLAG(&a, + nullptr, + NOT_IMPLEMENTED; return false, + return GPUSparseMatrix::AreEqual(*a.m_GPUMatrix, *b.m_GPUSparseMatrix, threshold), + NOT_IMPLEMENTED; return false, + return GPUSparseMatrix::AreEqual(*a.m_GPUSparseMatrix, *b.m_GPUMatrix, threshold) + ); + } + + } + + template + bool Matrix::HasElement(const Matrix& a, const ElemType value) + { + if (a.IsEmpty()) + return false; + + DISPATCH_MATRIX_ON_FLAG(&a, + &a, + return CPUMatrix::HasElement(*a.m_CPUMatrix, value), + return GPUMatrix::HasElement(*a.m_GPUMatrix, value), + NOT_IMPLEMENTED; return false, + NOT_IMPLEMENTED; return false + ); + } + + // diagnostics helper to check if matrix has a NaN + // This is very slow. + template + bool Matrix::HasNan (const char * name) const + { + // Not implemented for sparse matrices. + // Return false as a workaround to at + // least evaluate the dense matrices. + if (m_matrixType == MatrixType::SPARSE) + return false; + + if (IsEmpty()) + return false; + + // if GPU then first detect NaN there, will be faster + if (GetDeviceId() != CPUDEVICE) + { + Matrix sum; + sum.AssignSumOfElements(*this); + auto x = sum.Get00Element(); + if (!std::isnan(x)) + return false; + } + + // const auto & us = *this; + const Matrix & us = *this; + + foreach_coord (i, j, us) + if (std::isnan(us(i, j))) + { + fprintf(stderr, "HasNan: NaN detected at %s (%ld,%ld) in (%d,%d) matrix\n", name, i, j, (int)GetNumRows(), (int)GetNumCols()); + return true; + } + return false; + } +#define CheckNan(m) m.HasNan (#m) + + // another diagnostics helper to check if matrix has a NaN + // This is used at load and save time. This test is slow. + + template + size_t Matrix::CountNanInf() const + { + const auto & us = *this; + size_t n = 0; // number of NaNs/INF found + foreach_coord (i, j, us) + { + auto val = us(i,j); + if (std::isnan (val) || !std::isfinite (val)) + n++; + } + return n; + } + + template + DEVICEID_TYPE Matrix::GetBestGPUDeviceId() + { + return EnforceOneGPUOnly(GPUMatrix::GetBestGPUDeviceId()); // see EnforceOneGPUOnly() for comment on what this is + } + + // TODO: these are scalar operations--why are they in Matrix? + template + ElemType Matrix::Exp10(ElemType num) + { + return (ElemType)exp(num*2.302585093); + } + + template + ElemType Matrix::Mod(ElemType x, ElemType y) + { + assert(y > 0); + if (y <= 0) + LogicError("y is smaller than zero"); + + return x - y * floor(x / y); + } + + // TODO: use static LogAdd() as defined in TensorOps.h + // Not doing this currently because that one uses ElemType for all ops, while this one uses double inside. Must compare before making this change. + template + ElemType Matrix::LogAdd(ElemType x, ElemType y) + { + ElemType temp, diff, z; + + if (x < y) + { + temp = x; x = y; y = temp; // TODO: ::swap(x,y)? + } + diff = y - x; + if (diff < MINLOGEXP) + { + return (ElemType) ((x < LSMALL) ? LZERO : x); + } + else + { + z = exp(diff); + return (ElemType)(x + log(1.0 + z)); + } + } + + //Matrix& Matrix::Shift(const Matrix& a, size_t shift) + //[this]= (a right shift by n), padded with zeros + // shift left, shift needs to be negative value + // shift right, shift needs to be positive value + // BUGBUG: Leaves uninitialized values in the opened-up columns. + template + Matrix& Matrix::Shift(const Matrix& a, int shift) + { + if (a.IsEmpty()) + LogicError("Shift: Matrix is empty."); + else + LogicError("Shift: BUGBUG This function currently leaves uninitialized values. Fix the code or contact fseide@microsoft.com."); + + auto& us = *this; + if (this != &a) + { + Resize(a.GetNumRows(), a.GetNumCols()); + } + + long n = (long)GetNumCols(); + + if (shift >= 0 && shift < n) + us.ColumnSlice(shift, n - shift).SetValue(a.ColumnSlice(0, n - shift)); + if (shift < 0 && shift > -n) + us.ColumnSlice(0, n + shift).SetValue(a.ColumnSlice(-shift, n + shift)); + return *this; + } + + template + Matrix& Matrix::AssignElementProductOfWithShiftNeg(const Matrix& a, const Matrix& b, size_t shift, size_t negnumber) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOfWithShiftNeg: Matrix is empty."); + + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("The input matrix dimensions do not match."); + + if (a.GetNumRows() != 1) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); + + DecideAndMoveToRightDevice(a, b, *this); + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignElementProductOfWithShiftNeg(*a.m_CPUMatrix, *b.m_CPUMatrix, shift, negnumber), + m_GPUMatrix->AssignElementProductOfWithShiftNeg(*a.m_GPUMatrix, *b.m_GPUMatrix, shift, negnumber), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + return *this; + } + + template + Matrix& Matrix::AssignInnerProductOfWithShiftNeg(const Matrix& a, const Matrix& b, const bool isColWise, size_t shift, size_t negnumber) + { + InnerProductWithShiftNeg(a, b, *this, isColWise, shift, negnumber); + return *this; + } + + template + void Matrix::InnerProductWithShiftNeg(const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise, size_t shift, size_t negnumber) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrix is empty."); + + DecideAndMoveToRightDevice(a, b, c); + + if (a.GetMatrixType() != b.GetMatrixType()) + NOT_IMPLEMENTED; + + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::InnerProductWithShiftNeg(*a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, isColWise, shift, negnumber), + GPUMatrix::InnerProductWithShiftNeg(*a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, shift, negnumber), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::GetARowByIndex(const Matrix& a, size_t index) + { + if (a.IsEmpty()) + LogicError("GetARowByIndex: Matrix is empty."); + + + //WARNING: a and this must have same type + if (!(GetMatrixType() == a.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->GetARowByIndex(*a.m_CPUMatrix, index), + m_GPUMatrix->GetARowByIndex(*a.m_GPUMatrix, index), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + template + void Matrix::ConductRowElementMultiplyWithShift(const Matrix& a, const Matrix& b, Matrix& c, size_t shift, bool bFirstmatrixfixed) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("InnerProduct: one of the input matrix is empty."); + + DecideAndMoveToRightDevice(a, b, c); + + if (a.GetMatrixType() != b.GetMatrixType()) + NOT_IMPLEMENTED; + + c.SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(&c, + &c, + CPUMatrix::ConductRowElementMultiplyWithShift(*a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, shift, bFirstmatrixfixed), + GPUMatrix::ConductRowElementMultiplyWithShift(*a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, shift, bFirstmatrixfixed), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::AssignElementProductOfWithShift(const Matrix& a, const Matrix& b, size_t shift) + { + if (a.IsEmpty() || b.IsEmpty()) + LogicError("AssignElementProductOfWithShift: Matrix is empty."); + + assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()); + if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols())) + InvalidArgument("The input matrix dimensions do not match."); + + if (a.GetNumRows() != 1) + InvalidArgument("AssignElementProductOfWithShiftNeg: The input matrix must be a row vector."); + + DecideAndMoveToRightDevice(a, b, *this); + if (!(a.GetMatrixType() == b.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(a.GetMatrixType(), a.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignElementProductOfWithShift(*a.m_CPUMatrix, *b.m_CPUMatrix, shift), + m_GPUMatrix->AssignElementProductOfWithShift(*a.m_GPUMatrix, *b.m_GPUMatrix, shift), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + return *this; + } + + template + void Matrix::RCRFBackwardCompute(const Matrix& alpha, Matrix& beta, + Matrix& functionValues, const Matrix& lbls, + const Matrix& pos_scores, const Matrix& pair_scores, const int shift) + { + DecideAndMoveToRightDevice(alpha, beta); + functionValues._transferToDevice(alpha.GetDeviceId()); + beta._transferToDevice(alpha.GetDeviceId()); + + DISPATCH_MATRIX_ON_FLAG(&alpha, + &beta, + CPUMatrix::RCRFBackwardCompute( + *alpha.m_CPUMatrix, + *beta.m_CPUMatrix, + *lbls.m_CPUMatrix, + *pair_scores.m_CPUMatrix), + GPUMatrix::RCRFBackwardCompute( + *alpha.m_GPUMatrix, + *beta.m_GPUMatrix, + *lbls.m_GPUMatrix, + *pos_scores.m_GPUMatrix, + *pair_scores.m_GPUMatrix, shift), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::RCRFTransGrdCompute(const Matrix& lbls, + const Matrix& alpha, + const Matrix& beta, + const Matrix& pair_scores, + Matrix& grd, + const int startLbl, + const int shift) + { + DecideAndMoveToRightDevice(alpha, grd); + grd._transferToDevice(alpha.GetDeviceId()); + + DISPATCH_MATRIX_ON_FLAG(&alpha, + &grd, + CPUMatrix::RCRFTransGrdCompute( + *lbls.m_CPUMatrix, + *alpha.m_CPUMatrix, + *beta.m_CPUMatrix, + *pair_scores.m_CPUMatrix, + *grd.m_CPUMatrix), + GPUMatrix::RCRFTransGrdCompute( + *lbls.m_GPUMatrix, + *alpha.m_GPUMatrix, + *beta.m_GPUMatrix, + *pair_scores.m_GPUMatrix, + *grd.m_GPUMatrix, + startLbl, + shift), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + Matrix& Matrix::DropFrame(const Matrix& label, const Matrix& gamma, const ElemType & threshhold) + { + DecideAndMoveToRightDevice(*this, label, gamma); + + if (label.GetNumCols() != gamma.GetNumCols() || label.GetNumRows() != gamma.GetNumRows()) + LogicError("DropFrame: label matrix is not in the same size as gamm matrix."); + SwitchToMatrixType(label.GetMatrixType(), label.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->DropFrame(*label.m_CPUMatrix, *gamma.m_CPUMatrix, threshhold), + m_GPUMatrix->DropFrame(*label.m_GPUMatrix, *gamma.m_GPUMatrix, threshhold), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + + return *this; + } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + Matrix& Matrix::AssignSequenceError(const ElemType hsmoothingWeight, const Matrix& label, + const Matrix& dnnoutput, const Matrix& gamma, ElemType alpha) + { + DecideAndMoveToRightDevice(label, dnnoutput, gamma); + + if (!(label.GetMatrixType() == gamma.GetMatrixType())) + NOT_IMPLEMENTED; + + SwitchToMatrixType(label.GetMatrixType(), label.GetFormat(), false); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->AssignSequenceError(hsmoothingWeight, *label.m_CPUMatrix, *dnnoutput.m_CPUMatrix, *gamma.m_CPUMatrix, alpha), + m_GPUMatrix->AssignSequenceError(hsmoothingWeight, *label.m_GPUMatrix, *dnnoutput.m_GPUMatrix, *gamma.m_GPUMatrix, alpha), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + return *this; + } +#pragma endregion Static BLAS Functions + + template + void Matrix::TensorOp(ElemType beta, const Matrix& a, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 2> & regularStrides, + const SmallVector & reducingOpDims, const array, 2> & reducingStrides) + { + DecideAndMoveToRightDevice(*this, a); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::TensorOp(ElemType beta, const Matrix& a, const Matrix& b, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 3> & regularStrides, + const SmallVector & reducingOpDims, const array, 3> & reducingStrides) + { + DecideAndMoveToRightDevice(*this, a, b); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, *b.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, *b.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template + void Matrix::TensorOp(ElemType beta, const Matrix& a, const Matrix& b, const Matrix& c, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 4> & regularStrides, + const SmallVector & reducingOpDims, const array, 4> & reducingStrides) + { + DecideAndMoveToRightDevice(*this, a, b, c); + + DISPATCH_MATRIX_ON_FLAG(this, + this, + m_CPUMatrix->TensorOp(beta, *a.m_CPUMatrix, *b.m_CPUMatrix, *c.m_CPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + m_GPUMatrix->TensorOp(beta, *a.m_GPUMatrix, *b.m_GPUMatrix, *c.m_GPUMatrix, alpha, op, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides), + NOT_IMPLEMENTED, + NOT_IMPLEMENTED + ); + } + + template class Matrix; + template class Matrix; + + // We use Matrix as the backing store for QuantizedMatrix, and also as a flag matrix. + // Let's explicitly instantiate the methods we need for that purpose + template Matrix::Matrix(DEVICEID_TYPE); + template Matrix::Matrix(Matrix&&); + template Matrix::Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId, const MatrixType matrixType, const MatrixFormat matrixFormat); + template Matrix::Matrix(const size_t numRows, const size_t numCols, char *pArray, const size_t matrixFlags, DEVICEID_TYPE deviceId, const size_t nnz); + template Matrix::~Matrix(); + template Matrix& Matrix::operator=(Matrix&& moveFrom); + template Matrix& Matrix::operator=(const Matrix& deepCopyFrom); + template char* Matrix::BufferPointer() const; + template int Matrix::GetDeviceId() const; + template size_t Matrix::GetNumElements() const; + template Matrix Matrix::ColumnSlice(size_t startColumn, size_t numCols) const; + template void Matrix::_transferToDevice(int id_to, bool ismoved, bool emptyTransfer) const; + template size_t Matrix::GetNumRows() const; + template size_t Matrix::GetNumCols() const; + template void Matrix::SetValue(const char); + template void Matrix::SetValue(size_t numRows, const size_t numCols, int deviceId, char *pArray, size_t matrixFlags); + template bool Matrix::IsEmpty() const; + template void Matrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, bool growOnly); + +}}} diff --git a/Source/Math/Matrix.h b/Source/Math/Matrix.h index 94eb0dd53642..b9a1b2fa47d4 100644 --- a/Source/Math/Matrix.h +++ b/Source/Math/Matrix.h @@ -1,469 +1,469 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -// TODO: -// - remove empty-matrix checks: if an op is well-defined with empty matrices, then do it -// - Resize() must be cheap if it does nothing (I already did that for CPU; already done for GPU?) - -#pragma once - -#include "Basics.h" -#include "File.h" -#include "CommonMatrix.h" -#include "TensorShape.h" // only for SmallVector; I was hoping to keep this out -#include -#include // for shared_ptr -#include -#include - -// This class is exported from the Math.dll -namespace Microsoft { namespace MSR { namespace CNTK { - - enum CurrentDataLocation - { - NONE, CPU, GPU, BOTH - }; - - enum MatrixType - { - UNDETERMINED, DENSE, SPARSE - }; - - // TODO: create an -agnostic base class, then move generic functions such as getting dims, resizing, and getting/setting as scalars - class MATH_API MatrixBase - { - protected: - //virtual ~MatrixBase() { }; - // TODO: currently this causes link errors when building DLLs - }; - - // avoid pulling in these header files for consumers of this class - template class GPUMatrix; - template class CPUMatrix; - template class GPUSparseMatrix; - template class CPUSparseMatrix; - template class DeviceBoundNumber; - - //To compy with BLAS libraries matrices are stored in ColMajor. However, by default C/C++/C# use RowMajor - //convertion is need when passing data between Matrix and C++ matrices - //For the best performance compile CNTKMath project with NO_SYNC preprocessor directive - //!!!WARNING!!! This class is NOT THREAD SAFE. Test and add necessary modifications if using in multi-threaded environment - template - class MATH_API Matrix : public MatrixBase - { - private: - mutable BaseMatrix *m_baseMatrix; - mutable GPUMatrix *m_GPUMatrix; - mutable CPUMatrix *m_CPUMatrix; - mutable GPUSparseMatrix *m_GPUSparseMatrix; - mutable CPUSparseMatrix *m_CPUSparseMatrix; - mutable MatrixType m_matrixType; - mutable CurrentDataLocation m_currentDataLocation; //Indicates which matrix is current - mutable DEVICEID_TYPE m_preferredDeviceId; - - mutable size_t m_numTimesDeviceChanged; - mutable size_t m_numTimesMatrixTypeChanged; - mutable int m_devicesTransferedTo[2]; // TODO: what is this for? Seems only diagnostics - - //Moves matrix from device id_from to device with id_to. This method doesn't change preferred device Id - void _transferFromDeviceToDevice(int id_from, int id_to, bool ismoved=true,bool emptyTransfer=false) const; - //Moves matrix from current device to device with id_to. This method doesn't change preferred device Id - void _transferToDevice(int id_to, bool ismoved=true, bool emptyTransfer=false) const; - static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b); - static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b, const Matrix& c); - static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& d); - static void CopyElementsFromDenseToSparse(CPUMatrix& from, CPUSparseMatrix& dest); - - public: - //Constructors, destructors and other static matrix builders - //Each constructor can take deviceId as parameter. - //If deviceId<0 then the matrix will be based in RAM (CPUMatrix) - //Elseif deviceId>=0 and * baseMatrix, ElemType *pArray, DEVICEID_TYPE deviceId); // constructor for setting Matrix from a base matrix (externally managed butter pArray) - Matrix(FILE* f, const char * matrixName, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, const MatrixType matrixType = DENSE); //matrixName is used to verify that correct matrix is read. - Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId = AUTOPLACEMATRIX, const MatrixType matrixType = DENSE, const MatrixFormat matrixFormat = matrixFormatDense); - Matrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags=matrixFlagNormal, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, const size_t nnz=0); - Matrix(const Matrix& deepCopyFrom, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); //copy constructor, deep copy - Matrix& operator=(const Matrix& deepCopyFrom); //assignment operator, deep copy - Matrix(Matrix&& moveFrom); //move constructor, shallow copy - Matrix& operator=(Matrix&& moveFrom); //move coment operator, shallow copy - - static Matrix Ones(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); - static Matrix Zeros(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); - static Matrix Eye(const size_t rows, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); - -#define USE_TIME_BASED_SEED ULONG_MAX - static Matrix RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed = USE_TIME_BASED_SEED, DEVICEID_TYPE deviceId = AUTOPLACEMATRIX); - static Matrix RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); - - static void SetDevice(DEVICEID_TYPE deviceId); - - void Clear(); - ~Matrix(); - - private: - Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, const MatrixFormat matrixFormat, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix - Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix - Matrix(const MatrixFlags matrixFlags, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix - void Init(DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix - void SetDataLocation(CurrentDataLocation location, MatrixType type=UNDETERMINED) const; - - public: - MatrixType GetMatrixType() const { return m_matrixType; } - MatrixFormat GetFormat() const { return m_baseMatrix->GetFormat(); } - bool OwnBuffer() const { return m_baseMatrix->OwnBuffer(); } - int GetDeviceId() const; //-1 if CPU, otherwise GPU CUDA device id - DEVICEID_TYPE GetPreferredDeviceId() const { return m_preferredDeviceId; }; //-1 if CPU, otherwise GPU CUDA device id - void SetPreferredDeviceId(DEVICEID_TYPE preferredDeviceId) { m_preferredDeviceId = preferredDeviceId; } - //Moves matrix from device id_from to device with id_to. - //If emptyTransfer=true, then no data is ever moved, just corresponding GPU/CPU matrices are deleted and then created using empty constructor - void TransferFromDeviceToDevice(int id_from, int id_to, bool ismoved = false,/*if false then keep source and set location to BOTH*/ bool emptyTransfer = false, bool updatePreferredDevice = true) const; - //Same as TransferFromDeviceToDevice() but moves only if it is currently not on the target device - void TransferToDeviceIfNotThere(int id_to, bool ismoved = false, bool emptyTransfer = false, bool updatePreferredDevice = true) const; - void TransferToDeviceIfNotThereAndNotAutoPlace(int id_to, bool ismoved = false, bool emptyTransfer = false, bool updatePreferredDevice = true) const; - CurrentDataLocation GetCurrentMatrixLocation() const { return m_currentDataLocation; }; - void SwitchToMatrixType(MatrixType newMatrixType, MatrixFormat newMatrixFormat, bool keepValues); //sets matrix type between dense and sparse - size_t GetNumRows() const; - size_t GetNumCols() const; - size_t GetNumElements() const; - bool HasNoElements() const { return GetNumElements() == 0; } - wchar_t* GetMatrixName() const; - void SetMatrixName(const wchar_t* s); - bool IsEmpty() const; - size_t BufferSize() const; - ElemType* BufferPointer() const; - size_t NzCount() const; - - ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller - size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller - // colStride specifies leading dimension of dst. - // REVIEW alexeyk: GPU version copies from device to host only, implement all versions (device <-> host). - void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; - - Matrix ColumnSlice(size_t startColumn, size_t numCols) const; - - // difference between AssignColumnSlice and SetColumnSlice - // AssignColumnSlice : this(:, startColumn:startColumn+numCols-1) = fromMatrix(:, startColumn: startColumn+numCols-1) - // SetColumnSlice : this(:, startColumn:startColumn+numCols-1) = fromMatrix(:, 0: startColumn+numCols-1) - // AssignColumnSlice do not transfer data, it uses external data - // SetColumnSlice copies data - - Matrix& AssignColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols); - Matrix& SetColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols); - - void CopyColumnsStrided(const Matrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); - - Matrix Diagonal() const; - Matrix AssignDiagonalValuesTo(Matrix& diag) const; - void ShiftBy(int numShift); - - // TODO: all these scalars should be passed as doubles and cast down inside - void NormalGrad(Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum, const bool useNAG); - ElemType Adagrad(Matrix& gradients, const bool needAveMultiplier); - void FSAdagrad(size_t mbSize, Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum); - ElemType RmsProp(Matrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier); - - void Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve = 10000, bool growOnly = true); //by default we only reallocate if need to grow - void Resize(const Matrix& other) { Resize(other.GetNumRows(), other.GetNumCols()); } - void VerifySize(size_t rows, size_t cols) - { - m_baseMatrix->VerifySize(rows, cols); - } - - Matrix AsReference() const { return ColumnSlice(0, GetNumCols()); } // get a reference (e.g. this is not resizable but can be reshaped) - void Reshape(const size_t numRows, const size_t numCols); // note: reshapes in place. To get a reshaped reference, use Reshaped() - Matrix Reshaped(const size_t numRows, const size_t numCols) const // get a reshaped reference - { - Matrix result = AsReference(); - result.Reshape(numRows, numCols); - return result; - } - - // update number of columns - // TODO: a future version may want to enforce retaining the content, to allow dynamically growing layouts column by column (when size is not known upfront) - void ResizeColumns(const size_t numCols) { Resize(GetNumRows(), numCols); } - - // similarl to the repmat operation in matlab or octave - static Matrix RepMat(const Matrix& frmMat, const size_t rows, const size_t cols); - size_t GetAllocatedSize() const; - void Reset(); // reset for sparse matrix - - const ElemType operator() (const size_t row, const size_t col) const; - ElemType& operator() (const size_t row, const size_t col); - ElemType Get00Element() const; - - void SetValue(const ElemType v); - void SetValue(const DeviceBoundNumber& db_number); - void SetValue(const Matrix& deepCopyFrom, const MatrixFormat format=matrixFormatSparseCSR); - void SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags = matrixFlagNormal); - void SetValue(const size_t rIdx, const size_t cIdx, ElemType val); // set matrix sparsely - void SetValue(const size_t numRows, const size_t numCols, std::initializer_list l) { std::vector vals(l); assert(vals.size() == numRows * numCols); SetValue(numRows, numCols, GetDeviceId(), vals.data(), matrixFormatRowMajor); } // SetValue(2,3, {1,2,3, 4,5,6}); - static ElemType MakeNan(size_t payload); - void Invalidate() { SetValue(MakeNan(__LINE__)); } - void SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, - const size_t nz, const size_t numRows, const size_t numCols); - - void MaskColumnsValue(const Matrix& columnsMask, ElemType val); - - void SetColumn(const ElemType* colPointer, size_t colInd); - void SetColumn(const ElemType val, size_t colInd); - void SetColumn(const Matrix& valMat, size_t colInd); - - void SetDiagonalValue(const ElemType v); - void SetDiagonalValue(const Matrix& vector); - void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); - void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); - void AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); - Matrix& AssignNoiseContrastiveEstimation(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias, Matrix& tmp); - - Matrix& AssignNCEDerivative(const Matrix& tmp, const Matrix& a, const Matrix& b, const Matrix& c, size_t inputIndex); - Matrix& AssignSoftmaxSum(const Matrix& a, const Matrix& softmax); - Matrix& AssignNceUnnormalizedEval(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias); - - Matrix Transpose(); // This method doesn't change state of Matrix. It should be a const function - Matrix& AssignTransposeOf (const Matrix& a); - - Matrix& operator+= (const ElemType alpha); - Matrix operator+ (const ElemType alpha) const; - Matrix& AssignSumOf(const ElemType alpha, const Matrix& a); - - Matrix& operator+= (const Matrix& a); - Matrix operator+ (const Matrix& a) const; - Matrix& AssignSumOf(const Matrix& a, const Matrix& b); - - Matrix& operator-= (const ElemType alpha); - Matrix operator- (const ElemType alpha) const; - Matrix& AssignDifferenceOf(const ElemType alpha, const Matrix& a); - Matrix& AssignDifferenceOf(const Matrix& a, const ElemType alpha); - - Matrix& operator-= (const Matrix& a); - Matrix operator- (const Matrix& a) const; - Matrix& AssignDifferenceOf(const Matrix& a, const Matrix& b); - - Matrix& operator*= (const ElemType alpha); - Matrix operator* (const ElemType alpha) const; - Matrix& AssignProductOf(const ElemType alpha, const Matrix& a); - - Matrix operator* (const Matrix& a) const; - Matrix& AssignProductOf (const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB); // this = a * b - Matrix& Assign1x1ProductOf(const Matrix& a1x1, const Matrix& b); // this = a * b, where a is 1x1 - - Matrix& operator/= (ElemType alpha); - Matrix operator/ (ElemType alpha) const; - - Matrix& operator^= (ElemType alpha); //element-wise power - Matrix operator^ (ElemType alpha) const; //element-wise power - Matrix& AssignElementPowerOf(const Matrix& a, const ElemType power); - - Matrix& ElementMultiplyWith (const Matrix& a); - Matrix& AssignElementProductOf (const Matrix& a, const Matrix& b); - Matrix& AddElementProductOf (const Matrix& a, const Matrix& b); - - Matrix& AssignElementDivisionOf (const Matrix& a, const Matrix& b); - Matrix& ElementDivideBy(const Matrix& a); - - Matrix& ColumnElementMultiplyWith(const Matrix& a); - Matrix& RowElementMultiplyWith(const Matrix& a); - - Matrix& ColumnElementDivideBy(const Matrix& a); - Matrix& RowElementDivideBy(const Matrix& a); - - Matrix& ElementInverse (); - Matrix& AssignElementInverseOf (const Matrix& a); - - Matrix& InplaceLinearRectifierDerivative(); - Matrix& AssignLinearRectifierDerivativeOf (const Matrix& a); - - Matrix& InplaceSigmoidDerivative(); - Matrix& AssignSigmoidDerivativeOf (const Matrix& a); - - Matrix& InplaceSigmoid (); - Matrix& AssignSigmoidOf (const Matrix& a); - - Matrix& InplaceTanh (); - Matrix& AssignTanhOf (const Matrix& a); - - Matrix& InplaceLogSoftmax (const bool isColWise); - Matrix& AssignLogSoftmaxOf (const Matrix& a, const bool isColWise); - - Matrix& InplaceHardmax(const bool isColWise); - Matrix& AssignHardmaxOf(const Matrix& a, const bool isColWise); - - //sequence training - Matrix& DropFrame(const Matrix& label, const Matrix& gamma, const ElemType & threshhold); - Matrix& AssignSequenceError(const ElemType hsmoothingWeight, const Matrix& label, const Matrix& dnnoutput, const Matrix& gamma, ElemType alpha); - Matrix& InplaceSqrt (); - Matrix& AssignSqrtOf (const Matrix& a); - - Matrix& InplaceExp (); - Matrix& AssignExpOf (const Matrix& a); - - Matrix& InplaceLog (); - Matrix& AssignLogOf (const Matrix& a); - - Matrix& InplaceCosine (); - Matrix& AssignCosineOf (const Matrix& a); - - Matrix& InplaceNegativeSine (); - Matrix& AssignNegativeSineOf (const Matrix& a); - - Matrix& InplaceLog10 (); - Matrix& AssignLog10Of (const Matrix& a); - - Matrix& InplaceAbs (); - Matrix& AssignAbsOf (const Matrix& a); - - Matrix& InplaceTruncateBottom (const ElemType threshold); - Matrix& AssignTruncateBottomOf (const Matrix& a, const ElemType threshold); - Matrix& InplaceTruncateTop (const ElemType threshold); - Matrix& AssignTruncateTopOf (const Matrix& a, const ElemType threshold); - Matrix& InplaceTruncate (const ElemType threshold); - Matrix& InplaceSoftThreshold(const ElemType threshold); - void InplaceTranspose(); - - Matrix& SetToZeroIfAbsLessThan (const ElemType threshold); - - DeviceBoundNumber Sum_AsDeviceBoundNum() const; - ElemType SumOfAbsElements () const; //sum of all abs(elements) - ElemType SumOfElements () const; //sum of all elements - Matrix& AssignSumOfElements(const Matrix& a); - - ElemType LogAddSumOfElements() const; - - Matrix& AssignToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); - Matrix& AssignRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); - Matrix& AddToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); - Matrix& AddWithRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); - //Matrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); - - Matrix& AssignRepeatOf(const Matrix& a, const size_t numRowRepeats, const size_t numColRepeats); - Matrix& AddToRowRepeatValuesOf(const Matrix& a, const size_t numRepeats); - - Matrix& AssignPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - Matrix& AddFoldedPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); - - bool IsValid() const; - bool IsEqualTo(const Matrix& a, const ElemType threshold = 1e-8) const; - - static void VectorSum(const Matrix& a, Matrix& c, const bool isColWise); - - void VectorNorm1(Matrix& c, const bool isColWise) const; - Matrix& AssignVectorNorm1Of(Matrix& a, const bool isColWise); // TODO: arg should be const - - void VectorNorm2(Matrix& c, const bool isColWise) const; - Matrix& AssignVectorNorm2Of(Matrix& a, const bool isColWise); // TODO: arg should be const - - void VectorNormInf(Matrix& c, const bool isColWise) const; - Matrix& AssignVectorNormInfOf(Matrix& a, const bool isColWise); - - Matrix& AssignInnerProductOf(const Matrix& a, const Matrix& b, const bool isColWise); - Matrix& AssignKhatriRaoProductOf(const Matrix& a, const Matrix& b); - Matrix& AddColumnReshapeProductOf(const Matrix& a, const Matrix& b, const bool transposeAColumn); - - Matrix& AddWithScaleOf(ElemType alpha, const Matrix& a); // this += alpha * a - - ElemType FrobeniusNorm() const; - Matrix& AssignFrobeniusNormOf(const Matrix& a); - - ElemType MatrixNormInf() const; - ElemType MatrixNorm1() const; - ElemType MatrixNorm0() const; //number of non-zero elemets - Matrix& AssignSignOf(const Matrix& a); - Matrix& AddSignOf(const Matrix& a); - void VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise) const; - void VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise, int topK) const; - void VectorMin(Matrix& minIndexes, Matrix& minValues, const bool isColWise) const; - - Matrix& AssignNumOfDiff(const Matrix& a, const Matrix& b, bool searchInCol = false); - - Matrix& AssignInnerProductOfMatrices(const Matrix& a, const Matrix& b); //this method will resize(1,1) first - - bool HasNan (const char * name) const; - size_t CountNanInf() const; - - void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; - void Print(const char* matrixName = nullptr) const; //print whole matrix. can be expensive - - Matrix& AssignPackedConvolutionInput(const Matrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding = false); - Matrix& UnpackConvolutionInput(Matrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding = false) const; - Matrix& AssignMaxPoolingResult(const Matrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - Matrix& AddMaxPoolingGradient(const Matrix& outputGradientBatch, const Matrix& inputBatch, const Matrix& outputBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - Matrix& AssignAveragePoolingResult(const Matrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - Matrix& AddAveragePoolingGradient(const Matrix& outputGradientBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); - public: - // TODO: why are these not static? And why are they here? - ElemType Exp10(ElemType num); - ElemType Mod(ElemType x , ElemType y); - ElemType LogAdd(ElemType x, ElemType y); - - public: - static DEVICEID_TYPE GetBestGPUDeviceId(); - - //static BLAS functions - - // singular value decomposition of A as A = U*SIGMA*VT - static void SVD(const Matrix& A, Matrix& SIGMA, Matrix& U, Matrix& VT, Matrix& W); - - static void MultiplyAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, ElemType beta, Matrix& c); // SGEMM - static void MultiplyAndAdd(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, Matrix& c); - static void Multiply(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, Matrix& c); - static void Multiply(const Matrix& a, const Matrix& b, Matrix& c); - static void Multiply1x1AndWeightedAdd(ElemType alpha, const Matrix& a, const Matrix& b, ElemType beta, Matrix& c); - static void ConvolveAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, ElemType beta, Matrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise); - - static void ScaleAndAdd(ElemType alpha, const Matrix& a, Matrix& c); - static void ScaleAndAdd(ElemType alpha, const Matrix& a, ElemType beta, Matrix& c); - static void AddScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c); - static void AssignScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c); - static void AddScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c); // c += alpha * (a - b) - static void AssignScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c); - - static void AddElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); - //static void AddLogElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); - static void AssignElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); - static void MinusOneAt(Matrix& c, const size_t position); - - static void Scale(ElemType alpha, Matrix& a); - static void Scale(const Matrix& alpha, Matrix& a); //In this case Matrix alpha must be 1x1 - static void Scale(ElemType alpha, const Matrix& a, Matrix& c); - static void InnerProduct (const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise); - static ElemType InnerProductOfMatrices(const Matrix& a, const Matrix& b); - static void ElementWisePower (ElemType alpha, const Matrix& a, Matrix& c); - - static bool AreEqual(const Matrix& a, const Matrix& b, const ElemType threshold = 1e-8); - static bool HasElement(const Matrix& a, const ElemType value = 0.0); - - static void TensorShuffleScaleAndAdd(ElemType keepWeight, const Matrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const Matrix& b, Matrix& c); - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +// TODO: +// - remove empty-matrix checks: if an op is well-defined with empty matrices, then do it +// - Resize() must be cheap if it does nothing (I already did that for CPU; already done for GPU?) + +#pragma once + +#include "Basics.h" +#include "File.h" +#include "CommonMatrix.h" +#include "TensorShape.h" // only for SmallVector; I was hoping to keep this out +#include +#include // for shared_ptr +#include +#include + +// This class is exported from the Math.dll +namespace Microsoft { namespace MSR { namespace CNTK { + + enum CurrentDataLocation + { + NONE, CPU, GPU, BOTH + }; + + enum MatrixType + { + UNDETERMINED, DENSE, SPARSE + }; + + // TODO: create an -agnostic base class, then move generic functions such as getting dims, resizing, and getting/setting as scalars + class MATH_API MatrixBase + { + protected: + //virtual ~MatrixBase() { }; + // TODO: currently this causes link errors when building DLLs + }; + + // avoid pulling in these header files for consumers of this class + template class GPUMatrix; + template class CPUMatrix; + template class GPUSparseMatrix; + template class CPUSparseMatrix; + template class DeviceBoundNumber; + + //To compy with BLAS libraries matrices are stored in ColMajor. However, by default C/C++/C# use RowMajor + //convertion is need when passing data between Matrix and C++ matrices + //For the best performance compile CNTKMath project with NO_SYNC preprocessor directive + //!!!WARNING!!! This class is NOT THREAD SAFE. Test and add necessary modifications if using in multi-threaded environment + template + class MATH_API Matrix : public MatrixBase + { + private: + mutable BaseMatrix *m_baseMatrix; + mutable GPUMatrix *m_GPUMatrix; + mutable CPUMatrix *m_CPUMatrix; + mutable GPUSparseMatrix *m_GPUSparseMatrix; + mutable CPUSparseMatrix *m_CPUSparseMatrix; + mutable MatrixType m_matrixType; + mutable CurrentDataLocation m_currentDataLocation; //Indicates which matrix is current + mutable DEVICEID_TYPE m_preferredDeviceId; + + mutable size_t m_numTimesDeviceChanged; + mutable size_t m_numTimesMatrixTypeChanged; + mutable int m_devicesTransferedTo[2]; // TODO: what is this for? Seems only diagnostics + + //Moves matrix from device id_from to device with id_to. This method doesn't change preferred device Id + void _transferFromDeviceToDevice(int id_from, int id_to, bool ismoved=true,bool emptyTransfer=false) const; + //Moves matrix from current device to device with id_to. This method doesn't change preferred device Id + void _transferToDevice(int id_to, bool ismoved=true, bool emptyTransfer=false) const; + static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b); + static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b, const Matrix& c); + static void DecideAndMoveToRightDevice(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& d); + static void CopyElementsFromDenseToSparse(CPUMatrix& from, CPUSparseMatrix& dest); + + public: + //Constructors, destructors and other static matrix builders + //Each constructor can take deviceId as parameter. + //If deviceId<0 then the matrix will be based in RAM (CPUMatrix) + //Elseif deviceId>=0 and * baseMatrix, ElemType *pArray, DEVICEID_TYPE deviceId); // constructor for setting Matrix from a base matrix (externally managed butter pArray) + Matrix(FILE* f, const char * matrixName, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, const MatrixType matrixType = DENSE); //matrixName is used to verify that correct matrix is read. + Matrix(const size_t numRows, const size_t numCols, DEVICEID_TYPE deviceId = AUTOPLACEMATRIX, const MatrixType matrixType = DENSE, const MatrixFormat matrixFormat = matrixFormatDense); + Matrix(const size_t numRows, const size_t numCols, ElemType *pArray, const size_t matrixFlags=matrixFlagNormal, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX, const size_t nnz=0); + Matrix(const Matrix& deepCopyFrom, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); //copy constructor, deep copy + Matrix& operator=(const Matrix& deepCopyFrom); //assignment operator, deep copy + Matrix(Matrix&& moveFrom); //move constructor, shallow copy + Matrix& operator=(Matrix&& moveFrom); //move coment operator, shallow copy + + static Matrix Ones(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); + static Matrix Zeros(const size_t rows, const size_t cols, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); + static Matrix Eye(const size_t rows, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); + +#define USE_TIME_BASED_SEED ULONG_MAX + static Matrix RandomUniform(const size_t rows, const size_t cols, const ElemType low, const ElemType high, unsigned long seed = USE_TIME_BASED_SEED, DEVICEID_TYPE deviceId = AUTOPLACEMATRIX); + static Matrix RandomGaussian(const size_t rows, const size_t cols, const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED, DEVICEID_TYPE deviceId=AUTOPLACEMATRIX); + + static void SetDevice(DEVICEID_TYPE deviceId); + + void Clear(); + ~Matrix(); + + private: + Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, const MatrixFormat matrixFormat, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix + Matrix(const MatrixFlags matrixFlags, const MatrixType matrixType, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix + Matrix(const MatrixFlags matrixFlags, DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix + void Init(DEVICEID_TYPE deviceID); //only used internally to initialize a blank matrix + void SetDataLocation(CurrentDataLocation location, MatrixType type=UNDETERMINED) const; + + public: + MatrixType GetMatrixType() const { return m_matrixType; } + MatrixFormat GetFormat() const { return m_baseMatrix->GetFormat(); } + bool OwnBuffer() const { return m_baseMatrix->OwnBuffer(); } + int GetDeviceId() const; //-1 if CPU, otherwise GPU CUDA device id + DEVICEID_TYPE GetPreferredDeviceId() const { return m_preferredDeviceId; }; //-1 if CPU, otherwise GPU CUDA device id + void SetPreferredDeviceId(DEVICEID_TYPE preferredDeviceId) { m_preferredDeviceId = preferredDeviceId; } + //Moves matrix from device id_from to device with id_to. + //If emptyTransfer=true, then no data is ever moved, just corresponding GPU/CPU matrices are deleted and then created using empty constructor + void TransferFromDeviceToDevice(int id_from, int id_to, bool ismoved = false,/*if false then keep source and set location to BOTH*/ bool emptyTransfer = false, bool updatePreferredDevice = true) const; + //Same as TransferFromDeviceToDevice() but moves only if it is currently not on the target device + void TransferToDeviceIfNotThere(int id_to, bool ismoved = false, bool emptyTransfer = false, bool updatePreferredDevice = true) const; + void TransferToDeviceIfNotThereAndNotAutoPlace(int id_to, bool ismoved = false, bool emptyTransfer = false, bool updatePreferredDevice = true) const; + CurrentDataLocation GetCurrentMatrixLocation() const { return m_currentDataLocation; }; + void SwitchToMatrixType(MatrixType newMatrixType, MatrixFormat newMatrixFormat, bool keepValues); //sets matrix type between dense and sparse + size_t GetNumRows() const; + size_t GetNumCols() const; + size_t GetNumElements() const; + bool HasNoElements() const { return GetNumElements() == 0; } + wchar_t* GetMatrixName() const; + void SetMatrixName(const wchar_t* s); + bool IsEmpty() const; + size_t BufferSize() const; + ElemType* BufferPointer() const; + size_t NzCount() const; + + ElemType* CopyToArray() const; //allocated by the callee but need to be deleted by the caller + size_t CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const; //allocated by the callee but need to be deleted by the caller + // colStride specifies leading dimension of dst. + // REVIEW alexeyk: GPU version copies from device to host only, implement all versions (device <-> host). + void CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const; + + Matrix ColumnSlice(size_t startColumn, size_t numCols) const; + + // difference between AssignColumnSlice and SetColumnSlice + // AssignColumnSlice : this(:, startColumn:startColumn+numCols-1) = fromMatrix(:, startColumn: startColumn+numCols-1) + // SetColumnSlice : this(:, startColumn:startColumn+numCols-1) = fromMatrix(:, 0: startColumn+numCols-1) + // AssignColumnSlice do not transfer data, it uses external data + // SetColumnSlice copies data + + Matrix& AssignColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols); + Matrix& SetColumnSlice(const Matrix& fromMatrix, size_t startColumn, size_t numCols); + + void CopyColumnsStrided(const Matrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride); + + Matrix Diagonal() const; + Matrix AssignDiagonalValuesTo(Matrix& diag) const; + void ShiftBy(int numShift); + + // TODO: all these scalars should be passed as doubles and cast down inside + void NormalGrad(Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum, const bool useNAG); + ElemType Adagrad(Matrix& gradients, const bool needAveMultiplier); + void FSAdagrad(size_t mbSize, Matrix& gradients, Matrix& functionValues, const ElemType learnRatePerSample, const ElemType momentum); + ElemType RmsProp(Matrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier); + + void Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve = 10000, bool growOnly = true); //by default we only reallocate if need to grow + void Resize(const Matrix& other) { Resize(other.GetNumRows(), other.GetNumCols()); } + void VerifySize(size_t rows, size_t cols) + { + m_baseMatrix->VerifySize(rows, cols); + } + + Matrix AsReference() const { return ColumnSlice(0, GetNumCols()); } // get a reference (e.g. this is not resizable but can be reshaped) + void Reshape(const size_t numRows, const size_t numCols); // note: reshapes in place. To get a reshaped reference, use Reshaped() + Matrix Reshaped(const size_t numRows, const size_t numCols) const // get a reshaped reference + { + Matrix result = AsReference(); + result.Reshape(numRows, numCols); + return result; + } + + // update number of columns + // TODO: a future version may want to enforce retaining the content, to allow dynamically growing layouts column by column (when size is not known upfront) + void ResizeColumns(const size_t numCols) { Resize(GetNumRows(), numCols); } + + // similarl to the repmat operation in matlab or octave + static Matrix RepMat(const Matrix& frmMat, const size_t rows, const size_t cols); + size_t GetAllocatedSize() const; + void Reset(); // reset for sparse matrix + + const ElemType operator() (const size_t row, const size_t col) const; + ElemType& operator() (const size_t row, const size_t col); + ElemType Get00Element() const; + + void SetValue(const ElemType v); + void SetValue(const DeviceBoundNumber& db_number); + void SetValue(const Matrix& deepCopyFrom, const MatrixFormat format=matrixFormatSparseCSR); + void SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags = matrixFlagNormal); + void SetValue(const size_t rIdx, const size_t cIdx, ElemType val); // set matrix sparsely + void SetValue(const size_t numRows, const size_t numCols, std::initializer_list l) { std::vector vals(l); assert(vals.size() == numRows * numCols); SetValue(numRows, numCols, GetDeviceId(), vals.data(), matrixFormatRowMajor); } // SetValue(2,3, {1,2,3, 4,5,6}); + static ElemType MakeNan(size_t payload); + void Invalidate() { SetValue(MakeNan(__LINE__)); } + void SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, + const size_t nz, const size_t numRows, const size_t numCols); + + void MaskColumnsValue(const Matrix& columnsMask, ElemType val); + + void SetColumn(const ElemType* colPointer, size_t colInd); + void SetColumn(const ElemType val, size_t colInd); + void SetColumn(const Matrix& valMat, size_t colInd); + + void SetDiagonalValue(const ElemType v); + void SetDiagonalValue(const Matrix& vector); + void SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed=USE_TIME_BASED_SEED); + void SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + void SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed=USE_TIME_BASED_SEED); + void AddGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed=USE_TIME_BASED_SEED); + Matrix& AssignNoiseContrastiveEstimation(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias, Matrix& tmp); + + Matrix& AssignNCEDerivative(const Matrix& tmp, const Matrix& a, const Matrix& b, const Matrix& c, size_t inputIndex); + Matrix& AssignSoftmaxSum(const Matrix& a, const Matrix& softmax); + Matrix& AssignNceUnnormalizedEval(const Matrix& a, const Matrix& b, const Matrix& c, const Matrix& bias); + + Matrix Transpose(); // This method doesn't change state of Matrix. It should be a const function + Matrix& AssignTransposeOf (const Matrix& a); + + Matrix& operator+= (const ElemType alpha); + Matrix operator+ (const ElemType alpha) const; + Matrix& AssignSumOf(const ElemType alpha, const Matrix& a); + + Matrix& operator+= (const Matrix& a); + Matrix operator+ (const Matrix& a) const; + Matrix& AssignSumOf(const Matrix& a, const Matrix& b); + + Matrix& operator-= (const ElemType alpha); + Matrix operator- (const ElemType alpha) const; + Matrix& AssignDifferenceOf(const ElemType alpha, const Matrix& a); + Matrix& AssignDifferenceOf(const Matrix& a, const ElemType alpha); + + Matrix& operator-= (const Matrix& a); + Matrix operator- (const Matrix& a) const; + Matrix& AssignDifferenceOf(const Matrix& a, const Matrix& b); + + Matrix& operator*= (const ElemType alpha); + Matrix operator* (const ElemType alpha) const; + Matrix& AssignProductOf(const ElemType alpha, const Matrix& a); + + Matrix operator* (const Matrix& a) const; + Matrix& AssignProductOf (const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB); // this = a * b + Matrix& Assign1x1ProductOf(const Matrix& a1x1, const Matrix& b); // this = a * b, where a is 1x1 + + Matrix& operator/= (ElemType alpha); + Matrix operator/ (ElemType alpha) const; + + Matrix& operator^= (ElemType alpha); //element-wise power + Matrix operator^ (ElemType alpha) const; //element-wise power + Matrix& AssignElementPowerOf(const Matrix& a, const ElemType power); + + Matrix& ElementMultiplyWith (const Matrix& a); + Matrix& AssignElementProductOf (const Matrix& a, const Matrix& b); + Matrix& AddElementProductOf (const Matrix& a, const Matrix& b); + + Matrix& AssignElementDivisionOf (const Matrix& a, const Matrix& b); + Matrix& ElementDivideBy(const Matrix& a); + + Matrix& ColumnElementMultiplyWith(const Matrix& a); + Matrix& RowElementMultiplyWith(const Matrix& a); + + Matrix& ColumnElementDivideBy(const Matrix& a); + Matrix& RowElementDivideBy(const Matrix& a); + + Matrix& ElementInverse (); + Matrix& AssignElementInverseOf (const Matrix& a); + + Matrix& InplaceLinearRectifierDerivative(); + Matrix& AssignLinearRectifierDerivativeOf (const Matrix& a); + + Matrix& InplaceSigmoidDerivative(); + Matrix& AssignSigmoidDerivativeOf (const Matrix& a); + + Matrix& InplaceSigmoid (); + Matrix& AssignSigmoidOf (const Matrix& a); + + Matrix& InplaceTanh (); + Matrix& AssignTanhOf (const Matrix& a); + + Matrix& InplaceLogSoftmax (const bool isColWise); + Matrix& AssignLogSoftmaxOf (const Matrix& a, const bool isColWise); + + Matrix& InplaceHardmax(const bool isColWise); + Matrix& AssignHardmaxOf(const Matrix& a, const bool isColWise); + + //sequence training + Matrix& DropFrame(const Matrix& label, const Matrix& gamma, const ElemType & threshhold); + Matrix& AssignSequenceError(const ElemType hsmoothingWeight, const Matrix& label, const Matrix& dnnoutput, const Matrix& gamma, ElemType alpha); + Matrix& InplaceSqrt (); + Matrix& AssignSqrtOf (const Matrix& a); + + Matrix& InplaceExp (); + Matrix& AssignExpOf (const Matrix& a); + + Matrix& InplaceLog (); + Matrix& AssignLogOf (const Matrix& a); + + Matrix& InplaceCosine (); + Matrix& AssignCosineOf (const Matrix& a); + + Matrix& InplaceNegativeSine (); + Matrix& AssignNegativeSineOf (const Matrix& a); + + Matrix& InplaceLog10 (); + Matrix& AssignLog10Of (const Matrix& a); + + Matrix& InplaceAbs (); + Matrix& AssignAbsOf (const Matrix& a); + + Matrix& InplaceTruncateBottom (const ElemType threshold); + Matrix& AssignTruncateBottomOf (const Matrix& a, const ElemType threshold); + Matrix& InplaceTruncateTop (const ElemType threshold); + Matrix& AssignTruncateTopOf (const Matrix& a, const ElemType threshold); + Matrix& InplaceTruncate (const ElemType threshold); + Matrix& InplaceSoftThreshold(const ElemType threshold); + void InplaceTranspose(); + + Matrix& SetToZeroIfAbsLessThan (const ElemType threshold); + + DeviceBoundNumber Sum_AsDeviceBoundNum() const; + ElemType SumOfAbsElements () const; //sum of all abs(elements) + ElemType SumOfElements () const; //sum of all elements + Matrix& AssignSumOfElements(const Matrix& a); + + ElemType LogAddSumOfElements() const; + + Matrix& AssignToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); + Matrix& AssignRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); + Matrix& AddToRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); + Matrix& AddWithRowSliceValuesOf(const Matrix& a, const size_t startIndex, const size_t numRows); + //Matrix& AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols); + + Matrix& AssignRepeatOf(const Matrix& a, const size_t numRowRepeats, const size_t numColRepeats); + Matrix& AddToRowRepeatValuesOf(const Matrix& a, const size_t numRepeats); + + Matrix& AssignPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + Matrix& AddFoldedPositiveAndShiftedNegSample(const Matrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber); + + bool IsValid() const; + bool IsEqualTo(const Matrix& a, const ElemType threshold = 1e-8) const; + + static void VectorSum(const Matrix& a, Matrix& c, const bool isColWise); + + void VectorNorm1(Matrix& c, const bool isColWise) const; + Matrix& AssignVectorNorm1Of(Matrix& a, const bool isColWise); // TODO: arg should be const + + void VectorNorm2(Matrix& c, const bool isColWise) const; + Matrix& AssignVectorNorm2Of(Matrix& a, const bool isColWise); // TODO: arg should be const + + void VectorNormInf(Matrix& c, const bool isColWise) const; + Matrix& AssignVectorNormInfOf(Matrix& a, const bool isColWise); + + Matrix& AssignInnerProductOf(const Matrix& a, const Matrix& b, const bool isColWise); + Matrix& AssignKhatriRaoProductOf(const Matrix& a, const Matrix& b); + Matrix& AddColumnReshapeProductOf(const Matrix& a, const Matrix& b, const bool transposeAColumn); + + Matrix& AddWithScaleOf(ElemType alpha, const Matrix& a); // this += alpha * a + + ElemType FrobeniusNorm() const; + Matrix& AssignFrobeniusNormOf(const Matrix& a); + + ElemType MatrixNormInf() const; + ElemType MatrixNorm1() const; + ElemType MatrixNorm0() const; //number of non-zero elemets + Matrix& AssignSignOf(const Matrix& a); + Matrix& AddSignOf(const Matrix& a); + void VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise) const; + void VectorMax(Matrix& maxIndexes, Matrix& maxValues, const bool isColWise, int topK) const; + void VectorMin(Matrix& minIndexes, Matrix& minValues, const bool isColWise) const; + + Matrix& AssignNumOfDiff(const Matrix& a, const Matrix& b, bool searchInCol = false); + + Matrix& AssignInnerProductOfMatrices(const Matrix& a, const Matrix& b); //this method will resize(1,1) first + + bool HasNan (const char * name) const; + size_t CountNanInf() const; + + void Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const; + void Print(const char* matrixName = nullptr) const; //print whole matrix. can be expensive + + Matrix& AssignPackedConvolutionInput(const Matrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding = false); + Matrix& UnpackConvolutionInput(Matrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding = false) const; + Matrix& AssignMaxPoolingResult(const Matrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + Matrix& AddMaxPoolingGradient(const Matrix& outputGradientBatch, const Matrix& inputBatch, const Matrix& outputBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + Matrix& AssignAveragePoolingResult(const Matrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + Matrix& AddAveragePoolingGradient(const Matrix& outputGradientBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample); + public: + // TODO: why are these not static? And why are they here? + ElemType Exp10(ElemType num); + ElemType Mod(ElemType x , ElemType y); + ElemType LogAdd(ElemType x, ElemType y); + + public: + static DEVICEID_TYPE GetBestGPUDeviceId(); + + //static BLAS functions + + // singular value decomposition of A as A = U*SIGMA*VT + static void SVD(const Matrix& A, Matrix& SIGMA, Matrix& U, Matrix& VT, Matrix& W); + + static void MultiplyAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, ElemType beta, Matrix& c); // SGEMM + static void MultiplyAndAdd(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, Matrix& c); + static void Multiply(const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, Matrix& c); + static void Multiply(const Matrix& a, const Matrix& b, Matrix& c); + static void Multiply1x1AndWeightedAdd(ElemType alpha, const Matrix& a, const Matrix& b, ElemType beta, Matrix& c); + static void ConvolveAndWeightedAdd(ElemType alpha, const Matrix& a, const bool transposeA, const Matrix& b, const bool transposeB, ElemType beta, Matrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise); + + static void ScaleAndAdd(ElemType alpha, const Matrix& a, Matrix& c); + static void ScaleAndAdd(ElemType alpha, const Matrix& a, ElemType beta, Matrix& c); + static void AddScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c); + static void AssignScaledDifference(const ElemType alpha, const Matrix& a, const Matrix& b, Matrix& c); + static void AddScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c); // c += alpha * (a - b) + static void AssignScaledDifference(const Matrix& alpha, const Matrix& a, const Matrix& b, Matrix& c); + + static void AddElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); + //static void AddLogElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); + static void AssignElementToElement(const Matrix& a, const size_t ai, const size_t aj, Matrix& c, const size_t ci, const size_t cj); + static void MinusOneAt(Matrix& c, const size_t position); + + static void Scale(ElemType alpha, Matrix& a); + static void Scale(const Matrix& alpha, Matrix& a); //In this case Matrix alpha must be 1x1 + static void Scale(ElemType alpha, const Matrix& a, Matrix& c); + static void InnerProduct (const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise); + static ElemType InnerProductOfMatrices(const Matrix& a, const Matrix& b); + static void ElementWisePower (ElemType alpha, const Matrix& a, Matrix& c); + + static bool AreEqual(const Matrix& a, const Matrix& b, const ElemType threshold = 1e-8); + static bool HasElement(const Matrix& a, const ElemType value = 0.0); + + static void TensorShuffleScaleAndAdd(ElemType keepWeight, const Matrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const Matrix& b, Matrix& c); + void TensorOp(ElemType beta, const Matrix& a, ElemType alpha, ElementWiseOperator op, const std::array & offsets, const SmallVector & regularOpDims, const std::array, 2> & regularStrides, @@ -476,48 +476,48 @@ namespace Microsoft { namespace MSR { namespace CNTK { const std::array & offsets, const SmallVector & regularOpDims, const std::array, 4> & regularStrides, const SmallVector & reducingOpDims, const std::array, 4> & reducingStrides); - public: - void Read(File& stream); - void Write(File& stream) const; - - Matrix& Shift(const Matrix& a, int shift); - - Matrix& AssignElementProductOfWithShiftNeg(const Matrix& a, const Matrix& b, size_t shift, size_t negnumber); - Matrix& AssignInnerProductOfWithShiftNeg(const Matrix& a, const Matrix& b, const bool isColWise, size_t shift, size_t negnumber); - static void InnerProductWithShiftNeg(const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise, size_t shift, size_t negnumber); - Matrix& GetARowByIndex(const Matrix& a, size_t index); - static void ConductRowElementMultiplyWithShift(const Matrix& a, const Matrix& b, Matrix& c, size_t shift, bool bFirstmatrixfixed); - Matrix& AssignElementProductOfWithShift(const Matrix& a, const Matrix& b, size_t shift); - - public: - static void RCRFBackwardCompute(const Matrix& alpha, Matrix& beta, - Matrix& functionValues, const Matrix& lbls, - const Matrix& pos_scores, const Matrix& pair_scores, const int shift); - - static void RCRFTransGrdCompute(const Matrix& lbls, - const Matrix& alpha, - const Matrix& beta, - const Matrix& pair_scores, - Matrix& grd, - const int startLbl, /// the time 0 start symbol in the output layer - const int shift); - - template - friend class MatrixQuantizer; - - template - friend class QuantizedMatrix; - - template - friend class Matrix; - }; - - // overload I/O operators - template - File& operator>>(File& stream, Matrix& M) { M.Read(stream); return stream; } - template - File& operator<<(File& stream, const Matrix& M) { M.Write(stream); return stream; } - - typedef Matrix SingleMatrix; - typedef Matrix DoubleMatrix; -}}} + public: + void Read(File& stream); + void Write(File& stream) const; + + Matrix& Shift(const Matrix& a, int shift); + + Matrix& AssignElementProductOfWithShiftNeg(const Matrix& a, const Matrix& b, size_t shift, size_t negnumber); + Matrix& AssignInnerProductOfWithShiftNeg(const Matrix& a, const Matrix& b, const bool isColWise, size_t shift, size_t negnumber); + static void InnerProductWithShiftNeg(const Matrix& a, const Matrix& b, Matrix& c, const bool isColWise, size_t shift, size_t negnumber); + Matrix& GetARowByIndex(const Matrix& a, size_t index); + static void ConductRowElementMultiplyWithShift(const Matrix& a, const Matrix& b, Matrix& c, size_t shift, bool bFirstmatrixfixed); + Matrix& AssignElementProductOfWithShift(const Matrix& a, const Matrix& b, size_t shift); + + public: + static void RCRFBackwardCompute(const Matrix& alpha, Matrix& beta, + Matrix& functionValues, const Matrix& lbls, + const Matrix& pos_scores, const Matrix& pair_scores, const int shift); + + static void RCRFTransGrdCompute(const Matrix& lbls, + const Matrix& alpha, + const Matrix& beta, + const Matrix& pair_scores, + Matrix& grd, + const int startLbl, /// the time 0 start symbol in the output layer + const int shift); + + template + friend class MatrixQuantizer; + + template + friend class QuantizedMatrix; + + template + friend class Matrix; + }; + + // overload I/O operators + template + File& operator>>(File& stream, Matrix& M) { M.Read(stream); return stream; } + template + File& operator<<(File& stream, const Matrix& M) { M.Write(stream); return stream; } + + typedef Matrix SingleMatrix; + typedef Matrix DoubleMatrix; +}}} diff --git a/Source/Math/NoGPU.cpp b/Source/Math/NoGPU.cpp index 84c121f0d02e..36de0ba34052 100644 --- a/Source/Math/NoGPU.cpp +++ b/Source/Math/NoGPU.cpp @@ -1,1349 +1,1349 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// - -#include "stdafx.h" -#include "BestGpu.h" - -#ifdef CPUONLY - -#include "GPUMatrix.h" -#include "GPUSparseMatrix.h" -#include "MatrixQuantizerGPU.h" -#include "CuDnnConvolutionEngine.h" -#include "TensorShape.h" -#include "GPUDataTransferer.h" - -#pragma warning (disable: 4100) // unreferenced formal parameter, which is OK since all functions in here are dummies; disabling this allows to copy-paste prototypes here when we add new functions -#pragma warning (disable: 4702) // unreachable code, which we get from the NOT_IMPLEMENTED macro which is OK - -namespace Microsoft { namespace MSR { namespace CNTK { - - // the reset below are dummy implementations - - void PrepareDevice(DEVICEID_TYPE deviceId); - - template GPUSPARSE_INDEX_TYPE GPUSparseMatrix::SecondaryIndexValueAt(size_t idx) const { return (GPUSPARSE_INDEX_TYPE)0; } - -#pragma region Constructors and Destructor - - template GPUSparseMatrix::GPUSparseMatrix(const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/, - const DEVICEID_TYPE computeDevice /*= AUTOPLACEMATRIX*/) { } - - template void GPUSparseMatrix::ZeroInit(const MatrixFormat matrixFormat, const DEVICEID_TYPE computeDevice) { } - - template GPUSparseMatrix::GPUSparseMatrix(const GPUMatrix& deepCopy, const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/) { } - - - template GPUSparseMatrix::GPUSparseMatrix(const GPUSparseMatrix& deepCopy) { } - - template GPUSparseMatrix::GPUSparseMatrix(const size_t numRows, const size_t numCols, const size_t numNZ, const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/, const DEVICEID_TYPE computeDevice /*= AUTOPLACEMATRIX*/) { } - - // PrepareDevice - Setup the correct cuda context for an operation - // deviceId - the device on which the operation will take place - // defaults to -1, which means use matrices current device - template DEVICEID_TYPE GPUSparseMatrix::PrepareDevice(DEVICEID_TYPE deviceId /*=-1*/) const { return deviceId; } - - template void GPUSparseMatrix::DeepCopy(const GPUSparseMatrix& deepCopy) { } - - template void GPUSparseMatrix::SetValue(const GPUSparseMatrix& deepCopy) { } - - template void GPUSparseMatrix::SetValue(const GPUMatrix& denseMatrix) { } - - template void GPUSparseMatrix::SetValue(const GPUMatrix& denseMatrix, const MatrixFormat matrixFormat){} - - template void GPUSparseMatrix::SetValue(const CPUSparseMatrix& deepCopyFrom) {} - - template GPUSparseMatrix& GPUSparseMatrix::operator=(const GPUSparseMatrix& deepCopy) { return *this; } - - template GPUSparseMatrix::GPUSparseMatrix(GPUSparseMatrix&& moveFrom) { } - template GPUSparseMatrix& GPUSparseMatrix::operator=(GPUSparseMatrix&& moveFrom) { return *this; } - - template GPUSparseMatrix::~GPUSparseMatrix() { } - - template void GPUSparseMatrix::ReleaseMemory() { } - - //ResizeAsAndCopyIndexFrom - Resize this sparse matrix to have the same element structure as the passed matrix - // a - sparse matrix whose structure we want to clone - // remark: this was done for element wise operations where the structure will be identical after an operation - template void GPUSparseMatrix::ResizeAsAndCopyIndexFrom(const GPUSparseMatrix& a, const bool growOnly /*= true*/){} - - //------------------------------------------------------------------------- - // Start of new GPU Sparse Matrix code - //------------------------------------------------------------------------- - - template void GPUSparseMatrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, const MatrixFormat matrixFormat, const bool growOnly, bool keepExistingValues) {}//matrix format will affect the size to allocate - template void GPUSparseMatrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, const bool growOnly, bool keepExistingValues) {} - - template GPUMatrix GPUSparseMatrix::CopyToDenseMatrix() const - { - GPUMatrix < ElemType> res(0); - return res; - } - template void GPUSparseMatrix::CopyToDenseMatrix(GPUMatrix &denseMatrix) const {} - template void GPUSparseMatrix::CopyToCPUSparseMatrix(CPUSparseMatrix &cpuSparseMatrix) const {} - template void GPUSparseMatrix::ChangeDeviceTo(DEVICEID_TYPE toId) {} - - //Reset matrix so it can be reused - template void GPUSparseMatrix::Reset() { } - -#pragma endregion Constructors and Destructor - -#pragma region Static BLAS Functions - - // copy features to GPU matrix - template void GPUSparseMatrix::SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, - const size_t nz, const size_t numRows, const size_t numCols, const bool IsOnDevice /*= false*/, const DEVICEID_TYPE devId /*= -1*/) { } - - // forward pass from feature to hidden layer - template void GPUSparseMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, - const GPUSparseMatrix& rhs, const bool transposeB, ElemType beta, GPUMatrix& c) { } - - // backward pass from hidden layer to feature weight - template void GPUSparseMatrix::MultiplyAndAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, - const GPUSparseMatrix& rhs, const bool transposeB, GPUSparseMatrix& c) { } - - // used for gradients udpate - template void GPUSparseMatrix::ScaleAndAdd(const ElemType alpha, const GPUSparseMatrix& lhs, GPUMatrix& rhs) { } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncate(const ElemType threshold) { return *this; } - - // normal update for smoothed gradients c and current gradients (this) - template - void GPUSparseMatrix::NormalGrad(GPUMatrix& c, const ElemType momentum) { } - template - ElemType GPUSparseMatrix::Adagrad(GPUMatrix& c, const bool needAveMultiplier) {return 1;} - //template - //void GPUSparseMatrix::FSAdagrad(CPUMatrix& gradients, CPUMatrix&, ElemType, ElemType, ElemType, ElemType) { } - -#ifdef NO_SYNC - template bool GPUSparseMatrix::do_sync = false; -#else - template bool GPUSparseMatrix::do_sync = true; -#endif - - template void GPUSparseMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUSparseMatrix& a, const bool transposeA, - const GPUMatrix& b, const bool transposeD, ElemType beta, GPUMatrix& c) {} - template void GPUSparseMatrix::Multiply(const GPUSparseMatrix& S, const GPUMatrix& D, GPUMatrix& C) { } - template void GPUSparseMatrix::Multiply(const GPUMatrix& D, const GPUSparseMatrix& S, GPUMatrix& C) { } - - template size_t GPUSparseMatrix::ElemCountFromBufferSize(const size_t numRows, const size_t numCols, const MatrixFormat format, const size_t totalBufferSize) const { return 0; } - template size_t GPUSparseMatrix::ElemCountFromBufferSize() const { return 0; } - - - // PrepareBuffer - Get the dimensions start buffer, computes the starting row/column of each value - // m - rows in the source - // n - cols in the source - // canReuseBuffer - target matrix can be reused for temporary space - // func - function to call to count elements in the result (returns count, and fills csrRowPtr array) - template - void GPUSparseMatrix::PrepareBuffer(size_t m, size_t n, bool canReuseBuffer, std::function func) { } - - // Multiply - multiply one spares matrix by another sparse matrix - // S1 - first sparse matrix - // transposeS1 - transpose first matrix? - // S2 - second sparse matrix - // transposeS2 - tanspose second matrix? - // c - result matrix - // NOTE: if c has enough space allocated, it will be reused, otherwise it will be freed and a new memory block used - template void GPUSparseMatrix::Multiply(const GPUSparseMatrix& S1, bool transposeS1, const GPUSparseMatrix& S2, bool transposeS2, GPUSparseMatrix &c) { } - - template GPUSparseMatrix& GPUSparseMatrix::AssignProductOf(const GPUSparseMatrix& a, const bool transposeA, const GPUSparseMatrix& /*b*/, const bool transposeB) { return *this; } - - template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUSparseMatrix& a, ElemType beta, const GPUSparseMatrix& /*b*/, GPUSparseMatrix& c) { } - - template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUSparseMatrix& a, ElemType beta, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, ElemType beta, const GPUSparseMatrix& /*b*/, GPUMatrix& c) { } - - template void GPUSparseMatrix::Scale(ElemType alpha, GPUSparseMatrix& a) { } - - template void GPUSparseMatrix::ElementWisePower(ElemType alpha, const GPUSparseMatrix& a, GPUSparseMatrix& c) { } - - template ElemType GPUSparseMatrix::InnerProductOfMatrices(const GPUSparseMatrix& a, const GPUMatrix& /*b*/) - { - return ElemType(0); - } - - template ElemType GPUSparseMatrix::InnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUSparseMatrix& /*b*/) - { - return ElemType(0); - } - - template bool GPUSparseMatrix::AreEqual(const GPUSparseMatrix& a, const GPUSparseMatrix& /*b*/, - const ElemType threshold) - { - return false; - } - - template bool GPUSparseMatrix::AreEqual(const GPUMatrix& /*a*/, const GPUSparseMatrix& /*b*/, - const ElemType threshold) - { - return false; - } - - template bool GPUSparseMatrix::AreEqual(const GPUSparseMatrix& a, const GPUMatrix& /*b*/, - const ElemType threshold) - { - return false; - } - - template bool GPUSparseMatrix::IsEqualTo(const GPUSparseMatrix& a, const ElemType threshold) const - { - return false; - } - - template bool GPUSparseMatrix::IsEqualTo(const GPUMatrix& /*a*/, const ElemType threshold) const - { - return false; - } -#pragma endregion Static BLAS Functions - -#pragma region Member BLAS Functions - - template int GPUSparseMatrix::GetComputeDeviceId() const - { - return -1; - } - - template GPUMatrix GPUSparseMatrix::ElementProductOf(const GPUSparseMatrix& a, const GPUMatrix& /*b*/) - { - GPUMatrix c(0); - return c; - } - - template GPUMatrix GPUSparseMatrix::ElementProductOf(const GPUMatrix& a, const GPUSparseMatrix& b) - { - return GPUSparseMatrix::ElementProductOf(b, a); - } - - template GPUSparseMatrix GPUSparseMatrix::operator+ (const GPUSparseMatrix& a) const { return *this; } - - template GPUSparseMatrix GPUSparseMatrix::operator- (const GPUSparseMatrix& a) const { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::operator^=(ElemType alpha) { return *this; } - - template GPUSparseMatrix GPUSparseMatrix::operator^ (ElemType alpha) const { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::operator*=(ElemType alpha) { return *this; } - - template GPUSparseMatrix GPUSparseMatrix::operator* (ElemType alpha) const { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignElementPowerOf(const GPUSparseMatrix& a, const ElemType power) { return *this; } - - template GPUSparseMatrix GPUSparseMatrix::Transpose() const { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignTransposeOf(const GPUSparseMatrix& a) { return *this; } - - template void GPUSparseMatrix::InplaceTranspose() { } - - template - GPUSparseMatrix GPUSparseMatrix::ColumnSlice(size_t startColumn, size_t numCols) const - { - GPUSparseMatrix a; - return a; - } - template - GPUMatrix GPUSparseMatrix::CopyColumnSliceToDense(size_t startColumn, size_t numCols) const - { - GPUMatrix a(0); - return a; - } - template - GPUMatrix GPUSparseMatrix::DiagonalToDense() const - { - GPUMatrix a(0); - return a; - } - - template ElemType GPUSparseMatrix::SumOfAbsElements() const - { - return ElemType(0); - } - - template ElemType GPUSparseMatrix::SumOfElements() const - { - return ElemType(0); - } - - - template ElemType GPUSparseMatrix::FrobeniusNorm() const - { - return ElemType(0); - } - - template ElemType GPUSparseMatrix::MatrixNormInf() const - { - return ElemType(0); - } - - template ElemType GPUSparseMatrix::MatrixNorm1() const - { - return ElemType(0); - } - -#pragma endregion Member BLAS Functions - -#pragma region Other Functions - - template - GPUSparseMatrix& GPUSparseMatrix::ElementInverse() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignElementInverseOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceSigmoid() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignSigmoidOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceLinearRectifierDerivative() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignLinearRectifierDerivativeOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceTanh() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignTanhOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceSqrt() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignSqrtOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceExp() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignExpOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceLog() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignLogOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceAbs() { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignAbsOf(const GPUSparseMatrix& a) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncateBottom(const ElemType threshold) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::AssignTruncateBottomOf(const GPUSparseMatrix& a, const ElemType threshold) { return *this; } - - template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncateTop(const ElemType threshold) - { - return *this; - } - - template GPUSparseMatrix& GPUSparseMatrix::AssignTruncateTopOf(const GPUSparseMatrix& a, const ElemType threshold) - { - return *this; - } - - template GPUSparseMatrix& GPUSparseMatrix::SetToZeroIfAbsLessThan(const ElemType threshold) - { - return *this; - } - - template - GPUSparseMatrix& GPUSparseMatrix::InplaceSoftThreshold(const ElemType threshold) - { - return (*this); - } - - template - size_t GPUSparseMatrix::IdentifyRowsWithValues() const - { - return 0; - } - -#pragma endregion - -#pragma region Helper Functions - template void* GPUSparseMatrix::ReserveTempHostBuffer(const size_t sizeInByte) const { return nullptr; } - - template void GPUSparseMatrix::performElementWiseFunction(const ElementWiseOperator kind, const GPUSparseMatrix& src) { } - - template void GPUSparseMatrix::SetMatrixFromCSRFormat(const CPUSPARSE_INDEX_TYPE *h_CSRRow, const CPUSPARSE_INDEX_TYPE *h_Col, const ElemType *h_Val, - const size_t nz, const size_t numRows, const size_t numCols, const bool IsOnDevice /*= false*/, const DEVICEID_TYPE devId /*= -1*/) { } - - template void GPUSparseMatrix::GetMatrixFromCSRFormat(CPUSPARSE_INDEX_TYPE*& h_CSRRow, CPUSPARSE_INDEX_TYPE*& h_Col, ElemType*& h_Val, size_t &numElemAllocated, size_t &nz, size_t &numRows, size_t &numCols) const {} - - template void GPUSparseMatrix::GetMatrixFromCSCFormat(CPUSPARSE_INDEX_TYPE*& h_CSCCol, CPUSPARSE_INDEX_TYPE*& h_Row, ElemType*& h_Val, size_t &numElemAllocated, size_t &nz, size_t &numRows, size_t &numCols) const {} - - template void GPUSparseMatrix::ConvertToSparseFormat(MatrixFormat newFormat) {} - template void GPUSparseMatrix::ConvertToSparseFormat(MatrixFormat newFormat, GPUSparseMatrix& outMatrix) const {} - - template void GPUSparseMatrix::ConvolveAndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, const GPUSparseMatrix& rhs, const bool transposeB, ElemType beta, GPUMatrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise) { }; - template void GPUSparseMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUSparseMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUSparseMatrix& b, GPUSparseMatrix& c) { } - template void GPUSparseMatrix::Reshape(const size_t numRows, const size_t numCols) { } - - template bool GPUSparseMatrix::IsValid() const { return true; } - - template template - void GPUSparseMatrix::CopyBuffer(OutType * outBuffer, const InType * inBuffer, const size_t size){} - -#pragma endregion Helper Functions - - template class MATH_API GPUSparseMatrix; - template class MATH_API GPUSparseMatrix; - template class MATH_API GPUSparseMatrix; - - template - MATH_API File& operator>>(File& stream, GPUSparseMatrix& us) - { - return stream; - } - - template MATH_API File& operator>>(File& stream, GPUSparseMatrix& us); - template MATH_API File& operator>>(File& stream, GPUSparseMatrix& us); - - template - MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us) - { - return stream; - } - template MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us); - template MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us); - - -#pragma region DeviceBoundNumber class - - template DeviceBoundNumber::DeviceBoundNumber(const DeviceBoundNumber &deepCopy) - { - NOT_IMPLEMENTED; - } - - template DeviceBoundNumber::DeviceBoundNumber(DeviceBoundNumber &&shallowCopy) - { - this->ShallowCopyFrom(shallowCopy.m_data, shallowCopy.m_computeDevice); - shallowCopy.m_data = NULL; - } - - template void DeviceBoundNumber::ShallowCopyFrom(ElemType* newVal, int newValsDevceId) { } - - template DeviceBoundNumber::~DeviceBoundNumber() { } - -#pragma endregion DeviceBoundNumber class - -#pragma region Helper functions - - template - void GPUMatrix::SetDevice(DEVICEID_TYPE deviceId) { }; - - // GetBestGPUDeviceId - Get the best GPU DeviceId, based on cuda information - // TODO: should be replaced by BestGpu class instead, it's much better - template int GPUMatrix::GetBestGPUDeviceId() //returns -1 if no GPUs can be used - { - return EnforceOneGPUOnly(-1); // CPU - } - - // PrepareDevice - Setup the correct cuda context for an operation - // deviceId - the device on which the operation will take place - // defaults to -1, which means use matrices current device - template DEVICEID_TYPE GPUMatrix::PrepareDevice(DEVICEID_TYPE deviceId /*=-1*/) const - { - return deviceId; - } - - template ElemType* GPUMatrix::CopyToArray() const - { - return NULL; - } - - template void GPUMatrix::CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const - { - } - - //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done - //return number of elements copied - template size_t GPUMatrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const - { - return 0; - } - - template void GPUMatrix::ChangeDeviceTo(int to_id) { } - - template void GPUMatrix::performElementWiseFunction(const ElementWiseOperator kind, const ElemType* src) { } - - -#pragma endregion Helper functions - -#pragma region Constructors and Destructor - - //should only be used by constructors. - template void GPUMatrix::ZeroInit(int deviceId) { } - - template GPUMatrix::GPUMatrix(int deviceId) - {}; - - //matrixName is used to verify that correct matrix is read. - template GPUMatrix::GPUMatrix(FILE* f, const char * matrixName, int deviceId) { } - - template GPUMatrix::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId) { }; - - template GPUMatrix::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags) { }; - - template GPUMatrix::GPUMatrix(const GPUMatrix& deepCopyFrom) { } - - template GPUMatrix::GPUMatrix(GPUMatrix&& moveFrom) { } - - //assignment operator, deep copy - template GPUMatrix& GPUMatrix::operator=(const GPUMatrix& deepCopyFrom) { return *this; } - - //move assignment operator, shallow copy - template GPUMatrix& GPUMatrix::operator=(GPUMatrix&& moveFrom) { return *this; } - - template GPUMatrix::~GPUMatrix(void) { } - - template void GPUMatrix::Clear() { } -#pragma endregion Constructors and Destructor - - template int GPUMatrix::GetComputeDeviceId() const - { - return -1; - } - -#pragma region Basic Operators - template GPUMatrix GPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const - { - GPUMatrix slice(0); - return slice; - } - - template GPUMatrix& GPUMatrix::AssignColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols) { return *this; } - - template GPUMatrix& GPUMatrix::SetColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols) { return *this; } - template GPUMatrix GPUMatrix::Diagonal() const - { - GPUMatrix diag(0); - return diag; - } - - //for each column of a, we assign numRows starting from startIndex to this - template GPUMatrix& GPUMatrix::AssignRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } - //for each column of a, we assign all rows of a to this starting from startIndex - template GPUMatrix& GPUMatrix::AssignToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows) { return *this; } - - //for each column of a, we add all rows of a to this starting from startIndex - template GPUMatrix& GPUMatrix::AddToRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } - template GPUMatrix& GPUMatrix::AddWithRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } - //template GPUMatrix& GPUMatrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) { return *this; } - - template GPUMatrix& GPUMatrix::AssignRepeatOf(const GPUMatrix& /*a*/, const size_t numRowRepeats, const size_t numColRepeats) { return *this; } - template GPUMatrix& GPUMatrix::AddToRowRepeatValuesOf(const GPUMatrix& /*a*/, const size_t numRowRepeats) { return *this; } - - template GPUMatrix& GPUMatrix::AssignPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) { return *this; } - template GPUMatrix& GPUMatrix::AddFoldedPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) { return *this; } - - template GPUMatrix GPUMatrix::Transpose() const { return *this; } - - // GetCublasHandle - get a cublas handle for the given GPU, should only need one per GPU - // computeDevice - The compute device for which the cublas handle is desired - // returns: cublas handle - // NOTE: we currently don't bother to ever free the CUBLAS handle, it will be freed automatically by CUDA when the process ends - template cublasHandle_t GPUMatrix::GetCublasHandle(int computeDevice/*=-1*/) - { - cublasHandle_t cuHandle = 0; - return cuHandle; - } - - template GPUMatrix& GPUMatrix::AssignTransposeOf(const GPUMatrix& /*a*/) { return *this; } - - template void GPUMatrix::SetValue(const ElemType v) { } - - template void GPUMatrix::SetValue(const ElemType* d_v) //d_v is pointer to the the value in GPU memory - {} - - template void GPUMatrix::SetColumn(const ElemType* colPointer, size_t colInd) { } - template void GPUMatrix::SetColumn(const GPUMatrix& valMat, size_t colInd) { } - - template void GPUMatrix::MaskColumnsValue(const GPUMatrix& columnsMask, ElemType val) { } - - template void GPUMatrix:: CopyColumnsStrided(const GPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) { } - template void GPUMatrix::SetValue(const GPUMatrix& deepCopyFrom) { } - - template - void GPUMatrix::SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, size_t matrixFlags) { } - - - template void GPUMatrix::SetDiagonalValue(const ElemType v) { } - - template void GPUMatrix::SetDiagonalValue(const GPUMatrix& vector) { } - - template void GPUMatrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) { } - - template void GPUMatrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) { } - - //maskRate: percentage of values masked out (similar to dropout rate) - //scaleValue: which scale value to set to the left ones (unmasked items). - template void GPUMatrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) { } - - template ElemType GPUMatrix::Adagrad(GPUMatrix& gradients, const bool needAveMultiplier) { return 0; } - template void GPUMatrix::FSAdagrad(GPUMatrix& gradients, GPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul){} - - template ElemType GPUMatrix::RmsProp(GPUMatrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier) { return 0; } - - template void GPUMatrix::Reshape(const size_t numRows, const size_t numCols) { } - - template void GPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly) { } - - template size_t GPUMatrix::LocateElement(const size_t row, const size_t col) const - { - return 0; - } - - template std::unique_ptr> GPUMatrix::GetOrCreateWorkspace() const - { - return NULL; - } - - template void GPUMatrix::ReleaseWorkspace(std::unique_ptr> src) const { } - - template size_t GPUMatrix::LocateColumn(const size_t col) const - { - return 0; - } - - template ElemType GPUMatrix::Get00Element() const - { - ElemType res = 0; - return res; - } -#pragma endregion Basic Operators - -#pragma region Member BLAS Functions - template GPUMatrix& GPUMatrix::operator+= (ElemType alpha) { return *this; } - - template GPUMatrix GPUMatrix::operator+ (ElemType alpha) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignSumOf(const ElemType alpha, const GPUMatrix& /*a*/) - { - return (*this); - } - - - template GPUMatrix& GPUMatrix::operator+= (const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix GPUMatrix::operator+ (const GPUMatrix& /*a*/) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignSumOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) - { - return (*this); - } - - template GPUMatrix& GPUMatrix::operator-= (ElemType alpha) { return *this; } - - template GPUMatrix GPUMatrix::operator- (ElemType alpha) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignDifferenceOf(const ElemType alpha, const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::AssignDifferenceOf(const GPUMatrix& /*a*/, const ElemType alpha) { return *this; } - - template GPUMatrix& GPUMatrix::operator-= (const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix GPUMatrix::operator- (const GPUMatrix& /*a*/) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignDifferenceOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - - template GPUMatrix& GPUMatrix::operator*= (ElemType alpha) { return *this; } - - template GPUMatrix GPUMatrix::operator* (ElemType alpha) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignProductOf(const ElemType alpha, const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::AssignProductOf(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB) { return *this; } - - template GPUMatrix GPUMatrix::operator* (const GPUMatrix& /*a*/) const { return *this; } - - template GPUMatrix& GPUMatrix::operator/= (ElemType alpha) - { - return (*this); - } - - template GPUMatrix GPUMatrix::operator/ (ElemType alpha) const { return *this; } - - //element-wise power - template GPUMatrix& GPUMatrix::operator^= (ElemType alpha) { return *this; } - - template GPUMatrix GPUMatrix::operator^ (ElemType alpha) const { return *this; } - - template GPUMatrix& GPUMatrix::AssignElementPowerOf(const GPUMatrix& /*a*/, const ElemType power) { return *this; } - - - template GPUMatrix& GPUMatrix::AddElementProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - - template GPUMatrix& GPUMatrix::ColumnElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } - template GPUMatrix& GPUMatrix::RowElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::ColumnElementDivideBy(const GPUMatrix& /*a*/) { return *this; } - template GPUMatrix& GPUMatrix::RowElementDivideBy(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::ElementInverse() { return *this; } - - template GPUMatrix& GPUMatrix::AssignElementInverseOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceSigmoid() { return *this; } - - template GPUMatrix& GPUMatrix::AssignSigmoidOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceSigmoidDerivative() { return *this; } - - template GPUMatrix& GPUMatrix::AssignSigmoidDerivativeOf(const GPUMatrix& /*a*/) { return *this; } - - - template GPUMatrix& GPUMatrix::InplaceTanh() { return *this; } - - template GPUMatrix& GPUMatrix::AssignTanhOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceLogSoftmax(const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::AssignLogSoftmaxOf(const GPUMatrix& /*a*/, const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceHardmax(const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::AssignHardmaxOf(const GPUMatrix& /*a*/, const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::DropFrame(const GPUMatrix& label, const GPUMatrix& gamma, const ElemType & threshhold) { return *this; } - template GPUMatrix& GPUMatrix::AssignSequenceError(const ElemType hsmoothingWeight, const GPUMatrix& label, const GPUMatrix& dnnoutput, const GPUMatrix& gamma, ElemType alpha) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceSqrt() { return *this; } - - template GPUMatrix& GPUMatrix::AssignSqrtOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceExp() { return *this; } - - template GPUMatrix& GPUMatrix::AssignExpOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceLog() { return *this; } - - template GPUMatrix& GPUMatrix::AssignLogOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceAbs() { return *this; } - - template GPUMatrix& GPUMatrix::AssignAbsOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceLinearRectifierDerivative() { return *this; } - - template GPUMatrix& GPUMatrix::AssignLinearRectifierDerivativeOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceCosine() { return *this; } - - template GPUMatrix& GPUMatrix::AssignCosineOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceNegativeSine() { return *this; } - - template GPUMatrix& GPUMatrix::AssignNegativeSineOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceTruncateBottom(const ElemType threshold) { return *this; } - - template GPUMatrix& GPUMatrix::AssignTruncateBottomOf(const GPUMatrix& /*a*/, const ElemType threshold) { return *this; } - - template GPUMatrix& GPUMatrix::InplaceTruncateTop(const ElemType threshold) - { - return *this; - } - - template GPUMatrix& GPUMatrix::AssignTruncateTopOf(const GPUMatrix& /*a*/, const ElemType threshold) - { - return *this; - } - template GPUMatrix& GPUMatrix::SetToZeroIfAbsLessThan(const ElemType threshold) - { - return *this; - } - - template ElemType GPUMatrix::SumOfAbsElements() const - { - return ElemType(0); - } - - template ElemType GPUMatrix::SumOfElements() const - { - return ElemType(0); - } - - - template GPUMatrix& GPUMatrix::AssignSumOfElements(const GPUMatrix& /*a*/) - { - return (*this); - } - template - void GPUMatrix::MinusOneAt(GPUMatrix& c, const size_t position) - {} - - template - void GPUMatrix::VectorSum(const GPUMatrix& a, GPUMatrix& c, const bool isColWise) - {} - - template - GPUMatrix& GPUMatrix::InplaceTruncate(const ElemType threshold) - { - return (*this); - } - - template - GPUMatrix& GPUMatrix::InplaceSoftThreshold(const ElemType threshold) - { - return (*this); - } - - template - GPUMatrix& GPUMatrix::GetARowByIndex(const GPUMatrix& a, const size_t m) - { - return (*this); - } - - template - GPUMatrix& GPUMatrix::AssignElementProductOfWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, const size_t shift, const size_t nt) - { - return (*this); - } - - template - GPUMatrix& GPUMatrix::AssignElementProductOfWithShift(const GPUMatrix& a, const GPUMatrix& b, const size_t shift) - { - return (*this); - } - - template - void GPUMatrix::InnerProductWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const size_t nt) - {} - - template - void GPUMatrix::ConductRowElementMultiplyWithShift(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const bool isafixed) - {} - - template DeviceBoundNumber GPUMatrix::Sum_AsDeviceBoundNum() const - { - DeviceBoundNumber result; - return result; - } - - template ElemType GPUMatrix::Max() const - { - return ElemType(0); - } - - - template GPUMatrix& GPUMatrix::ElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::AssignElementProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - - template GPUMatrix& GPUMatrix::AssignElementDivisionOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - template GPUMatrix& GPUMatrix::ElementDivideBy(const GPUMatrix& /*a*/) { return *this; } - - template bool GPUMatrix::IsEqualTo(const GPUMatrix& a, const ElemType threshold /*= 1e-8*/) const - { - return AreEqual(*this, a, threshold); - } - - template void GPUMatrix::VectorNorm1(GPUMatrix& c, const bool isColWise) const - { - } - - template GPUMatrix& GPUMatrix::AssignVectorNorm1Of(GPUMatrix& /*a*/, const bool isColWise) { return *this; } - - template void GPUMatrix::VectorNorm2(GPUMatrix& c, const bool isColWise) const - {} - - template GPUMatrix& GPUMatrix::AssignVectorNorm2Of(GPUMatrix& /*a*/, const bool isColWise) { return *this; } - - template void GPUMatrix::VectorNormInf(GPUMatrix& c, const bool isColWise) const - {} - - template GPUMatrix& GPUMatrix::AssignVectorNormInfOf(GPUMatrix& /*a*/, const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::AssignInnerProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const bool isColWise) { return *this; } - - template GPUMatrix& GPUMatrix::AssignKhatriRaoProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - - //column-wise reshaped product. Used to compute KhatriRaoProduct Gradient - // this = reshape each column of a from (K1xK2,1) to (K1, K2) - // if each column of a is not transposed, each (K1, K2) times each column of b (K2, frames). - // the output is a (K1, frames) matrix - // if each column of a is tranposed, each (K1, K2)^T times each column of b(K1, frames) and output is (K2, frames) - template GPUMatrix& GPUMatrix::AddColumnReshapeProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const bool transposeAColumn) { return *this; } - - template GPUMatrix& GPUMatrix::AddWithScaleOf(ElemType alpha, const GPUMatrix& /*a*/) { return *this; } - - template ElemType GPUMatrix::FrobeniusNorm() const - { - ElemType h_sum = 0; - return (h_sum); - } - - template GPUMatrix& GPUMatrix::AssignFrobeniusNormOf(const GPUMatrix& /*a*/) { return *this; } - - template ElemType GPUMatrix::MatrixNormInf() const - { - ElemType h_maxAbs = 0; - return h_maxAbs; - } - - template ElemType GPUMatrix::MatrixNorm1() const - { - return ElemType(0); - } - - template ElemType GPUMatrix::MatrixNorm0() const - { - return ElemType(0); - } - - template GPUMatrix& GPUMatrix::AssignSignOf(const GPUMatrix& /*a*/) { return *this; } - - template GPUMatrix& GPUMatrix::AddSignOf(const GPUMatrix& /*a*/) { return *this; } - - template void GPUMatrix::VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise) const - {} - - template void GPUMatrix::VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise, int topK) const - {} - - template void GPUMatrix::VectorMin(GPUMatrix& minIndexes, GPUMatrix& minValues, const bool isColWise) const - {} - - template GPUMatrix& GPUMatrix::AssignNumOfDiff(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, bool /*searchInCol = false*/) { return *this; } - -#pragma endregion Member BLAS Functions - -#pragma region Other helper functions - template void GPUMatrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const - {} - - template void GPUMatrix::Print(const char* matrixName /*=nullptr*/) const - {} - - // file I/O - //matrixName is used to verify that correct matrix is read. - template void GPUMatrix::ReadFromFile(FILE* f, const char * matrixName) - {} - - //matrixName is used to verify that correct matrix is read. - template void GPUMatrix::WriteToFile(FILE* f, const char * matrixName) - {} - - //helpfer function used for convolution neural network - template GPUMatrix& GPUMatrix::AssignPackedConvolutionInput(const GPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) { - return *this; - } - - //helpfer function used for convolution neural network - template GPUMatrix& GPUMatrix::UnpackConvolutionInput(GPUMatrix& inputSubBatch, - const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, - const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, - const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, - const bool zeroPadding) const - { - return inputSubBatch; - } - - template GPUMatrix& GPUMatrix::AssignMaxPoolingResult(const GPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { - return *this; - } - - template GPUMatrix& GPUMatrix::AddMaxPoolingGradient(const GPUMatrix& outputGradientBatch, const GPUMatrix& inputBatch, const GPUMatrix& outputBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { - return *this; - } - - template GPUMatrix& GPUMatrix::AssignAveragePoolingResult(const GPUMatrix& inputBatch, const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { - return *this; - } - - template GPUMatrix& GPUMatrix::AddAveragePoolingGradient(const GPUMatrix& outputGradientBatch, - const size_t channels, - const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, - const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, - const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { - return *this; - } - -#pragma endregion Other helper functions - -#pragma region Static BLAS Functions - template void GPUMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, - ElemType beta, GPUMatrix& c) - { - } - template void GPUMatrix::Multiply1x1AndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const GPUMatrix& rhs, ElemType beta, GPUMatrix& c) { } - - template void GPUMatrix::MultiplyAndAdd(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, GPUMatrix& c) { } - - template void GPUMatrix::Multiply(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, GPUMatrix& c) { } - - template void GPUMatrix::Multiply(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c - /// if a is a column vector, add to all columns of c - /// if a is a row vector, add to all rows of c - /// if a is a scalar, add to all elements of c - /// Scalar - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template void GPUMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } - - /// Matrix-scalar multiply with col-major matrices: c = alpha * a + b - /// if a is a column vector, add to all columns of b - /// if a is a row vector, add to all rows of b - /// if a is a scalar, add to all elements of b - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template void GPUMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template void GPUMatrix::AddScaledDifference(const ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void GPUMatrix::AssignScaledDifference(const ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - /// c += alpha * (a-b) - /// if a, b, c must have same dim - /// 1X1 matrix - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template void GPUMatrix::AddScaledDifference(const GPUMatrix& /*alpha*/, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - /// c = alpha * (a-b) - /// if a, b, c must have same dim - /// Scalar - /// Input matrix - /// Input matrix - /// Resulting matrix, user is responsible for allocating this - template - void GPUMatrix::AssignScaledDifference(const GPUMatrix& /*alpha*/, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } - - //c[ci,cj] += a[ai,aj] - template void GPUMatrix::AddElementToElement(const GPUMatrix& /*a*/, const size_t ai, const size_t aj, GPUMatrix& c, const size_t ci, const size_t cj) { } - - template void GPUMatrix::Scale(ElemType alpha, GPUMatrix& /*a*/) { } - - - template void GPUMatrix::Scale(GPUMatrix& /*alpha*/, GPUMatrix& /*a*/) { } - - template //c = alpha * a - void GPUMatrix::Scale(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } - - template bool GPUMatrix::HasElement(const GPUMatrix& a, const ElemType value) { return false; } - - template void GPUMatrix::InnerProduct(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c, const bool isColWise) { } - - template ElemType GPUMatrix::InnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) - { - return ElemType(0); - } - - template GPUMatrix& GPUMatrix::AssignInnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } - - template void GPUMatrix::ElementWisePower(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } - - template bool GPUMatrix::AreEqual(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const ElemType threshold /*= 1e-8*/) - { - return false; - } - - template void GPUMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUMatrix& b, GPUMatrix& c) { } - - template - void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 2> & regularStrides, - const SmallVector & reducingOpDims, const array, 2> & reducingStrides) { } - template - void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, const GPUMatrix& b, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 3> & regularStrides, - const SmallVector & reducingOpDims, const array, 3> & reducingStrides) { } - template - void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, const GPUMatrix& b, const GPUMatrix& c, ElemType alpha, ElementWiseOperator op, - const array & offsets, - const SmallVector & regularOpDims, const array, 4> & regularStrides, - const SmallVector & reducingOpDims, const array, 4> & reducingStrides) { } - - template - void GPUMatrix::CreateCurandObject(unsigned long seed, const char *caller) - { - } - - template - void GPUMatrix::ResetCurandObject(unsigned long seed, const char *caller) - { - } - - template GPUMatrix GPUMatrix::Ones(const size_t rows, const size_t cols, int deviceId) - { - GPUMatrix mat(0); - return mat; - } - - template GPUMatrix GPUMatrix::Zeros(const size_t rows, const size_t cols, int deviceId) - { - GPUMatrix mat(0); - return mat; - } - - template GPUMatrix GPUMatrix::Eye(const size_t rows, int deviceId) - { - GPUMatrix mat(0); - return mat; - } - - template GPUMatrix GPUMatrix::RandomUniform(const size_t rows, const size_t cols, int deviceId, const ElemType low, const ElemType high, unsigned long seed) - { - GPUMatrix mat(0); - return mat; - } - - template GPUMatrix GPUMatrix::RandomGaussian(const size_t rows, const size_t cols, int deviceId, const ElemType mean, const ElemType sigma, unsigned long seed) - { - GPUMatrix mat(0); - return mat; - } - - template ElemType GPUMatrix::GetLearnRateForBlock_Helper(const GPUMatrix &Gradients, const GPUMatrix &SmoothedGradients) - { - return ElemType(0); - } - - template - ElemType GPUMatrix::LogAddSumOfElements() const - { - return ElemType(0); - } - - template - void GPUMatrix::RCRFBackwardCompute( - const GPUMatrix& alpha, GPUMatrix& beta, - const GPUMatrix& lbls, - const GPUMatrix& pos_scores, const GPUMatrix& pair_scores, const int shift) - {} - - template - void GPUMatrix::RCRFTransGrdCompute(const GPUMatrix& lbls, - const GPUMatrix& alpha, - const GPUMatrix& beta, - const GPUMatrix& pair_scores, - GPUMatrix& grd, - const int startLbl, - const int shift) - {} - - template - void GPUMatrix::AssignNoiseContrastiveEstimation(const GPUMatrix& a, -const GPUMatrix& b, const GPUMatrix& bias, size_t sampleCount, GPUMatrix& tmp, GPUMatrix& c) - { - } - - template - void GPUMatrix::AssignNCEDerivative(GPUMatrix& tmp, const GPUMatrix& a, - const GPUMatrix& b, size_t inputIndex, GPUMatrix& c) - { - - } - - template - void GPUMatrix::AssignSoftmaxSum(const GPUMatrix& a, GPUMatrix& c) - { - } - - template - void GPUMatrix::AssignNCEUnnormalizedEval(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c) - { - } -#pragma endregion Static BLAS Functions - -#pragma region MatrixQuantizerGPU functions - template - MatrixQuantizerGPU::MatrixQuantizerGPU(int deviceId, bool useDedicatedComputeStream, bool forceSync) - { - } - - template - MatrixQuantizerGPU::~MatrixQuantizerGPU() - { - } - - template - void MatrixQuantizerGPU::QuantizeAsync(const Matrix& inMatrix, const Matrix& inResidual, QuantizedMatrix& outQMatrix, Matrix& outResidual, bool zeroThresholdFor1Bit) - { - } - - template - void MatrixQuantizerGPU::WaitQuantizeAsyncDone() - { - } - - template - void MatrixQuantizerGPU::UnquantizeAsync(QuantizedMatrix& inQMatrix, Matrix& outMatrix, bool add /*= false*/) - { - } - - template - void MatrixQuantizerGPU::WaitUnquantizeAsyncDone() - { - } - -#pragma endregion MatrixQuantizerGPU functions - -#pragma region GPUMatrixComputeStreamEvent functions - - GPUMatrixComputeStreamEvent::GPUMatrixComputeStreamEvent(int deviceId) - : MatrixComputeStreamEvent(deviceId) - { - } - - GPUMatrixComputeStreamEvent::~GPUMatrixComputeStreamEvent() { }; - void GPUMatrixComputeStreamEvent::SynchronizeEvent() { }; - template<> void GPUMatrixComputeStreamEvent::SynchronizeQuantizationComputeStreamWithEvent() { }; - template<> void GPUMatrixComputeStreamEvent::SynchronizeQuantizationComputeStreamWithEvent() { }; - template<> void GPUMatrixComputeStreamEvent::SynchronizeDataTransferFetchStreamWithEvent() { }; - template<> void GPUMatrixComputeStreamEvent::SynchronizeDataTransferFetchStreamWithEvent() { }; - -#pragma endregion GPUMatrixComputeStreamEvent functions - -#pragma region GPUDataTransferer functions - - template - GPUDataTransferer::GPUDataTransferer(int, bool) - { - } - - template - GPUDataTransferer::~GPUDataTransferer() - { - } - - template - void GPUDataTransferer::CopyGPUToCPUAsync(ElemType*, size_t, ElemType*) - { - } - - template - void GPUDataTransferer::WaitForCopyGPUToCPUAsync() - { - } - - template - void GPUDataTransferer::CopyCPUToGPUAsync(ElemType*, size_t, ElemType*) - { - } - - template - void GPUDataTransferer::WaitForCopyCPUToGPUAsync() - { - } - -#pragma endregion GPUDataTransferer functions - - template class GPUMatrix; - template class GPUMatrix; - template class GPUMatrix; - template class DeviceBoundNumber; - template class DeviceBoundNumber; - template MatrixQuantizerGPU::~MatrixQuantizerGPU(); - template MatrixQuantizerGPU::~MatrixQuantizerGPU(); - template void MatrixQuantizerGPU::QuantizeAsync(const Matrix&, const Matrix&, QuantizedMatrix&, Matrix&, bool); - template void MatrixQuantizerGPU::QuantizeAsync(const Matrix&, const Matrix&, QuantizedMatrix&, Matrix&, bool); - - template class GPUDataTransferer; - template class GPUDataTransferer; - - template cublasHandle_t GPUMatrix::s_cuHandle[GPUMatrix::MaxGpus] = { 0 }; - - template void* GPUMatrix::s_curandGenerator = NULL; - - template - typename CuDnnConvolutionEngineFactory::Tensor4DPtr CuDnnConvolutionEngineFactory::CreateTensor(size_t, size_t, size_t, size_t) - { - RuntimeError("The code is compiled with CPUONLY macro."); - } - - template - typename CuDnnConvolutionEngineFactory::FilterPtr CuDnnConvolutionEngineFactory::CreateFilter(size_t, size_t, size_t, size_t) - { - RuntimeError("The code is compiled without CPUONLY macro."); - } - - template - typename CuDnnConvolutionEngineFactory::ConvDescPtr CuDnnConvolutionEngineFactory::CreateConvDescriptor( - const Tensor4D&, const Filter&, size_t, size_t, bool) - { - RuntimeError("The code is compiled without CPUONLY macro."); - } - - template - typename CuDnnConvolutionEngineFactory::PoolDescPtr CuDnnConvolutionEngineFactory::CreatePoolDescriptor( - typename PoolDesc::PoolKind, size_t, size_t, size_t, size_t, size_t, size_t) - { - RuntimeError("The code is compiled without CPUONLY macro."); - } - - template - typename CuDnnConvolutionEngineFactory::ConvEnginePtr CuDnnConvolutionEngineFactory::CreateConvEngine(DEVICEID_TYPE, size_t) - { - RuntimeError("The code is compiled without CPUONLY macro."); - } - - template - typename CuDnnConvolutionEngineFactory::PoolEnginePtr CuDnnConvolutionEngineFactory::CreatePoolEngine(DEVICEID_TYPE) - { - RuntimeError("The code is compiled without CPUONLY macro."); - } - - template - bool CuDnnConvolutionEngineFactory::IsSupported(DEVICEID_TYPE) - { - return false; - } - - template class CuDnnConvolutionEngineFactory; - template class CuDnnConvolutionEngineFactory; - -}}} - -// define a dummy GPUWatcher class too -#include "GPUWatcher.h" - -int GPUWatcher::GetGPUIdWithTheMostFreeMemory() -{ - return 0; -} - - -size_t GPUWatcher::GetFreeMemoryOnCUDADevice(int /*devId*/) -{ - return 0; -} - -GPUWatcher::GPUWatcher(void) -{ -} - -GPUWatcher::~GPUWatcher(void) -{ -} - -#endif // CPUONLY +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// + +#include "stdafx.h" +#include "BestGpu.h" + +#ifdef CPUONLY + +#include "GPUMatrix.h" +#include "GPUSparseMatrix.h" +#include "MatrixQuantizerGPU.h" +#include "CuDnnConvolutionEngine.h" +#include "TensorShape.h" +#include "GPUDataTransferer.h" + +#pragma warning (disable: 4100) // unreferenced formal parameter, which is OK since all functions in here are dummies; disabling this allows to copy-paste prototypes here when we add new functions +#pragma warning (disable: 4702) // unreachable code, which we get from the NOT_IMPLEMENTED macro which is OK + +namespace Microsoft { namespace MSR { namespace CNTK { + + // the reset below are dummy implementations + + void PrepareDevice(DEVICEID_TYPE deviceId); + + template GPUSPARSE_INDEX_TYPE GPUSparseMatrix::SecondaryIndexValueAt(size_t idx) const { return (GPUSPARSE_INDEX_TYPE)0; } + +#pragma region Constructors and Destructor + + template GPUSparseMatrix::GPUSparseMatrix(const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/, + const DEVICEID_TYPE computeDevice /*= AUTOPLACEMATRIX*/) { } + + template void GPUSparseMatrix::ZeroInit(const MatrixFormat matrixFormat, const DEVICEID_TYPE computeDevice) { } + + template GPUSparseMatrix::GPUSparseMatrix(const GPUMatrix& deepCopy, const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/) { } + + + template GPUSparseMatrix::GPUSparseMatrix(const GPUSparseMatrix& deepCopy) { } + + template GPUSparseMatrix::GPUSparseMatrix(const size_t numRows, const size_t numCols, const size_t numNZ, const MatrixFormat matrixFormat /*= MatrixFormat::matrixFormatSparseCSR*/, const DEVICEID_TYPE computeDevice /*= AUTOPLACEMATRIX*/) { } + + // PrepareDevice - Setup the correct cuda context for an operation + // deviceId - the device on which the operation will take place + // defaults to -1, which means use matrices current device + template DEVICEID_TYPE GPUSparseMatrix::PrepareDevice(DEVICEID_TYPE deviceId /*=-1*/) const { return deviceId; } + + template void GPUSparseMatrix::DeepCopy(const GPUSparseMatrix& deepCopy) { } + + template void GPUSparseMatrix::SetValue(const GPUSparseMatrix& deepCopy) { } + + template void GPUSparseMatrix::SetValue(const GPUMatrix& denseMatrix) { } + + template void GPUSparseMatrix::SetValue(const GPUMatrix& denseMatrix, const MatrixFormat matrixFormat){} + + template void GPUSparseMatrix::SetValue(const CPUSparseMatrix& deepCopyFrom) {} + + template GPUSparseMatrix& GPUSparseMatrix::operator=(const GPUSparseMatrix& deepCopy) { return *this; } + + template GPUSparseMatrix::GPUSparseMatrix(GPUSparseMatrix&& moveFrom) { } + template GPUSparseMatrix& GPUSparseMatrix::operator=(GPUSparseMatrix&& moveFrom) { return *this; } + + template GPUSparseMatrix::~GPUSparseMatrix() { } + + template void GPUSparseMatrix::ReleaseMemory() { } + + //ResizeAsAndCopyIndexFrom - Resize this sparse matrix to have the same element structure as the passed matrix + // a - sparse matrix whose structure we want to clone + // remark: this was done for element wise operations where the structure will be identical after an operation + template void GPUSparseMatrix::ResizeAsAndCopyIndexFrom(const GPUSparseMatrix& a, const bool growOnly /*= true*/){} + + //------------------------------------------------------------------------- + // Start of new GPU Sparse Matrix code + //------------------------------------------------------------------------- + + template void GPUSparseMatrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, const MatrixFormat matrixFormat, const bool growOnly, bool keepExistingValues) {}//matrix format will affect the size to allocate + template void GPUSparseMatrix::Resize(const size_t numRows, const size_t numCols, const size_t numNZElemToReserve, const bool growOnly, bool keepExistingValues) {} + + template GPUMatrix GPUSparseMatrix::CopyToDenseMatrix() const + { + GPUMatrix < ElemType> res(0); + return res; + } + template void GPUSparseMatrix::CopyToDenseMatrix(GPUMatrix &denseMatrix) const {} + template void GPUSparseMatrix::CopyToCPUSparseMatrix(CPUSparseMatrix &cpuSparseMatrix) const {} + template void GPUSparseMatrix::ChangeDeviceTo(DEVICEID_TYPE toId) {} + + //Reset matrix so it can be reused + template void GPUSparseMatrix::Reset() { } + +#pragma endregion Constructors and Destructor + +#pragma region Static BLAS Functions + + // copy features to GPU matrix + template void GPUSparseMatrix::SetMatrixFromCSCFormat(const CPUSPARSE_INDEX_TYPE *h_CSCCol, const CPUSPARSE_INDEX_TYPE *h_Row, const ElemType *h_Val, + const size_t nz, const size_t numRows, const size_t numCols, const bool IsOnDevice /*= false*/, const DEVICEID_TYPE devId /*= -1*/) { } + + // forward pass from feature to hidden layer + template void GPUSparseMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, + const GPUSparseMatrix& rhs, const bool transposeB, ElemType beta, GPUMatrix& c) { } + + // backward pass from hidden layer to feature weight + template void GPUSparseMatrix::MultiplyAndAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, + const GPUSparseMatrix& rhs, const bool transposeB, GPUSparseMatrix& c) { } + + // used for gradients udpate + template void GPUSparseMatrix::ScaleAndAdd(const ElemType alpha, const GPUSparseMatrix& lhs, GPUMatrix& rhs) { } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncate(const ElemType threshold) { return *this; } + + // normal update for smoothed gradients c and current gradients (this) + template + void GPUSparseMatrix::NormalGrad(GPUMatrix& c, const ElemType momentum) { } + template + ElemType GPUSparseMatrix::Adagrad(GPUMatrix& c, const bool needAveMultiplier) {return 1;} + //template + //void GPUSparseMatrix::FSAdagrad(CPUMatrix& gradients, CPUMatrix&, ElemType, ElemType, ElemType, ElemType) { } + +#ifdef NO_SYNC + template bool GPUSparseMatrix::do_sync = false; +#else + template bool GPUSparseMatrix::do_sync = true; +#endif + + template void GPUSparseMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUSparseMatrix& a, const bool transposeA, + const GPUMatrix& b, const bool transposeD, ElemType beta, GPUMatrix& c) {} + template void GPUSparseMatrix::Multiply(const GPUSparseMatrix& S, const GPUMatrix& D, GPUMatrix& C) { } + template void GPUSparseMatrix::Multiply(const GPUMatrix& D, const GPUSparseMatrix& S, GPUMatrix& C) { } + + template size_t GPUSparseMatrix::ElemCountFromBufferSize(const size_t numRows, const size_t numCols, const MatrixFormat format, const size_t totalBufferSize) const { return 0; } + template size_t GPUSparseMatrix::ElemCountFromBufferSize() const { return 0; } + + + // PrepareBuffer - Get the dimensions start buffer, computes the starting row/column of each value + // m - rows in the source + // n - cols in the source + // canReuseBuffer - target matrix can be reused for temporary space + // func - function to call to count elements in the result (returns count, and fills csrRowPtr array) + template + void GPUSparseMatrix::PrepareBuffer(size_t m, size_t n, bool canReuseBuffer, std::function func) { } + + // Multiply - multiply one spares matrix by another sparse matrix + // S1 - first sparse matrix + // transposeS1 - transpose first matrix? + // S2 - second sparse matrix + // transposeS2 - tanspose second matrix? + // c - result matrix + // NOTE: if c has enough space allocated, it will be reused, otherwise it will be freed and a new memory block used + template void GPUSparseMatrix::Multiply(const GPUSparseMatrix& S1, bool transposeS1, const GPUSparseMatrix& S2, bool transposeS2, GPUSparseMatrix &c) { } + + template GPUSparseMatrix& GPUSparseMatrix::AssignProductOf(const GPUSparseMatrix& a, const bool transposeA, const GPUSparseMatrix& /*b*/, const bool transposeB) { return *this; } + + template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUSparseMatrix& a, ElemType beta, const GPUSparseMatrix& /*b*/, GPUSparseMatrix& c) { } + + template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUSparseMatrix& a, ElemType beta, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + template void GPUSparseMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, ElemType beta, const GPUSparseMatrix& /*b*/, GPUMatrix& c) { } + + template void GPUSparseMatrix::Scale(ElemType alpha, GPUSparseMatrix& a) { } + + template void GPUSparseMatrix::ElementWisePower(ElemType alpha, const GPUSparseMatrix& a, GPUSparseMatrix& c) { } + + template ElemType GPUSparseMatrix::InnerProductOfMatrices(const GPUSparseMatrix& a, const GPUMatrix& /*b*/) + { + return ElemType(0); + } + + template ElemType GPUSparseMatrix::InnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUSparseMatrix& /*b*/) + { + return ElemType(0); + } + + template bool GPUSparseMatrix::AreEqual(const GPUSparseMatrix& a, const GPUSparseMatrix& /*b*/, + const ElemType threshold) + { + return false; + } + + template bool GPUSparseMatrix::AreEqual(const GPUMatrix& /*a*/, const GPUSparseMatrix& /*b*/, + const ElemType threshold) + { + return false; + } + + template bool GPUSparseMatrix::AreEqual(const GPUSparseMatrix& a, const GPUMatrix& /*b*/, + const ElemType threshold) + { + return false; + } + + template bool GPUSparseMatrix::IsEqualTo(const GPUSparseMatrix& a, const ElemType threshold) const + { + return false; + } + + template bool GPUSparseMatrix::IsEqualTo(const GPUMatrix& /*a*/, const ElemType threshold) const + { + return false; + } +#pragma endregion Static BLAS Functions + +#pragma region Member BLAS Functions + + template int GPUSparseMatrix::GetComputeDeviceId() const + { + return -1; + } + + template GPUMatrix GPUSparseMatrix::ElementProductOf(const GPUSparseMatrix& a, const GPUMatrix& /*b*/) + { + GPUMatrix c(0); + return c; + } + + template GPUMatrix GPUSparseMatrix::ElementProductOf(const GPUMatrix& a, const GPUSparseMatrix& b) + { + return GPUSparseMatrix::ElementProductOf(b, a); + } + + template GPUSparseMatrix GPUSparseMatrix::operator+ (const GPUSparseMatrix& a) const { return *this; } + + template GPUSparseMatrix GPUSparseMatrix::operator- (const GPUSparseMatrix& a) const { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::operator^=(ElemType alpha) { return *this; } + + template GPUSparseMatrix GPUSparseMatrix::operator^ (ElemType alpha) const { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::operator*=(ElemType alpha) { return *this; } + + template GPUSparseMatrix GPUSparseMatrix::operator* (ElemType alpha) const { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignElementPowerOf(const GPUSparseMatrix& a, const ElemType power) { return *this; } + + template GPUSparseMatrix GPUSparseMatrix::Transpose() const { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignTransposeOf(const GPUSparseMatrix& a) { return *this; } + + template void GPUSparseMatrix::InplaceTranspose() { } + + template + GPUSparseMatrix GPUSparseMatrix::ColumnSlice(size_t startColumn, size_t numCols) const + { + GPUSparseMatrix a; + return a; + } + template + GPUMatrix GPUSparseMatrix::CopyColumnSliceToDense(size_t startColumn, size_t numCols) const + { + GPUMatrix a(0); + return a; + } + template + GPUMatrix GPUSparseMatrix::DiagonalToDense() const + { + GPUMatrix a(0); + return a; + } + + template ElemType GPUSparseMatrix::SumOfAbsElements() const + { + return ElemType(0); + } + + template ElemType GPUSparseMatrix::SumOfElements() const + { + return ElemType(0); + } + + + template ElemType GPUSparseMatrix::FrobeniusNorm() const + { + return ElemType(0); + } + + template ElemType GPUSparseMatrix::MatrixNormInf() const + { + return ElemType(0); + } + + template ElemType GPUSparseMatrix::MatrixNorm1() const + { + return ElemType(0); + } + +#pragma endregion Member BLAS Functions + +#pragma region Other Functions + + template + GPUSparseMatrix& GPUSparseMatrix::ElementInverse() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignElementInverseOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceSigmoid() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignSigmoidOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceLinearRectifierDerivative() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignLinearRectifierDerivativeOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceTanh() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignTanhOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceSqrt() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignSqrtOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceExp() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignExpOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceLog() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignLogOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceAbs() { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignAbsOf(const GPUSparseMatrix& a) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncateBottom(const ElemType threshold) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::AssignTruncateBottomOf(const GPUSparseMatrix& a, const ElemType threshold) { return *this; } + + template GPUSparseMatrix& GPUSparseMatrix::InplaceTruncateTop(const ElemType threshold) + { + return *this; + } + + template GPUSparseMatrix& GPUSparseMatrix::AssignTruncateTopOf(const GPUSparseMatrix& a, const ElemType threshold) + { + return *this; + } + + template GPUSparseMatrix& GPUSparseMatrix::SetToZeroIfAbsLessThan(const ElemType threshold) + { + return *this; + } + + template + GPUSparseMatrix& GPUSparseMatrix::InplaceSoftThreshold(const ElemType threshold) + { + return (*this); + } + + template + size_t GPUSparseMatrix::IdentifyRowsWithValues() const + { + return 0; + } + +#pragma endregion + +#pragma region Helper Functions + template void* GPUSparseMatrix::ReserveTempHostBuffer(const size_t sizeInByte) const { return nullptr; } + + template void GPUSparseMatrix::performElementWiseFunction(const ElementWiseOperator kind, const GPUSparseMatrix& src) { } + + template void GPUSparseMatrix::SetMatrixFromCSRFormat(const CPUSPARSE_INDEX_TYPE *h_CSRRow, const CPUSPARSE_INDEX_TYPE *h_Col, const ElemType *h_Val, + const size_t nz, const size_t numRows, const size_t numCols, const bool IsOnDevice /*= false*/, const DEVICEID_TYPE devId /*= -1*/) { } + + template void GPUSparseMatrix::GetMatrixFromCSRFormat(CPUSPARSE_INDEX_TYPE*& h_CSRRow, CPUSPARSE_INDEX_TYPE*& h_Col, ElemType*& h_Val, size_t &numElemAllocated, size_t &nz, size_t &numRows, size_t &numCols) const {} + + template void GPUSparseMatrix::GetMatrixFromCSCFormat(CPUSPARSE_INDEX_TYPE*& h_CSCCol, CPUSPARSE_INDEX_TYPE*& h_Row, ElemType*& h_Val, size_t &numElemAllocated, size_t &nz, size_t &numRows, size_t &numCols) const {} + + template void GPUSparseMatrix::ConvertToSparseFormat(MatrixFormat newFormat) {} + template void GPUSparseMatrix::ConvertToSparseFormat(MatrixFormat newFormat, GPUSparseMatrix& outMatrix) const {} + + template void GPUSparseMatrix::ConvolveAndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const bool transposeA, const GPUSparseMatrix& rhs, const bool transposeB, ElemType beta, GPUMatrix& c, size_t numChannels, size_t horizontalSubsample, bool padding, bool channelwise) { }; + template void GPUSparseMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUSparseMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUSparseMatrix& b, GPUSparseMatrix& c) { } + template void GPUSparseMatrix::Reshape(const size_t numRows, const size_t numCols) { } + + template bool GPUSparseMatrix::IsValid() const { return true; } + + template template + void GPUSparseMatrix::CopyBuffer(OutType * outBuffer, const InType * inBuffer, const size_t size){} + +#pragma endregion Helper Functions + + template class MATH_API GPUSparseMatrix; + template class MATH_API GPUSparseMatrix; + template class MATH_API GPUSparseMatrix; + + template + MATH_API File& operator>>(File& stream, GPUSparseMatrix& us) + { + return stream; + } + + template MATH_API File& operator>>(File& stream, GPUSparseMatrix& us); + template MATH_API File& operator>>(File& stream, GPUSparseMatrix& us); + + template + MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us) + { + return stream; + } + template MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us); + template MATH_API File& operator<<(File& stream, const GPUSparseMatrix& us); + + +#pragma region DeviceBoundNumber class + + template DeviceBoundNumber::DeviceBoundNumber(const DeviceBoundNumber &deepCopy) + { + NOT_IMPLEMENTED; + } + + template DeviceBoundNumber::DeviceBoundNumber(DeviceBoundNumber &&shallowCopy) + { + this->ShallowCopyFrom(shallowCopy.m_data, shallowCopy.m_computeDevice); + shallowCopy.m_data = NULL; + } + + template void DeviceBoundNumber::ShallowCopyFrom(ElemType* newVal, int newValsDevceId) { } + + template DeviceBoundNumber::~DeviceBoundNumber() { } + +#pragma endregion DeviceBoundNumber class + +#pragma region Helper functions + + template + void GPUMatrix::SetDevice(DEVICEID_TYPE deviceId) { }; + + // GetBestGPUDeviceId - Get the best GPU DeviceId, based on cuda information + // TODO: should be replaced by BestGpu class instead, it's much better + template int GPUMatrix::GetBestGPUDeviceId() //returns -1 if no GPUs can be used + { + return EnforceOneGPUOnly(-1); // CPU + } + + // PrepareDevice - Setup the correct cuda context for an operation + // deviceId - the device on which the operation will take place + // defaults to -1, which means use matrices current device + template DEVICEID_TYPE GPUMatrix::PrepareDevice(DEVICEID_TYPE deviceId /*=-1*/) const + { + return deviceId; + } + + template ElemType* GPUMatrix::CopyToArray() const + { + return NULL; + } + + template void GPUMatrix::CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const + { + } + + //memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done + //return number of elements copied + template size_t GPUMatrix::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const + { + return 0; + } + + template void GPUMatrix::ChangeDeviceTo(int to_id) { } + + template void GPUMatrix::performElementWiseFunction(const ElementWiseOperator kind, const ElemType* src) { } + + +#pragma endregion Helper functions + +#pragma region Constructors and Destructor + + //should only be used by constructors. + template void GPUMatrix::ZeroInit(int deviceId) { } + + template GPUMatrix::GPUMatrix(int deviceId) + {}; + + //matrixName is used to verify that correct matrix is read. + template GPUMatrix::GPUMatrix(FILE* f, const char * matrixName, int deviceId) { } + + template GPUMatrix::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId) { }; + + template GPUMatrix::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, const size_t matrixFlags) { }; + + template GPUMatrix::GPUMatrix(const GPUMatrix& deepCopyFrom) { } + + template GPUMatrix::GPUMatrix(GPUMatrix&& moveFrom) { } + + //assignment operator, deep copy + template GPUMatrix& GPUMatrix::operator=(const GPUMatrix& deepCopyFrom) { return *this; } + + //move assignment operator, shallow copy + template GPUMatrix& GPUMatrix::operator=(GPUMatrix&& moveFrom) { return *this; } + + template GPUMatrix::~GPUMatrix(void) { } + + template void GPUMatrix::Clear() { } +#pragma endregion Constructors and Destructor + + template int GPUMatrix::GetComputeDeviceId() const + { + return -1; + } + +#pragma region Basic Operators + template GPUMatrix GPUMatrix::ColumnSlice(size_t startColumn, size_t numCols) const + { + GPUMatrix slice(0); + return slice; + } + + template GPUMatrix& GPUMatrix::AssignColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols) { return *this; } + + template GPUMatrix& GPUMatrix::SetColumnSlice(const GPUMatrix& fromMatrix, size_t startColumn, size_t numCols) { return *this; } + template GPUMatrix GPUMatrix::Diagonal() const + { + GPUMatrix diag(0); + return diag; + } + + //for each column of a, we assign numRows starting from startIndex to this + template GPUMatrix& GPUMatrix::AssignRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } + //for each column of a, we assign all rows of a to this starting from startIndex + template GPUMatrix& GPUMatrix::AssignToRowSliceValuesOf(const GPUMatrix& a, const size_t startIndex, const size_t numRows) { return *this; } + + //for each column of a, we add all rows of a to this starting from startIndex + template GPUMatrix& GPUMatrix::AddToRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } + template GPUMatrix& GPUMatrix::AddWithRowSliceValuesOf(const GPUMatrix& /*a*/, const size_t startIndex, const size_t numRows) { return *this; } + //template GPUMatrix& GPUMatrix::AssignRowStackValuesOf(const std::vector*>& inputMatrices, const size_t sliceStartCol, const size_t sliceNumCols) { return *this; } + + template GPUMatrix& GPUMatrix::AssignRepeatOf(const GPUMatrix& /*a*/, const size_t numRowRepeats, const size_t numColRepeats) { return *this; } + template GPUMatrix& GPUMatrix::AddToRowRepeatValuesOf(const GPUMatrix& /*a*/, const size_t numRowRepeats) { return *this; } + + template GPUMatrix& GPUMatrix::AssignPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) { return *this; } + template GPUMatrix& GPUMatrix::AddFoldedPositiveAndShiftedNegSample(const GPUMatrix& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber) { return *this; } + + template GPUMatrix GPUMatrix::Transpose() const { return *this; } + + // GetCublasHandle - get a cublas handle for the given GPU, should only need one per GPU + // computeDevice - The compute device for which the cublas handle is desired + // returns: cublas handle + // NOTE: we currently don't bother to ever free the CUBLAS handle, it will be freed automatically by CUDA when the process ends + template cublasHandle_t GPUMatrix::GetCublasHandle(int computeDevice/*=-1*/) + { + cublasHandle_t cuHandle = 0; + return cuHandle; + } + + template GPUMatrix& GPUMatrix::AssignTransposeOf(const GPUMatrix& /*a*/) { return *this; } + + template void GPUMatrix::SetValue(const ElemType v) { } + + template void GPUMatrix::SetValue(const ElemType* d_v) //d_v is pointer to the the value in GPU memory + {} + + template void GPUMatrix::SetColumn(const ElemType* colPointer, size_t colInd) { } + template void GPUMatrix::SetColumn(const GPUMatrix& valMat, size_t colInd) { } + + template void GPUMatrix::MaskColumnsValue(const GPUMatrix& columnsMask, ElemType val) { } + + template void GPUMatrix:: CopyColumnsStrided(const GPUMatrix& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride) { } + template void GPUMatrix::SetValue(const GPUMatrix& deepCopyFrom) { } + + template + void GPUMatrix::SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType *pArray, size_t matrixFlags) { } + + + template void GPUMatrix::SetDiagonalValue(const ElemType v) { } + + template void GPUMatrix::SetDiagonalValue(const GPUMatrix& vector) { } + + template void GPUMatrix::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed) { } + + template void GPUMatrix::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed) { } + + //maskRate: percentage of values masked out (similar to dropout rate) + //scaleValue: which scale value to set to the left ones (unmasked items). + template void GPUMatrix::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, unsigned long seed) { } + + template ElemType GPUMatrix::Adagrad(GPUMatrix& gradients, const bool needAveMultiplier) { return 0; } + template void GPUMatrix::FSAdagrad(GPUMatrix& gradients, GPUMatrix& functionValues, ElemType learnRatePerSample, ElemType momentum, ElemType adaWeight, ElemType adaMul){} + + template ElemType GPUMatrix::RmsProp(GPUMatrix& gradients, ElemType RMS_GAMMA, ElemType RMS_WGT_INC, ElemType RMS_WGT_MAX, ElemType RMS_WGT_DEC, ElemType RMS_WGT_MIN, const bool needAveMultiplier) { return 0; } + + template void GPUMatrix::Reshape(const size_t numRows, const size_t numCols) { } + + template void GPUMatrix::Resize(const size_t numRows, const size_t numCols, bool growOnly) { } + + template size_t GPUMatrix::LocateElement(const size_t row, const size_t col) const + { + return 0; + } + + template std::unique_ptr> GPUMatrix::GetOrCreateWorkspace() const + { + return NULL; + } + + template void GPUMatrix::ReleaseWorkspace(std::unique_ptr> src) const { } + + template size_t GPUMatrix::LocateColumn(const size_t col) const + { + return 0; + } + + template ElemType GPUMatrix::Get00Element() const + { + ElemType res = 0; + return res; + } +#pragma endregion Basic Operators + +#pragma region Member BLAS Functions + template GPUMatrix& GPUMatrix::operator+= (ElemType alpha) { return *this; } + + template GPUMatrix GPUMatrix::operator+ (ElemType alpha) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignSumOf(const ElemType alpha, const GPUMatrix& /*a*/) + { + return (*this); + } + + + template GPUMatrix& GPUMatrix::operator+= (const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix GPUMatrix::operator+ (const GPUMatrix& /*a*/) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignSumOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) + { + return (*this); + } + + template GPUMatrix& GPUMatrix::operator-= (ElemType alpha) { return *this; } + + template GPUMatrix GPUMatrix::operator- (ElemType alpha) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignDifferenceOf(const ElemType alpha, const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::AssignDifferenceOf(const GPUMatrix& /*a*/, const ElemType alpha) { return *this; } + + template GPUMatrix& GPUMatrix::operator-= (const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix GPUMatrix::operator- (const GPUMatrix& /*a*/) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignDifferenceOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + + template GPUMatrix& GPUMatrix::operator*= (ElemType alpha) { return *this; } + + template GPUMatrix GPUMatrix::operator* (ElemType alpha) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignProductOf(const ElemType alpha, const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::AssignProductOf(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB) { return *this; } + + template GPUMatrix GPUMatrix::operator* (const GPUMatrix& /*a*/) const { return *this; } + + template GPUMatrix& GPUMatrix::operator/= (ElemType alpha) + { + return (*this); + } + + template GPUMatrix GPUMatrix::operator/ (ElemType alpha) const { return *this; } + + //element-wise power + template GPUMatrix& GPUMatrix::operator^= (ElemType alpha) { return *this; } + + template GPUMatrix GPUMatrix::operator^ (ElemType alpha) const { return *this; } + + template GPUMatrix& GPUMatrix::AssignElementPowerOf(const GPUMatrix& /*a*/, const ElemType power) { return *this; } + + + template GPUMatrix& GPUMatrix::AddElementProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + + template GPUMatrix& GPUMatrix::ColumnElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } + template GPUMatrix& GPUMatrix::RowElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::ColumnElementDivideBy(const GPUMatrix& /*a*/) { return *this; } + template GPUMatrix& GPUMatrix::RowElementDivideBy(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::ElementInverse() { return *this; } + + template GPUMatrix& GPUMatrix::AssignElementInverseOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceSigmoid() { return *this; } + + template GPUMatrix& GPUMatrix::AssignSigmoidOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceSigmoidDerivative() { return *this; } + + template GPUMatrix& GPUMatrix::AssignSigmoidDerivativeOf(const GPUMatrix& /*a*/) { return *this; } + + + template GPUMatrix& GPUMatrix::InplaceTanh() { return *this; } + + template GPUMatrix& GPUMatrix::AssignTanhOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceLogSoftmax(const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::AssignLogSoftmaxOf(const GPUMatrix& /*a*/, const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceHardmax(const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::AssignHardmaxOf(const GPUMatrix& /*a*/, const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::DropFrame(const GPUMatrix& label, const GPUMatrix& gamma, const ElemType & threshhold) { return *this; } + template GPUMatrix& GPUMatrix::AssignSequenceError(const ElemType hsmoothingWeight, const GPUMatrix& label, const GPUMatrix& dnnoutput, const GPUMatrix& gamma, ElemType alpha) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceSqrt() { return *this; } + + template GPUMatrix& GPUMatrix::AssignSqrtOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceExp() { return *this; } + + template GPUMatrix& GPUMatrix::AssignExpOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceLog() { return *this; } + + template GPUMatrix& GPUMatrix::AssignLogOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceAbs() { return *this; } + + template GPUMatrix& GPUMatrix::AssignAbsOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceLinearRectifierDerivative() { return *this; } + + template GPUMatrix& GPUMatrix::AssignLinearRectifierDerivativeOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceCosine() { return *this; } + + template GPUMatrix& GPUMatrix::AssignCosineOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceNegativeSine() { return *this; } + + template GPUMatrix& GPUMatrix::AssignNegativeSineOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceTruncateBottom(const ElemType threshold) { return *this; } + + template GPUMatrix& GPUMatrix::AssignTruncateBottomOf(const GPUMatrix& /*a*/, const ElemType threshold) { return *this; } + + template GPUMatrix& GPUMatrix::InplaceTruncateTop(const ElemType threshold) + { + return *this; + } + + template GPUMatrix& GPUMatrix::AssignTruncateTopOf(const GPUMatrix& /*a*/, const ElemType threshold) + { + return *this; + } + template GPUMatrix& GPUMatrix::SetToZeroIfAbsLessThan(const ElemType threshold) + { + return *this; + } + + template ElemType GPUMatrix::SumOfAbsElements() const + { + return ElemType(0); + } + + template ElemType GPUMatrix::SumOfElements() const + { + return ElemType(0); + } + + + template GPUMatrix& GPUMatrix::AssignSumOfElements(const GPUMatrix& /*a*/) + { + return (*this); + } + template + void GPUMatrix::MinusOneAt(GPUMatrix& c, const size_t position) + {} + + template + void GPUMatrix::VectorSum(const GPUMatrix& a, GPUMatrix& c, const bool isColWise) + {} + + template + GPUMatrix& GPUMatrix::InplaceTruncate(const ElemType threshold) + { + return (*this); + } + + template + GPUMatrix& GPUMatrix::InplaceSoftThreshold(const ElemType threshold) + { + return (*this); + } + + template + GPUMatrix& GPUMatrix::GetARowByIndex(const GPUMatrix& a, const size_t m) + { + return (*this); + } + + template + GPUMatrix& GPUMatrix::AssignElementProductOfWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, const size_t shift, const size_t nt) + { + return (*this); + } + + template + GPUMatrix& GPUMatrix::AssignElementProductOfWithShift(const GPUMatrix& a, const GPUMatrix& b, const size_t shift) + { + return (*this); + } + + template + void GPUMatrix::InnerProductWithShiftNeg(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const size_t nt) + {} + + template + void GPUMatrix::ConductRowElementMultiplyWithShift(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c, const size_t shift, const bool isafixed) + {} + + template DeviceBoundNumber GPUMatrix::Sum_AsDeviceBoundNum() const + { + DeviceBoundNumber result; + return result; + } + + template ElemType GPUMatrix::Max() const + { + return ElemType(0); + } + + + template GPUMatrix& GPUMatrix::ElementMultiplyWith(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::AssignElementProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + + template GPUMatrix& GPUMatrix::AssignElementDivisionOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + template GPUMatrix& GPUMatrix::ElementDivideBy(const GPUMatrix& /*a*/) { return *this; } + + template bool GPUMatrix::IsEqualTo(const GPUMatrix& a, const ElemType threshold /*= 1e-8*/) const + { + return AreEqual(*this, a, threshold); + } + + template void GPUMatrix::VectorNorm1(GPUMatrix& c, const bool isColWise) const + { + } + + template GPUMatrix& GPUMatrix::AssignVectorNorm1Of(GPUMatrix& /*a*/, const bool isColWise) { return *this; } + + template void GPUMatrix::VectorNorm2(GPUMatrix& c, const bool isColWise) const + {} + + template GPUMatrix& GPUMatrix::AssignVectorNorm2Of(GPUMatrix& /*a*/, const bool isColWise) { return *this; } + + template void GPUMatrix::VectorNormInf(GPUMatrix& c, const bool isColWise) const + {} + + template GPUMatrix& GPUMatrix::AssignVectorNormInfOf(GPUMatrix& /*a*/, const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::AssignInnerProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const bool isColWise) { return *this; } + + template GPUMatrix& GPUMatrix::AssignKhatriRaoProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + + //column-wise reshaped product. Used to compute KhatriRaoProduct Gradient + // this = reshape each column of a from (K1xK2,1) to (K1, K2) + // if each column of a is not transposed, each (K1, K2) times each column of b (K2, frames). + // the output is a (K1, frames) matrix + // if each column of a is tranposed, each (K1, K2)^T times each column of b(K1, frames) and output is (K2, frames) + template GPUMatrix& GPUMatrix::AddColumnReshapeProductOf(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const bool transposeAColumn) { return *this; } + + template GPUMatrix& GPUMatrix::AddWithScaleOf(ElemType alpha, const GPUMatrix& /*a*/) { return *this; } + + template ElemType GPUMatrix::FrobeniusNorm() const + { + ElemType h_sum = 0; + return (h_sum); + } + + template GPUMatrix& GPUMatrix::AssignFrobeniusNormOf(const GPUMatrix& /*a*/) { return *this; } + + template ElemType GPUMatrix::MatrixNormInf() const + { + ElemType h_maxAbs = 0; + return h_maxAbs; + } + + template ElemType GPUMatrix::MatrixNorm1() const + { + return ElemType(0); + } + + template ElemType GPUMatrix::MatrixNorm0() const + { + return ElemType(0); + } + + template GPUMatrix& GPUMatrix::AssignSignOf(const GPUMatrix& /*a*/) { return *this; } + + template GPUMatrix& GPUMatrix::AddSignOf(const GPUMatrix& /*a*/) { return *this; } + + template void GPUMatrix::VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise) const + {} + + template void GPUMatrix::VectorMax(GPUMatrix& maxIndexes, GPUMatrix& maxValues, const bool isColWise, int topK) const + {} + + template void GPUMatrix::VectorMin(GPUMatrix& minIndexes, GPUMatrix& minValues, const bool isColWise) const + {} + + template GPUMatrix& GPUMatrix::AssignNumOfDiff(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, bool /*searchInCol = false*/) { return *this; } + +#pragma endregion Member BLAS Functions + +#pragma region Other helper functions + template void GPUMatrix::Print(const char* matrixName, size_t rowStart, size_t rowEnd, size_t colStart, size_t colEnd) const + {} + + template void GPUMatrix::Print(const char* matrixName /*=nullptr*/) const + {} + + // file I/O + //matrixName is used to verify that correct matrix is read. + template void GPUMatrix::ReadFromFile(FILE* f, const char * matrixName) + {} + + //matrixName is used to verify that correct matrix is read. + template void GPUMatrix::WriteToFile(FILE* f, const char * matrixName) + {} + + //helpfer function used for convolution neural network + template GPUMatrix& GPUMatrix::AssignPackedConvolutionInput(const GPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) { + return *this; + } + + //helpfer function used for convolution neural network + template GPUMatrix& GPUMatrix::UnpackConvolutionInput(GPUMatrix& inputSubBatch, + const size_t inputWidth, const size_t inputHeight, const size_t inputChannels, + const size_t outputWidth, const size_t outputHeight, const size_t outputChannels, + const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample, + const bool zeroPadding) const + { + return inputSubBatch; + } + + template GPUMatrix& GPUMatrix::AssignMaxPoolingResult(const GPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { + return *this; + } + + template GPUMatrix& GPUMatrix::AddMaxPoolingGradient(const GPUMatrix& outputGradientBatch, const GPUMatrix& inputBatch, const GPUMatrix& outputBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { + return *this; + } + + template GPUMatrix& GPUMatrix::AssignAveragePoolingResult(const GPUMatrix& inputBatch, const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { + return *this; + } + + template GPUMatrix& GPUMatrix::AddAveragePoolingGradient(const GPUMatrix& outputGradientBatch, + const size_t channels, + const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample, + const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample, + const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample) { + return *this; + } + +#pragma endregion Other helper functions + +#pragma region Static BLAS Functions + template void GPUMatrix::MultiplyAndWeightedAdd(ElemType alpha, const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, + ElemType beta, GPUMatrix& c) + { + } + template void GPUMatrix::Multiply1x1AndWeightedAdd(ElemType alpha, const GPUMatrix& lhs, const GPUMatrix& rhs, ElemType beta, GPUMatrix& c) { } + + template void GPUMatrix::MultiplyAndAdd(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, GPUMatrix& c) { } + + template void GPUMatrix::Multiply(const GPUMatrix& /*a*/, const bool transposeA, const GPUMatrix& /*b*/, const bool transposeB, GPUMatrix& c) { } + + template void GPUMatrix::Multiply(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + c + /// if a is a column vector, add to all columns of c + /// if a is a row vector, add to all rows of c + /// if a is a scalar, add to all elements of c + /// Scalar + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template void GPUMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } + + /// Matrix-scalar multiply with col-major matrices: c = alpha * a + b + /// if a is a column vector, add to all columns of b + /// if a is a row vector, add to all rows of b + /// if a is a scalar, add to all elements of b + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template void GPUMatrix::ScaleAndAdd(ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template void GPUMatrix::AddScaledDifference(const ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void GPUMatrix::AssignScaledDifference(const ElemType alpha, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + /// c += alpha * (a-b) + /// if a, b, c must have same dim + /// 1X1 matrix + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template void GPUMatrix::AddScaledDifference(const GPUMatrix& /*alpha*/, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + /// c = alpha * (a-b) + /// if a, b, c must have same dim + /// Scalar + /// Input matrix + /// Input matrix + /// Resulting matrix, user is responsible for allocating this + template + void GPUMatrix::AssignScaledDifference(const GPUMatrix& /*alpha*/, const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c) { } + + //c[ci,cj] += a[ai,aj] + template void GPUMatrix::AddElementToElement(const GPUMatrix& /*a*/, const size_t ai, const size_t aj, GPUMatrix& c, const size_t ci, const size_t cj) { } + + template void GPUMatrix::Scale(ElemType alpha, GPUMatrix& /*a*/) { } + + + template void GPUMatrix::Scale(GPUMatrix& /*alpha*/, GPUMatrix& /*a*/) { } + + template //c = alpha * a + void GPUMatrix::Scale(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } + + template bool GPUMatrix::HasElement(const GPUMatrix& a, const ElemType value) { return false; } + + template void GPUMatrix::InnerProduct(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, GPUMatrix& c, const bool isColWise) { } + + template ElemType GPUMatrix::InnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) + { + return ElemType(0); + } + + template GPUMatrix& GPUMatrix::AssignInnerProductOfMatrices(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/) { return *this; } + + template void GPUMatrix::ElementWisePower(ElemType alpha, const GPUMatrix& /*a*/, GPUMatrix& c) { } + + template bool GPUMatrix::AreEqual(const GPUMatrix& /*a*/, const GPUMatrix& /*b*/, const ElemType threshold /*= 1e-8*/) + { + return false; + } + + template void GPUMatrix::TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUMatrix& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUMatrix& b, GPUMatrix& c) { } + + template + void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 2> & regularStrides, + const SmallVector & reducingOpDims, const array, 2> & reducingStrides) { } + template + void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, const GPUMatrix& b, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 3> & regularStrides, + const SmallVector & reducingOpDims, const array, 3> & reducingStrides) { } + template + void GPUMatrix::TensorOp(ElemType beta, const GPUMatrix& a, const GPUMatrix& b, const GPUMatrix& c, ElemType alpha, ElementWiseOperator op, + const array & offsets, + const SmallVector & regularOpDims, const array, 4> & regularStrides, + const SmallVector & reducingOpDims, const array, 4> & reducingStrides) { } + + template + void GPUMatrix::CreateCurandObject(unsigned long seed, const char *caller) + { + } + + template + void GPUMatrix::ResetCurandObject(unsigned long seed, const char *caller) + { + } + + template GPUMatrix GPUMatrix::Ones(const size_t rows, const size_t cols, int deviceId) + { + GPUMatrix mat(0); + return mat; + } + + template GPUMatrix GPUMatrix::Zeros(const size_t rows, const size_t cols, int deviceId) + { + GPUMatrix mat(0); + return mat; + } + + template GPUMatrix GPUMatrix::Eye(const size_t rows, int deviceId) + { + GPUMatrix mat(0); + return mat; + } + + template GPUMatrix GPUMatrix::RandomUniform(const size_t rows, const size_t cols, int deviceId, const ElemType low, const ElemType high, unsigned long seed) + { + GPUMatrix mat(0); + return mat; + } + + template GPUMatrix GPUMatrix::RandomGaussian(const size_t rows, const size_t cols, int deviceId, const ElemType mean, const ElemType sigma, unsigned long seed) + { + GPUMatrix mat(0); + return mat; + } + + template ElemType GPUMatrix::GetLearnRateForBlock_Helper(const GPUMatrix &Gradients, const GPUMatrix &SmoothedGradients) + { + return ElemType(0); + } + + template + ElemType GPUMatrix::LogAddSumOfElements() const + { + return ElemType(0); + } + + template + void GPUMatrix::RCRFBackwardCompute( + const GPUMatrix& alpha, GPUMatrix& beta, + const GPUMatrix& lbls, + const GPUMatrix& pos_scores, const GPUMatrix& pair_scores, const int shift) + {} + + template + void GPUMatrix::RCRFTransGrdCompute(const GPUMatrix& lbls, + const GPUMatrix& alpha, + const GPUMatrix& beta, + const GPUMatrix& pair_scores, + GPUMatrix& grd, + const int startLbl, + const int shift) + {} + + template + void GPUMatrix::AssignNoiseContrastiveEstimation(const GPUMatrix& a, +const GPUMatrix& b, const GPUMatrix& bias, size_t sampleCount, GPUMatrix& tmp, GPUMatrix& c) + { + } + + template + void GPUMatrix::AssignNCEDerivative(GPUMatrix& tmp, const GPUMatrix& a, + const GPUMatrix& b, size_t inputIndex, GPUMatrix& c) + { + + } + + template + void GPUMatrix::AssignSoftmaxSum(const GPUMatrix& a, GPUMatrix& c) + { + } + + template + void GPUMatrix::AssignNCEUnnormalizedEval(const GPUMatrix& a, const GPUMatrix& b, GPUMatrix& c) + { + } +#pragma endregion Static BLAS Functions + +#pragma region MatrixQuantizerGPU functions + template + MatrixQuantizerGPU::MatrixQuantizerGPU(int deviceId, bool useDedicatedComputeStream, bool forceSync) + { + } + + template + MatrixQuantizerGPU::~MatrixQuantizerGPU() + { + } + + template + void MatrixQuantizerGPU::QuantizeAsync(const Matrix& inMatrix, const Matrix& inResidual, QuantizedMatrix& outQMatrix, Matrix& outResidual, bool zeroThresholdFor1Bit) + { + } + + template + void MatrixQuantizerGPU::WaitQuantizeAsyncDone() + { + } + + template + void MatrixQuantizerGPU::UnquantizeAsync(QuantizedMatrix& inQMatrix, Matrix& outMatrix, bool add /*= false*/) + { + } + + template + void MatrixQuantizerGPU::WaitUnquantizeAsyncDone() + { + } + +#pragma endregion MatrixQuantizerGPU functions + +#pragma region GPUMatrixComputeStreamEvent functions + + GPUMatrixComputeStreamEvent::GPUMatrixComputeStreamEvent(int deviceId) + : MatrixComputeStreamEvent(deviceId) + { + } + + GPUMatrixComputeStreamEvent::~GPUMatrixComputeStreamEvent() { }; + void GPUMatrixComputeStreamEvent::SynchronizeEvent() { }; + template<> void GPUMatrixComputeStreamEvent::SynchronizeQuantizationComputeStreamWithEvent() { }; + template<> void GPUMatrixComputeStreamEvent::SynchronizeQuantizationComputeStreamWithEvent() { }; + template<> void GPUMatrixComputeStreamEvent::SynchronizeDataTransferFetchStreamWithEvent() { }; + template<> void GPUMatrixComputeStreamEvent::SynchronizeDataTransferFetchStreamWithEvent() { }; + +#pragma endregion GPUMatrixComputeStreamEvent functions + +#pragma region GPUDataTransferer functions + + template + GPUDataTransferer::GPUDataTransferer(int, bool) + { + } + + template + GPUDataTransferer::~GPUDataTransferer() + { + } + + template + void GPUDataTransferer::CopyGPUToCPUAsync(ElemType*, size_t, ElemType*) + { + } + + template + void GPUDataTransferer::WaitForCopyGPUToCPUAsync() + { + } + + template + void GPUDataTransferer::CopyCPUToGPUAsync(ElemType*, size_t, ElemType*) + { + } + + template + void GPUDataTransferer::WaitForCopyCPUToGPUAsync() + { + } + +#pragma endregion GPUDataTransferer functions + + template class GPUMatrix; + template class GPUMatrix; + template class GPUMatrix; + template class DeviceBoundNumber; + template class DeviceBoundNumber; + template MatrixQuantizerGPU::~MatrixQuantizerGPU(); + template MatrixQuantizerGPU::~MatrixQuantizerGPU(); + template void MatrixQuantizerGPU::QuantizeAsync(const Matrix&, const Matrix&, QuantizedMatrix&, Matrix&, bool); + template void MatrixQuantizerGPU::QuantizeAsync(const Matrix&, const Matrix&, QuantizedMatrix&, Matrix&, bool); + + template class GPUDataTransferer; + template class GPUDataTransferer; + + template cublasHandle_t GPUMatrix::s_cuHandle[GPUMatrix::MaxGpus] = { 0 }; + + template void* GPUMatrix::s_curandGenerator = NULL; + + template + typename CuDnnConvolutionEngineFactory::Tensor4DPtr CuDnnConvolutionEngineFactory::CreateTensor(size_t, size_t, size_t, size_t) + { + RuntimeError("The code is compiled with CPUONLY macro."); + } + + template + typename CuDnnConvolutionEngineFactory::FilterPtr CuDnnConvolutionEngineFactory::CreateFilter(size_t, size_t, size_t, size_t) + { + RuntimeError("The code is compiled without CPUONLY macro."); + } + + template + typename CuDnnConvolutionEngineFactory::ConvDescPtr CuDnnConvolutionEngineFactory::CreateConvDescriptor( + const Tensor4D&, const Filter&, size_t, size_t, bool) + { + RuntimeError("The code is compiled without CPUONLY macro."); + } + + template + typename CuDnnConvolutionEngineFactory::PoolDescPtr CuDnnConvolutionEngineFactory::CreatePoolDescriptor( + typename PoolDesc::PoolKind, size_t, size_t, size_t, size_t, size_t, size_t) + { + RuntimeError("The code is compiled without CPUONLY macro."); + } + + template + typename CuDnnConvolutionEngineFactory::ConvEnginePtr CuDnnConvolutionEngineFactory::CreateConvEngine(DEVICEID_TYPE, size_t) + { + RuntimeError("The code is compiled without CPUONLY macro."); + } + + template + typename CuDnnConvolutionEngineFactory::PoolEnginePtr CuDnnConvolutionEngineFactory::CreatePoolEngine(DEVICEID_TYPE) + { + RuntimeError("The code is compiled without CPUONLY macro."); + } + + template + bool CuDnnConvolutionEngineFactory::IsSupported(DEVICEID_TYPE) + { + return false; + } + + template class CuDnnConvolutionEngineFactory; + template class CuDnnConvolutionEngineFactory; + +}}} + +// define a dummy GPUWatcher class too +#include "GPUWatcher.h" + +int GPUWatcher::GetGPUIdWithTheMostFreeMemory() +{ + return 0; +} + + +size_t GPUWatcher::GetFreeMemoryOnCUDADevice(int /*devId*/) +{ + return 0; +} + +GPUWatcher::GPUWatcher(void) +{ +} + +GPUWatcher::~GPUWatcher(void) +{ +} + +#endif // CPUONLY diff --git a/Source/Math/ValueQuantizer.h b/Source/Math/ValueQuantizer.h index 8a4c28fd563e..da972f6a005c 100644 --- a/Source/Math/ValueQuantizer.h +++ b/Source/Math/ValueQuantizer.h @@ -1,221 +1,221 @@ -#pragma once -#ifndef __VALLUE_QUANTIZER_H__ -#define __VALLUE_QUANTIZER_H__ - -#include "Basics.h" -#include "BestGpu.h" // for CPUONLY -#ifndef CPUONLY -#include -#include -#include -#include -#endif // !CPUONLY - -#include -#include - -#pragma warning (disable: 4127) // conditional expression is constant - -namespace Microsoft { namespace MSR { namespace CNTK { - - #ifdef __device__ // this can be used in CUDA; if this is not defined, then we are compiling in a non-CUDA context - #define cudacode __device__ // CUDA: we assume we ONLY run these functions on CUDA (otherwise we'd need to mess with specifiers of matrixref) - #define cudasharedcode __device__ __host__ // shared on both CUDA and CPU; note that such functions cannot call into __device__ only functions like matrixref::operator(,) - #undef assert - #define assert(c) - #else - #define cudacode // non-CUDA context: defines to nothing - #define cudasharedcode - //#define QUANTUSEPPL - #endif - - #ifdef QUANTUSEPPL - #include // in non-CUDA: also use PPL lib - #endif - - template - class QuantizedWordHelper; - - template<> - class QuantizedWordHelper - { - public: - typedef unsigned int ValueType; - typedef int ValueTypeSigned; - static_assert(sizeof(float) == sizeof(ValueType), "Quantized word size != size of ElemType=float"); - }; - - template<> - class QuantizedWordHelper - { - public: - typedef unsigned long long ValueType; - typedef long long ValueTypeSigned; - static_assert(sizeof(double) == sizeof(ValueType), "Quantized word size != size of ElemType=double"); - }; - - template - class ValueQuantizer - { - public: - typedef typename QuantizedWordHelper::ValueType QWord; - typedef typename QuantizedWordHelper::ValueType QWordVal; - typedef typename QuantizedWordHelper::ValueTypeSigned QWordValSigned; - static const size_t QWordNumBits = 8 * sizeof(QWord); - - public: - cudasharedcode ValueQuantizer(size_t ldNbits, ElemType lower, ElemType upper) - : ldNbits(ldNbits), Nbits(1 << ldNbits), quantimin(lower), quantimax(upper) - { - rangeend = ((QWordVal)1) << Nbits; - - // post-fix for incorrect shift for no-quant hack (Nbits=32): << arg is taken mod 32! - // in this case, it's only used as (rangeend-1) which is now correct (before it was 0!) - if (Nbits >= (8 * sizeof(rangeend))) - { - rangeend = 0; - } - - // must protect against NaN: interval is 0 -> quantization is futile, just emit 0 - if (((quantimax - quantimin) < 1e-36f) || (rangeend == 0)) - { - qfactor = ufactor = (ElemType)0.0; - } - else - { - // precompute this for quantize() (see comment there) - qfactor = rangeend / (quantimax - quantimin); - // and for unquantize() - ufactor = (quantimax - quantimin) / rangeend; - } - - // set the quantization threshold for the special case of 1-bit - quantimid = 0.5f * (quantimax + quantimin); - } - - // quantize one value - // TODO: we can optimize for 1 bit here - very simply use a template arg 'isonebit' - template - cudasharedcode QWordVal Quantize(ElemType u) const - { - if (Nbits == QWordNumBits) - { - return QuantizeToFullQWord(u); - } - // TODO: we may need to optimize this by a template arg - else if (ldNbits == 0) - { - return Quantize1(u) ? 1 : 0; - } - else - { - if (u <= quantimin) - { - return 0; - } - else if (u >= quantimax) - { - return (rangeend - 1); - } - else - { - return (QWordVal)((QWordValSigned)((u - quantimin) * qfactor)); - } - } - } - - // unquantize one value - cudasharedcode ElemType Unquantize(QWordVal u) const - { - if (Nbits == QWordNumBits) - { - return *(ElemType*)&u; - } - - // Note: in 1-bit case, we want 0.5 -> mean0, 1.5 -> mean1 - return ((u + (ElemType)0.5) * ufactor) + quantimin; - } - - // quantize one value --special version for 1 bit - template - cudasharedcode bool Quantize1(ElemType u) const - { - assert(Nbits == 1); - if (!ZeroThresholdFor1Bit) - { - return u >= quantimid; - } - else - { - return u >= (ElemType)0.0; - } - } - - // unquantize one value --special case for 1 bit - static cudasharedcode ElemType Unquantize1(bool u, ElemType val0, ElemType val1) - { - return u ? val1 : val0; - } - - //how many bits we are quanatizing to - cudasharedcode size_t NBits() const - { - return Nbits; - } - - //max value of quantize value; 2^Nbits - cudasharedcode QWordVal QuanRangeEnd() const - { - return rangeend; - } - - // helper: compute the binary log of a power of two (utility function to convert 'Nbits' into 'ldNbits' - static size_t ld(size_t v) - { - if (v == 1) - { - return 0; - } - else if (v & 1) // not a power of two - { - RuntimeError("ld: 'bits' must be a power of two"); - } - else - { - return 1 + ld(v >> 1); - } - } - - protected: - // quantize for full ElemType size bits case (special case that allows to bypass quantization, for testing/debugging purposes) - cudasharedcode QWordVal QuantizeToFullQWord(ElemType u) const - { - assert(Nbits == QWordNumBits); - - // we return the bit pattern that encodes the float value - return *(QWordVal*)&u; - } - - protected: - // NBits must be power of two - size_t ldNbits; - size_t Nbits; - - QWordVal rangeend; - - // quantization range - ElemType quantimin; - ElemType quantimax; - - // quantization threshold for 1-bit case - ElemType quantimid; - - // precomputed factor for quantizing - ElemType qfactor; - - // and for unquantizing - ElemType ufactor; - }; -}}} - -#endif // __VALUE_QUANTIZER_H__ +#pragma once +#ifndef __VALLUE_QUANTIZER_H__ +#define __VALLUE_QUANTIZER_H__ + +#include "Basics.h" +#include "BestGpu.h" // for CPUONLY +#ifndef CPUONLY +#include +#include +#include +#include +#endif // !CPUONLY + +#include +#include + +#pragma warning (disable: 4127) // conditional expression is constant + +namespace Microsoft { namespace MSR { namespace CNTK { + + #ifdef __device__ // this can be used in CUDA; if this is not defined, then we are compiling in a non-CUDA context + #define cudacode __device__ // CUDA: we assume we ONLY run these functions on CUDA (otherwise we'd need to mess with specifiers of matrixref) + #define cudasharedcode __device__ __host__ // shared on both CUDA and CPU; note that such functions cannot call into __device__ only functions like matrixref::operator(,) + #undef assert + #define assert(c) + #else + #define cudacode // non-CUDA context: defines to nothing + #define cudasharedcode + //#define QUANTUSEPPL + #endif + + #ifdef QUANTUSEPPL + #include // in non-CUDA: also use PPL lib + #endif + + template + class QuantizedWordHelper; + + template<> + class QuantizedWordHelper + { + public: + typedef unsigned int ValueType; + typedef int ValueTypeSigned; + static_assert(sizeof(float) == sizeof(ValueType), "Quantized word size != size of ElemType=float"); + }; + + template<> + class QuantizedWordHelper + { + public: + typedef unsigned long long ValueType; + typedef long long ValueTypeSigned; + static_assert(sizeof(double) == sizeof(ValueType), "Quantized word size != size of ElemType=double"); + }; + + template + class ValueQuantizer + { + public: + typedef typename QuantizedWordHelper::ValueType QWord; + typedef typename QuantizedWordHelper::ValueType QWordVal; + typedef typename QuantizedWordHelper::ValueTypeSigned QWordValSigned; + static const size_t QWordNumBits = 8 * sizeof(QWord); + + public: + cudasharedcode ValueQuantizer(size_t ldNbits, ElemType lower, ElemType upper) + : ldNbits(ldNbits), Nbits(1 << ldNbits), quantimin(lower), quantimax(upper) + { + rangeend = ((QWordVal)1) << Nbits; + + // post-fix for incorrect shift for no-quant hack (Nbits=32): << arg is taken mod 32! + // in this case, it's only used as (rangeend-1) which is now correct (before it was 0!) + if (Nbits >= (8 * sizeof(rangeend))) + { + rangeend = 0; + } + + // must protect against NaN: interval is 0 -> quantization is futile, just emit 0 + if (((quantimax - quantimin) < 1e-36f) || (rangeend == 0)) + { + qfactor = ufactor = (ElemType)0.0; + } + else + { + // precompute this for quantize() (see comment there) + qfactor = rangeend / (quantimax - quantimin); + // and for unquantize() + ufactor = (quantimax - quantimin) / rangeend; + } + + // set the quantization threshold for the special case of 1-bit + quantimid = 0.5f * (quantimax + quantimin); + } + + // quantize one value + // TODO: we can optimize for 1 bit here - very simply use a template arg 'isonebit' + template + cudasharedcode QWordVal Quantize(ElemType u) const + { + if (Nbits == QWordNumBits) + { + return QuantizeToFullQWord(u); + } + // TODO: we may need to optimize this by a template arg + else if (ldNbits == 0) + { + return Quantize1(u) ? 1 : 0; + } + else + { + if (u <= quantimin) + { + return 0; + } + else if (u >= quantimax) + { + return (rangeend - 1); + } + else + { + return (QWordVal)((QWordValSigned)((u - quantimin) * qfactor)); + } + } + } + + // unquantize one value + cudasharedcode ElemType Unquantize(QWordVal u) const + { + if (Nbits == QWordNumBits) + { + return *(ElemType*)&u; + } + + // Note: in 1-bit case, we want 0.5 -> mean0, 1.5 -> mean1 + return ((u + (ElemType)0.5) * ufactor) + quantimin; + } + + // quantize one value --special version for 1 bit + template + cudasharedcode bool Quantize1(ElemType u) const + { + assert(Nbits == 1); + if (!ZeroThresholdFor1Bit) + { + return u >= quantimid; + } + else + { + return u >= (ElemType)0.0; + } + } + + // unquantize one value --special case for 1 bit + static cudasharedcode ElemType Unquantize1(bool u, ElemType val0, ElemType val1) + { + return u ? val1 : val0; + } + + //how many bits we are quanatizing to + cudasharedcode size_t NBits() const + { + return Nbits; + } + + //max value of quantize value; 2^Nbits + cudasharedcode QWordVal QuanRangeEnd() const + { + return rangeend; + } + + // helper: compute the binary log of a power of two (utility function to convert 'Nbits' into 'ldNbits' + static size_t ld(size_t v) + { + if (v == 1) + { + return 0; + } + else if (v & 1) // not a power of two + { + RuntimeError("ld: 'bits' must be a power of two"); + } + else + { + return 1 + ld(v >> 1); + } + } + + protected: + // quantize for full ElemType size bits case (special case that allows to bypass quantization, for testing/debugging purposes) + cudasharedcode QWordVal QuantizeToFullQWord(ElemType u) const + { + assert(Nbits == QWordNumBits); + + // we return the bit pattern that encodes the float value + return *(QWordVal*)&u; + } + + protected: + // NBits must be power of two + size_t ldNbits; + size_t Nbits; + + QWordVal rangeend; + + // quantization range + ElemType quantimin; + ElemType quantimax; + + // quantization threshold for 1-bit case + ElemType quantimid; + + // precomputed factor for quantizing + ElemType qfactor; + + // and for unquantizing + ElemType ufactor; + }; +}}} + +#endif // __VALUE_QUANTIZER_H__ diff --git a/Source/Math/cudabasetypes.h b/Source/Math/cudabasetypes.h index 32eb13bd3438..79aef9a8db86 100644 --- a/Source/Math/cudabasetypes.h +++ b/Source/Math/cudabasetypes.h @@ -1,89 +1,89 @@ -// cudabasetypes.h -- basic types used on both CUDA and PC side -// -// F. Seide, V-hansu - -#pragma once - -#ifdef __CUDA_ARCH__ // we are compiling under CUDA -#define ON_CUDA 1 -#ifdef __device__ -#define cudacode __device__ -#define cudasharedcode __device__ __host__ -#else -#define cudacode -#define cudasharedcode -#endif -#else -#define ON_CUDA 0 // TODO: this does not work for some combination--fix this -#ifdef __device__ -#define cudacode __device__ -#define cudasharedcode __device__ __host__ -#else -#define cudacode -#define cudasharedcode -#endif -#endif - -#include - -namespace msra { namespace cuda { - -typedef size_t cuda_size_t; // TODO: verify if this is consistent across CPU/CUDA, or use uint32 or so - -// we wrap CUDA pointers so that we don't accidentally use them in CPU code -template class cuda_ptr -{ - T * p; // CUDA pointers are the same as host (e.g. Win32 is restricted to 32-bit CUDA pointers) -public: - void swap (cuda_ptr & other) { T * tmp = p; p = other.p; other.p = tmp; } - cudacode T & operator[] (size_t index) { return p[index]; } - cudacode const T & operator[] (size_t index) const { return p[index]; } - cudasharedcode cuda_ptr operator+ (size_t index) const { return cuda_ptr (p + index); } - cudasharedcode cuda_ptr operator- (size_t index) const { return cuda_ptr (p - index); } - cuda_ptr (T * pp) : p (pp) {} - T * get() const { return p; } -}; - -// reference to a vector (without allocation) that lives in CUDA RAM -// This can be directly passed by value to CUDA functions. -template class vectorref -{ - cuda_ptr p; // pointer in CUDA space of this device - cuda_size_t n; // number of elements -public: - cudasharedcode size_t size() const throw() { return n; } - cudacode T & operator[] (size_t i) { return p[i]; } - cudacode const T & operator[] (size_t i) const { return p[i]; } - cuda_ptr get() const throw() { return p; } - cuda_ptr reset (cuda_ptr pp, size_t nn) throw() { p.swap (pp); n = nn; return pp; } - vectorref (cuda_ptr pp, size_t nn) : p (pp), n (nn) { } - vectorref() : p (0), n (0) { } -}; - -// reference to a matrix -template class matrixref -{ -protected: - cuda_ptr p; // pointer in CUDA space of this device - size_t numrows; // rows() - size_t numcols; // cols() - size_t colstride; // height of column = rows() rounded to multiples of 4 - cudasharedcode size_t locate (size_t i, size_t j) const - { - return j * colstride + i; - } // matrix in column-wise storage - matrixref() : p (0), numrows (0), numcols (0), colstride (0) {} -public: - matrixref(T* p, size_t numRows, size_t numCols, size_t colStride) - : p(p), numrows(numRows), numcols(numCols), colstride(colStride) - { - } - cuda_ptr get() const throw() { return p; } - cudasharedcode size_t rows() const throw() { return numrows; } - cudasharedcode size_t cols() const throw() { return numcols; } - cudasharedcode void reshape(const size_t newrows, const size_t newcols) { assert (rows() * cols() == newrows * newcols); numrows=newrows; numcols = newcols;}; - cudasharedcode size_t getcolstride() const throw() { return colstride; } - cudacode T & operator() (size_t i, size_t j) { return p[locate(i,j)]; } - cudacode const T & operator() (size_t i, size_t j) const { return p[locate(i,j)]; } -}; -}} +// cudabasetypes.h -- basic types used on both CUDA and PC side +// +// F. Seide, V-hansu + +#pragma once + +#ifdef __CUDA_ARCH__ // we are compiling under CUDA +#define ON_CUDA 1 +#ifdef __device__ +#define cudacode __device__ +#define cudasharedcode __device__ __host__ +#else +#define cudacode +#define cudasharedcode +#endif +#else +#define ON_CUDA 0 // TODO: this does not work for some combination--fix this +#ifdef __device__ +#define cudacode __device__ +#define cudasharedcode __device__ __host__ +#else +#define cudacode +#define cudasharedcode +#endif +#endif + +#include + +namespace msra { namespace cuda { + +typedef size_t cuda_size_t; // TODO: verify if this is consistent across CPU/CUDA, or use uint32 or so + +// we wrap CUDA pointers so that we don't accidentally use them in CPU code +template class cuda_ptr +{ + T * p; // CUDA pointers are the same as host (e.g. Win32 is restricted to 32-bit CUDA pointers) +public: + void swap (cuda_ptr & other) { T * tmp = p; p = other.p; other.p = tmp; } + cudacode T & operator[] (size_t index) { return p[index]; } + cudacode const T & operator[] (size_t index) const { return p[index]; } + cudasharedcode cuda_ptr operator+ (size_t index) const { return cuda_ptr (p + index); } + cudasharedcode cuda_ptr operator- (size_t index) const { return cuda_ptr (p - index); } + cuda_ptr (T * pp) : p (pp) {} + T * get() const { return p; } +}; + +// reference to a vector (without allocation) that lives in CUDA RAM +// This can be directly passed by value to CUDA functions. +template class vectorref +{ + cuda_ptr p; // pointer in CUDA space of this device + cuda_size_t n; // number of elements +public: + cudasharedcode size_t size() const throw() { return n; } + cudacode T & operator[] (size_t i) { return p[i]; } + cudacode const T & operator[] (size_t i) const { return p[i]; } + cuda_ptr get() const throw() { return p; } + cuda_ptr reset (cuda_ptr pp, size_t nn) throw() { p.swap (pp); n = nn; return pp; } + vectorref (cuda_ptr pp, size_t nn) : p (pp), n (nn) { } + vectorref() : p (0), n (0) { } +}; + +// reference to a matrix +template class matrixref +{ +protected: + cuda_ptr p; // pointer in CUDA space of this device + size_t numrows; // rows() + size_t numcols; // cols() + size_t colstride; // height of column = rows() rounded to multiples of 4 + cudasharedcode size_t locate (size_t i, size_t j) const + { + return j * colstride + i; + } // matrix in column-wise storage + matrixref() : p (0), numrows (0), numcols (0), colstride (0) {} +public: + matrixref(T* p, size_t numRows, size_t numCols, size_t colStride) + : p(p), numrows(numRows), numcols(numCols), colstride(colStride) + { + } + cuda_ptr get() const throw() { return p; } + cudasharedcode size_t rows() const throw() { return numrows; } + cudasharedcode size_t cols() const throw() { return numcols; } + cudasharedcode void reshape(const size_t newrows, const size_t newcols) { assert (rows() * cols() == newrows * newcols); numrows=newrows; numcols = newcols;}; + cudasharedcode size_t getcolstride() const throw() { return colstride; } + cudacode T & operator() (size_t i, size_t j) { return p[locate(i,j)]; } + cudacode const T & operator() (size_t i, size_t j) const { return p[locate(i,j)]; } +}; +}} diff --git a/Source/Math/cudadevice.h b/Source/Math/cudadevice.h index 86ea28e22076..64c88a3dce8f 100644 --- a/Source/Math/cudadevice.h +++ b/Source/Math/cudadevice.h @@ -1,38 +1,38 @@ -// cudadevice.h - holds the buffers, events, and streams used on a per device basis -// -// F. Seide, V-hansu - -#pragma once - -#include -#include -#include -#include -#include - -namespace msra { namespace cuda { - -const int deviceMax = 8; - -// an object that lives in a device --this class just implements setdevice() and associated storage, and is shared across matrix and vectorbaseimpl -class objectondevice -{ -protected: - size_t deviceid; // CUDA card in which this matrix lives ("virtual" index amongst cards allocated to this process); default: 0 -protected: - objectondevice (size_t d) : deviceid (d) { } -public: - size_t getdevice() const {return deviceid;} -}; - -// auto-class to set device (through context) inside a function -// usage at each function that calls CUDA: -// ondevice no (deviceid); -class ondevice -{ -public: - ondevice (size_t deviceid) { cudaSetDevice((int)deviceid) || "cudaSetDevice failed!"; } - ~ondevice() {} -}; - -}} +// cudadevice.h - holds the buffers, events, and streams used on a per device basis +// +// F. Seide, V-hansu + +#pragma once + +#include +#include +#include +#include +#include + +namespace msra { namespace cuda { + +const int deviceMax = 8; + +// an object that lives in a device --this class just implements setdevice() and associated storage, and is shared across matrix and vectorbaseimpl +class objectondevice +{ +protected: + size_t deviceid; // CUDA card in which this matrix lives ("virtual" index amongst cards allocated to this process); default: 0 +protected: + objectondevice (size_t d) : deviceid (d) { } +public: + size_t getdevice() const {return deviceid;} +}; + +// auto-class to set device (through context) inside a function +// usage at each function that calls CUDA: +// ondevice no (deviceid); +class ondevice +{ +public: + ondevice (size_t deviceid) { cudaSetDevice((int)deviceid) || "cudaSetDevice failed!"; } + ~ondevice() {} +}; + +}} diff --git a/Source/Math/cudalattice.cpp b/Source/Math/cudalattice.cpp index 1d9b2ce0c17e..4e1f0e15818e 100644 --- a/Source/Math/cudalattice.cpp +++ b/Source/Math/cudalattice.cpp @@ -1,215 +1,215 @@ -// cudalattice.cpp -- lattice forward/backward functions for CUDA execution (glue code) -// -// F. Seide, V-hansu - -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings - -#define DLLEXPORT -#define __kernel_emulation__ // allow the compilation of CUDA kernels on the CPU -#include "latticefunctionskernels.h" // for the actual inner kernels and any argument types that are not yet defined in latticestorage.h -#undef __kernel_emulation__ -#include "cudalattice.h" // this exports the class -#include "cudalatticeops.h" // brings in the actual lattice functions/kernels -#include "cudalib.h" // generic CUDA helpers -#include "cudadevice.h" -#include -#include // for auto_ptr -#include -#include - -namespace msra { namespace cuda { - -extern void operator|| (cudaError_t rc, const char * msg); // TODO: imported from cudamatrix.cpp --better move to cudalib.h - -// this implements the basic operations of exported interface vectorbase<>, from which all vectors derive -// TODO: This really should not be in cudalattice, since it is more general; we need a cudavector.cpp/h -template class vectorbaseimpl : - public /*interface*/VECTORTYPE, // user-type interface; must derive from vectorbase - public OPSTYPE, // type of class that implements the kernels; must derive from vectorref - public objectondevice // setdevice() -{ - typedef typename VECTORTYPE::elemtype elemtype; // (for convenience) - size_t capacity; // amount of allocated storage (like capacity() vs. vectorref::n = size()) - void release() { ondevice no (deviceid); free (this->reset (NULL, 0)); } -public: - vectorbaseimpl(size_t deviceid) : capacity(0), objectondevice(deviceid) {} - ~vectorbaseimpl() { release(); } - void allocate (size_t sz) - { - if (sz > capacity) // need to grow - { - ondevice no (deviceid); // switch to desired CUDA card - cuda_ptr pnew = malloc (sz); // allocate memory inside CUDA device (or throw) - capacity = sz; // if succeeded then: remember - cuda_ptr p = this->reset (pnew, sz); // and swap the pointers and update n - free (p); // then release the old one - } - else // not growing: keep same allocation - this->reset (this->get(), sz); - } - size_t size() const throw() { return vectorref::size(); } - void assign (const elemtype * p, size_t nelem, bool synchronize) - { - allocate (nelem); // assign will resize the target appropriately - ondevice no (deviceid); // switch to desired CUDA card - if (nelem > 0) - memcpy (this->get(), 0, p, nelem); - if (synchronize) - join(); - } - void fetch (elemtype * p, size_t nelem, bool synchronize) const - { - if (nelem != size()) // fetch() cannot resize the target; caller must do that - LogicError("fetch: vector size mismatch"); - ondevice no (deviceid); // switch to desired CUDA card - if (nelem > 0) - memcpy (p, this->get(), 0, nelem); - if (synchronize) - join(); - }; -}; - -// --------------------------------------------------------------------------- -// glue code for lattice-related classes -// The XXXvectorimpl classes must derive from vectorbaseimpl. -// For classes without kernels that operate on the vector, XXXvectorimpl is not -// needed, use vectorbaseimpl> instead, where -// XXXvector is an alias for vectorbase (but better keep that alias in cudalattice.h -// to document which vectors are implemented). -// --------------------------------------------------------------------------- - -matrixref tomatrixref(const Microsoft::MSR::CNTK::Matrix& m) -{ - return matrixref(m.BufferPointer(), m.GetNumRows(), m.GetNumCols(), m.GetNumRows()); -} - -class latticefunctionsimpl : public vectorbaseimpl -{ -public: - latticefunctionsimpl(size_t deviceid) : vectorbaseimpl(deviceid) {} - -private: - void edgealignment (const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, - const size_t silalignunitid, const Microsoft::MSR::CNTK::Matrix& logLLs, const nodeinfovector & nodes, - const edgeinfowithscoresvector & edges, const aligninfovector & aligns, - const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, - ushortvector & alignresult, floatvector & edgeacscores) // output - { - ondevice no (deviceid); - - matrixref logLLsMatrixRef = tomatrixref(logLLs); - latticefunctionsops::edgealignment (dynamic_cast> &> (hmms), - dynamic_cast> &> (transPs), - spalignunitid, silalignunitid, logLLsMatrixRef, - dynamic_cast> &> (nodes), - dynamic_cast> &> (edges), - dynamic_cast> &> (aligns), - dynamic_cast> &> (alignoffsets), - dynamic_cast> &> (backptrstorage), - dynamic_cast> &> (backptroffsets), - dynamic_cast> &> (alignresult), - dynamic_cast> &> (edgeacscores)); - } - - void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, - const size_t numlaunchforward, const size_t numlaunchbackward, - const size_t spalignunitid, const size_t silalignunitid, - const floatvector & edgeacscores, const edgeinfowithscoresvector & edges, - const nodeinfovector & nodes, const aligninfovector & aligns, - const ushortvector & alignments, const uintvector & alignoffsets, - doublevector & logpps, doublevector & logalphas, doublevector & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, - const ushortvector & uids, const ushortvector & senone2classmap, doublevector & logaccalphas, - doublevector & logaccbetas, doublevector & logframescorrectedge, - doublevector & logEframescorrect, doublevector & Eframescorrectbuf, double & logEframescorrecttotal, double & totalfwscore) - { - ondevice no (deviceid); - latticefunctionsops::forwardbackwardlattice (batchsizeforward, batchsizebackward, numlaunchforward, numlaunchbackward, - spalignunitid, silalignunitid, - dynamic_cast> &> (edgeacscores), - dynamic_cast> &> (edges), - dynamic_cast> &> (nodes), - dynamic_cast> &> (aligns), - dynamic_cast> &> (alignments), - dynamic_cast> &> (alignoffsets), - dynamic_cast> &> (logpps), - dynamic_cast> &> (logalphas), - dynamic_cast> &> (logbetas), - lmf, wp, amf, boostingfactor, returnEframescorrect, - dynamic_cast> &> (uids), - dynamic_cast> &> (senone2classmap), - dynamic_cast> &> (logaccalphas), - dynamic_cast> &> (logaccbetas), - dynamic_cast> &> (logframescorrectedge), - dynamic_cast> &> (logEframescorrect), - dynamic_cast> &> (Eframescorrectbuf), - logEframescorrecttotal, totalfwscore); - } - - void sMBRerrorsignal (const ushortvector & alignstateids, - const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logpps, const float amf, const doublevector & logEframescorrect, - const double logEframescorrecttotal, Microsoft::MSR::CNTK::Matrix& dengammas, Microsoft::MSR::CNTK::Matrix& dengammasbuf) - { - ondevice no (deviceid); - - matrixref dengammasMatrixRef = tomatrixref(dengammas); - matrixref dengammasbufMatrixRef = tomatrixref(dengammasbuf); - latticefunctionsops::sMBRerrorsignal (dynamic_cast> &> (alignstateids), - dynamic_cast> &> (alignoffsets), - dynamic_cast> &> (edges), - dynamic_cast> &> (nodes), - dynamic_cast> &> (logpps), - amf, - dynamic_cast> &> (logEframescorrect), - logEframescorrecttotal, dengammasMatrixRef, dengammasbufMatrixRef); - } - - void mmierrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logpps, Microsoft::MSR::CNTK::Matrix& dengammas) - { - ondevice no (deviceid); - - matrixref dengammasMatrixRef = tomatrixref(dengammas); - latticefunctionsops::mmierrorsignal (dynamic_cast> &> (alignstateids), - dynamic_cast> &> (alignoffsets), - dynamic_cast> &> (edges), - dynamic_cast> &> (nodes), - dynamic_cast> &> (logpps), - dengammasMatrixRef); - } - - void stateposteriors (const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logqs, Microsoft::MSR::CNTK::Matrix& logacc) - { - ondevice no (deviceid); - - matrixref logaccMatrixRef = tomatrixref(logacc); - latticefunctionsops::stateposteriors (dynamic_cast> &> (alignstateids), - dynamic_cast> &> (alignoffsets), - dynamic_cast> &> (edges), - dynamic_cast> &> (nodes), - dynamic_cast> &> (logqs), - logaccMatrixRef); - } -}; - -latticefunctions * newlatticefunctions(size_t deviceid) { return new latticefunctionsimpl(deviceid); } - -// implementation of lrhmmdefvector -// Class has no vector-level member functions, so no need for an extra type -lrhmmdefvector * newlrhmmdefvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -lr3transPvector * newlr3transPvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -ushortvector * newushortvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -uintvector * newuintvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -floatvector * newfloatvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -doublevector * newdoublevector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -sizetvector * newsizetvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -nodeinfovector * newnodeinfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -edgeinfowithscoresvector * newedgeinfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } -aligninfovector * newaligninfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } - -};}; +// cudalattice.cpp -- lattice forward/backward functions for CUDA execution (glue code) +// +// F. Seide, V-hansu + +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings + +#define DLLEXPORT +#define __kernel_emulation__ // allow the compilation of CUDA kernels on the CPU +#include "latticefunctionskernels.h" // for the actual inner kernels and any argument types that are not yet defined in latticestorage.h +#undef __kernel_emulation__ +#include "cudalattice.h" // this exports the class +#include "cudalatticeops.h" // brings in the actual lattice functions/kernels +#include "cudalib.h" // generic CUDA helpers +#include "cudadevice.h" +#include +#include // for auto_ptr +#include +#include + +namespace msra { namespace cuda { + +extern void operator|| (cudaError_t rc, const char * msg); // TODO: imported from cudamatrix.cpp --better move to cudalib.h + +// this implements the basic operations of exported interface vectorbase<>, from which all vectors derive +// TODO: This really should not be in cudalattice, since it is more general; we need a cudavector.cpp/h +template class vectorbaseimpl : + public /*interface*/VECTORTYPE, // user-type interface; must derive from vectorbase + public OPSTYPE, // type of class that implements the kernels; must derive from vectorref + public objectondevice // setdevice() +{ + typedef typename VECTORTYPE::elemtype elemtype; // (for convenience) + size_t capacity; // amount of allocated storage (like capacity() vs. vectorref::n = size()) + void release() { ondevice no (deviceid); free (this->reset (NULL, 0)); } +public: + vectorbaseimpl(size_t deviceid) : capacity(0), objectondevice(deviceid) {} + ~vectorbaseimpl() { release(); } + void allocate (size_t sz) + { + if (sz > capacity) // need to grow + { + ondevice no (deviceid); // switch to desired CUDA card + cuda_ptr pnew = malloc (sz); // allocate memory inside CUDA device (or throw) + capacity = sz; // if succeeded then: remember + cuda_ptr p = this->reset (pnew, sz); // and swap the pointers and update n + free (p); // then release the old one + } + else // not growing: keep same allocation + this->reset (this->get(), sz); + } + size_t size() const throw() { return vectorref::size(); } + void assign (const elemtype * p, size_t nelem, bool synchronize) + { + allocate (nelem); // assign will resize the target appropriately + ondevice no (deviceid); // switch to desired CUDA card + if (nelem > 0) + memcpy (this->get(), 0, p, nelem); + if (synchronize) + join(); + } + void fetch (elemtype * p, size_t nelem, bool synchronize) const + { + if (nelem != size()) // fetch() cannot resize the target; caller must do that + LogicError("fetch: vector size mismatch"); + ondevice no (deviceid); // switch to desired CUDA card + if (nelem > 0) + memcpy (p, this->get(), 0, nelem); + if (synchronize) + join(); + }; +}; + +// --------------------------------------------------------------------------- +// glue code for lattice-related classes +// The XXXvectorimpl classes must derive from vectorbaseimpl. +// For classes without kernels that operate on the vector, XXXvectorimpl is not +// needed, use vectorbaseimpl> instead, where +// XXXvector is an alias for vectorbase (but better keep that alias in cudalattice.h +// to document which vectors are implemented). +// --------------------------------------------------------------------------- + +matrixref tomatrixref(const Microsoft::MSR::CNTK::Matrix& m) +{ + return matrixref(m.BufferPointer(), m.GetNumRows(), m.GetNumCols(), m.GetNumRows()); +} + +class latticefunctionsimpl : public vectorbaseimpl +{ +public: + latticefunctionsimpl(size_t deviceid) : vectorbaseimpl(deviceid) {} + +private: + void edgealignment (const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, + const size_t silalignunitid, const Microsoft::MSR::CNTK::Matrix& logLLs, const nodeinfovector & nodes, + const edgeinfowithscoresvector & edges, const aligninfovector & aligns, + const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, + ushortvector & alignresult, floatvector & edgeacscores) // output + { + ondevice no (deviceid); + + matrixref logLLsMatrixRef = tomatrixref(logLLs); + latticefunctionsops::edgealignment (dynamic_cast> &> (hmms), + dynamic_cast> &> (transPs), + spalignunitid, silalignunitid, logLLsMatrixRef, + dynamic_cast> &> (nodes), + dynamic_cast> &> (edges), + dynamic_cast> &> (aligns), + dynamic_cast> &> (alignoffsets), + dynamic_cast> &> (backptrstorage), + dynamic_cast> &> (backptroffsets), + dynamic_cast> &> (alignresult), + dynamic_cast> &> (edgeacscores)); + } + + void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, + const size_t numlaunchforward, const size_t numlaunchbackward, + const size_t spalignunitid, const size_t silalignunitid, + const floatvector & edgeacscores, const edgeinfowithscoresvector & edges, + const nodeinfovector & nodes, const aligninfovector & aligns, + const ushortvector & alignments, const uintvector & alignoffsets, + doublevector & logpps, doublevector & logalphas, doublevector & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, + const ushortvector & uids, const ushortvector & senone2classmap, doublevector & logaccalphas, + doublevector & logaccbetas, doublevector & logframescorrectedge, + doublevector & logEframescorrect, doublevector & Eframescorrectbuf, double & logEframescorrecttotal, double & totalfwscore) + { + ondevice no (deviceid); + latticefunctionsops::forwardbackwardlattice (batchsizeforward, batchsizebackward, numlaunchforward, numlaunchbackward, + spalignunitid, silalignunitid, + dynamic_cast> &> (edgeacscores), + dynamic_cast> &> (edges), + dynamic_cast> &> (nodes), + dynamic_cast> &> (aligns), + dynamic_cast> &> (alignments), + dynamic_cast> &> (alignoffsets), + dynamic_cast> &> (logpps), + dynamic_cast> &> (logalphas), + dynamic_cast> &> (logbetas), + lmf, wp, amf, boostingfactor, returnEframescorrect, + dynamic_cast> &> (uids), + dynamic_cast> &> (senone2classmap), + dynamic_cast> &> (logaccalphas), + dynamic_cast> &> (logaccbetas), + dynamic_cast> &> (logframescorrectedge), + dynamic_cast> &> (logEframescorrect), + dynamic_cast> &> (Eframescorrectbuf), + logEframescorrecttotal, totalfwscore); + } + + void sMBRerrorsignal (const ushortvector & alignstateids, + const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logpps, const float amf, const doublevector & logEframescorrect, + const double logEframescorrecttotal, Microsoft::MSR::CNTK::Matrix& dengammas, Microsoft::MSR::CNTK::Matrix& dengammasbuf) + { + ondevice no (deviceid); + + matrixref dengammasMatrixRef = tomatrixref(dengammas); + matrixref dengammasbufMatrixRef = tomatrixref(dengammasbuf); + latticefunctionsops::sMBRerrorsignal (dynamic_cast> &> (alignstateids), + dynamic_cast> &> (alignoffsets), + dynamic_cast> &> (edges), + dynamic_cast> &> (nodes), + dynamic_cast> &> (logpps), + amf, + dynamic_cast> &> (logEframescorrect), + logEframescorrecttotal, dengammasMatrixRef, dengammasbufMatrixRef); + } + + void mmierrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logpps, Microsoft::MSR::CNTK::Matrix& dengammas) + { + ondevice no (deviceid); + + matrixref dengammasMatrixRef = tomatrixref(dengammas); + latticefunctionsops::mmierrorsignal (dynamic_cast> &> (alignstateids), + dynamic_cast> &> (alignoffsets), + dynamic_cast> &> (edges), + dynamic_cast> &> (nodes), + dynamic_cast> &> (logpps), + dengammasMatrixRef); + } + + void stateposteriors (const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logqs, Microsoft::MSR::CNTK::Matrix& logacc) + { + ondevice no (deviceid); + + matrixref logaccMatrixRef = tomatrixref(logacc); + latticefunctionsops::stateposteriors (dynamic_cast> &> (alignstateids), + dynamic_cast> &> (alignoffsets), + dynamic_cast> &> (edges), + dynamic_cast> &> (nodes), + dynamic_cast> &> (logqs), + logaccMatrixRef); + } +}; + +latticefunctions * newlatticefunctions(size_t deviceid) { return new latticefunctionsimpl(deviceid); } + +// implementation of lrhmmdefvector +// Class has no vector-level member functions, so no need for an extra type +lrhmmdefvector * newlrhmmdefvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +lr3transPvector * newlr3transPvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +ushortvector * newushortvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +uintvector * newuintvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +floatvector * newfloatvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +doublevector * newdoublevector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +sizetvector * newsizetvector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +nodeinfovector * newnodeinfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +edgeinfowithscoresvector * newedgeinfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } +aligninfovector * newaligninfovector(size_t deviceid) { return new vectorbaseimpl>(deviceid); } + +};}; diff --git a/Source/Math/cudalattice.h b/Source/Math/cudalattice.h index db7f01093070..33fdfcfd3baa 100644 --- a/Source/Math/cudalattice.h +++ b/Source/Math/cudalattice.h @@ -1,118 +1,118 @@ -// cudalattice.h -- lattice forward/backward functions CUDA execution -// -// F. Seide, V-hansu - -#pragma once -#include // (for NOCUDA version only) -#include "latticestorage.h" // basic data types for storing lattices -#define __kernel_emulation__ // allow the compilation of CUDA kernels on the CPU -#include "latticefunctionskernels.h" // for data types passed to kernel functions -#undef __kernel_emulation__ - -#include "Matrix.h" - -namespace msra { namespace cuda { - -// --------------------------------------------------------------------------- -// how to add vector types here: -// - for types with methods that operate on the vector, follow the example of 'somedatavector' -// - for types without extra methods, follow the example of 'ushortvector' -// - the total set of classes that need to be defined is: -// - XXXvector --the interface that the calling code sees (in this header file) -// - XXXvectorimpl --glue for each method: switch CUDA device, then call kernel (in XXXvectorops, cudalattice.cpp) -// - XXXvectorops --contains the actual kernel functions (cudalatticeops.h, cudalatticeops.cu.h) -// - comments: -// - follow precisely this pattern -// - for non-vector elements you need to pass by reference, you can still use this with a size-1 vector -// - and for functions that have no 'this', use a size-0 vector -// - note that you cannot add data members to XXXvector, as there is no mechanism to sync between GPU and CPU; only vector elements are synced -// - remember to Get Latest in both projects to see the change, for cudalattice.h and latticestorage.h -// --------------------------------------------------------------------------- - - -// a vector type; use this as a basetype -// Note that this will only be instantiated for types known inside this lib, and a newvector<> function must be exported for each. -template struct/*interface*/ vectorbase -{ - virtual ~vectorbase() { } - virtual void allocate(size_t n) = 0; - virtual size_t size() const throw() = 0; - virtual void assign(const ELEMTYPE * p, size_t n, bool synchronize) = 0; - template void assign(const VECTOR & v, bool synchronize) { allocate(v.size()); if (!v.empty()) assign(&v[0], v.size(), synchronize); } - virtual void fetch(ELEMTYPE * p, size_t n, bool synchronize) const = 0; - template void fetch(VECTOR & v, bool synchronize) const { v.resize(size()); if (!v.empty()) fetch(&v[0], v.size(), synchronize); } - typedef ELEMTYPE elemtype; -}; - -// --------------------------------------------------------------------------- -// vectors of these with custom functions -// The XXXvector classes must derive from vectorbase. -// --------------------------------------------------------------------------- - -struct somedatavector : public vectorbase -{ - // must implement all members of vectorbase<>, and can add operations here - virtual int somedataoperation (size_t arg) = 0; -}; - -typedef vectorbase ushortvector; -typedef vectorbase floatvector; -typedef vectorbase doublevector; -typedef vectorbase uintvector; -typedef vectorbase sizetvector; -typedef vectorbase lrhmmdefvector; -typedef vectorbase lr3transPvector; -typedef vectorbase nodeinfovector; -typedef vectorbase edgeinfowithscoresvector; -typedef vectorbase aligninfovector; - -struct latticefunctions : public vectorbase -{ - virtual void edgealignment (const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, - const size_t silalignunitid, const Microsoft::MSR::CNTK::Matrix& logLLs, const nodeinfovector & nodes, - const edgeinfowithscoresvector & edges, const aligninfovector & aligns, - const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, - ushortvector & alignresult, floatvector & edgeacscores) = 0; // output - virtual void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, - const size_t numlaunchforward, const size_t numlaunchbackward, - const size_t spalignunitid, const size_t silalignunitid, - const floatvector & edgeacscores, const edgeinfowithscoresvector & edges, - const nodeinfovector & nodes, const aligninfovector & aligns, - const ushortvector & alignoutput, const uintvector & alignoffsets, - doublevector & logpps, doublevector & logalphas, doublevector & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, - const ushortvector & uids, const ushortvector & senone2classmap, - doublevector & logaccalphas, doublevector & logaccbetas, - doublevector & logframescorrectedge, doublevector & logEframescorrect, - doublevector & Eframescorrectbuf, double & logEframescorrecttotal, double & totalfwscore) = 0; - virtual void sMBRerrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logpps, const float amf, const doublevector & logEframescorrect, - const double logEframescorrecttotal, Microsoft::MSR::CNTK::Matrix& dengammas, Microsoft::MSR::CNTK::Matrix& dengammasbuf) = 0; - virtual void mmierrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logpps, Microsoft::MSR::CNTK::Matrix& dengammas) = 0; - virtual void stateposteriors (const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, - const doublevector & logqs, Microsoft::MSR::CNTK::Matrix& logacc) = 0; - -}; - -// --------------------------------------------------------------------------- -// factor methods -// --------------------------------------------------------------------------- - -somedatavector * newsomedatavector(size_t deviceid); -ushortvector * newushortvector(size_t deviceid); -uintvector * newuintvector(size_t deviceid); -floatvector * newfloatvector(size_t deviceid); -doublevector * newdoublevector(size_t deviceid); -sizetvector * newsizetvector(size_t deviceid); -latticefunctions * newlatticefunctions(size_t deviceid); -lrhmmdefvector * newlrhmmdefvector(size_t deviceid); -lr3transPvector * newlr3transPvector(size_t deviceid); -nodeinfovector * newnodeinfovector(size_t deviceid); -edgeinfowithscoresvector * newedgeinfovector(size_t deviceid); -aligninfovector * newaligninfovector(size_t deviceid); - -};}; +// cudalattice.h -- lattice forward/backward functions CUDA execution +// +// F. Seide, V-hansu + +#pragma once +#include // (for NOCUDA version only) +#include "latticestorage.h" // basic data types for storing lattices +#define __kernel_emulation__ // allow the compilation of CUDA kernels on the CPU +#include "latticefunctionskernels.h" // for data types passed to kernel functions +#undef __kernel_emulation__ + +#include "Matrix.h" + +namespace msra { namespace cuda { + +// --------------------------------------------------------------------------- +// how to add vector types here: +// - for types with methods that operate on the vector, follow the example of 'somedatavector' +// - for types without extra methods, follow the example of 'ushortvector' +// - the total set of classes that need to be defined is: +// - XXXvector --the interface that the calling code sees (in this header file) +// - XXXvectorimpl --glue for each method: switch CUDA device, then call kernel (in XXXvectorops, cudalattice.cpp) +// - XXXvectorops --contains the actual kernel functions (cudalatticeops.h, cudalatticeops.cu.h) +// - comments: +// - follow precisely this pattern +// - for non-vector elements you need to pass by reference, you can still use this with a size-1 vector +// - and for functions that have no 'this', use a size-0 vector +// - note that you cannot add data members to XXXvector, as there is no mechanism to sync between GPU and CPU; only vector elements are synced +// - remember to Get Latest in both projects to see the change, for cudalattice.h and latticestorage.h +// --------------------------------------------------------------------------- + + +// a vector type; use this as a basetype +// Note that this will only be instantiated for types known inside this lib, and a newvector<> function must be exported for each. +template struct/*interface*/ vectorbase +{ + virtual ~vectorbase() { } + virtual void allocate(size_t n) = 0; + virtual size_t size() const throw() = 0; + virtual void assign(const ELEMTYPE * p, size_t n, bool synchronize) = 0; + template void assign(const VECTOR & v, bool synchronize) { allocate(v.size()); if (!v.empty()) assign(&v[0], v.size(), synchronize); } + virtual void fetch(ELEMTYPE * p, size_t n, bool synchronize) const = 0; + template void fetch(VECTOR & v, bool synchronize) const { v.resize(size()); if (!v.empty()) fetch(&v[0], v.size(), synchronize); } + typedef ELEMTYPE elemtype; +}; + +// --------------------------------------------------------------------------- +// vectors of these with custom functions +// The XXXvector classes must derive from vectorbase. +// --------------------------------------------------------------------------- + +struct somedatavector : public vectorbase +{ + // must implement all members of vectorbase<>, and can add operations here + virtual int somedataoperation (size_t arg) = 0; +}; + +typedef vectorbase ushortvector; +typedef vectorbase floatvector; +typedef vectorbase doublevector; +typedef vectorbase uintvector; +typedef vectorbase sizetvector; +typedef vectorbase lrhmmdefvector; +typedef vectorbase lr3transPvector; +typedef vectorbase nodeinfovector; +typedef vectorbase edgeinfowithscoresvector; +typedef vectorbase aligninfovector; + +struct latticefunctions : public vectorbase +{ + virtual void edgealignment (const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, + const size_t silalignunitid, const Microsoft::MSR::CNTK::Matrix& logLLs, const nodeinfovector & nodes, + const edgeinfowithscoresvector & edges, const aligninfovector & aligns, + const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, + ushortvector & alignresult, floatvector & edgeacscores) = 0; // output + virtual void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, + const size_t numlaunchforward, const size_t numlaunchbackward, + const size_t spalignunitid, const size_t silalignunitid, + const floatvector & edgeacscores, const edgeinfowithscoresvector & edges, + const nodeinfovector & nodes, const aligninfovector & aligns, + const ushortvector & alignoutput, const uintvector & alignoffsets, + doublevector & logpps, doublevector & logalphas, doublevector & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, + const ushortvector & uids, const ushortvector & senone2classmap, + doublevector & logaccalphas, doublevector & logaccbetas, + doublevector & logframescorrectedge, doublevector & logEframescorrect, + doublevector & Eframescorrectbuf, double & logEframescorrecttotal, double & totalfwscore) = 0; + virtual void sMBRerrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logpps, const float amf, const doublevector & logEframescorrect, + const double logEframescorrecttotal, Microsoft::MSR::CNTK::Matrix& dengammas, Microsoft::MSR::CNTK::Matrix& dengammasbuf) = 0; + virtual void mmierrorsignal (const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logpps, Microsoft::MSR::CNTK::Matrix& dengammas) = 0; + virtual void stateposteriors (const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, const nodeinfovector & nodes, + const doublevector & logqs, Microsoft::MSR::CNTK::Matrix& logacc) = 0; + +}; + +// --------------------------------------------------------------------------- +// factor methods +// --------------------------------------------------------------------------- + +somedatavector * newsomedatavector(size_t deviceid); +ushortvector * newushortvector(size_t deviceid); +uintvector * newuintvector(size_t deviceid); +floatvector * newfloatvector(size_t deviceid); +doublevector * newdoublevector(size_t deviceid); +sizetvector * newsizetvector(size_t deviceid); +latticefunctions * newlatticefunctions(size_t deviceid); +lrhmmdefvector * newlrhmmdefvector(size_t deviceid); +lr3transPvector * newlr3transPvector(size_t deviceid); +nodeinfovector * newnodeinfovector(size_t deviceid); +edgeinfowithscoresvector * newedgeinfovector(size_t deviceid); +aligninfovector * newaligninfovector(size_t deviceid); + +};}; diff --git a/Source/Math/cudalatticeops.cu b/Source/Math/cudalatticeops.cu index 4e70b98adfc5..48f643ba95b7 100644 --- a/Source/Math/cudalatticeops.cu +++ b/Source/Math/cudalatticeops.cu @@ -1,27 +1,27 @@ -// .cu file --#includes all actual .cu files which we store as .cu.h so we get syntax highlighting (VS does not recognize .cu files) -// -// F. Seide, V-hansu - -#include -#include "Basics.h" -#include "BestGpu.h" - -#ifndef CPUONLY - -namespace msra { namespace cuda { - - // call this after all kernel launches - // This is non-blocking. It catches launch failures, but not crashes during execution. - static void checklaunch (const char * fn) - { - cudaError_t rc = cudaGetLastError(); - if (rc != cudaSuccess) - RuntimeError("%s: launch failure: %s (cuda error %d)", fn, cudaGetErrorString(rc), (int)rc); - } - -};}; - -// now include actual code which is in those files to allow for code highlighting etc. -#include "cudalatticeops.cu.h" - -#endif // CPUONLY +// .cu file --#includes all actual .cu files which we store as .cu.h so we get syntax highlighting (VS does not recognize .cu files) +// +// F. Seide, V-hansu + +#include +#include "Basics.h" +#include "BestGpu.h" + +#ifndef CPUONLY + +namespace msra { namespace cuda { + + // call this after all kernel launches + // This is non-blocking. It catches launch failures, but not crashes during execution. + static void checklaunch (const char * fn) + { + cudaError_t rc = cudaGetLastError(); + if (rc != cudaSuccess) + RuntimeError("%s: launch failure: %s (cuda error %d)", fn, cudaGetErrorString(rc), (int)rc); + } + +};}; + +// now include actual code which is in those files to allow for code highlighting etc. +#include "cudalatticeops.cu.h" + +#endif // CPUONLY diff --git a/Source/Math/cudalatticeops.cu.h b/Source/Math/cudalatticeops.cu.h index 8027fe40b5ca..be57cd57f8da 100644 --- a/Source/Math/cudalatticeops.cu.h +++ b/Source/Math/cudalatticeops.cu.h @@ -1,454 +1,454 @@ -// cudamatrix.cu(.h) -- CUDA kernels for lattice ops. Consider this a .cu/.cpp file. -// -// F. Seide, V-hansu - -#if 0 // [v-hansu] set aside codes with log -#endif - -#undef DIRECT_MODE // [v-hansu] use the direct formula for smbr mode, proven makes no difference - -#include -#include -#include "cudalib.h" -#include "cudabasetypes.h" -#include "latticestorage.h" -#include "latticefunctionskernels.h" -#include "cudalatticeops.h" -#include "math.h" -#include -#include - -#ifdef _WIN32 -#include // for timer -#endif - -#if __unix__ -#include -#endif - -namespace msra { namespace cuda { - - cudaStream_t GetCurrentStream(); - - // auto_timer timer; run(); double seconds = timer; // now can abandon the object - #ifdef __unix__ - typedef timeval LARGE_INTEGER; - #endif - class auto_timer - { - LARGE_INTEGER freq, start; - auto_timer (const auto_timer &); void operator= (const auto_timer &); - public: - auto_timer() - { - #ifdef _WIN32 - if (!QueryPerformanceFrequency (&freq)) // count ticks per second - RuntimeError("auto_timer: QueryPerformanceFrequency failure"); - QueryPerformanceCounter (&start); - #endif - #ifdef __unix__ - gettimeofday (&start, NULL); - #endif - } - operator double() const // each read gives time elapsed since start, in seconds - { - LARGE_INTEGER end; - #ifdef _WIN32 - QueryPerformanceCounter (&end); - return (end.QuadPart - start.QuadPart) / (double) freq.QuadPart; - #endif - #ifdef __unix__ - gettimeofday (&end,NULL); - return (end.tv_sec - start.tv_sec) + (end.tv_usec - start.tv_usec)/(1000*1000); - #endif - } - void show (const std::string & msg) const - { - double elapsed = *this; - fprintf (stderr, "%s: %.6f ms\n", msg.c_str(), elapsed * 1000.0/*to ms*/); - } - }; - - // ----------------------------------------------------------------------- - // edgealignment --do alignment on a per edge level, only support normal left to right hmms and ergodic silence hmm - // output alignresult - // ----------------------------------------------------------------------- - - __global__ void edgealignmentj (const vectorref hmms, const vectorref transPs, const size_t spalignunitid, const size_t silalignunitid, - const matrixref logLLs, const vectorref nodes, const vectorref edges, - const vectorref aligns, const vectorref alignoffsets, - vectorref backptrstorage, const vectorref backptroffsets, - vectorref alignresult, vectorref edgeacscores) // output - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::edgealignmentj (j, hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); - } - } - - void latticefunctionsops::edgealignment (const vectorref & hmms, const vectorref & transPs, const size_t spalignunitid, - const size_t silalignunitid, const matrixref & logLLs, - const vectorref & nodes, const vectorref & edges, - const vectorref & aligns, const vectorref & alignoffsets, - vectorref & backptrstorage, const vectorref & backptroffsets, - vectorref & alignresult, vectorref & edgeacscores) const // output - { - // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. - // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. - const size_t numedges = edges.size(); - dim3 t (32,8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - //cudaarrayref logLLsarray; // TODO: pass this in, of course - //passtextureref texref (logLLstex, logLLsarray); // use the same name as that global texref one, so it will match the name inside the kernel - edgealignmentj << > > (hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); - checklaunch ("edgealignment"); - } - - // setvalue --helper to initialize an array to a constant value, e.g. LOGZERO - __global__ void setvaluej (vectorref arraytoset, double value, size_t nelem) - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < nelem) - { - msra::lattices::latticefunctionskernels::setvaluej(j, arraytoset, value); - } - } - - __global__ void expfi (matrixref mata) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i < mata.rows()) - { - const size_t m = mata.cols(); - for (size_t j = 0; j < m; j++) - mata(i,j) = expf (mata(i,j)); - } - } - - __global__ void dotprodi (matrixref mata, matrixref matb) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i < mata.rows()) - { - const size_t m = mata.cols(); - for (size_t j = 0; j < m; j++) - mata(i,j) = mata(i,j) * matb(i,j); - } - } - - __global__ void setunseeni (matrixref errorsignal, matrixref errorsignalauxbuf) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i < errorsignal.rows()) - { - const size_t m = errorsignal.cols(); - for (size_t j = 0; j < m; j++) - if (errorsignal(i,j) == logf(CUDART_MIN_DENORM_F) && errorsignalauxbuf(i,j) == logf(CUDART_MIN_DENORM_F)) - errorsignalauxbuf(i,j) = LOGZERO; - } - } - - // errorsignal(i,j) = (exp(errorsignal(i,j)) - exp(errorsignal(i,j))) / amf - __global__ void errorcomputationi (matrixref errorsignal, matrixref errorsignalauxbuf, float amf) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i < errorsignal.rows()) - { - const size_t m = errorsignal.cols(); - for (size_t j = 0; j < m; j++) - errorsignal(i,j) = msra::lattices::latticefunctionskernels::expdiff (errorsignal(i,j), errorsignalauxbuf(i,j)) / amf; - } - } - - // exp(errorsignal(i,j)) - exp(logEframescorrecttotal+errorsignalauxbuf(i,j))/amf - __global__ void directerrorcomputationi (matrixref errorsignal, matrixref errorsignalauxbuf, float logEframescorrecttotal,float amf) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i < errorsignal.rows()) - { - const size_t m = errorsignal.cols(); - for (size_t j = 0; j < m; j++) - errorsignal(i,j) = msra::lattices::latticefunctionskernels::expdiff (errorsignal(i,j), logEframescorrecttotal + errorsignalauxbuf(i,j)) / amf; - } - } - - // compute the final error signal from gammas and state-consolidated Eframescorrect - // in-place operation is supported (i.e. output = one of the inputs) - __global__ void computesMBRerrorsignals (const matrixref loggammas, const matrixref logEframescorrect, - const double logEframecorrecttotal, const float kappa, matrixref errorsignal) - { - const size_t s = threadIdx.x + (blockIdx.x * blockDim.x); - if (s < loggammas.rows()) - msra::lattices::latticefunctionskernels::computesMBRerrorsignals (s, loggammas, logEframescorrect, logEframecorrecttotal, kappa, errorsignal); - } - - __global__ void forwardlatticej (const size_t batchsize, const size_t startindex, const vectorref edgeacscores, - const size_t spalignunitid, const size_t silalignunitid, - vectorref edges, vectorref nodes, - const vectorref aligns, vectorref alignments, - vectorref alignmentoffsets, vectorref logalphas, float lmf, float wp, float amf, - const float boostingfactor, const vectorref uids, const vectorref senone2classmap, - const bool returnEframescorrect, vectorref logframescorrectedge, vectorref logaccalphas) - { - const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < batchsize) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::forwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignmentoffsets, - logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas); - } - } - - __global__ void backwardlatticej (const size_t batchsize, const size_t startindex, const vectorref edgeacscores, - const size_t spalignunitid, const size_t silalignunitid, - vectorref edges, vectorref nodes, - vectorref aligns, const double totalfwscore, - vectorref logpps, vectorref logalphas, vectorref logbetas, - float lmf, float wp, float amf, const float boostingfactor, const bool returnEframescorrect, - vectorref logframescorrectedge, vectorref logaccalphas, - vectorref logEframescorrect, vectorref logaccbetas) - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - size_t j = jinblock + blockIdx.x * tpb; - if (j < batchsize) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::backwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, - edges, nodes, aligns, totalfwscore, logpps, logalphas, logbetas, - lmf, wp, amf, boostingfactor, returnEframescorrect, - logframescorrectedge, logaccalphas, - logEframescorrect, logaccbetas); - } - } - - void latticefunctionsops::forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, - const size_t numlaunchforward, const size_t numlaunchbackward, - const size_t spalignunitid, const size_t silalignunitid, - const vectorref & edgeacscores, - const vectorref & edges, - const vectorref & nodes, - const vectorref & aligns, - const vectorref & alignments, - const vectorref & aligmentoffsets, - vectorref & logpps, vectorref & logalphas, vectorref & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, - const bool returnEframescorrect, const vectorref & uids, - const vectorref & senone2classmap, vectorref & logaccalphas, - vectorref & logaccbetas, vectorref & logframescorrectedge, - vectorref & logEframescorrect, vectorref & /*Eframescorrectbuf*/, - double & logEframescorrecttotal, double & totalfwscore) const - { - // initialize log{,acc}(alhas/betas) - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((logalphas.size() + tpb - 1) / tpb)); - - // TODO: is this really efficient? One thread per value? - setvaluej<<>> (logalphas, LOGZERO, logalphas.size()); - checklaunch ("setvaluej"); - setvaluej<<>> (logbetas, LOGZERO, logalphas.size()); - checklaunch ("setvaluej"); - if (returnEframescorrect) - { - setvaluej<<>> (logaccalphas, LOGZERO, logalphas.size()); - checklaunch ("setvaluej"); - setvaluej<<>> (logaccbetas, LOGZERO, logalphas.size()); - checklaunch ("setvaluej"); - } - // set initial tokens to probability 1 (0 in log) - double log1 = 0.0; - memcpy (logalphas.get(), 0, &log1, 1); - memcpy (logbetas.get(), nodes.size()-1, &log1, 1); - - // forward pass - size_t startindex = 0; - for (size_t i = 0; i < numlaunchforward; i++) - { - dim3 b ((unsigned int) ((batchsizeforward[i] + tpb - 1) / tpb)); - forwardlatticej <<>> (batchsizeforward[i], startindex, edgeacscores, - spalignunitid, silalignunitid, edges, nodes, aligns, - alignments, aligmentoffsets, logalphas, lmf, wp, amf, - boostingfactor, uids, senone2classmap, returnEframescorrect, - logframescorrectedge, logaccalphas); - checklaunch ("edgealignment"); - startindex += batchsizeforward[i]; - } - memcpy(&totalfwscore, logalphas.get(), nodes.size() - 1, 1); - double totalfwacc = 0; - if(returnEframescorrect) - { - memcpy(&totalfwacc, logaccalphas.get(), nodes.size() - 1, 1); - totalfwacc -= totalfwscore; - } - - // backward pass - startindex = edges.size(); - for (size_t i = 0; i < numlaunchbackward; i++) - { - dim3 b ((unsigned int) ((batchsizebackward[i] + tpb - 1) / tpb)); - backwardlatticej <<>> (batchsizebackward[i], startindex - batchsizebackward[i], - edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, - totalfwscore, logpps, logalphas, logbetas, - lmf, wp, amf, boostingfactor, returnEframescorrect, logframescorrectedge, - logaccalphas, logEframescorrect, logaccbetas); - checklaunch ("edgealignment"); - startindex -= batchsizebackward[i]; - } - double totalbwscore = 0; - memcpy(&totalbwscore, logbetas.get(), 0, 1); - double totalbwacc = 0; - if (returnEframescorrect) - { - memcpy(&totalbwacc, logaccbetas.get(), 0, 1); - totalbwacc -= totalbwscore; - logEframescorrecttotal = totalbwacc; - } - - double difffwbwscore = totalfwscore - totalbwscore; - double absdifffwbwscore = difffwbwscore > 0 ? difffwbwscore : 0 - difffwbwscore; - - if (absdifffwbwscore / nodes.size() > 1e-4) - fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); - - if (returnEframescorrect) - { - double difffwbwacc = totalfwacc- totalbwacc; - double absdifffwbwacc = difffwbwacc > 0 ? difffwbwacc : 0 - difffwbwacc; - - if (absdifffwbwacc / nodes.size() > 1e-4) - fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw acc %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); - } - } - - // ----------------------------------------------------------------------- - // sMBRerrorsignal -- accumulate difference of logEframescorrect and logEframescorrecttotal into errorsignal - // ----------------------------------------------------------------------- - - __global__ void sMBRerrorsignalj (const vectorref alignstateids, const vectorref alignoffsets, - const vectorref edges, const vectorref nodes, - vectorref logpps, const float amf, const vectorref logEframescorrect, const double logEframescorrecttotal, - matrixref errorsignal, matrixref errorsignalneg) - { - const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::sMBRerrorsignalj (j, alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); - } - } - - // ----------------------------------------------------------------------- - // stateposteriors --accumulate a per-edge quantity into the states that the edge is aligned with - // ----------------------------------------------------------------------- - - __global__ void stateposteriorsj (const vectorref alignstateids, const vectorref alignoffsets, - const vectorref edges, const vectorref nodes, - const vectorref logqs, matrixref logacc) - { - const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::stateposteriorsj (j, alignstateids, alignoffsets, edges, nodes, logqs, logacc); - } - } - - __global__ void setvaluei(matrixref us, float value) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i >= us.rows()) - return; - // set all columns - const size_t m = us.cols(); - for (size_t j = 0; j < m; j++) - us(i, j) = value; - } - - void latticefunctionsops::stateposteriors (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logqs, matrixref & logacc) const - { - // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. - // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. - const size_t numedges = edges.size(); - dim3 t (32,8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - - setvaluei <<>> (logacc, LOGZERO); - checklaunch ("setvaluei"); - - stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logqs, logacc); - checklaunch ("stateposteriors"); - } - - void latticefunctionsops::sMBRerrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logpps, const float amf, const vectorref & logEframescorrect, const double logEframescorrecttotal, - matrixref & errorsignal, matrixref & errorsignalauxbuf) const - { - // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. - // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. - const size_t numedges = edges.size(); - dim3 t (32,8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - -#ifdef DIRECT_MODE // compute Eframescorrect in a more direct way, proven to get same result as below - setvaluei <<>> (errorsignal, LOGZERO); - checklaunch ("setvaluei"); - - sMBRerrorsignalj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalauxbuf); - checklaunch ("sMBRerrorsignal"); // now we get state based logEframescorrect - - matrixref & loggammas = errorsignalauxbuf; - setvaluei <<>> (errorsignalauxbuf, LOGZERO); - checklaunch ("setvaluei"); - - stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, loggammas); - checklaunch ("stateposteriorsj"); // now we get state based loggammas - - directerrorcomputationi <<>> (errorsignal, errorsignalauxbuf, logEframescorrecttotal, amf); - checklaunch ("errorcomputationj"); -#else // this saves some computation compared with DIRECT_MODE - setvaluei <<>> (errorsignal, LOGZERO); - checklaunch ("setvaluei"); - setvaluei <<>> (errorsignalauxbuf, LOGZERO); - checklaunch ("setvaluei"); - sMBRerrorsignalj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalauxbuf); - checklaunch ("sMBRerrorsignal"); - - setunseeni <<>> (errorsignal, errorsignalauxbuf); - checklaunch ("setunseenj"); - - errorcomputationi <<>> (errorsignal, errorsignalauxbuf, amf); - checklaunch ("errorcomputationj"); - -#endif - } - - void latticefunctionsops::mmierrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logpps, matrixref & errorsignal) const - { - const size_t numedges = edges.size(); - dim3 t (32,8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - - matrixref & loggammas = errorsignal; // remember--this is an alias to 'errorsignal' - setvaluei <<>> (loggammas, LOGZERO); - checklaunch ("setvaluei"); - stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, loggammas); - checklaunch ("stateposteriorsj"); - - expfi <<>> (errorsignal); - checklaunch ("expfi"); - } -};}; +// cudamatrix.cu(.h) -- CUDA kernels for lattice ops. Consider this a .cu/.cpp file. +// +// F. Seide, V-hansu + +#if 0 // [v-hansu] set aside codes with log +#endif + +#undef DIRECT_MODE // [v-hansu] use the direct formula for smbr mode, proven makes no difference + +#include +#include +#include "cudalib.h" +#include "cudabasetypes.h" +#include "latticestorage.h" +#include "latticefunctionskernels.h" +#include "cudalatticeops.h" +#include "math.h" +#include +#include + +#ifdef _WIN32 +#include // for timer +#endif + +#if __unix__ +#include +#endif + +namespace msra { namespace cuda { + + cudaStream_t GetCurrentStream(); + + // auto_timer timer; run(); double seconds = timer; // now can abandon the object + #ifdef __unix__ + typedef timeval LARGE_INTEGER; + #endif + class auto_timer + { + LARGE_INTEGER freq, start; + auto_timer (const auto_timer &); void operator= (const auto_timer &); + public: + auto_timer() + { + #ifdef _WIN32 + if (!QueryPerformanceFrequency (&freq)) // count ticks per second + RuntimeError("auto_timer: QueryPerformanceFrequency failure"); + QueryPerformanceCounter (&start); + #endif + #ifdef __unix__ + gettimeofday (&start, NULL); + #endif + } + operator double() const // each read gives time elapsed since start, in seconds + { + LARGE_INTEGER end; + #ifdef _WIN32 + QueryPerformanceCounter (&end); + return (end.QuadPart - start.QuadPart) / (double) freq.QuadPart; + #endif + #ifdef __unix__ + gettimeofday (&end,NULL); + return (end.tv_sec - start.tv_sec) + (end.tv_usec - start.tv_usec)/(1000*1000); + #endif + } + void show (const std::string & msg) const + { + double elapsed = *this; + fprintf (stderr, "%s: %.6f ms\n", msg.c_str(), elapsed * 1000.0/*to ms*/); + } + }; + + // ----------------------------------------------------------------------- + // edgealignment --do alignment on a per edge level, only support normal left to right hmms and ergodic silence hmm + // output alignresult + // ----------------------------------------------------------------------- + + __global__ void edgealignmentj (const vectorref hmms, const vectorref transPs, const size_t spalignunitid, const size_t silalignunitid, + const matrixref logLLs, const vectorref nodes, const vectorref edges, + const vectorref aligns, const vectorref alignoffsets, + vectorref backptrstorage, const vectorref backptroffsets, + vectorref alignresult, vectorref edgeacscores) // output + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::edgealignmentj (j, hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); + } + } + + void latticefunctionsops::edgealignment (const vectorref & hmms, const vectorref & transPs, const size_t spalignunitid, + const size_t silalignunitid, const matrixref & logLLs, + const vectorref & nodes, const vectorref & edges, + const vectorref & aligns, const vectorref & alignoffsets, + vectorref & backptrstorage, const vectorref & backptroffsets, + vectorref & alignresult, vectorref & edgeacscores) const // output + { + // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. + // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. + const size_t numedges = edges.size(); + dim3 t (32,8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + //cudaarrayref logLLsarray; // TODO: pass this in, of course + //passtextureref texref (logLLstex, logLLsarray); // use the same name as that global texref one, so it will match the name inside the kernel + edgealignmentj << > > (hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); + checklaunch ("edgealignment"); + } + + // setvalue --helper to initialize an array to a constant value, e.g. LOGZERO + __global__ void setvaluej (vectorref arraytoset, double value, size_t nelem) + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < nelem) + { + msra::lattices::latticefunctionskernels::setvaluej(j, arraytoset, value); + } + } + + __global__ void expfi (matrixref mata) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i < mata.rows()) + { + const size_t m = mata.cols(); + for (size_t j = 0; j < m; j++) + mata(i,j) = expf (mata(i,j)); + } + } + + __global__ void dotprodi (matrixref mata, matrixref matb) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i < mata.rows()) + { + const size_t m = mata.cols(); + for (size_t j = 0; j < m; j++) + mata(i,j) = mata(i,j) * matb(i,j); + } + } + + __global__ void setunseeni (matrixref errorsignal, matrixref errorsignalauxbuf) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i < errorsignal.rows()) + { + const size_t m = errorsignal.cols(); + for (size_t j = 0; j < m; j++) + if (errorsignal(i,j) == logf(CUDART_MIN_DENORM_F) && errorsignalauxbuf(i,j) == logf(CUDART_MIN_DENORM_F)) + errorsignalauxbuf(i,j) = LOGZERO; + } + } + + // errorsignal(i,j) = (exp(errorsignal(i,j)) - exp(errorsignal(i,j))) / amf + __global__ void errorcomputationi (matrixref errorsignal, matrixref errorsignalauxbuf, float amf) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i < errorsignal.rows()) + { + const size_t m = errorsignal.cols(); + for (size_t j = 0; j < m; j++) + errorsignal(i,j) = msra::lattices::latticefunctionskernels::expdiff (errorsignal(i,j), errorsignalauxbuf(i,j)) / amf; + } + } + + // exp(errorsignal(i,j)) - exp(logEframescorrecttotal+errorsignalauxbuf(i,j))/amf + __global__ void directerrorcomputationi (matrixref errorsignal, matrixref errorsignalauxbuf, float logEframescorrecttotal,float amf) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i < errorsignal.rows()) + { + const size_t m = errorsignal.cols(); + for (size_t j = 0; j < m; j++) + errorsignal(i,j) = msra::lattices::latticefunctionskernels::expdiff (errorsignal(i,j), logEframescorrecttotal + errorsignalauxbuf(i,j)) / amf; + } + } + + // compute the final error signal from gammas and state-consolidated Eframescorrect + // in-place operation is supported (i.e. output = one of the inputs) + __global__ void computesMBRerrorsignals (const matrixref loggammas, const matrixref logEframescorrect, + const double logEframecorrecttotal, const float kappa, matrixref errorsignal) + { + const size_t s = threadIdx.x + (blockIdx.x * blockDim.x); + if (s < loggammas.rows()) + msra::lattices::latticefunctionskernels::computesMBRerrorsignals (s, loggammas, logEframescorrect, logEframecorrecttotal, kappa, errorsignal); + } + + __global__ void forwardlatticej (const size_t batchsize, const size_t startindex, const vectorref edgeacscores, + const size_t spalignunitid, const size_t silalignunitid, + vectorref edges, vectorref nodes, + const vectorref aligns, vectorref alignments, + vectorref alignmentoffsets, vectorref logalphas, float lmf, float wp, float amf, + const float boostingfactor, const vectorref uids, const vectorref senone2classmap, + const bool returnEframescorrect, vectorref logframescorrectedge, vectorref logaccalphas) + { + const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < batchsize) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::forwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignmentoffsets, + logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas); + } + } + + __global__ void backwardlatticej (const size_t batchsize, const size_t startindex, const vectorref edgeacscores, + const size_t spalignunitid, const size_t silalignunitid, + vectorref edges, vectorref nodes, + vectorref aligns, const double totalfwscore, + vectorref logpps, vectorref logalphas, vectorref logbetas, + float lmf, float wp, float amf, const float boostingfactor, const bool returnEframescorrect, + vectorref logframescorrectedge, vectorref logaccalphas, + vectorref logEframescorrect, vectorref logaccbetas) + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + size_t j = jinblock + blockIdx.x * tpb; + if (j < batchsize) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::backwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, + edges, nodes, aligns, totalfwscore, logpps, logalphas, logbetas, + lmf, wp, amf, boostingfactor, returnEframescorrect, + logframescorrectedge, logaccalphas, + logEframescorrect, logaccbetas); + } + } + + void latticefunctionsops::forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, + const size_t numlaunchforward, const size_t numlaunchbackward, + const size_t spalignunitid, const size_t silalignunitid, + const vectorref & edgeacscores, + const vectorref & edges, + const vectorref & nodes, + const vectorref & aligns, + const vectorref & alignments, + const vectorref & aligmentoffsets, + vectorref & logpps, vectorref & logalphas, vectorref & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, + const bool returnEframescorrect, const vectorref & uids, + const vectorref & senone2classmap, vectorref & logaccalphas, + vectorref & logaccbetas, vectorref & logframescorrectedge, + vectorref & logEframescorrect, vectorref & /*Eframescorrectbuf*/, + double & logEframescorrecttotal, double & totalfwscore) const + { + // initialize log{,acc}(alhas/betas) + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((logalphas.size() + tpb - 1) / tpb)); + + // TODO: is this really efficient? One thread per value? + setvaluej<<>> (logalphas, LOGZERO, logalphas.size()); + checklaunch ("setvaluej"); + setvaluej<<>> (logbetas, LOGZERO, logalphas.size()); + checklaunch ("setvaluej"); + if (returnEframescorrect) + { + setvaluej<<>> (logaccalphas, LOGZERO, logalphas.size()); + checklaunch ("setvaluej"); + setvaluej<<>> (logaccbetas, LOGZERO, logalphas.size()); + checklaunch ("setvaluej"); + } + // set initial tokens to probability 1 (0 in log) + double log1 = 0.0; + memcpy (logalphas.get(), 0, &log1, 1); + memcpy (logbetas.get(), nodes.size()-1, &log1, 1); + + // forward pass + size_t startindex = 0; + for (size_t i = 0; i < numlaunchforward; i++) + { + dim3 b ((unsigned int) ((batchsizeforward[i] + tpb - 1) / tpb)); + forwardlatticej <<>> (batchsizeforward[i], startindex, edgeacscores, + spalignunitid, silalignunitid, edges, nodes, aligns, + alignments, aligmentoffsets, logalphas, lmf, wp, amf, + boostingfactor, uids, senone2classmap, returnEframescorrect, + logframescorrectedge, logaccalphas); + checklaunch ("edgealignment"); + startindex += batchsizeforward[i]; + } + memcpy(&totalfwscore, logalphas.get(), nodes.size() - 1, 1); + double totalfwacc = 0; + if(returnEframescorrect) + { + memcpy(&totalfwacc, logaccalphas.get(), nodes.size() - 1, 1); + totalfwacc -= totalfwscore; + } + + // backward pass + startindex = edges.size(); + for (size_t i = 0; i < numlaunchbackward; i++) + { + dim3 b ((unsigned int) ((batchsizebackward[i] + tpb - 1) / tpb)); + backwardlatticej <<>> (batchsizebackward[i], startindex - batchsizebackward[i], + edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, + totalfwscore, logpps, logalphas, logbetas, + lmf, wp, amf, boostingfactor, returnEframescorrect, logframescorrectedge, + logaccalphas, logEframescorrect, logaccbetas); + checklaunch ("edgealignment"); + startindex -= batchsizebackward[i]; + } + double totalbwscore = 0; + memcpy(&totalbwscore, logbetas.get(), 0, 1); + double totalbwacc = 0; + if (returnEframescorrect) + { + memcpy(&totalbwacc, logaccbetas.get(), 0, 1); + totalbwacc -= totalbwscore; + logEframescorrecttotal = totalbwacc; + } + + double difffwbwscore = totalfwscore - totalbwscore; + double absdifffwbwscore = difffwbwscore > 0 ? difffwbwscore : 0 - difffwbwscore; + + if (absdifffwbwscore / nodes.size() > 1e-4) + fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); + + if (returnEframescorrect) + { + double difffwbwacc = totalfwacc- totalbwacc; + double absdifffwbwacc = difffwbwacc > 0 ? difffwbwacc : 0 - difffwbwacc; + + if (absdifffwbwacc / nodes.size() > 1e-4) + fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw acc %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); + } + } + + // ----------------------------------------------------------------------- + // sMBRerrorsignal -- accumulate difference of logEframescorrect and logEframescorrecttotal into errorsignal + // ----------------------------------------------------------------------- + + __global__ void sMBRerrorsignalj (const vectorref alignstateids, const vectorref alignoffsets, + const vectorref edges, const vectorref nodes, + vectorref logpps, const float amf, const vectorref logEframescorrect, const double logEframescorrecttotal, + matrixref errorsignal, matrixref errorsignalneg) + { + const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::sMBRerrorsignalj (j, alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); + } + } + + // ----------------------------------------------------------------------- + // stateposteriors --accumulate a per-edge quantity into the states that the edge is aligned with + // ----------------------------------------------------------------------- + + __global__ void stateposteriorsj (const vectorref alignstateids, const vectorref alignoffsets, + const vectorref edges, const vectorref nodes, + const vectorref logqs, matrixref logacc) + { + const size_t shufflemode = 1; // [v-hansu] this gives us about 100% speed up than shufflemode = 0 (no shuffle) + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::stateposteriorsj (j, alignstateids, alignoffsets, edges, nodes, logqs, logacc); + } + } + + __global__ void setvaluei(matrixref us, float value) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i >= us.rows()) + return; + // set all columns + const size_t m = us.cols(); + for (size_t j = 0; j < m; j++) + us(i, j) = value; + } + + void latticefunctionsops::stateposteriors (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logqs, matrixref & logacc) const + { + // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. + // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. + const size_t numedges = edges.size(); + dim3 t (32,8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + + setvaluei <<>> (logacc, LOGZERO); + checklaunch ("setvaluei"); + + stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logqs, logacc); + checklaunch ("stateposteriors"); + } + + void latticefunctionsops::sMBRerrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logpps, const float amf, const vectorref & logEframescorrect, const double logEframescorrecttotal, + matrixref & errorsignal, matrixref & errorsignalauxbuf) const + { + // Layout: each thread block takes 1024 threads; and we have #edges/1024 blocks. + // This limits us to 16 million edges. If you need more, please adjust to either use wider thread blocks or a second dimension for the grid. Don't forget to adjust the kernel as well. + const size_t numedges = edges.size(); + dim3 t (32,8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + +#ifdef DIRECT_MODE // compute Eframescorrect in a more direct way, proven to get same result as below + setvaluei <<>> (errorsignal, LOGZERO); + checklaunch ("setvaluei"); + + sMBRerrorsignalj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalauxbuf); + checklaunch ("sMBRerrorsignal"); // now we get state based logEframescorrect + + matrixref & loggammas = errorsignalauxbuf; + setvaluei <<>> (errorsignalauxbuf, LOGZERO); + checklaunch ("setvaluei"); + + stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, loggammas); + checklaunch ("stateposteriorsj"); // now we get state based loggammas + + directerrorcomputationi <<>> (errorsignal, errorsignalauxbuf, logEframescorrecttotal, amf); + checklaunch ("errorcomputationj"); +#else // this saves some computation compared with DIRECT_MODE + setvaluei <<>> (errorsignal, LOGZERO); + checklaunch ("setvaluei"); + setvaluei <<>> (errorsignalauxbuf, LOGZERO); + checklaunch ("setvaluei"); + sMBRerrorsignalj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalauxbuf); + checklaunch ("sMBRerrorsignal"); + + setunseeni <<>> (errorsignal, errorsignalauxbuf); + checklaunch ("setunseenj"); + + errorcomputationi <<>> (errorsignal, errorsignalauxbuf, amf); + checklaunch ("errorcomputationj"); + +#endif + } + + void latticefunctionsops::mmierrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logpps, matrixref & errorsignal) const + { + const size_t numedges = edges.size(); + dim3 t (32,8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + + matrixref & loggammas = errorsignal; // remember--this is an alias to 'errorsignal' + setvaluei <<>> (loggammas, LOGZERO); + checklaunch ("setvaluei"); + stateposteriorsj <<>> (alignstateids, alignoffsets, edges, nodes, logpps, loggammas); + checklaunch ("stateposteriorsj"); + + expfi <<>> (errorsignal); + checklaunch ("expfi"); + } +};}; diff --git a/Source/Math/cudalatticeops.h b/Source/Math/cudalatticeops.h index 7a56079f7c56..a8d2edc6866f 100644 --- a/Source/Math/cudalatticeops.h +++ b/Source/Math/cudalatticeops.h @@ -1,63 +1,63 @@ -// cudalatticeops.h -- contains all actual CUDA-side lattice ops -// -// F. Seide, V-hansu - -#pragma once - -#include "cudabasetypes.h" // for vectorref<> -#include "latticestorage.h" // for the lattice types -#include "latticefunctionskernels.h" // for the actual inner kernels and any argument types that are not yet defined in latticestorage.h - -using namespace msra::lattices; - -// Forward declarations -namespace Microsoft { namespace MSR { namespace CNTK { - template class Matrix; -}}} - -namespace msra { namespace math { - class ssematrixbase; -}} - -namespace msra { namespace cuda { - -// The XXXvectorops classes must derive from vectorref. - -class latticefunctionsops : protected vectorref -{ -protected: - void edgealignment (const vectorref & hmms, const vectorref & transPs, const size_t spalignunitid, - const size_t silalignunitid, const matrixref & logLLs, - const vectorref & nodes, const vectorref & edges, - const vectorref & aligns, const vectorref & alignoffsets, - vectorref & backptrstorage, const vectorref & backptroffsets, - vectorref & alignresult, vectorref & edgeacscores) const; // output - - void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, - const size_t numlaunchforward, const size_t numlaunchbackward, - const size_t spalignunitid, const size_t silalignunitid, - const vectorref & edgeacscores, const vectorref & edges, - const vectorref & nodes, - const vectorref & aligns, const vectorref & aligments, - const vectorref & aligmentoffsets, - vectorref & logpps, vectorref & logalphas, vectorref & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, - const vectorref & uids, const vectorref & senone2classmap, - vectorref & logaccalphas, vectorref & logaccbetas, - vectorref & logframescorrectedge, vectorref & logEframescorrect, vectorref & Eframescorrectbuf, - double & logEframescorrecttotal, double & totalfwscore) const; - - void sMBRerrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logpps, const float amf, const vectorref & logEframescorrect, const double logEframescorrecttotal, - matrixref & errorsignal, matrixref & errorsignalneg) const; - - void mmierrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logpps, matrixref & errorsignal) const; - - void stateposteriors (const vectorref & alignstateids, const vectorref & alignoffsets, - const vectorref & edges, const vectorref & nodes, - const vectorref & logqs, matrixref & logacc) const; -}; -};}; +// cudalatticeops.h -- contains all actual CUDA-side lattice ops +// +// F. Seide, V-hansu + +#pragma once + +#include "cudabasetypes.h" // for vectorref<> +#include "latticestorage.h" // for the lattice types +#include "latticefunctionskernels.h" // for the actual inner kernels and any argument types that are not yet defined in latticestorage.h + +using namespace msra::lattices; + +// Forward declarations +namespace Microsoft { namespace MSR { namespace CNTK { + template class Matrix; +}}} + +namespace msra { namespace math { + class ssematrixbase; +}} + +namespace msra { namespace cuda { + +// The XXXvectorops classes must derive from vectorref. + +class latticefunctionsops : protected vectorref +{ +protected: + void edgealignment (const vectorref & hmms, const vectorref & transPs, const size_t spalignunitid, + const size_t silalignunitid, const matrixref & logLLs, + const vectorref & nodes, const vectorref & edges, + const vectorref & aligns, const vectorref & alignoffsets, + vectorref & backptrstorage, const vectorref & backptroffsets, + vectorref & alignresult, vectorref & edgeacscores) const; // output + + void forwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, + const size_t numlaunchforward, const size_t numlaunchbackward, + const size_t spalignunitid, const size_t silalignunitid, + const vectorref & edgeacscores, const vectorref & edges, + const vectorref & nodes, + const vectorref & aligns, const vectorref & aligments, + const vectorref & aligmentoffsets, + vectorref & logpps, vectorref & logalphas, vectorref & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, + const vectorref & uids, const vectorref & senone2classmap, + vectorref & logaccalphas, vectorref & logaccbetas, + vectorref & logframescorrectedge, vectorref & logEframescorrect, vectorref & Eframescorrectbuf, + double & logEframescorrecttotal, double & totalfwscore) const; + + void sMBRerrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logpps, const float amf, const vectorref & logEframescorrect, const double logEframescorrecttotal, + matrixref & errorsignal, matrixref & errorsignalneg) const; + + void mmierrorsignal (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logpps, matrixref & errorsignal) const; + + void stateposteriors (const vectorref & alignstateids, const vectorref & alignoffsets, + const vectorref & edges, const vectorref & nodes, + const vectorref & logqs, matrixref & logacc) const; +}; +};}; diff --git a/Source/Math/cudalib.cpp b/Source/Math/cudalib.cpp index 8606964af1d5..7d2d3ea4bb10 100644 --- a/Source/Math/cudalib.cpp +++ b/Source/Math/cudalib.cpp @@ -1,83 +1,83 @@ -// cudalib.cpp -- all CUDA calls (but not cublas) are encapsulated here -// All actual CUDA API calls go here, to keep the header out of our other headers. -// -// F. Seide, V-hansu - -#define _CRT_SECURE_NO_WARNINGS 1 // so we can use getenv()... - -#include "Basics.h" -#include // for CUDA API -#include // for device API -#include "cudalib.h" -#include "cudadevice.h" -#include -#include -#include - -#undef NOMULTIDEVICE // define this to disable any context/driver stuff - -#ifndef NOMULTIDEVICE -#pragma comment (lib, "cuda.lib") // link CUDA device API -#endif -#pragma comment (lib, "cudart.lib") // link CUDA runtime -#pragma comment (lib, "cublas.lib") - -namespace msra { namespace cuda { - -static int devicesallocated = -1; // -1 means not initialized - -// allows to write cudaFunction() || "error" (CUDA runtime) -static void operator|| (cudaError_t rc, const char * msg) -{ - if (rc != cudaSuccess) - RuntimeError("%s: %s (cuda error %d)", msg, cudaGetErrorString(rc), (int)rc); -} - -cudaStream_t GetCurrentStream() { return cudaStreamDefault; } - -// synchronize with ongoing thread -void join() -{ - cudaDeviceSynchronize() || "cudaDeviceSynchronize failed"; -} - -// allocate a stack to store the devices that have been pushed -const int stackSize = 20; -static int curStack = 0; -static size_t deviceStack[stackSize] = {0}; - - -// memory allocation -void * mallocbytes (size_t nelem, size_t sz) -{ - for (size_t retry = 0; ; retry++) - { - try - { - //fprintf (stderr, "mallocbytes: allocating %d elements of size %d, %d bytes\n", (int) nelem, (int) sz, (int) (nelem * sz)); // comment out by [v-hansu] to get rid out annoying output - void * p; - cudaMalloc (&p, nelem * sz) || "cudaMalloc failed"; - return p; - } - catch (const std::exception & e) - { - fprintf (stderr, "mallocbytes: failed with error %s\n", e.what()); - if (retry >= 5) - throw; - } - } -} - -void freebytes (void * p) { cudaFree (p) || "cudaFree failed"; } - -void memcpyh2d (void * dst, size_t byteoffset, const void * src, size_t nbytes) -{ - cudaMemcpy (byteoffset + (char*) dst, src, nbytes, cudaMemcpyHostToDevice) || "cudaMemcpy failed"; -} - -void memcpyd2h (void * dst, const void * src, size_t byteoffset, size_t nbytes) -{ - cudaMemcpy (dst, byteoffset + (const char *) src, nbytes, cudaMemcpyDeviceToHost) || "cudaMemcpy failed"; -} - -};}; +// cudalib.cpp -- all CUDA calls (but not cublas) are encapsulated here +// All actual CUDA API calls go here, to keep the header out of our other headers. +// +// F. Seide, V-hansu + +#define _CRT_SECURE_NO_WARNINGS 1 // so we can use getenv()... + +#include "Basics.h" +#include // for CUDA API +#include // for device API +#include "cudalib.h" +#include "cudadevice.h" +#include +#include +#include + +#undef NOMULTIDEVICE // define this to disable any context/driver stuff + +#ifndef NOMULTIDEVICE +#pragma comment (lib, "cuda.lib") // link CUDA device API +#endif +#pragma comment (lib, "cudart.lib") // link CUDA runtime +#pragma comment (lib, "cublas.lib") + +namespace msra { namespace cuda { + +static int devicesallocated = -1; // -1 means not initialized + +// allows to write cudaFunction() || "error" (CUDA runtime) +static void operator|| (cudaError_t rc, const char * msg) +{ + if (rc != cudaSuccess) + RuntimeError("%s: %s (cuda error %d)", msg, cudaGetErrorString(rc), (int)rc); +} + +cudaStream_t GetCurrentStream() { return cudaStreamDefault; } + +// synchronize with ongoing thread +void join() +{ + cudaDeviceSynchronize() || "cudaDeviceSynchronize failed"; +} + +// allocate a stack to store the devices that have been pushed +const int stackSize = 20; +static int curStack = 0; +static size_t deviceStack[stackSize] = {0}; + + +// memory allocation +void * mallocbytes (size_t nelem, size_t sz) +{ + for (size_t retry = 0; ; retry++) + { + try + { + //fprintf (stderr, "mallocbytes: allocating %d elements of size %d, %d bytes\n", (int) nelem, (int) sz, (int) (nelem * sz)); // comment out by [v-hansu] to get rid out annoying output + void * p; + cudaMalloc (&p, nelem * sz) || "cudaMalloc failed"; + return p; + } + catch (const std::exception & e) + { + fprintf (stderr, "mallocbytes: failed with error %s\n", e.what()); + if (retry >= 5) + throw; + } + } +} + +void freebytes (void * p) { cudaFree (p) || "cudaFree failed"; } + +void memcpyh2d (void * dst, size_t byteoffset, const void * src, size_t nbytes) +{ + cudaMemcpy (byteoffset + (char*) dst, src, nbytes, cudaMemcpyHostToDevice) || "cudaMemcpy failed"; +} + +void memcpyd2h (void * dst, const void * src, size_t byteoffset, size_t nbytes) +{ + cudaMemcpy (dst, byteoffset + (const char *) src, nbytes, cudaMemcpyDeviceToHost) || "cudaMemcpy failed"; +} + +};}; diff --git a/Source/Math/cudalib.h b/Source/Math/cudalib.h index d45763ada745..2a2164fbb080 100644 --- a/Source/Math/cudalib.h +++ b/Source/Math/cudalib.h @@ -1,57 +1,57 @@ -// cudalib.h -- wrappers for CUDA to hide NVidia stuff -// -// F. Seide, V-hansu - -#pragma once - -#include "cudabasetypes.h" -#include -#include -#include - -namespace msra { namespace cuda { - -// helper functions -void join(); // wait until current launch or other async operation is completed - -// memory allocation and copying -void * mallocbytes (size_t nelem, size_t sz); -void freebytes (void * p); -template cuda_ptr malloc (size_t nelem) { return (T*) mallocbytes (nelem, sizeof (T)); } -template void free (cuda_ptr p) { if (p.get() != NULL) freebytes ((void*)p.get()); } - -void memcpyh2d (void * dst, size_t byteoffset, const void * src, size_t nbytes); -void memcpyd2h (void * dst, const void * src, size_t byteoffset, size_t nbytes); -template void memcpy (cuda_ptr dst, size_t dstoffset, const T * src, size_t nelem) -{ - memcpyh2d ((void*) dst.get(), dstoffset * sizeof (T), (const void *) src, nelem * sizeof (T)); -} -template void memcpy (T * dst, const cuda_ptr src, size_t srcoffset, size_t nelem) -{ - memcpyd2h ((void*) dst, (const void *) src.get(), srcoffset * sizeof (T), nelem * sizeof (T)); -} -// [v-hansu] for debug use, change false to true to activate -template void peek (vectorref v) -{ - bool dopeek = false; - if (dopeek) - { - std::vector vp(v.size()); - memcpy (&vp[0], v.get(), 0, v.size()); - T vp0 = vp[0]; // for reference - vp[0] = vp0; - } -} -template void peek (matrixref v) -{ - bool dopeek = false; - if (dopeek) - { - std::vector vp(v.cols() * v.rows()); - memcpy (&vp[0], v.get(), 0, v.cols() * v.rows()); - T vp0 = vp[0]; // for reference - vp[0] = vp0; - } -} - -};}; +// cudalib.h -- wrappers for CUDA to hide NVidia stuff +// +// F. Seide, V-hansu + +#pragma once + +#include "cudabasetypes.h" +#include +#include +#include + +namespace msra { namespace cuda { + +// helper functions +void join(); // wait until current launch or other async operation is completed + +// memory allocation and copying +void * mallocbytes (size_t nelem, size_t sz); +void freebytes (void * p); +template cuda_ptr malloc (size_t nelem) { return (T*) mallocbytes (nelem, sizeof (T)); } +template void free (cuda_ptr p) { if (p.get() != NULL) freebytes ((void*)p.get()); } + +void memcpyh2d (void * dst, size_t byteoffset, const void * src, size_t nbytes); +void memcpyd2h (void * dst, const void * src, size_t byteoffset, size_t nbytes); +template void memcpy (cuda_ptr dst, size_t dstoffset, const T * src, size_t nelem) +{ + memcpyh2d ((void*) dst.get(), dstoffset * sizeof (T), (const void *) src, nelem * sizeof (T)); +} +template void memcpy (T * dst, const cuda_ptr src, size_t srcoffset, size_t nelem) +{ + memcpyd2h ((void*) dst, (const void *) src.get(), srcoffset * sizeof (T), nelem * sizeof (T)); +} +// [v-hansu] for debug use, change false to true to activate +template void peek (vectorref v) +{ + bool dopeek = false; + if (dopeek) + { + std::vector vp(v.size()); + memcpy (&vp[0], v.get(), 0, v.size()); + T vp0 = vp[0]; // for reference + vp[0] = vp0; + } +} +template void peek (matrixref v) +{ + bool dopeek = false; + if (dopeek) + { + std::vector vp(v.cols() * v.rows()); + memcpy (&vp[0], v.get(), 0, v.cols() * v.rows()); + T vp0 = vp[0]; // for reference + vp[0] = vp0; + } +} + +};}; diff --git a/Source/Math/latticefunctionskernels.h b/Source/Math/latticefunctionskernels.h index b2b7d4b08297..b599955bb954 100644 --- a/Source/Math/latticefunctionskernels.h +++ b/Source/Math/latticefunctionskernels.h @@ -1,923 +1,923 @@ -// latticefunctionskernels.cu(.h) -- kernels for lattice ops intended for use with CUDA, to be called from actual CUDA kernels. -// -// To make this compile for the CPU (emulation for testing), add this line before #including this: -// #define __device__ -// -// F. Seide, V-hansu - -#define FORBID_INVALID_SIL_PATHS // [v-hansu] prune path that start from sil(sp) and go into sil, only used with addsil is adopted - -#pragma once - -#pragma push_macro ("__device__") -#pragma push_macro ("atomicAdd") -#pragma push_macro ("atomicCAS") - -#include "latticestorage.h" -#include - -namespace msra { namespace cuda { class passtextureref; }} - -#ifdef CPUONLY -#define __kernel_emulation__ -#endif - -#ifdef __kernel_emulation__ -#include "math.h" // to get exp() and log() compiled correctly with c++ -#include -using namespace std; -#ifndef __device__ -#define __device__ -#endif -#define CUDART_MIN_DENORM_F numeric_limits::denorm_min() -#define atomicAdd(address,value) (*(address)+=(value)) // don't forget to #undef (#praga pop_macro)! Otherwise CUDA might compile with this... -#define atomicCAS(address, compare, val) *address; *address = *address == compare ? val : *address; -#define __double_as_longlong(in) (*(unsigned long long int *) &in) -#define __longlong_as_double(in) (*(double *) &in) -#define __float_as_int(in) (*(int *) &in) -#define __int_as_float(in) (*(float *) &in) -#else // TODO: remove this once we got this figured out -#include "math_constants.h" -#if __CUDA_ARCH__ < 200 -//#warning Sequence training not supported on 1.x CUDA machines. -#define force_crash() (*((int*)-1)=0) // TODO: this does not in fact seem to crash it... -#define atomicAdd(a,v) (force_crash(),*(a)=v) // force a crash if used with 1.x devices -#define atomicCAS(address, compare, val) (*(address) = compare + val, *((int*)-1)=0) -#define __double_as_longlong(in) (force_crash(), in) -#define __longlong_as_double(in) (force_crash(), in) -#define __float_as_int(in) (force_crash(), in) -#define __int_as_float(in) (force_crash(), in) -#endif -#endif - -namespace msra { namespace lattices { - -struct somedata // example type to have a pattern to copy from -{ - size_t fortytwo; -}; - -struct empty {}; - -// Note that the code that uses this (edgealignmentj) will assume 3-state left-to-right -// except for /sil/ and /sp/ which are treated specially. -// TODO: either check when creating this whether this assumption is true, or control this through a flag in here. -struct lr3transP // lr3 = 3-state left-to-right architecture -{ - - static const size_t MAXSTATES = 3; - size_t numstates; - float loga[MAXSTATES+1][MAXSTATES+1]; - - - lr3transP () - { -#ifdef INITIAL_STRANGE - numstates = 3; - for (size_t i = 0; i < NUMSTATES+1; i++) - for (size_t j = 0; j < NUMSTATES+1; j++) - { - loga[i][j] = LOGZERO; - } -#endif - } -}; - -struct lrhmmdef // left-to-right HMM (no /sil/) -{ - static const size_t MAXSTATES = 3; // we use a fixed memory allocation since it's almost always 3 anyway - - unsigned char transPindex; // index of monophone to find transP matrix - unsigned char numstates; // number of states; code supports only either 1 or 3 - unsigned short senoneids[MAXSTATES]; // [0..numstates-1] senone indices - - size_t getsenoneid (size_t i) const { return (size_t) senoneids[i]; } - size_t getnumstates() const { return (size_t) numstates; } - const struct lr3transP & gettransP(const lr3transP * transPs) const { return transPs[transPindex]; } - - lrhmmdef () - { -#ifdef INITIAL_STRANGE - transPindex = unsigned char (-1); - numstates = unsigned char (-1); - for (size_t i = 0; i < MAXSTATES; i++) - { - senoneids[i] = unsigned short (-1); - } -#endif - } -}; - -#if 1 // straight-forward version -#else // CUDA hacked version -hmm gethmm (hmms, i) -{ - ushort4 u4 = *((ushort4) &hmms[i]); - lrhmmdef hmm; - hmm.transPindex = u4.x & 0xff; - hmm.numstates = u4.x >> 8; - hmm.senoneids[0] = u4.y; - hmm.senoneids[1] = u4.z; - hmm.senoneids[2] = u4.w; -} -#endif - -#ifndef LOGZERO -#define LOGZERO -1e30f -#endif - -class bpmatrixref -{ -private: - unsigned short * p; // pointer in CUDA space of this device - size_t numrows; // rows() - size_t numcols; // cols() - size_t colstride; // height of column = rows() rounded to multiples of 4 - __device__ void checkbounds (size_t i, size_t j) const - { - if (i >= numrows || j >= numcols) -#ifdef __kernel_emulation__ - throw::logic_error ("out of boundary!!!"); -#else - *((int*)-1)=0; -#endif - } - __device__ size_t locate (size_t i, size_t j) const - { - checkbounds (i,j); - return j * colstride + i; - } // matrix in column-wise storage -public: - __device__ bpmatrixref (unsigned short * address, size_t n, size_t m) - { - numrows = n; - numcols = m; - colstride = n; - p = address; - } - - __device__ unsigned short & operator() (size_t i, size_t j) { return p[locate(i,j)]; } - __device__ const unsigned short & operator() (size_t i, size_t j) const { return p[locate(i,j)]; } -}; - -// this class contains all-static methods that are inner pieces of thread kernels for use with CUDA -struct latticefunctionskernels -{ - // [v-hansu] mimic the float version of atomicCAS - static __device__ float atomicCASfloatdouble (float *address, float compare, float val) - { - int * intaddress = (int *) address; - int intcompare = __float_as_int(compare); // __double_as_longlong : read double as unsigned long long - int intval = __float_as_int(val); - int result = atomicCAS (intaddress, intcompare, intval); - return __int_as_float(result); - } - - static __device__ double atomicCASfloatdouble (double *address, double compare, double val) - { - unsigned long long int * longlongintaddress = (unsigned long long int *) address; - unsigned long long int longlongintcompare = __double_as_longlong (compare); // __double_as_longlong : read double as unsigned long long - unsigned long long int longlongintval = __double_as_longlong (val); - unsigned long long int result = atomicCAS(longlongintaddress, longlongintcompare, longlongintval); - return __longlong_as_double(result); - } - - static __device__ void logaddratio (double & loga, double diff) - { - if (diff < -37.0f) return; // log (2^-53), 52-bit mantissa -> cut of after 53th bit - loga += log (1.0 + exp (diff)); - } - static __device__ void logaddratio (float & loga, float diff) - { - if (diff < -17.0f) return; // log (2^-24), 23-bit mantissa -> cut of after 24th bit - loga += logf (1.0f + expf (diff)); - } - - template static __device__ void swap (FLOAT & left, FLOAT & right) - { // exchange values stored at _Left and _Right - FLOAT tmp = left; - left = right; - right = tmp; - } - - // overloads for exp() for float and double, so that we can use templates - static __device__ float expfd (float x) { return ::expf (x); } // TODO: ain't there an overload for this? - static __device__ double expfd (double x) { return ::exp (x); } - - // Compute the difference of two numbers, which are represented as their logs. - // The return value is a non-log value. exp(loga) - exp(logb) - template - static __device__ FLOAT expdiff (FLOAT loga, FLOAT logb) - { - if (logb < loga) // logb - loga < 0 => exp(logb-loga) < 1 - return expfd (loga) * (1 - expfd (logb - loga)); - else - return -expfd (logb) * (1 - expfd (loga - logb)); - } - - // loga <- log (exp (loga) + exp (logb)) = log (exp (loga) * (1.0 + exp (logb - loga)) = loga + log (1.0 + exp (logb - loga)) - template static __device__ void logadd (FLOAT & loga, FLOAT logb) - { - if (logb > loga) // we add smaller to bigger - swap (loga, logb); // loga is bigger - if (loga <= LOGZERO) // both are 0 - return; - logaddratio (loga, logb - loga); - } - - // does the same as above but if the bigger one is too small, we assign a small value to it - template static __device__ void logaddseen (FLOAT & loga, FLOAT logb) - { - if (logb > loga) // we add smaller to bigger - swap (loga, logb); // loga is bigger - if (loga <= LOGZERO) // both are 0 - { - loga = logf(CUDART_MIN_DENORM_F); // [v-hansu] we hope to separate LOGZERO (unseen states) and logf(CUDART_MIN_DENORM_F) (seen states with small prob) - return; - } - logaddratio (loga, logb - loga); - } - -#if 1 - static inline __device__ float bitsasfloat ( int b) { return __int_as_float (b); } - static inline __device__ double bitsasfloat (unsigned long long int b) { return __longlong_as_double (b); } - static inline __device__ int floatasbits (float f) { return __float_as_int (f); } - static inline __device__ unsigned long long int floatasbits (double f) { return __double_as_longlong (f); } - - template // adapted from [http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#ixzz32EuzZjxV] - static __device__ FLOAT atomicLogAdd (FLOAT * address, FLOAT val) // direct adaptation from NVidia source code - { - typedef decltype (floatasbits (val)) bitstype; - bitstype * address_as_ull = (bitstype *) address; - bitstype old = *address_as_ull, assumed; - do { - assumed = old; - FLOAT sum = bitsasfloat (assumed); - logaddseen (sum, val); - old = atomicCAS (address_as_ull, assumed, floatasbits (sum)); - } while (assumed != old); - // note: critically, ^^ this comparison must copare the bits ('int') instead of the converted float values, since this will fail for NaNs (NaN != NaN is true always) - return bitsasfloat(old); - } -#else // this code does not work because (assumed != old) will not compare correctly in case of NaNs - //same pattern as atomicAdd(), but performing the log-add operation instead - template static __device__ FLOAT atomicLogAdd (FLOAT * address, FLOAT val) - { - FLOAT old = *address; - FLOAT assumed; - FLOAT logaddresult; - do { - assumed = old; // old is the assumed value at address - logaddresult = assumed; // for next step to compute logaddresult, assumed shall be the same as before - logaddseen (logaddresult, val); - old = atomicCASfloatdouble (address, assumed, logaddresult); - } while (assumed != old); // if old == assumed, the *address is not changed in this loop, so this is safe - return old; - } -#endif - - // [v-hansu] shuffling accessing order for a item in cubic(Ni, Nj, Nk) with index i, j, k according to shufflemode - static inline __device__ size_t shuffle (size_t i, size_t Ni, size_t j, size_t Nj, size_t k, size_t Nk, size_t shufflemode) - { - if (shufflemode == 0) - return i + j * Ni + k * Nj * Ni; - else if (shufflemode == 1) // inverse - return k + j * Nk + i * Nj * Nk; - else if (shufflemode == 2) // flip i and j - return j + i * Nj + k * Ni * Nj; - else if (shufflemode == 3) // flip j and k - return i + k * Ni + j * Nk * Ni; - else if (shufflemode == 4) - return j + k * Nj + i * Nk * Nj; - else - *((int*)-1)=0; // shall not get here, WRONG - return 0; - } - - template - static __device__ void setvaluej (size_t j, doublevector & thisvector, double value) - { - thisvector[j] = value; - } - - //zhaorui - static inline __device__ float getlogtransp (lr3transP transP, int from, int to) - { - /*if (from < -1 || from >= transP.MAXSTATES || to > transP.MAXSTATES) - { - //printf("from: %d to: %d\n", from, to); - return LOGZERO; - }*/ - return transP.loga[from+1][to]; - } - template - static inline __device__ void edgealignmentj (size_t j, const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, - const size_t silalignunitid, const matrix & logLLs, const nodeinfovector & nodes, - const edgeinfowithscoresvector & edges, const aligninfovector & aligns, - const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, - ushortvector & alignresult, floatvector & edgeacscores) - { // TODO: alignresult will change to (start,end) - // mostly finished - // some preparation - size_t as = edges[j].firstalign; // align start - size_t ae = (j+1) < edges.size() ? (size_t) edges[j+1].firstalign : aligns.size(); - if (as == ae) // the last empty alignment - return; - size_t ts = nodes[edges[j].S].t; - - float fwscore = 0.0f; // score passed across phone boundaries - size_t alignindex = alignoffsets[j]; // index to set (result) -#ifndef PARALLEL_SIL - const bool isSil = (aligns[as].unit == silalignunitid || aligns[ae-1].unit == silalignunitid); - if (isSil) return; // we do not support silence edge now, which is computed by cpu, may change when we support it -#endif - // Viterbi alignment - for (size_t k = as; k < ae; k++) - { - const aligninfo align = aligns[k]; - const size_t numframes = align.frames; - const bool isSp = (align.unit == spalignunitid); - const bool isSil = (align.unit == silalignunitid); - - const lrhmmdef hmm = hmms[align.unit]; - const lr3transP transP = transPs[hmm.transPindex]; - - - // pre-fetch senone ids into registers - size_t senoneid0 = hmm.senoneids[0]; - size_t senoneid1 = 0; - size_t senoneid2 = 0; - if (!isSp) // fetch only if needed--may save some memory cycles - { - senoneid1 = hmm.senoneids[1]; - senoneid2 = hmm.senoneids[2]; - } - - const size_t te = ts + numframes; // end time of current unit - - size_t state1step0to1 = te; // inflection point from state 0 to 1, record in state 1 - //size_t state1stepm1to1 = te; - size_t state2step0to1 = te; // inflection point from state 0 to 1, record in state 2 - //size_t state2stepm1to1 = te; // inflection point from state 0 to 1, record in state 2 - size_t state2step1to2 = te; // inflection point from state 1 to 2, record in state 2 - size_t state2step0to2 = te; - - //now we only support transition from -1 to 0 or 2 for sil - float pathscore0 = fwscore; // log pp in state 0 - float pathscore1 = fwscore; // log pp in state 1 - float pathscore2 = fwscore; // log pp in state 2 - - - - // first frame - if (ts != te) // for t = ts, initialization - { - /* if (isSil) //for sil, -1 to 2 and -1 to 0 is permitted - { - pathscore0 += getlogtransp(transP,-1,0) + logLLs(senoneid0,ts); - pathscore2 += getlogtransp(transP,-1,2) + logLLs(senoneid2,ts); - } - else //for others, only -1 to 0 is permitted - { - pathscore0 += getlogtransp(transP, -1, 0) + logLLs(senoneid0, ts); - pathscore1 += getlogtransp(transP, -1, 1) + logLLs(senoneid1, ts); - - }*/ - pathscore2 += getlogtransp(transP, -1, 2) + logLLs(senoneid2, ts); - pathscore1 += getlogtransp(transP, -1, 1) + logLLs(senoneid1, ts); - //state1stepm1to1 = ts; - pathscore0 += getlogtransp(transP, -1, 0) + logLLs(senoneid0, ts); - - - } - - - float pathscore2last = pathscore2; // allocate last for state 2 because the order of computation below is 2->1->0, last state 2 is needed because from 2 to 0 or 1 is permitedted for sil. - float pathscore1last = pathscore1; // allocate last for state 1 because the order of computation below is 2->1->0, last state 1 is needed because from 1 to 0 is permitedted for sil. - - size_t backptroffset = backptroffsets[j]; // we make use of backptrstorage in backptroffsets[j] for viterbi of ergodic model (silence) - - bpmatrixref backptrmatrix (&backptrstorage[backptroffset], hmm.MAXSTATES, numframes); - //subsequent frames - for (size_t t = ts + 1; t < te; t++) - { - if (!isSp) - { - // state [2] - pathscore2 += getlogtransp(transP,2,2); // log pp from state 2 to 2 - if (isSil) - backptrmatrix (2, t-ts-1) = 2; - const float pathscore12 = pathscore1 + getlogtransp(transP,1,2); // log pp from state 1 to 2 - if (pathscore12 >= pathscore2) // if state 1->2 - { - pathscore2 = pathscore12; - state2step0to1 = state1step0to1; // record the inflection point - //state2stepm1to1 = state1stepm1to1; - state2step1to2 = t; // record the inflection point - state2step0to2 = te; - if (isSil) - backptrmatrix (2, t-ts-1) = 1; - } - //if (isSil) // only silence have path from 0 to 2 - { - const float pathscore02 = pathscore0 + getlogtransp(transP,0,2); // log pp from state 0 to 2 - if (pathscore02 >= pathscore2) // if state 0->2 - { - pathscore2 = pathscore02; - if (isSil) - backptrmatrix (2, t-ts-1) = 0; - state2step0to2 = t; - state2step1to2 = te; - } - } - - // state [1] - pathscore1 += getlogtransp(transP,1,1); // log pp from state 1 to 1 - if (isSil) - backptrmatrix (1, t-ts-1) = 1; - const float pathscore01 = pathscore0 + getlogtransp(transP,0,1); // log pp from state 0 to 1 - if (pathscore01 >= pathscore1) // if state 0 -> 1 - { - pathscore1 = pathscore01; - state1step0to1 = t; // record the inflection point - //state1stepm1to1 = te; - if (isSil) - backptrmatrix (1, t-ts-1) = 0; - } - - if (isSil) // only silence have path from 2 to 1 - { - const float pathscore21 = pathscore2last + getlogtransp(transP,2,1); - if (pathscore21 >= pathscore1) // if state 2 -> 1 - { - pathscore1 = pathscore21; - backptrmatrix (1, t-ts-1) = 2; - } - } - } - // state [0] - pathscore0 += getlogtransp(transP,0,0); - if(isSil) // only silence have path from 2 or 1 to 0 - { - backptrmatrix (0, t-ts-1) = 0; - const float pathscore20 = pathscore2last + getlogtransp(transP,2,0); // log pp from state 2 to 0 - if (pathscore20 >= pathscore0) - { - pathscore0 = pathscore20; - backptrmatrix (0, t-ts-1) = 2; - } - const float pathscore10 = pathscore1last + getlogtransp(transP,1,0); // log pp from state 1 to 0 - if (pathscore10 >= pathscore0) - { - pathscore0 = pathscore10; - backptrmatrix (0, t-ts-1) = 1; - } - } - - // add log LLs - pathscore0 += logLLs(senoneid0,t); - if (!isSp) // only fetch if needed, saves mem access - { - pathscore1 += logLLs(senoneid1,t); - pathscore2 += logLLs(senoneid2,t); - } - pathscore1last = pathscore1; // update pathscore1last - pathscore2last = pathscore2; // update pathscore2last - } - - // final 'next' transition that exits from last frame - - if (ts == te) // if sp tee model, will not in next loop - { - pathscore2 = pathscore0 + getlogtransp(transP,-1,1); - } - else if (isSp) - { - pathscore2 = pathscore0 + getlogtransp(transP,0,1) ; // sp model, from 0 to 1 - //printf(" sp, %f\n", pathscore2); - } - - else if(isSil) //for sil, the exit state can be 0 or 2. - { - const float pathscore03 = pathscore0 + getlogtransp(transP,0,3); - pathscore2 += getlogtransp(transP,2,3); - if(pathscore03 > pathscore2) - { - pathscore2 = pathscore03; - } - } - else - pathscore2 += getlogtransp(transP,2,3); - - fwscore = pathscore2; // propagate across phone boundaries - - // emit alignment - - if (!isSil) - { - if (state2step0to2 < te) //from 0 to 2 - { - state2step0to2 += alignindex - ts; - for (size_t t = alignindex; t < alignindex + numframes; t++) // set the final alignment - { - size_t senoneid; - if (t < state2step0to2) // in state 0 - senoneid = senoneid0; - else // in state 2 - senoneid = senoneid2; - alignresult[t] = (unsigned short)senoneid; - } - } - else //from 1 to 2 - { - state2step0to1 += alignindex - ts; // convert to align measure - state2step1to2 += alignindex - ts; - for (size_t t = alignindex; t < alignindex + numframes; t++) // set the final alignment - { - size_t senoneid; - if (state2step0to1 = pathscore2) //exit state is 0 - { - alignresult[alignindex + numframes - 1] = (unsigned short) senoneid0; - lastpointer = 0; - } - else //exit state is 2 - alignresult[alignindex + numframes - 1] = (unsigned short) senoneid2; - - for (size_t t = alignindex + numframes - 2; (t + 1) > alignindex; t--) // set the final alignment - { - lastpointer = backptrmatrix (lastpointer, t-alignindex); - size_t senoneid = (size_t) (-1); - if (lastpointer == 0) - senoneid = senoneid0; - else if (lastpointer == 1) - senoneid = senoneid1; - else if (lastpointer == 2) - senoneid = senoneid2; - alignresult[t] = (unsigned short) senoneid; - } - } - ts = te; - alignindex += numframes; - } - edgeacscores[j] = fwscore; - } - - // compute the final error signal from gammas and state-consolidated Eframescorrect - // in-place operation is supported (i.e. output = one of the inputs) - template - static inline __device__ void computesMBRerrorsignals (const size_t s, const matrix & loggammas, const matrix & logEframescorrect, - const double logEframescorrecttotal, const float kappa, matrix & errorsignal) - { - const float Eframescorrecttotal = expf ((float)logEframescorrecttotal); - const size_t T = errorsignal.cols(); - for (size_t t = 0; t < T; t++) - errorsignal(s,t) = expf (loggammas(s,t)) * (expf (logEframescorrect(s,t)) - Eframescorrecttotal) * kappa; - } - - // test if a state is silence [v-hansu] - // WARNING, this function only support models with 9304 states - // TODO: change this later on - static inline __device__ bool issilencestate (size_t stateid, size_t numsenones) - { - if (numsenones == 9304 && (stateid == 7670 || stateid == 7671 || stateid == 7672)) - return true; - else - return false; - } - - // compare two states and check if they are of the same class [v-hansu] - template - static inline __device__ bool isofsameclass (size_t statea, size_t stateb, ushortvector senone2classmap) - { - if (senone2classmap.size() == 0) // no map provided, we just do normal comparison - return (statea == stateb); - else - return senone2classmap[statea] == senone2classmap[stateb]; - } - - // Phase 1 of forwardbackward algorithm - // returnEframescorrect means sMBR mode - template - static inline __device__ void forwardlatticej (const size_t j, const floatvector & edgeacscores, - const size_t /*spalignunitid --unused*/, const size_t silalignunitid, - const edgeinforvector & edges, const nodeinfovector & nodes, const aligninfovector & aligns, - const ushortvector & alignments, const uintvector & alignmentoffsets, - doublevector & logalphas, float lmf, float wp, float amf, const float boostingfactor, - const ushortvector & uids, const ushortvector senone2classmap, const bool returnEframescorrect, - doublevector & logframescorrectedge, doublevector & logaccalphas) - { - // edge info - const edgeinfowithscores & e = edges[j]; - double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned - //zhaorui to deal with the abnormal score for sent start. - if(e.l < -200.0f) - edgescore = (0.0 * lmf + wp + edgeacscores[j]) / amf; - const bool boostmmi = (boostingfactor != 0.0f); - // compute the frames-correct count for this edge - double logframescorrectedgej = LOGZERO; - const size_t numsenones = 9304; // WARNING: this is a hack, please fix this once smbr or bmmi is working! [v-hansu] - bool skipsilence = true; // currently we skip silence for BMMI and sMBR [v-hansu] - if (returnEframescorrect || boostmmi) - { - size_t ts = nodes[e.S].t; - size_t te = nodes[e.E].t; - size_t framescorrect = 0; // count raw number of correct frames - size_t startindex = alignmentoffsets[j]; - - size_t as = e.firstalign; // align start - size_t ae = (j+1) < edges.size() ? (size_t) edges[j+1].firstalign : aligns.size(); - const bool isSil = (ae == as + 1 && aligns[as].unit == silalignunitid); // the order of this judgement shall be changed to save memory access - if (!(isSil && skipsilence)) // we don't count silence when 1. is silence; 2, skip them - { - for (size_t t = ts; t < te; t++) - { - if (!(skipsilence && issilencestate (alignments[t-ts+startindex], numsenones))) - framescorrect += isofsameclass (alignments[t-ts+startindex], uids[t], senone2classmap); // we only count correct && non-silence state - } - } - logframescorrectedgej = (framescorrect > 0) ? log ((double) framescorrect) : LOGZERO; // remember for backward pass - logframescorrectedge[j] = logframescorrectedgej; - } - if (boostmmi) - edgescore -= boostingfactor * exp (logframescorrectedge[j]); - -#ifdef FORBID_INVALID_SIL_PATHS - const bool forbidinvalidsilpath = (logalphas.size() > nodes.size()); // we constrain sil to sil path if node space has been blown up - const bool isaddedsil = forbidinvalidsilpath && (e.unused == 1); // HACK: 'unused' indicates artificially added sil/sp edge - - // original mode - if (!isaddedsil) -#endif - { - const size_t S = e.S; - const size_t E = e.E; - - const double inscore = logalphas[S]; - const double pathscore = inscore + edgescore; - - atomicLogAdd (&logalphas[E], pathscore); - - if (returnEframescorrect) - { -#ifdef DIRECT_MODE - double loginaccs = logaccalphas[e.S] + edgescore; - double logpathaccs = logalphas[e.S] + edgescore + logframescorrectedgej; - logadd (logpathaccs, loginaccs); - atomicLogAdd (&logaccalphas[e.E], logpathaccs); -#else - double loginaccs = logaccalphas[S] - logalphas[S]; - logadd (loginaccs, logframescorrectedgej); - double logpathacc = loginaccs + logalphas[S] + edgescore; - atomicLogAdd (&logaccalphas[E], logpathacc); -#endif - } - } - -#ifdef FORBID_INVALID_SIL_PATHS - // silence edge or second speech edge - if ((isaddedsil && e.E != nodes.size() -1) || (forbidinvalidsilpath && e.S != 0)) - { - const size_t S = (size_t) (!isaddedsil ? e.S + nodes.size() : e.S); // second speech edge comes from special 'silence state' node - const size_t E = (size_t) (isaddedsil ? e.E + nodes.size() : e.E); // silence edge goes into special 'silence state' node - // remaining lines here are 100% code dup from above, just operating on different (S, E) - const double inscore = logalphas[S]; - const double pathscore = inscore + edgescore; - atomicLogAdd (&logalphas[E], pathscore); - - if (returnEframescorrect) - { - double loginaccs = logaccalphas[S] - logalphas[S]; - logadd (loginaccs, logframescorrectedgej); - double logpathacc = loginaccs + logalphas[S] + edgescore; - atomicLogAdd (&logaccalphas[E], logpathacc); - } - } -#endif - } - - template - static inline __device__ void backwardlatticej (size_t j, const floatvector & edgeacscores, - const size_t /*spalignunitid --unused*/, const size_t /*silalignunitid --unused*/, - const edgeinforvector & edges, const nodeinfovector & nodes, - const aligninfovector & /*aligns -- unused*/, const double totalfwscore, doublevector & logpps, - doublevector & logalphas, doublevector & logbetas, float lmf, float wp, - float amf, const float boostingfactor, const bool returnEframescorrect, - doublevector & logframescorrectedge, doublevector & logaccalphas, - doublevector & logEframescorrect, doublevector & logaccbetas) - { - // output values - double logpp = LOGZERO; - double logEframescorrectj = LOGZERO; - const bool boostmmi = (boostingfactor != 0.0f); - - // edge info - const edgeinfowithscores & e = edges[j]; - double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - //zhaorui to deal with the abnormal score for sent start. - if (e.l < -200.0f) - edgescore = (0.0 * lmf + wp + edgeacscores[j]) / amf; - if (boostmmi) - edgescore -= boostingfactor * exp (logframescorrectedge[j]); - - // get the frames-correct count for this edge that was computed during the forward pass - double logframescorrectedgej = (returnEframescorrect || boostmmi) ? logframescorrectedge[j] : LOGZERO; - -#ifdef FORBID_INVALID_SIL_PATHS - // original mode - const bool forbidinvalidsilpath = (logalphas.size() > nodes.size()); // we prune sil to sil path if alphabetablowup != 1 - const bool isaddedsil = forbidinvalidsilpath && (e.unused == 1); // HACK: 'unused' indicates artificially added sil/sp edge - - if (!isaddedsil) // original mode -#endif - { - const size_t S = e.S; - const size_t E = e.E; - - // backward pass - const double inscore = logbetas[E]; - const double pathscore = inscore + edgescore; - atomicLogAdd (&logbetas[S], pathscore); - - // compute lattice posteriors on the fly since we are at it - logpp = logalphas[S] + edgescore + logbetas[E] - totalfwscore; - - // similar logic for Eframescorrect - if (returnEframescorrect) - { -#ifdef DIRECT_MODE - double loginaccs = logaccbetas[e.E] + edgescore; - double logpathaccs = logbetas[e.E] + edgescore + logframescorrectedgej; - logadd (logpathaccs, loginaccs); - atomicLogAdd (&logaccbetas[e.S], logpathaccs); - - double logecorrect = logaccalphas[e.S] + edgescore + logbetas[e.E]; - logadd (logecorrect, logalphas[e.S] + edgescore + logframescorrectedgej + logbetas[e.E]); - logadd (logecorrect, logalphas[e.S] + edgescore + logaccbetas[e.E]); - logEframescorrectj = logecorrect - totalfwscore; // be careful, this includes the denominator -#else - // backward pass - double loginaccs = logaccbetas[E] - logbetas[E]; - logadd (loginaccs, logframescorrectedgej); - double logpathacc = loginaccs + logbetas[E] + edgescore; - atomicLogAdd (&logaccbetas[S], logpathacc); - - // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) - double logsum = logframescorrectedgej; // sum over this edge, left partial (alpha), right partial (beta) - - double edgelogaccalpha = logaccalphas[S] - logalphas[S]; // incoming partial expected frames correct - logadd (logsum, edgelogaccalpha); - - double edgelogaccbeta = logaccbetas[E] - logbetas[E]; // partial expected frames correct from the end - logadd (logsum, edgelogaccbeta); - - logEframescorrectj = logsum; // that's it -#endif - } - } - -#ifdef FORBID_INVALID_SIL_PATHS - double logpp2 = LOGZERO; - double logEframescorrectj2 = LOGZERO; - - // silence edge or second speech edge - if ((isaddedsil && e.E != nodes.size() -1) || (forbidinvalidsilpath && e.S != 0)) - { - const size_t S = (size_t) (!isaddedsil ? e.S + nodes.size() : e.S); // second speech edge comes from special 'silence state' node - const size_t E = (size_t) (isaddedsil ? e.E + nodes.size() : e.E); // silence edge goes into special 'silence state' node - // remaining lines here are code dup from above, with two changes: logadd2/logEframescorrectj2 instead of logadd/logEframescorrectj - - // backward pass - const double inscore = logbetas[E]; - const double pathscore = inscore + edgescore; - atomicLogAdd (&logbetas[S], pathscore); - - // compute lattice posteriors on the fly since we are at it - logpp2 = logalphas[S] + edgescore + logbetas[E] - totalfwscore; // second edge (logpp2) - - // similar logic for Eframescorrect - if (returnEframescorrect) - { - // backward pass - double loginaccs = logaccbetas[E] - logbetas[E]; - logadd (loginaccs, logframescorrectedgej); - double logpathacc = loginaccs + logbetas[E] + edgescore; - atomicLogAdd (&logaccbetas[S], logpathacc); - - // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) - double logsum = logframescorrectedgej; // sum over this edge, left partial (alpha), right partial (beta) - - double edgelogaccalpha = logaccalphas[S] - logalphas[S]; // incoming partial expected frames correct - logadd (logsum, edgelogaccalpha); - - double edgelogaccbeta = logaccbetas[E] - logbetas[E]; // partial expected frames correct from the end - logadd (logsum, edgelogaccbeta); - - logEframescorrectj2 = logsum; // that's it for this second edge - } - - // sum logpp2 and logEframescorrectj2 - // Eframescorrect must be summed up in a weighted fashion, weighted by PP - double numer = logEframescorrectj + logpp; - logadd (numer, logEframescorrectj2 + logpp2); // weighted sum, weighted by respective (log)pp - logadd (logpp, logpp2); // (log)pp is just the sum of the two posteriors - double denom = logpp; // and that is also the denominator for the weighted sum - logEframescorrectj = numer - denom; // weighted sum - } -#else - nodes; -#endif - - // write back return values - if (logpp > 0.0) // clip to log 1 (may be possible due to small numeric inaccuracies, although it really shouldn't happen) - logpp = 0.0; - logpps[j] = logpp; - if (returnEframescorrect) - logEframescorrect[j] = logEframescorrectj; - } - - template - static inline __device__ void sMBRerrorsignalj (size_t j, const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, - const nodeinfovector & nodes, const doublevector & logpps, const float amf, - const doublevector & logEframescorrect, const double logEframescorrecttotal, - matrix & errorsignal, matrix & errorsignalneg) - { - size_t ts = nodes[edges[j].S].t; - size_t te = nodes[edges[j].E].t; - if (ts != te) - { -#ifdef DIRECT_MODE - float logEframescorrectj = logEframescorrect[j]; - size_t offset = alignoffsets[j]; - for (size_t t = ts; t < te; t++) - { - const size_t s = (size_t) alignstateids[t - ts + offset]; - atomicLogAdd (&errorsignal(s,t), logEframescorrectj); - } -#else - const double diff = expdiff (logEframescorrect[j], logEframescorrecttotal); - // Note: the contribution of the states of an edge to their senones is the same for all states - // so we compute it once and add it to all; this will not be the case without hard alignments. - double absdiff = fabs (diff); - if (absdiff == 0.0f) - return; - const float logedgecorrect = (float) (logpps[j] + log (absdiff)); - size_t offset = alignoffsets[j]; - for (size_t t = ts; t < te; t++) - { - const size_t s = (size_t) alignstateids[t - ts + offset]; - if (diff > 0.0) - atomicLogAdd(&errorsignal(s,t), logedgecorrect); - else - atomicLogAdd(&errorsignalneg(s,t), logedgecorrect); - } - absdiff = amf; // just to reference it because we hope to support linear mode as well -#endif - } - } - - // accumulate a per-edge quantity into the states that the edge is aligned with - // Use this for MMI passing the edge posteriors logpps[] as logq, or for sMBR passing logEframescorrect[]. - // j=edge index, alignment in (alignstateids, alignoffsets) - template - static inline __device__ void stateposteriorsj (size_t j, const ushortvector & alignstateids, const uintvector & alignoffsets, - const edgeinfowithscoresvector & edges, - const nodeinfovector & nodes, const doublevector & logqs /*quantity to accumulate*/, - matrix & logacc /*accumulator to accumulate into*/) - { - size_t ts = nodes[edges[j].S].t; - size_t te = nodes[edges[j].E].t; - if (ts != te) - { - const float logq = (float) logqs[j]; // per-edge quantity to accumulate, e.g. edge posteriors -> state posteriors - size_t offset = alignoffsets[j]; - for (size_t t = ts; t < te; t++) - { - const size_t s = (size_t) alignstateids[t - ts + offset]; // get state for this (j,t) - atomicLogAdd (&logacc(s,t), logq); - } - } - } -}; - -};}; - - -#pragma pop_macro ("atomicCAS") -#pragma pop_macro ("atomicAdd") -#pragma pop_macro ("__device__") - +// latticefunctionskernels.cu(.h) -- kernels for lattice ops intended for use with CUDA, to be called from actual CUDA kernels. +// +// To make this compile for the CPU (emulation for testing), add this line before #including this: +// #define __device__ +// +// F. Seide, V-hansu + +#define FORBID_INVALID_SIL_PATHS // [v-hansu] prune path that start from sil(sp) and go into sil, only used with addsil is adopted + +#pragma once + +#pragma push_macro ("__device__") +#pragma push_macro ("atomicAdd") +#pragma push_macro ("atomicCAS") + +#include "latticestorage.h" +#include + +namespace msra { namespace cuda { class passtextureref; }} + +#ifdef CPUONLY +#define __kernel_emulation__ +#endif + +#ifdef __kernel_emulation__ +#include "math.h" // to get exp() and log() compiled correctly with c++ +#include +using namespace std; +#ifndef __device__ +#define __device__ +#endif +#define CUDART_MIN_DENORM_F numeric_limits::denorm_min() +#define atomicAdd(address,value) (*(address)+=(value)) // don't forget to #undef (#praga pop_macro)! Otherwise CUDA might compile with this... +#define atomicCAS(address, compare, val) *address; *address = *address == compare ? val : *address; +#define __double_as_longlong(in) (*(unsigned long long int *) &in) +#define __longlong_as_double(in) (*(double *) &in) +#define __float_as_int(in) (*(int *) &in) +#define __int_as_float(in) (*(float *) &in) +#else // TODO: remove this once we got this figured out +#include "math_constants.h" +#if __CUDA_ARCH__ < 200 +//#warning Sequence training not supported on 1.x CUDA machines. +#define force_crash() (*((int*)-1)=0) // TODO: this does not in fact seem to crash it... +#define atomicAdd(a,v) (force_crash(),*(a)=v) // force a crash if used with 1.x devices +#define atomicCAS(address, compare, val) (*(address) = compare + val, *((int*)-1)=0) +#define __double_as_longlong(in) (force_crash(), in) +#define __longlong_as_double(in) (force_crash(), in) +#define __float_as_int(in) (force_crash(), in) +#define __int_as_float(in) (force_crash(), in) +#endif +#endif + +namespace msra { namespace lattices { + +struct somedata // example type to have a pattern to copy from +{ + size_t fortytwo; +}; + +struct empty {}; + +// Note that the code that uses this (edgealignmentj) will assume 3-state left-to-right +// except for /sil/ and /sp/ which are treated specially. +// TODO: either check when creating this whether this assumption is true, or control this through a flag in here. +struct lr3transP // lr3 = 3-state left-to-right architecture +{ + + static const size_t MAXSTATES = 3; + size_t numstates; + float loga[MAXSTATES+1][MAXSTATES+1]; + + + lr3transP () + { +#ifdef INITIAL_STRANGE + numstates = 3; + for (size_t i = 0; i < NUMSTATES+1; i++) + for (size_t j = 0; j < NUMSTATES+1; j++) + { + loga[i][j] = LOGZERO; + } +#endif + } +}; + +struct lrhmmdef // left-to-right HMM (no /sil/) +{ + static const size_t MAXSTATES = 3; // we use a fixed memory allocation since it's almost always 3 anyway + + unsigned char transPindex; // index of monophone to find transP matrix + unsigned char numstates; // number of states; code supports only either 1 or 3 + unsigned short senoneids[MAXSTATES]; // [0..numstates-1] senone indices + + size_t getsenoneid (size_t i) const { return (size_t) senoneids[i]; } + size_t getnumstates() const { return (size_t) numstates; } + const struct lr3transP & gettransP(const lr3transP * transPs) const { return transPs[transPindex]; } + + lrhmmdef () + { +#ifdef INITIAL_STRANGE + transPindex = unsigned char (-1); + numstates = unsigned char (-1); + for (size_t i = 0; i < MAXSTATES; i++) + { + senoneids[i] = unsigned short (-1); + } +#endif + } +}; + +#if 1 // straight-forward version +#else // CUDA hacked version +hmm gethmm (hmms, i) +{ + ushort4 u4 = *((ushort4) &hmms[i]); + lrhmmdef hmm; + hmm.transPindex = u4.x & 0xff; + hmm.numstates = u4.x >> 8; + hmm.senoneids[0] = u4.y; + hmm.senoneids[1] = u4.z; + hmm.senoneids[2] = u4.w; +} +#endif + +#ifndef LOGZERO +#define LOGZERO -1e30f +#endif + +class bpmatrixref +{ +private: + unsigned short * p; // pointer in CUDA space of this device + size_t numrows; // rows() + size_t numcols; // cols() + size_t colstride; // height of column = rows() rounded to multiples of 4 + __device__ void checkbounds (size_t i, size_t j) const + { + if (i >= numrows || j >= numcols) +#ifdef __kernel_emulation__ + throw::logic_error ("out of boundary!!!"); +#else + *((int*)-1)=0; +#endif + } + __device__ size_t locate (size_t i, size_t j) const + { + checkbounds (i,j); + return j * colstride + i; + } // matrix in column-wise storage +public: + __device__ bpmatrixref (unsigned short * address, size_t n, size_t m) + { + numrows = n; + numcols = m; + colstride = n; + p = address; + } + + __device__ unsigned short & operator() (size_t i, size_t j) { return p[locate(i,j)]; } + __device__ const unsigned short & operator() (size_t i, size_t j) const { return p[locate(i,j)]; } +}; + +// this class contains all-static methods that are inner pieces of thread kernels for use with CUDA +struct latticefunctionskernels +{ + // [v-hansu] mimic the float version of atomicCAS + static __device__ float atomicCASfloatdouble (float *address, float compare, float val) + { + int * intaddress = (int *) address; + int intcompare = __float_as_int(compare); // __double_as_longlong : read double as unsigned long long + int intval = __float_as_int(val); + int result = atomicCAS (intaddress, intcompare, intval); + return __int_as_float(result); + } + + static __device__ double atomicCASfloatdouble (double *address, double compare, double val) + { + unsigned long long int * longlongintaddress = (unsigned long long int *) address; + unsigned long long int longlongintcompare = __double_as_longlong (compare); // __double_as_longlong : read double as unsigned long long + unsigned long long int longlongintval = __double_as_longlong (val); + unsigned long long int result = atomicCAS(longlongintaddress, longlongintcompare, longlongintval); + return __longlong_as_double(result); + } + + static __device__ void logaddratio (double & loga, double diff) + { + if (diff < -37.0f) return; // log (2^-53), 52-bit mantissa -> cut of after 53th bit + loga += log (1.0 + exp (diff)); + } + static __device__ void logaddratio (float & loga, float diff) + { + if (diff < -17.0f) return; // log (2^-24), 23-bit mantissa -> cut of after 24th bit + loga += logf (1.0f + expf (diff)); + } + + template static __device__ void swap (FLOAT & left, FLOAT & right) + { // exchange values stored at _Left and _Right + FLOAT tmp = left; + left = right; + right = tmp; + } + + // overloads for exp() for float and double, so that we can use templates + static __device__ float expfd (float x) { return ::expf (x); } // TODO: ain't there an overload for this? + static __device__ double expfd (double x) { return ::exp (x); } + + // Compute the difference of two numbers, which are represented as their logs. + // The return value is a non-log value. exp(loga) - exp(logb) + template + static __device__ FLOAT expdiff (FLOAT loga, FLOAT logb) + { + if (logb < loga) // logb - loga < 0 => exp(logb-loga) < 1 + return expfd (loga) * (1 - expfd (logb - loga)); + else + return -expfd (logb) * (1 - expfd (loga - logb)); + } + + // loga <- log (exp (loga) + exp (logb)) = log (exp (loga) * (1.0 + exp (logb - loga)) = loga + log (1.0 + exp (logb - loga)) + template static __device__ void logadd (FLOAT & loga, FLOAT logb) + { + if (logb > loga) // we add smaller to bigger + swap (loga, logb); // loga is bigger + if (loga <= LOGZERO) // both are 0 + return; + logaddratio (loga, logb - loga); + } + + // does the same as above but if the bigger one is too small, we assign a small value to it + template static __device__ void logaddseen (FLOAT & loga, FLOAT logb) + { + if (logb > loga) // we add smaller to bigger + swap (loga, logb); // loga is bigger + if (loga <= LOGZERO) // both are 0 + { + loga = logf(CUDART_MIN_DENORM_F); // [v-hansu] we hope to separate LOGZERO (unseen states) and logf(CUDART_MIN_DENORM_F) (seen states with small prob) + return; + } + logaddratio (loga, logb - loga); + } + +#if 1 + static inline __device__ float bitsasfloat ( int b) { return __int_as_float (b); } + static inline __device__ double bitsasfloat (unsigned long long int b) { return __longlong_as_double (b); } + static inline __device__ int floatasbits (float f) { return __float_as_int (f); } + static inline __device__ unsigned long long int floatasbits (double f) { return __double_as_longlong (f); } + + template // adapted from [http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#ixzz32EuzZjxV] + static __device__ FLOAT atomicLogAdd (FLOAT * address, FLOAT val) // direct adaptation from NVidia source code + { + typedef decltype (floatasbits (val)) bitstype; + bitstype * address_as_ull = (bitstype *) address; + bitstype old = *address_as_ull, assumed; + do { + assumed = old; + FLOAT sum = bitsasfloat (assumed); + logaddseen (sum, val); + old = atomicCAS (address_as_ull, assumed, floatasbits (sum)); + } while (assumed != old); + // note: critically, ^^ this comparison must copare the bits ('int') instead of the converted float values, since this will fail for NaNs (NaN != NaN is true always) + return bitsasfloat(old); + } +#else // this code does not work because (assumed != old) will not compare correctly in case of NaNs + //same pattern as atomicAdd(), but performing the log-add operation instead + template static __device__ FLOAT atomicLogAdd (FLOAT * address, FLOAT val) + { + FLOAT old = *address; + FLOAT assumed; + FLOAT logaddresult; + do { + assumed = old; // old is the assumed value at address + logaddresult = assumed; // for next step to compute logaddresult, assumed shall be the same as before + logaddseen (logaddresult, val); + old = atomicCASfloatdouble (address, assumed, logaddresult); + } while (assumed != old); // if old == assumed, the *address is not changed in this loop, so this is safe + return old; + } +#endif + + // [v-hansu] shuffling accessing order for a item in cubic(Ni, Nj, Nk) with index i, j, k according to shufflemode + static inline __device__ size_t shuffle (size_t i, size_t Ni, size_t j, size_t Nj, size_t k, size_t Nk, size_t shufflemode) + { + if (shufflemode == 0) + return i + j * Ni + k * Nj * Ni; + else if (shufflemode == 1) // inverse + return k + j * Nk + i * Nj * Nk; + else if (shufflemode == 2) // flip i and j + return j + i * Nj + k * Ni * Nj; + else if (shufflemode == 3) // flip j and k + return i + k * Ni + j * Nk * Ni; + else if (shufflemode == 4) + return j + k * Nj + i * Nk * Nj; + else + *((int*)-1)=0; // shall not get here, WRONG + return 0; + } + + template + static __device__ void setvaluej (size_t j, doublevector & thisvector, double value) + { + thisvector[j] = value; + } + + //zhaorui + static inline __device__ float getlogtransp (lr3transP transP, int from, int to) + { + /*if (from < -1 || from >= transP.MAXSTATES || to > transP.MAXSTATES) + { + //printf("from: %d to: %d\n", from, to); + return LOGZERO; + }*/ + return transP.loga[from+1][to]; + } + template + static inline __device__ void edgealignmentj (size_t j, const lrhmmdefvector & hmms, const lr3transPvector & transPs, const size_t spalignunitid, + const size_t silalignunitid, const matrix & logLLs, const nodeinfovector & nodes, + const edgeinfowithscoresvector & edges, const aligninfovector & aligns, + const uintvector & alignoffsets, ushortvector & backptrstorage, const sizetvector & backptroffsets, + ushortvector & alignresult, floatvector & edgeacscores) + { // TODO: alignresult will change to (start,end) + // mostly finished + // some preparation + size_t as = edges[j].firstalign; // align start + size_t ae = (j+1) < edges.size() ? (size_t) edges[j+1].firstalign : aligns.size(); + if (as == ae) // the last empty alignment + return; + size_t ts = nodes[edges[j].S].t; + + float fwscore = 0.0f; // score passed across phone boundaries + size_t alignindex = alignoffsets[j]; // index to set (result) +#ifndef PARALLEL_SIL + const bool isSil = (aligns[as].unit == silalignunitid || aligns[ae-1].unit == silalignunitid); + if (isSil) return; // we do not support silence edge now, which is computed by cpu, may change when we support it +#endif + // Viterbi alignment + for (size_t k = as; k < ae; k++) + { + const aligninfo align = aligns[k]; + const size_t numframes = align.frames; + const bool isSp = (align.unit == spalignunitid); + const bool isSil = (align.unit == silalignunitid); + + const lrhmmdef hmm = hmms[align.unit]; + const lr3transP transP = transPs[hmm.transPindex]; + + + // pre-fetch senone ids into registers + size_t senoneid0 = hmm.senoneids[0]; + size_t senoneid1 = 0; + size_t senoneid2 = 0; + if (!isSp) // fetch only if needed--may save some memory cycles + { + senoneid1 = hmm.senoneids[1]; + senoneid2 = hmm.senoneids[2]; + } + + const size_t te = ts + numframes; // end time of current unit + + size_t state1step0to1 = te; // inflection point from state 0 to 1, record in state 1 + //size_t state1stepm1to1 = te; + size_t state2step0to1 = te; // inflection point from state 0 to 1, record in state 2 + //size_t state2stepm1to1 = te; // inflection point from state 0 to 1, record in state 2 + size_t state2step1to2 = te; // inflection point from state 1 to 2, record in state 2 + size_t state2step0to2 = te; + + //now we only support transition from -1 to 0 or 2 for sil + float pathscore0 = fwscore; // log pp in state 0 + float pathscore1 = fwscore; // log pp in state 1 + float pathscore2 = fwscore; // log pp in state 2 + + + + // first frame + if (ts != te) // for t = ts, initialization + { + /* if (isSil) //for sil, -1 to 2 and -1 to 0 is permitted + { + pathscore0 += getlogtransp(transP,-1,0) + logLLs(senoneid0,ts); + pathscore2 += getlogtransp(transP,-1,2) + logLLs(senoneid2,ts); + } + else //for others, only -1 to 0 is permitted + { + pathscore0 += getlogtransp(transP, -1, 0) + logLLs(senoneid0, ts); + pathscore1 += getlogtransp(transP, -1, 1) + logLLs(senoneid1, ts); + + }*/ + pathscore2 += getlogtransp(transP, -1, 2) + logLLs(senoneid2, ts); + pathscore1 += getlogtransp(transP, -1, 1) + logLLs(senoneid1, ts); + //state1stepm1to1 = ts; + pathscore0 += getlogtransp(transP, -1, 0) + logLLs(senoneid0, ts); + + + } + + + float pathscore2last = pathscore2; // allocate last for state 2 because the order of computation below is 2->1->0, last state 2 is needed because from 2 to 0 or 1 is permitedted for sil. + float pathscore1last = pathscore1; // allocate last for state 1 because the order of computation below is 2->1->0, last state 1 is needed because from 1 to 0 is permitedted for sil. + + size_t backptroffset = backptroffsets[j]; // we make use of backptrstorage in backptroffsets[j] for viterbi of ergodic model (silence) + + bpmatrixref backptrmatrix (&backptrstorage[backptroffset], hmm.MAXSTATES, numframes); + //subsequent frames + for (size_t t = ts + 1; t < te; t++) + { + if (!isSp) + { + // state [2] + pathscore2 += getlogtransp(transP,2,2); // log pp from state 2 to 2 + if (isSil) + backptrmatrix (2, t-ts-1) = 2; + const float pathscore12 = pathscore1 + getlogtransp(transP,1,2); // log pp from state 1 to 2 + if (pathscore12 >= pathscore2) // if state 1->2 + { + pathscore2 = pathscore12; + state2step0to1 = state1step0to1; // record the inflection point + //state2stepm1to1 = state1stepm1to1; + state2step1to2 = t; // record the inflection point + state2step0to2 = te; + if (isSil) + backptrmatrix (2, t-ts-1) = 1; + } + //if (isSil) // only silence have path from 0 to 2 + { + const float pathscore02 = pathscore0 + getlogtransp(transP,0,2); // log pp from state 0 to 2 + if (pathscore02 >= pathscore2) // if state 0->2 + { + pathscore2 = pathscore02; + if (isSil) + backptrmatrix (2, t-ts-1) = 0; + state2step0to2 = t; + state2step1to2 = te; + } + } + + // state [1] + pathscore1 += getlogtransp(transP,1,1); // log pp from state 1 to 1 + if (isSil) + backptrmatrix (1, t-ts-1) = 1; + const float pathscore01 = pathscore0 + getlogtransp(transP,0,1); // log pp from state 0 to 1 + if (pathscore01 >= pathscore1) // if state 0 -> 1 + { + pathscore1 = pathscore01; + state1step0to1 = t; // record the inflection point + //state1stepm1to1 = te; + if (isSil) + backptrmatrix (1, t-ts-1) = 0; + } + + if (isSil) // only silence have path from 2 to 1 + { + const float pathscore21 = pathscore2last + getlogtransp(transP,2,1); + if (pathscore21 >= pathscore1) // if state 2 -> 1 + { + pathscore1 = pathscore21; + backptrmatrix (1, t-ts-1) = 2; + } + } + } + // state [0] + pathscore0 += getlogtransp(transP,0,0); + if(isSil) // only silence have path from 2 or 1 to 0 + { + backptrmatrix (0, t-ts-1) = 0; + const float pathscore20 = pathscore2last + getlogtransp(transP,2,0); // log pp from state 2 to 0 + if (pathscore20 >= pathscore0) + { + pathscore0 = pathscore20; + backptrmatrix (0, t-ts-1) = 2; + } + const float pathscore10 = pathscore1last + getlogtransp(transP,1,0); // log pp from state 1 to 0 + if (pathscore10 >= pathscore0) + { + pathscore0 = pathscore10; + backptrmatrix (0, t-ts-1) = 1; + } + } + + // add log LLs + pathscore0 += logLLs(senoneid0,t); + if (!isSp) // only fetch if needed, saves mem access + { + pathscore1 += logLLs(senoneid1,t); + pathscore2 += logLLs(senoneid2,t); + } + pathscore1last = pathscore1; // update pathscore1last + pathscore2last = pathscore2; // update pathscore2last + } + + // final 'next' transition that exits from last frame + + if (ts == te) // if sp tee model, will not in next loop + { + pathscore2 = pathscore0 + getlogtransp(transP,-1,1); + } + else if (isSp) + { + pathscore2 = pathscore0 + getlogtransp(transP,0,1) ; // sp model, from 0 to 1 + //printf(" sp, %f\n", pathscore2); + } + + else if(isSil) //for sil, the exit state can be 0 or 2. + { + const float pathscore03 = pathscore0 + getlogtransp(transP,0,3); + pathscore2 += getlogtransp(transP,2,3); + if(pathscore03 > pathscore2) + { + pathscore2 = pathscore03; + } + } + else + pathscore2 += getlogtransp(transP,2,3); + + fwscore = pathscore2; // propagate across phone boundaries + + // emit alignment + + if (!isSil) + { + if (state2step0to2 < te) //from 0 to 2 + { + state2step0to2 += alignindex - ts; + for (size_t t = alignindex; t < alignindex + numframes; t++) // set the final alignment + { + size_t senoneid; + if (t < state2step0to2) // in state 0 + senoneid = senoneid0; + else // in state 2 + senoneid = senoneid2; + alignresult[t] = (unsigned short)senoneid; + } + } + else //from 1 to 2 + { + state2step0to1 += alignindex - ts; // convert to align measure + state2step1to2 += alignindex - ts; + for (size_t t = alignindex; t < alignindex + numframes; t++) // set the final alignment + { + size_t senoneid; + if (state2step0to1 = pathscore2) //exit state is 0 + { + alignresult[alignindex + numframes - 1] = (unsigned short) senoneid0; + lastpointer = 0; + } + else //exit state is 2 + alignresult[alignindex + numframes - 1] = (unsigned short) senoneid2; + + for (size_t t = alignindex + numframes - 2; (t + 1) > alignindex; t--) // set the final alignment + { + lastpointer = backptrmatrix (lastpointer, t-alignindex); + size_t senoneid = (size_t) (-1); + if (lastpointer == 0) + senoneid = senoneid0; + else if (lastpointer == 1) + senoneid = senoneid1; + else if (lastpointer == 2) + senoneid = senoneid2; + alignresult[t] = (unsigned short) senoneid; + } + } + ts = te; + alignindex += numframes; + } + edgeacscores[j] = fwscore; + } + + // compute the final error signal from gammas and state-consolidated Eframescorrect + // in-place operation is supported (i.e. output = one of the inputs) + template + static inline __device__ void computesMBRerrorsignals (const size_t s, const matrix & loggammas, const matrix & logEframescorrect, + const double logEframescorrecttotal, const float kappa, matrix & errorsignal) + { + const float Eframescorrecttotal = expf ((float)logEframescorrecttotal); + const size_t T = errorsignal.cols(); + for (size_t t = 0; t < T; t++) + errorsignal(s,t) = expf (loggammas(s,t)) * (expf (logEframescorrect(s,t)) - Eframescorrecttotal) * kappa; + } + + // test if a state is silence [v-hansu] + // WARNING, this function only support models with 9304 states + // TODO: change this later on + static inline __device__ bool issilencestate (size_t stateid, size_t numsenones) + { + if (numsenones == 9304 && (stateid == 7670 || stateid == 7671 || stateid == 7672)) + return true; + else + return false; + } + + // compare two states and check if they are of the same class [v-hansu] + template + static inline __device__ bool isofsameclass (size_t statea, size_t stateb, ushortvector senone2classmap) + { + if (senone2classmap.size() == 0) // no map provided, we just do normal comparison + return (statea == stateb); + else + return senone2classmap[statea] == senone2classmap[stateb]; + } + + // Phase 1 of forwardbackward algorithm + // returnEframescorrect means sMBR mode + template + static inline __device__ void forwardlatticej (const size_t j, const floatvector & edgeacscores, + const size_t /*spalignunitid --unused*/, const size_t silalignunitid, + const edgeinforvector & edges, const nodeinfovector & nodes, const aligninfovector & aligns, + const ushortvector & alignments, const uintvector & alignmentoffsets, + doublevector & logalphas, float lmf, float wp, float amf, const float boostingfactor, + const ushortvector & uids, const ushortvector senone2classmap, const bool returnEframescorrect, + doublevector & logframescorrectedge, doublevector & logaccalphas) + { + // edge info + const edgeinfowithscores & e = edges[j]; + double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned + //zhaorui to deal with the abnormal score for sent start. + if(e.l < -200.0f) + edgescore = (0.0 * lmf + wp + edgeacscores[j]) / amf; + const bool boostmmi = (boostingfactor != 0.0f); + // compute the frames-correct count for this edge + double logframescorrectedgej = LOGZERO; + const size_t numsenones = 9304; // WARNING: this is a hack, please fix this once smbr or bmmi is working! [v-hansu] + bool skipsilence = true; // currently we skip silence for BMMI and sMBR [v-hansu] + if (returnEframescorrect || boostmmi) + { + size_t ts = nodes[e.S].t; + size_t te = nodes[e.E].t; + size_t framescorrect = 0; // count raw number of correct frames + size_t startindex = alignmentoffsets[j]; + + size_t as = e.firstalign; // align start + size_t ae = (j+1) < edges.size() ? (size_t) edges[j+1].firstalign : aligns.size(); + const bool isSil = (ae == as + 1 && aligns[as].unit == silalignunitid); // the order of this judgement shall be changed to save memory access + if (!(isSil && skipsilence)) // we don't count silence when 1. is silence; 2, skip them + { + for (size_t t = ts; t < te; t++) + { + if (!(skipsilence && issilencestate (alignments[t-ts+startindex], numsenones))) + framescorrect += isofsameclass (alignments[t-ts+startindex], uids[t], senone2classmap); // we only count correct && non-silence state + } + } + logframescorrectedgej = (framescorrect > 0) ? log ((double) framescorrect) : LOGZERO; // remember for backward pass + logframescorrectedge[j] = logframescorrectedgej; + } + if (boostmmi) + edgescore -= boostingfactor * exp (logframescorrectedge[j]); + +#ifdef FORBID_INVALID_SIL_PATHS + const bool forbidinvalidsilpath = (logalphas.size() > nodes.size()); // we constrain sil to sil path if node space has been blown up + const bool isaddedsil = forbidinvalidsilpath && (e.unused == 1); // HACK: 'unused' indicates artificially added sil/sp edge + + // original mode + if (!isaddedsil) +#endif + { + const size_t S = e.S; + const size_t E = e.E; + + const double inscore = logalphas[S]; + const double pathscore = inscore + edgescore; + + atomicLogAdd (&logalphas[E], pathscore); + + if (returnEframescorrect) + { +#ifdef DIRECT_MODE + double loginaccs = logaccalphas[e.S] + edgescore; + double logpathaccs = logalphas[e.S] + edgescore + logframescorrectedgej; + logadd (logpathaccs, loginaccs); + atomicLogAdd (&logaccalphas[e.E], logpathaccs); +#else + double loginaccs = logaccalphas[S] - logalphas[S]; + logadd (loginaccs, logframescorrectedgej); + double logpathacc = loginaccs + logalphas[S] + edgescore; + atomicLogAdd (&logaccalphas[E], logpathacc); +#endif + } + } + +#ifdef FORBID_INVALID_SIL_PATHS + // silence edge or second speech edge + if ((isaddedsil && e.E != nodes.size() -1) || (forbidinvalidsilpath && e.S != 0)) + { + const size_t S = (size_t) (!isaddedsil ? e.S + nodes.size() : e.S); // second speech edge comes from special 'silence state' node + const size_t E = (size_t) (isaddedsil ? e.E + nodes.size() : e.E); // silence edge goes into special 'silence state' node + // remaining lines here are 100% code dup from above, just operating on different (S, E) + const double inscore = logalphas[S]; + const double pathscore = inscore + edgescore; + atomicLogAdd (&logalphas[E], pathscore); + + if (returnEframescorrect) + { + double loginaccs = logaccalphas[S] - logalphas[S]; + logadd (loginaccs, logframescorrectedgej); + double logpathacc = loginaccs + logalphas[S] + edgescore; + atomicLogAdd (&logaccalphas[E], logpathacc); + } + } +#endif + } + + template + static inline __device__ void backwardlatticej (size_t j, const floatvector & edgeacscores, + const size_t /*spalignunitid --unused*/, const size_t /*silalignunitid --unused*/, + const edgeinforvector & edges, const nodeinfovector & nodes, + const aligninfovector & /*aligns -- unused*/, const double totalfwscore, doublevector & logpps, + doublevector & logalphas, doublevector & logbetas, float lmf, float wp, + float amf, const float boostingfactor, const bool returnEframescorrect, + doublevector & logframescorrectedge, doublevector & logaccalphas, + doublevector & logEframescorrect, doublevector & logaccbetas) + { + // output values + double logpp = LOGZERO; + double logEframescorrectj = LOGZERO; + const bool boostmmi = (boostingfactor != 0.0f); + + // edge info + const edgeinfowithscores & e = edges[j]; + double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + //zhaorui to deal with the abnormal score for sent start. + if (e.l < -200.0f) + edgescore = (0.0 * lmf + wp + edgeacscores[j]) / amf; + if (boostmmi) + edgescore -= boostingfactor * exp (logframescorrectedge[j]); + + // get the frames-correct count for this edge that was computed during the forward pass + double logframescorrectedgej = (returnEframescorrect || boostmmi) ? logframescorrectedge[j] : LOGZERO; + +#ifdef FORBID_INVALID_SIL_PATHS + // original mode + const bool forbidinvalidsilpath = (logalphas.size() > nodes.size()); // we prune sil to sil path if alphabetablowup != 1 + const bool isaddedsil = forbidinvalidsilpath && (e.unused == 1); // HACK: 'unused' indicates artificially added sil/sp edge + + if (!isaddedsil) // original mode +#endif + { + const size_t S = e.S; + const size_t E = e.E; + + // backward pass + const double inscore = logbetas[E]; + const double pathscore = inscore + edgescore; + atomicLogAdd (&logbetas[S], pathscore); + + // compute lattice posteriors on the fly since we are at it + logpp = logalphas[S] + edgescore + logbetas[E] - totalfwscore; + + // similar logic for Eframescorrect + if (returnEframescorrect) + { +#ifdef DIRECT_MODE + double loginaccs = logaccbetas[e.E] + edgescore; + double logpathaccs = logbetas[e.E] + edgescore + logframescorrectedgej; + logadd (logpathaccs, loginaccs); + atomicLogAdd (&logaccbetas[e.S], logpathaccs); + + double logecorrect = logaccalphas[e.S] + edgescore + logbetas[e.E]; + logadd (logecorrect, logalphas[e.S] + edgescore + logframescorrectedgej + logbetas[e.E]); + logadd (logecorrect, logalphas[e.S] + edgescore + logaccbetas[e.E]); + logEframescorrectj = logecorrect - totalfwscore; // be careful, this includes the denominator +#else + // backward pass + double loginaccs = logaccbetas[E] - logbetas[E]; + logadd (loginaccs, logframescorrectedgej); + double logpathacc = loginaccs + logbetas[E] + edgescore; + atomicLogAdd (&logaccbetas[S], logpathacc); + + // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) + double logsum = logframescorrectedgej; // sum over this edge, left partial (alpha), right partial (beta) + + double edgelogaccalpha = logaccalphas[S] - logalphas[S]; // incoming partial expected frames correct + logadd (logsum, edgelogaccalpha); + + double edgelogaccbeta = logaccbetas[E] - logbetas[E]; // partial expected frames correct from the end + logadd (logsum, edgelogaccbeta); + + logEframescorrectj = logsum; // that's it +#endif + } + } + +#ifdef FORBID_INVALID_SIL_PATHS + double logpp2 = LOGZERO; + double logEframescorrectj2 = LOGZERO; + + // silence edge or second speech edge + if ((isaddedsil && e.E != nodes.size() -1) || (forbidinvalidsilpath && e.S != 0)) + { + const size_t S = (size_t) (!isaddedsil ? e.S + nodes.size() : e.S); // second speech edge comes from special 'silence state' node + const size_t E = (size_t) (isaddedsil ? e.E + nodes.size() : e.E); // silence edge goes into special 'silence state' node + // remaining lines here are code dup from above, with two changes: logadd2/logEframescorrectj2 instead of logadd/logEframescorrectj + + // backward pass + const double inscore = logbetas[E]; + const double pathscore = inscore + edgescore; + atomicLogAdd (&logbetas[S], pathscore); + + // compute lattice posteriors on the fly since we are at it + logpp2 = logalphas[S] + edgescore + logbetas[E] - totalfwscore; // second edge (logpp2) + + // similar logic for Eframescorrect + if (returnEframescorrect) + { + // backward pass + double loginaccs = logaccbetas[E] - logbetas[E]; + logadd (loginaccs, logframescorrectedgej); + double logpathacc = loginaccs + logbetas[E] + edgescore; + atomicLogAdd (&logaccbetas[S], logpathacc); + + // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) + double logsum = logframescorrectedgej; // sum over this edge, left partial (alpha), right partial (beta) + + double edgelogaccalpha = logaccalphas[S] - logalphas[S]; // incoming partial expected frames correct + logadd (logsum, edgelogaccalpha); + + double edgelogaccbeta = logaccbetas[E] - logbetas[E]; // partial expected frames correct from the end + logadd (logsum, edgelogaccbeta); + + logEframescorrectj2 = logsum; // that's it for this second edge + } + + // sum logpp2 and logEframescorrectj2 + // Eframescorrect must be summed up in a weighted fashion, weighted by PP + double numer = logEframescorrectj + logpp; + logadd (numer, logEframescorrectj2 + logpp2); // weighted sum, weighted by respective (log)pp + logadd (logpp, logpp2); // (log)pp is just the sum of the two posteriors + double denom = logpp; // and that is also the denominator for the weighted sum + logEframescorrectj = numer - denom; // weighted sum + } +#else + nodes; +#endif + + // write back return values + if (logpp > 0.0) // clip to log 1 (may be possible due to small numeric inaccuracies, although it really shouldn't happen) + logpp = 0.0; + logpps[j] = logpp; + if (returnEframescorrect) + logEframescorrect[j] = logEframescorrectj; + } + + template + static inline __device__ void sMBRerrorsignalj (size_t j, const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, + const nodeinfovector & nodes, const doublevector & logpps, const float amf, + const doublevector & logEframescorrect, const double logEframescorrecttotal, + matrix & errorsignal, matrix & errorsignalneg) + { + size_t ts = nodes[edges[j].S].t; + size_t te = nodes[edges[j].E].t; + if (ts != te) + { +#ifdef DIRECT_MODE + float logEframescorrectj = logEframescorrect[j]; + size_t offset = alignoffsets[j]; + for (size_t t = ts; t < te; t++) + { + const size_t s = (size_t) alignstateids[t - ts + offset]; + atomicLogAdd (&errorsignal(s,t), logEframescorrectj); + } +#else + const double diff = expdiff (logEframescorrect[j], logEframescorrecttotal); + // Note: the contribution of the states of an edge to their senones is the same for all states + // so we compute it once and add it to all; this will not be the case without hard alignments. + double absdiff = fabs (diff); + if (absdiff == 0.0f) + return; + const float logedgecorrect = (float) (logpps[j] + log (absdiff)); + size_t offset = alignoffsets[j]; + for (size_t t = ts; t < te; t++) + { + const size_t s = (size_t) alignstateids[t - ts + offset]; + if (diff > 0.0) + atomicLogAdd(&errorsignal(s,t), logedgecorrect); + else + atomicLogAdd(&errorsignalneg(s,t), logedgecorrect); + } + absdiff = amf; // just to reference it because we hope to support linear mode as well +#endif + } + } + + // accumulate a per-edge quantity into the states that the edge is aligned with + // Use this for MMI passing the edge posteriors logpps[] as logq, or for sMBR passing logEframescorrect[]. + // j=edge index, alignment in (alignstateids, alignoffsets) + template + static inline __device__ void stateposteriorsj (size_t j, const ushortvector & alignstateids, const uintvector & alignoffsets, + const edgeinfowithscoresvector & edges, + const nodeinfovector & nodes, const doublevector & logqs /*quantity to accumulate*/, + matrix & logacc /*accumulator to accumulate into*/) + { + size_t ts = nodes[edges[j].S].t; + size_t te = nodes[edges[j].E].t; + if (ts != te) + { + const float logq = (float) logqs[j]; // per-edge quantity to accumulate, e.g. edge posteriors -> state posteriors + size_t offset = alignoffsets[j]; + for (size_t t = ts; t < te; t++) + { + const size_t s = (size_t) alignstateids[t - ts + offset]; // get state for this (j,t) + atomicLogAdd (&logacc(s,t), logq); + } + } + } +}; + +};}; + + +#pragma pop_macro ("atomicCAS") +#pragma pop_macro ("atomicAdd") +#pragma pop_macro ("__device__") + diff --git a/Source/Readers/BinaryReader/BinaryReader.vcxproj b/Source/Readers/BinaryReader/BinaryReader.vcxproj index ac0f40baceec..06c234763f26 100644 --- a/Source/Readers/BinaryReader/BinaryReader.vcxproj +++ b/Source/Readers/BinaryReader/BinaryReader.vcxproj @@ -1,152 +1,152 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {1D5787D4-52E4-45DB-951B-82F220EE0C6A} - - - - - - - - - Win32Proj - UCIReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - - - NotUsing - NotUsing - - - - false - - - false - - - - - Create - Create - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {1D5787D4-52E4-45DB-951B-82F220EE0C6A} + + + + + + + + + Win32Proj + UCIReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + + + NotUsing + NotUsing + + + + false + + + false + + + + + Create + Create + + + + + \ No newline at end of file diff --git a/Source/Readers/BinaryReader/BinaryReader.vcxproj.filters b/Source/Readers/BinaryReader/BinaryReader.vcxproj.filters index 6802fb2a56d9..d2c85d02debd 100644 --- a/Source/Readers/BinaryReader/BinaryReader.vcxproj.filters +++ b/Source/Readers/BinaryReader/BinaryReader.vcxproj.filters @@ -1,55 +1,55 @@ - - - - - - - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - {7a294e4e-7a00-4bd3-8e0a-86f08db10a98} - - - {ecc1d590-3964-477c-9487-9a78e9a497ba} - - + + + + + + + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + {7a294e4e-7a00-4bd3-8e0a-86f08db10a98} + + + {ecc1d590-3964-477c-9487-9a78e9a497ba} + + \ No newline at end of file diff --git a/Source/Readers/DSSMReader/DSSMReader.cpp b/Source/Readers/DSSMReader/DSSMReader.cpp index 9698abbd2766..0a27c134c090 100644 --- a/Source/Readers/DSSMReader/DSSMReader.cpp +++ b/Source/Readers/DSSMReader/DSSMReader.cpp @@ -1,685 +1,685 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// DSSMReader.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#define DATAREADER_EXPORTS // creating the exports here -#include "DataReader.h" -#include "DSSMReader.h" -#ifdef LEAKDETECT -#include // leak detection -#endif -#include "fileutil.h" // for fexists() - -namespace Microsoft { namespace MSR { namespace CNTK { - -DWORD HIDWORD(size_t size) {return size>>32;} -DWORD LODWORD(size_t size) {return size&0xFFFFFFFF;} - -std::string ws2s(const std::wstring& wstr) -{ - int size_needed = WideCharToMultiByte(CP_ACP, 0, wstr.c_str(), int(wstr.length() + 1), 0, 0, 0, 0); - std::string strTo(size_needed, 0); - WideCharToMultiByte(CP_ACP, 0, wstr.c_str(), int(wstr.length() + 1), &strTo[0], size_needed, 0, 0); - return strTo; -} - -template -size_t DSSMReader::RandomizeSweep(size_t mbStartSample) -{ - //size_t randomRangePerEpoch = (m_epochSize+m_randomizeRange-1)/m_randomizeRange; - //return m_epoch*randomRangePerEpoch + epochSample/m_randomizeRange; - return mbStartSample/m_randomizeRange; -} - -// ReadLine - Read a line -// readSample - sample to read in global sample space -// returns - true if we successfully read a record, otherwise false -template -bool DSSMReader::ReadRecord(size_t /*readSample*/) -{ - return false; // not used -} - -// RecordsToRead - Determine number of records to read to populate record buffers -// mbStartSample - the starting sample from which to read -// tail - we are checking for possible remainer records to read (default false) -// returns - true if we have more to read, false if we hit the end of the dataset -template -size_t DSSMReader::RecordsToRead(size_t mbStartSample, bool tail) -{ - assert(mbStartSample >= m_epochStartSample); - // determine how far ahead we need to read - bool randomize = Randomize(); - // need to read to the end of the next minibatch - size_t epochSample = mbStartSample; - epochSample %= m_epochSize; - - // determine number left to read for this epoch - size_t numberToEpoch = m_epochSize - epochSample; - // we will take either a minibatch or the number left in the epoch - size_t numberToRead = min(numberToEpoch, m_mbSize); - if (numberToRead == 0 && !tail) - numberToRead = m_mbSize; - - if (randomize) - { - size_t randomizeSweep = RandomizeSweep(mbStartSample); - // if first read or read takes us to another randomization range - // we need to read at least randomization range records - if (randomizeSweep != m_randomordering.CurrentSeed()) // the range has changed since last time - { - numberToRead = RoundUp(epochSample, m_randomizeRange) - epochSample; - if (numberToRead == 0 && !tail) - numberToRead = m_randomizeRange; - } - } - return numberToRead; -} - -template -void DSSMReader::WriteLabelFile() -{ - // write out the label file if they don't have one - if (!m_labelFileToWrite.empty()) - { - if (m_mapIdToLabel.size() > 0) - { - File labelFile(m_labelFileToWrite, fileOptionsWrite | fileOptionsText); - for (int i=0; i < m_mapIdToLabel.size(); ++i) - { - labelFile << m_mapIdToLabel[i] << '\n'; - } - fprintf(stderr, "label file %ls written to disk\n", m_labelFileToWrite.c_str()); - m_labelFileToWrite.clear(); - } - else if (!m_cachingWriter) - { - fprintf(stderr, "WARNING: file %ls NOT written to disk yet, will be written the first time the end of the entire dataset is found.\n", m_labelFileToWrite.c_str()); - } - } -} - -// Destroy - cleanup and remove this class -// NOTE: this destroys the object, and it can't be used past this point -template -void DSSMReader::Destroy() -{ - delete this; -} - -// Init - Reader Initialize for multiple data sets -// config - [in] configuration parameters for the datareader -// Sample format below: -//# Parameter values for the reader -//reader=[ -// # reader to use -// readerType=DSSMReader -// miniBatchMode=Partial -// randomize=None -// features=[ -// dim=784 -// start=1 -// file=c:\speech\mnist\mnist_test.txt -// ] -// labels=[ -// dim=1 -// start=0 -// file=c:\speech\mnist\mnist_test.txt -// labelMappingFile=c:\speech\mnist\labels.txt -// labelDim=10 -// labelType=Category -// ] -//] -template -template -void DSSMReader::InitFromConfig(const ConfigRecordType & readerConfig) -{ - std::vector features; - std::vector labels; - - // Determine the names of the features and lables sections in the config file. - // features - [in,out] a vector of feature name strings - // labels - [in,out] a vector of label name strings - // For DSSM dataset, we only need features. No label is necessary. The following "labels" just serves as a place holder - GetFileConfigNames(readerConfig, features, labels); - - // For DSSM dataset, it must have exactly two features - // In the config file, we must specify query features first, then document features. The sequence is different here. Pay attention - if (features.size() == 2 && labels.size() == 1) - { - m_featuresNameQuery = features[1]; - m_featuresNameDoc = features[0]; - m_labelsName = labels[0]; - } - else - { - RuntimeError("DSSM requires exactly two features and one label. Their names should match those in NDL definition"); - return; - } - - - m_mbStartSample = m_epoch = m_totalSamples = m_epochStartSample = 0; - m_labelIdMax = m_labelDim = 0; - m_partialMinibatch = m_endReached = false; - m_labelType = labelCategory; - m_readNextSample = 0; - m_traceLevel = readerConfig(L"traceLevel", 0); - - if (readerConfig.Exists(L"randomize")) - { - // BUGBUG: reading out string and number... ugh - wstring randomizeString = readerConfig(L"randomize"); - if (randomizeString == L"None") - { - m_randomizeRange = randomizeNone; - } - else if (randomizeString == L"Auto") - { - m_randomizeRange = randomizeAuto; - } - else - { - m_randomizeRange = readerConfig(L"randomize"); - } - } - else - { - m_randomizeRange = randomizeNone; - } - - std::string minibatchMode(readerConfig(L"minibatchMode", "Partial")); - m_partialMinibatch = !_stricmp(minibatchMode.c_str(), "Partial"); - - - // Get the config parameters for query feature and doc feature - ConfigParameters configFeaturesQuery = readerConfig(m_featuresNameQuery, ""); - ConfigParameters configFeaturesDoc = readerConfig(m_featuresNameDoc, ""); - - if (configFeaturesQuery.size() == 0) - RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); - if (configFeaturesDoc.size() == 0) - RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); - - // Read in feature size information - // This information will be used to handle OOVs - m_featuresDimQuery = configFeaturesQuery(L"dim"); - m_featuresDimDoc = configFeaturesDoc(L"dim"); - - std::wstring fileQ = configFeaturesQuery("file"); - std::wstring fileD = configFeaturesDoc("file"); - - dssm_queryInput.Init(fileQ, m_featuresDimQuery); - dssm_docInput.Init(fileD, m_featuresDimDoc); - - m_totalSamples = dssm_queryInput.numRows; - if (read_order == NULL) - { - read_order = new int[m_totalSamples]; - for (int c = 0; c < m_totalSamples; c++) - { - read_order[c] = c; - } - } - m_mbSize = 0; - -} -// destructor - virtual so it gets called properly -template -DSSMReader::~DSSMReader() -{ - ReleaseMemory(); -} - -// ReleaseMemory - release the memory footprint of DSSMReader -// used when the caching reader is taking over -template -void DSSMReader::ReleaseMemory() -{ - if (m_qfeaturesBuffer!=NULL) - delete[] m_qfeaturesBuffer; - m_qfeaturesBuffer=NULL; - if (m_dfeaturesBuffer!=NULL) - delete[] m_dfeaturesBuffer; - m_dfeaturesBuffer=NULL; - if (m_labelsBuffer!=NULL) - delete[] m_labelsBuffer; - m_labelsBuffer=NULL; - if (m_labelsIdBuffer!=NULL) - delete[] m_labelsIdBuffer; - m_labelsIdBuffer=NULL; - m_featureData.clear(); - m_labelIdData.clear(); - m_labelData.clear(); -} - -//SetupEpoch - Setup the proper position in the file, and other variable settings to start a particular epoch -template -void DSSMReader::SetupEpoch() -{ -} - -// utility function to round an integer up to a multiple of size -size_t RoundUp(size_t value, size_t size) -{ - return ((value + size -1)/size)*size; -} - -//StartMinibatchLoop - Startup a minibatch loop -// mbSize - [in] size of the minibatch (number of Samples, etc.) -// epoch - [in] epoch number for this loop, if > 0 the requestedEpochSamples must be specified (unless epoch zero was completed this run) -// requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset -// this value must be a multiple of mbSize, if it is not, it will be rounded up to one. -template -void DSSMReader::StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples) -{ - size_t mbStartSample = m_epoch * m_epochSize; - if (m_totalSamples == 0) - { - m_totalSamples = dssm_queryInput.numRows; - } - - size_t fileRecord = m_totalSamples ? mbStartSample % m_totalSamples : 0; - fprintf(stderr, "starting epoch %lld at record count %lld, and file position %lld\n", m_epoch, mbStartSample, fileRecord); - - - - // reset the next read sample - m_readNextSample = 0; - m_epochStartSample = m_mbStartSample = mbStartSample; - m_mbSize = mbSize; - m_epochSize = requestedEpochSamples; - dssm_queryInput.SetupEpoch(mbSize); - dssm_docInput.SetupEpoch(mbSize); - if (m_epochSize > (size_t)dssm_queryInput.numRows) - { - m_epochSize = (size_t)dssm_queryInput.numRows; - } - if (Randomize()) - { - random_shuffle(&read_order[0], &read_order[m_epochSize]); - } - m_epoch = epoch; - m_mbStartSample = epoch*m_epochSize; - -} - -// function to store the LabelType in an ElemType -// required for string labels, which can't be stored in ElemType arrays -template -void DSSMReader::StoreLabel(ElemType& labelStore, const LabelType& labelValue) -{ - labelStore = (ElemType)m_mapLabelToId[labelValue]; -} - -// GetMinibatch - Get the next minibatch (features and labels) -// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, -// [out] each matrix resized if necessary containing data. -// returns - true if there are more minibatches, false if no more minibatchs remain -template -bool DSSMReader::GetMinibatch(std::map*>& matrices) -{ - if (m_readNextSample >= m_totalSamples) - { - return false; - } - // In my unit test example, the input matrices contain 5: N, S, fD, fQ and labels - // Both N and S serve as a pre-set constant values, no need to change them - // In this node, we only need to fill in these matrices: fD, fQ, labels - Matrix& featuresQ = *matrices[m_featuresNameQuery]; - Matrix& featuresD = *matrices[m_featuresNameDoc]; - Matrix& labels = *matrices[m_labelsName]; // will change this part later. - - size_t actualMBSize = ( m_readNextSample + m_mbSize > m_totalSamples ) ? m_totalSamples - m_readNextSample : m_mbSize; - - - featuresQ.SwitchToMatrixType(MatrixType::SPARSE, MatrixFormat::matrixFormatSparseCSC, false); - featuresD.SwitchToMatrixType(MatrixType::SPARSE, MatrixFormat::matrixFormatSparseCSC, false); - - /* - featuresQ.Resize(dssm_queryInput.numRows, actualMBSize); - featuresD.Resize(dssm_docInput.numRows, actualMBSize); - */ - - //fprintf(stderr, "featuresQ\n"); - dssm_queryInput.Next_Batch(featuresQ, m_readNextSample, actualMBSize, read_order); - //fprintf(stderr, "\n\n\nfeaturesD\n"); - dssm_docInput.Next_Batch(featuresD, m_readNextSample, actualMBSize, read_order); - //fprintf(stderr, "\n\n\n\n\n"); - m_readNextSample += actualMBSize; - /* - featuresQ.Print("featuresQ"); - fprintf(stderr, "\n"); - featuresD.Print("featuresD"); - fprintf(stderr, "\n"); - */ - - /* - GPUSPARSE_INDEX_TYPE* h_CSCCol; - GPUSPARSE_INDEX_TYPE* h_Row; - ElemType* h_val; - size_t nz; - size_t nrs; - size_t ncols; - featuresQ.GetMatrixFromCSCFormat(&h_CSCCol, &h_Row, &h_val, &nz, &nrs, &ncols); - - for (int j = 0, k=0; j < nz; j++) - { - if (h_CSCCol[k] >= j) - { - fprintf(stderr, "\n"); - k++; - } - fprintf(stderr, "%d:%.f ", h_Row[j], h_val[j]); - - } - */ - - /* - featuresQ.TransferFromDeviceToDevice(featuresQ.GetDeviceId(), -1); - featuresQ.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); - featuresQ.Print("featuresQ"); - - featuresD.TransferFromDeviceToDevice(featuresD.GetDeviceId(), -1); - featuresD.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); - featuresD.Print("featuresD"); - - exit(1); - */ - - if (actualMBSize > m_mbSize || m_labelsBuffer == NULL) { - size_t rows = labels.GetNumRows(); - m_labelsBuffer = new ElemType[rows * actualMBSize]; - memset(m_labelsBuffer, 0, sizeof(ElemType)* rows * actualMBSize); - for (int i = 0; i < actualMBSize; i++) - { - m_labelsBuffer[i * rows] = 1; - } - } - if (actualMBSize != labels.GetNumCols()) - { - size_t rows = labels.GetNumRows(); - labels.Resize(rows, actualMBSize); - labels.SetValue(0.0); - labels.SetValue(rows, actualMBSize, labels.GetDeviceId(), m_labelsBuffer, 0); - } - /* - featuresQ.Print("featuresQ"); - featuresD.Print("featuresD"); - labels.print("labels"); - */ - - return true; -} - - -// GetLabelMapping - Gets the label mapping from integer index to label type -// returns - a map from numeric datatype to native label type -template -const std::map::LabelIdType, typename IDataReader::LabelType>& DSSMReader::GetLabelMapping(const std::wstring& sectionName) -{ - if (m_cachingReader) - { - return m_cachingReader->GetLabelMapping(sectionName); - } - return m_mapIdToLabel; -} - -// SetLabelMapping - Sets the label mapping from integer index to label -// labelMapping - mapping table from label values to IDs (must be 0-n) -// note: for tasks with labels, the mapping table must be the same between a training run and a testing run -template -void DSSMReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, typename LabelType>& labelMapping) -{ - if (m_cachingReader) - { - RuntimeError("Cannot set mapping table when the caching reader is being used"); - } - m_mapIdToLabel = labelMapping; - m_mapLabelToId.clear(); - for (std::pair var : labelMapping) - { - m_mapLabelToId[var.second] = var.first; - } -} - -template -bool DSSMReader::DataEnd(EndDataType endDataType) -{ - bool ret = false; - switch (endDataType) - { - case endDataNull: - assert(false); - break; - case endDataEpoch: - //ret = (m_mbStartSample / m_epochSize < m_epoch); - ret = (m_readNextSample >= m_totalSamples); - break; - case endDataSet: - ret = (m_readNextSample >= m_totalSamples); - break; - case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true - ret = true; - break; - } - return ret; -} - -template -DSSM_BinaryInput::DSSM_BinaryInput(){ -} -template -DSSM_BinaryInput::~DSSM_BinaryInput(){ - Dispose(); -} -template -void DSSM_BinaryInput::Init(wstring fileName, size_t dim){ - - m_dim = dim; - mbSize = 0; - /* - m_hndl = CreateFileA(fileName.c_str(), GENERIC_READ, - FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); - */ - m_hndl = CreateFile(fileName.c_str(), GENERIC_READ, - FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); - if (m_hndl == INVALID_HANDLE_VALUE) - { - char message[256]; - sprintf_s(message, "Unable to Open/Create file %ls, error %x", fileName.c_str(), GetLastError()); - RuntimeError(message); - } - - m_filemap = CreateFileMapping(m_hndl, NULL, PAGE_READONLY, 0, 0, NULL); - - SYSTEM_INFO sysinfo; - GetSystemInfo(&sysinfo); - DWORD sysGran = sysinfo.dwAllocationGranularity; - - header_buffer = MapViewOfFile(m_filemap, // handle to map object - FILE_MAP_READ, // get correct permissions - HIDWORD(0), - LODWORD(0), - sizeof(int64_t)* 2 + sizeof(int32_t)); - - //cout << "After mapviewoffile" << endl; - - memcpy(&numRows, header_buffer, sizeof(int64_t)); - memcpy(&numCols, (char*)header_buffer + sizeof(int64_t), sizeof(int32_t)); - memcpy(&totalNNz, (char*)header_buffer + sizeof(int64_t)+sizeof(int32_t), sizeof(int64_t)); - - //cout << "After gotvalues" << endl; - int64_t base_offset = sizeof(int64_t)* 2 + sizeof(int32_t); - - int64_t offsets_padding = base_offset % sysGran; - base_offset -= offsets_padding; - - int64_t header_size = numRows*sizeof(int64_t)+offsets_padding; - - void* offsets_orig = MapViewOfFile(m_filemap, // handle to map object - FILE_MAP_READ, // get correct permissions - HIDWORD(base_offset), - LODWORD(base_offset), - header_size); - - offsets_buffer = (char*)offsets_orig + offsets_padding; - - if (offsets != NULL){ - free(offsets); - } - offsets = (int64_t*)malloc(sizeof(int64_t)*numRows); - memcpy(offsets, offsets_buffer, numRows*sizeof(int64_t)); - - - int64_t header_offset = base_offset + offsets_padding + numRows * sizeof(int64_t); - - int64_t data_padding = header_offset % sysGran; - header_offset -= data_padding; - - void* data_orig = MapViewOfFile(m_filemap, // handle to map object - FILE_MAP_READ, // get correct permissions - HIDWORD(header_offset), - LODWORD(header_offset), - 0); - data_buffer = (char*)data_orig + data_padding; - -} -template -bool DSSM_BinaryInput::SetupEpoch( size_t minibatchSize){ - if (values == NULL || mbSize < minibatchSize) - { - if (values != NULL) - { - free(values); - free(colIndices); - free(rowIndices); - } - - values = (ElemType*)malloc(sizeof(ElemType)*MAX_BUFFER*minibatchSize); - colIndices = (int32_t*)malloc(sizeof(int32_t)*(minibatchSize+1)); - rowIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*minibatchSize); - //fprintf(stderr, "values size: %d",sizeof(ElemType)*MAX_BUFFER*minibatchSize); - //fprintf(stderr, "colindi size: %d",sizeof(int32_t)*MAX_BUFFER*(1+minibatchSize)); - //fprintf(stderr, "rowindi size: %d",sizeof(int32_t)*MAX_BUFFER*minibatchSize); - } - if (minibatchSize > mbSize) - { - mbSize = minibatchSize; - } - - return true; -} -template -bool DSSM_BinaryInput::Next_Batch(Matrix& matrices, size_t cur, size_t numToRead, int* /*ordering*/){ - /* - int devId = matrices.GetDeviceId(); - matrices.TransferFromDeviceToDevice(devId, -1); - */ - - int32_t cur_index = 0; - - for (int c = 0; c < numToRead; c++,cur++) - { - //int64_t cur_offset = offsets[ordering[cur]]; - int64_t cur_offset = offsets[cur]; - //int64_t cur_offset = offsets[ordering[c]]; - //int32_t nnz; - colIndices[c] = cur_index; - int32_t nnz = *(int32_t*)((char*)data_buffer + cur_offset); - //memcpy(&nnz, (char*)data_buffer + cur_offset, sizeof(int32_t)); - memcpy(values+cur_index, (char*)data_buffer + cur_offset + sizeof(int32_t), sizeof(ElemType)*nnz); - memcpy(rowIndices+cur_index, (char*)data_buffer + cur_offset + sizeof(int32_t)+sizeof(ElemType)*nnz, sizeof(int32_t)*nnz); - /** - fprintf(stderr, "%4d (%3d, %6d): ", c, nnz, cur_index + nnz); - for (int i = 0; i < nnz; i++) - { - fprintf(stderr, "%d:%.f ", rowIndices[cur_index+i], values[cur_index+i]); - //matrices.SetValue(rowIndices[cur_index + i], c, values[cur_index + i]); - } - fprintf(stderr, "\n"); - **/ - - cur_index += nnz; - } - colIndices[numToRead] = cur_index; - /* - int col = 0; - for (int c = 0; c < cur_index; c++) - { - if (colIndices[col] == c) - { - fprintf(stderr, "\n%4d: ", col); - col++; - } - fprintf(stderr, "%d:%.f ", rowIndices[c], values[c]); - } - */ - /* - fprintf(stderr, "\nXXXX nnz: %d\n", cur_index); - fprintf(stderr, "XXXX max values read: %d vs %d\n", sizeof(ElemType)*cur_index, sizeof(ElemType)*MAX_BUFFER*numToRead); - fprintf(stderr, "XXXX max indices read: %d vs %d\n", sizeof(int32_t)*cur_index, sizeof(int32_t)*MAX_BUFFER*numToRead); - fprintf(stderr, "XXXX sizeof(int32_t) = %d, sizeof(int) = %d\n", sizeof(int32_t), sizeof(int)); - */ - /* - values = (ElemType*)malloc(sizeof(ElemType)*MAX_BUFFER*minibatchSize); - colIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*(minibatchSize+1)); - rowIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*minibatchSize); - */ - - matrices.SetMatrixFromCSCFormat(colIndices, rowIndices, values, cur_index, m_dim, numToRead); - //matrices.Print("actual values"); - //exit(1); - /* - matrices.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); - matrices.Print("featuresQ"); - exit(1); - matrices.TransferFromDeviceToDevice(-1,devId); - */ - return true; -} - -template -void DSSM_BinaryInput::Dispose(){ - if (offsets_orig != NULL){ - UnmapViewOfFile(offsets_orig); - } - if (data_orig != NULL) - { - UnmapViewOfFile(data_orig); - } - - if (offsets!= NULL) - { - free(offsets);// = (ElemType*)malloc(sizeof(float)* 230 * 1024); - } - if (values != NULL) - { - free(values);// = (ElemType*)malloc(sizeof(float)* 230 * 1024); - } - if (rowIndices != NULL){ - free(rowIndices);// = (int*)malloc(sizeof(float)* 230 * 1024); - } - if (colIndices != NULL){ - free(colIndices);// = (int*)malloc(sizeof(float)* 230 * 1024); - } -} - -template -bool DSSMReader::GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart) -{ - if (m_cachingReader) - { - return m_cachingReader->GetData(sectionName, numRecords, data, dataBufferSize, recordStart); - } - RuntimeError("GetData not supported in DSSMReader"); -} - -// instantiate all the combinations we expect to be used -template class DSSMReader; -template class DSSMReader; -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// DSSMReader.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#define DATAREADER_EXPORTS // creating the exports here +#include "DataReader.h" +#include "DSSMReader.h" +#ifdef LEAKDETECT +#include // leak detection +#endif +#include "fileutil.h" // for fexists() + +namespace Microsoft { namespace MSR { namespace CNTK { + +DWORD HIDWORD(size_t size) {return size>>32;} +DWORD LODWORD(size_t size) {return size&0xFFFFFFFF;} + +std::string ws2s(const std::wstring& wstr) +{ + int size_needed = WideCharToMultiByte(CP_ACP, 0, wstr.c_str(), int(wstr.length() + 1), 0, 0, 0, 0); + std::string strTo(size_needed, 0); + WideCharToMultiByte(CP_ACP, 0, wstr.c_str(), int(wstr.length() + 1), &strTo[0], size_needed, 0, 0); + return strTo; +} + +template +size_t DSSMReader::RandomizeSweep(size_t mbStartSample) +{ + //size_t randomRangePerEpoch = (m_epochSize+m_randomizeRange-1)/m_randomizeRange; + //return m_epoch*randomRangePerEpoch + epochSample/m_randomizeRange; + return mbStartSample/m_randomizeRange; +} + +// ReadLine - Read a line +// readSample - sample to read in global sample space +// returns - true if we successfully read a record, otherwise false +template +bool DSSMReader::ReadRecord(size_t /*readSample*/) +{ + return false; // not used +} + +// RecordsToRead - Determine number of records to read to populate record buffers +// mbStartSample - the starting sample from which to read +// tail - we are checking for possible remainer records to read (default false) +// returns - true if we have more to read, false if we hit the end of the dataset +template +size_t DSSMReader::RecordsToRead(size_t mbStartSample, bool tail) +{ + assert(mbStartSample >= m_epochStartSample); + // determine how far ahead we need to read + bool randomize = Randomize(); + // need to read to the end of the next minibatch + size_t epochSample = mbStartSample; + epochSample %= m_epochSize; + + // determine number left to read for this epoch + size_t numberToEpoch = m_epochSize - epochSample; + // we will take either a minibatch or the number left in the epoch + size_t numberToRead = min(numberToEpoch, m_mbSize); + if (numberToRead == 0 && !tail) + numberToRead = m_mbSize; + + if (randomize) + { + size_t randomizeSweep = RandomizeSweep(mbStartSample); + // if first read or read takes us to another randomization range + // we need to read at least randomization range records + if (randomizeSweep != m_randomordering.CurrentSeed()) // the range has changed since last time + { + numberToRead = RoundUp(epochSample, m_randomizeRange) - epochSample; + if (numberToRead == 0 && !tail) + numberToRead = m_randomizeRange; + } + } + return numberToRead; +} + +template +void DSSMReader::WriteLabelFile() +{ + // write out the label file if they don't have one + if (!m_labelFileToWrite.empty()) + { + if (m_mapIdToLabel.size() > 0) + { + File labelFile(m_labelFileToWrite, fileOptionsWrite | fileOptionsText); + for (int i=0; i < m_mapIdToLabel.size(); ++i) + { + labelFile << m_mapIdToLabel[i] << '\n'; + } + fprintf(stderr, "label file %ls written to disk\n", m_labelFileToWrite.c_str()); + m_labelFileToWrite.clear(); + } + else if (!m_cachingWriter) + { + fprintf(stderr, "WARNING: file %ls NOT written to disk yet, will be written the first time the end of the entire dataset is found.\n", m_labelFileToWrite.c_str()); + } + } +} + +// Destroy - cleanup and remove this class +// NOTE: this destroys the object, and it can't be used past this point +template +void DSSMReader::Destroy() +{ + delete this; +} + +// Init - Reader Initialize for multiple data sets +// config - [in] configuration parameters for the datareader +// Sample format below: +//# Parameter values for the reader +//reader=[ +// # reader to use +// readerType=DSSMReader +// miniBatchMode=Partial +// randomize=None +// features=[ +// dim=784 +// start=1 +// file=c:\speech\mnist\mnist_test.txt +// ] +// labels=[ +// dim=1 +// start=0 +// file=c:\speech\mnist\mnist_test.txt +// labelMappingFile=c:\speech\mnist\labels.txt +// labelDim=10 +// labelType=Category +// ] +//] +template +template +void DSSMReader::InitFromConfig(const ConfigRecordType & readerConfig) +{ + std::vector features; + std::vector labels; + + // Determine the names of the features and lables sections in the config file. + // features - [in,out] a vector of feature name strings + // labels - [in,out] a vector of label name strings + // For DSSM dataset, we only need features. No label is necessary. The following "labels" just serves as a place holder + GetFileConfigNames(readerConfig, features, labels); + + // For DSSM dataset, it must have exactly two features + // In the config file, we must specify query features first, then document features. The sequence is different here. Pay attention + if (features.size() == 2 && labels.size() == 1) + { + m_featuresNameQuery = features[1]; + m_featuresNameDoc = features[0]; + m_labelsName = labels[0]; + } + else + { + RuntimeError("DSSM requires exactly two features and one label. Their names should match those in NDL definition"); + return; + } + + + m_mbStartSample = m_epoch = m_totalSamples = m_epochStartSample = 0; + m_labelIdMax = m_labelDim = 0; + m_partialMinibatch = m_endReached = false; + m_labelType = labelCategory; + m_readNextSample = 0; + m_traceLevel = readerConfig(L"traceLevel", 0); + + if (readerConfig.Exists(L"randomize")) + { + // BUGBUG: reading out string and number... ugh + wstring randomizeString = readerConfig(L"randomize"); + if (randomizeString == L"None") + { + m_randomizeRange = randomizeNone; + } + else if (randomizeString == L"Auto") + { + m_randomizeRange = randomizeAuto; + } + else + { + m_randomizeRange = readerConfig(L"randomize"); + } + } + else + { + m_randomizeRange = randomizeNone; + } + + std::string minibatchMode(readerConfig(L"minibatchMode", "Partial")); + m_partialMinibatch = !_stricmp(minibatchMode.c_str(), "Partial"); + + + // Get the config parameters for query feature and doc feature + ConfigParameters configFeaturesQuery = readerConfig(m_featuresNameQuery, ""); + ConfigParameters configFeaturesDoc = readerConfig(m_featuresNameDoc, ""); + + if (configFeaturesQuery.size() == 0) + RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); + if (configFeaturesDoc.size() == 0) + RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); + + // Read in feature size information + // This information will be used to handle OOVs + m_featuresDimQuery = configFeaturesQuery(L"dim"); + m_featuresDimDoc = configFeaturesDoc(L"dim"); + + std::wstring fileQ = configFeaturesQuery("file"); + std::wstring fileD = configFeaturesDoc("file"); + + dssm_queryInput.Init(fileQ, m_featuresDimQuery); + dssm_docInput.Init(fileD, m_featuresDimDoc); + + m_totalSamples = dssm_queryInput.numRows; + if (read_order == NULL) + { + read_order = new int[m_totalSamples]; + for (int c = 0; c < m_totalSamples; c++) + { + read_order[c] = c; + } + } + m_mbSize = 0; + +} +// destructor - virtual so it gets called properly +template +DSSMReader::~DSSMReader() +{ + ReleaseMemory(); +} + +// ReleaseMemory - release the memory footprint of DSSMReader +// used when the caching reader is taking over +template +void DSSMReader::ReleaseMemory() +{ + if (m_qfeaturesBuffer!=NULL) + delete[] m_qfeaturesBuffer; + m_qfeaturesBuffer=NULL; + if (m_dfeaturesBuffer!=NULL) + delete[] m_dfeaturesBuffer; + m_dfeaturesBuffer=NULL; + if (m_labelsBuffer!=NULL) + delete[] m_labelsBuffer; + m_labelsBuffer=NULL; + if (m_labelsIdBuffer!=NULL) + delete[] m_labelsIdBuffer; + m_labelsIdBuffer=NULL; + m_featureData.clear(); + m_labelIdData.clear(); + m_labelData.clear(); +} + +//SetupEpoch - Setup the proper position in the file, and other variable settings to start a particular epoch +template +void DSSMReader::SetupEpoch() +{ +} + +// utility function to round an integer up to a multiple of size +size_t RoundUp(size_t value, size_t size) +{ + return ((value + size -1)/size)*size; +} + +//StartMinibatchLoop - Startup a minibatch loop +// mbSize - [in] size of the minibatch (number of Samples, etc.) +// epoch - [in] epoch number for this loop, if > 0 the requestedEpochSamples must be specified (unless epoch zero was completed this run) +// requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset +// this value must be a multiple of mbSize, if it is not, it will be rounded up to one. +template +void DSSMReader::StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples) +{ + size_t mbStartSample = m_epoch * m_epochSize; + if (m_totalSamples == 0) + { + m_totalSamples = dssm_queryInput.numRows; + } + + size_t fileRecord = m_totalSamples ? mbStartSample % m_totalSamples : 0; + fprintf(stderr, "starting epoch %lld at record count %lld, and file position %lld\n", m_epoch, mbStartSample, fileRecord); + + + + // reset the next read sample + m_readNextSample = 0; + m_epochStartSample = m_mbStartSample = mbStartSample; + m_mbSize = mbSize; + m_epochSize = requestedEpochSamples; + dssm_queryInput.SetupEpoch(mbSize); + dssm_docInput.SetupEpoch(mbSize); + if (m_epochSize > (size_t)dssm_queryInput.numRows) + { + m_epochSize = (size_t)dssm_queryInput.numRows; + } + if (Randomize()) + { + random_shuffle(&read_order[0], &read_order[m_epochSize]); + } + m_epoch = epoch; + m_mbStartSample = epoch*m_epochSize; + +} + +// function to store the LabelType in an ElemType +// required for string labels, which can't be stored in ElemType arrays +template +void DSSMReader::StoreLabel(ElemType& labelStore, const LabelType& labelValue) +{ + labelStore = (ElemType)m_mapLabelToId[labelValue]; +} + +// GetMinibatch - Get the next minibatch (features and labels) +// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, +// [out] each matrix resized if necessary containing data. +// returns - true if there are more minibatches, false if no more minibatchs remain +template +bool DSSMReader::GetMinibatch(std::map*>& matrices) +{ + if (m_readNextSample >= m_totalSamples) + { + return false; + } + // In my unit test example, the input matrices contain 5: N, S, fD, fQ and labels + // Both N and S serve as a pre-set constant values, no need to change them + // In this node, we only need to fill in these matrices: fD, fQ, labels + Matrix& featuresQ = *matrices[m_featuresNameQuery]; + Matrix& featuresD = *matrices[m_featuresNameDoc]; + Matrix& labels = *matrices[m_labelsName]; // will change this part later. + + size_t actualMBSize = ( m_readNextSample + m_mbSize > m_totalSamples ) ? m_totalSamples - m_readNextSample : m_mbSize; + + + featuresQ.SwitchToMatrixType(MatrixType::SPARSE, MatrixFormat::matrixFormatSparseCSC, false); + featuresD.SwitchToMatrixType(MatrixType::SPARSE, MatrixFormat::matrixFormatSparseCSC, false); + + /* + featuresQ.Resize(dssm_queryInput.numRows, actualMBSize); + featuresD.Resize(dssm_docInput.numRows, actualMBSize); + */ + + //fprintf(stderr, "featuresQ\n"); + dssm_queryInput.Next_Batch(featuresQ, m_readNextSample, actualMBSize, read_order); + //fprintf(stderr, "\n\n\nfeaturesD\n"); + dssm_docInput.Next_Batch(featuresD, m_readNextSample, actualMBSize, read_order); + //fprintf(stderr, "\n\n\n\n\n"); + m_readNextSample += actualMBSize; + /* + featuresQ.Print("featuresQ"); + fprintf(stderr, "\n"); + featuresD.Print("featuresD"); + fprintf(stderr, "\n"); + */ + + /* + GPUSPARSE_INDEX_TYPE* h_CSCCol; + GPUSPARSE_INDEX_TYPE* h_Row; + ElemType* h_val; + size_t nz; + size_t nrs; + size_t ncols; + featuresQ.GetMatrixFromCSCFormat(&h_CSCCol, &h_Row, &h_val, &nz, &nrs, &ncols); + + for (int j = 0, k=0; j < nz; j++) + { + if (h_CSCCol[k] >= j) + { + fprintf(stderr, "\n"); + k++; + } + fprintf(stderr, "%d:%.f ", h_Row[j], h_val[j]); + + } + */ + + /* + featuresQ.TransferFromDeviceToDevice(featuresQ.GetDeviceId(), -1); + featuresQ.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); + featuresQ.Print("featuresQ"); + + featuresD.TransferFromDeviceToDevice(featuresD.GetDeviceId(), -1); + featuresD.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); + featuresD.Print("featuresD"); + + exit(1); + */ + + if (actualMBSize > m_mbSize || m_labelsBuffer == NULL) { + size_t rows = labels.GetNumRows(); + m_labelsBuffer = new ElemType[rows * actualMBSize]; + memset(m_labelsBuffer, 0, sizeof(ElemType)* rows * actualMBSize); + for (int i = 0; i < actualMBSize; i++) + { + m_labelsBuffer[i * rows] = 1; + } + } + if (actualMBSize != labels.GetNumCols()) + { + size_t rows = labels.GetNumRows(); + labels.Resize(rows, actualMBSize); + labels.SetValue(0.0); + labels.SetValue(rows, actualMBSize, labels.GetDeviceId(), m_labelsBuffer, 0); + } + /* + featuresQ.Print("featuresQ"); + featuresD.Print("featuresD"); + labels.print("labels"); + */ + + return true; +} + + +// GetLabelMapping - Gets the label mapping from integer index to label type +// returns - a map from numeric datatype to native label type +template +const std::map::LabelIdType, typename IDataReader::LabelType>& DSSMReader::GetLabelMapping(const std::wstring& sectionName) +{ + if (m_cachingReader) + { + return m_cachingReader->GetLabelMapping(sectionName); + } + return m_mapIdToLabel; +} + +// SetLabelMapping - Sets the label mapping from integer index to label +// labelMapping - mapping table from label values to IDs (must be 0-n) +// note: for tasks with labels, the mapping table must be the same between a training run and a testing run +template +void DSSMReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, typename LabelType>& labelMapping) +{ + if (m_cachingReader) + { + RuntimeError("Cannot set mapping table when the caching reader is being used"); + } + m_mapIdToLabel = labelMapping; + m_mapLabelToId.clear(); + for (std::pair var : labelMapping) + { + m_mapLabelToId[var.second] = var.first; + } +} + +template +bool DSSMReader::DataEnd(EndDataType endDataType) +{ + bool ret = false; + switch (endDataType) + { + case endDataNull: + assert(false); + break; + case endDataEpoch: + //ret = (m_mbStartSample / m_epochSize < m_epoch); + ret = (m_readNextSample >= m_totalSamples); + break; + case endDataSet: + ret = (m_readNextSample >= m_totalSamples); + break; + case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true + ret = true; + break; + } + return ret; +} + +template +DSSM_BinaryInput::DSSM_BinaryInput(){ +} +template +DSSM_BinaryInput::~DSSM_BinaryInput(){ + Dispose(); +} +template +void DSSM_BinaryInput::Init(wstring fileName, size_t dim){ + + m_dim = dim; + mbSize = 0; + /* + m_hndl = CreateFileA(fileName.c_str(), GENERIC_READ, + FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); + */ + m_hndl = CreateFile(fileName.c_str(), GENERIC_READ, + FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); + if (m_hndl == INVALID_HANDLE_VALUE) + { + char message[256]; + sprintf_s(message, "Unable to Open/Create file %ls, error %x", fileName.c_str(), GetLastError()); + RuntimeError(message); + } + + m_filemap = CreateFileMapping(m_hndl, NULL, PAGE_READONLY, 0, 0, NULL); + + SYSTEM_INFO sysinfo; + GetSystemInfo(&sysinfo); + DWORD sysGran = sysinfo.dwAllocationGranularity; + + header_buffer = MapViewOfFile(m_filemap, // handle to map object + FILE_MAP_READ, // get correct permissions + HIDWORD(0), + LODWORD(0), + sizeof(int64_t)* 2 + sizeof(int32_t)); + + //cout << "After mapviewoffile" << endl; + + memcpy(&numRows, header_buffer, sizeof(int64_t)); + memcpy(&numCols, (char*)header_buffer + sizeof(int64_t), sizeof(int32_t)); + memcpy(&totalNNz, (char*)header_buffer + sizeof(int64_t)+sizeof(int32_t), sizeof(int64_t)); + + //cout << "After gotvalues" << endl; + int64_t base_offset = sizeof(int64_t)* 2 + sizeof(int32_t); + + int64_t offsets_padding = base_offset % sysGran; + base_offset -= offsets_padding; + + int64_t header_size = numRows*sizeof(int64_t)+offsets_padding; + + void* offsets_orig = MapViewOfFile(m_filemap, // handle to map object + FILE_MAP_READ, // get correct permissions + HIDWORD(base_offset), + LODWORD(base_offset), + header_size); + + offsets_buffer = (char*)offsets_orig + offsets_padding; + + if (offsets != NULL){ + free(offsets); + } + offsets = (int64_t*)malloc(sizeof(int64_t)*numRows); + memcpy(offsets, offsets_buffer, numRows*sizeof(int64_t)); + + + int64_t header_offset = base_offset + offsets_padding + numRows * sizeof(int64_t); + + int64_t data_padding = header_offset % sysGran; + header_offset -= data_padding; + + void* data_orig = MapViewOfFile(m_filemap, // handle to map object + FILE_MAP_READ, // get correct permissions + HIDWORD(header_offset), + LODWORD(header_offset), + 0); + data_buffer = (char*)data_orig + data_padding; + +} +template +bool DSSM_BinaryInput::SetupEpoch( size_t minibatchSize){ + if (values == NULL || mbSize < minibatchSize) + { + if (values != NULL) + { + free(values); + free(colIndices); + free(rowIndices); + } + + values = (ElemType*)malloc(sizeof(ElemType)*MAX_BUFFER*minibatchSize); + colIndices = (int32_t*)malloc(sizeof(int32_t)*(minibatchSize+1)); + rowIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*minibatchSize); + //fprintf(stderr, "values size: %d",sizeof(ElemType)*MAX_BUFFER*minibatchSize); + //fprintf(stderr, "colindi size: %d",sizeof(int32_t)*MAX_BUFFER*(1+minibatchSize)); + //fprintf(stderr, "rowindi size: %d",sizeof(int32_t)*MAX_BUFFER*minibatchSize); + } + if (minibatchSize > mbSize) + { + mbSize = minibatchSize; + } + + return true; +} +template +bool DSSM_BinaryInput::Next_Batch(Matrix& matrices, size_t cur, size_t numToRead, int* /*ordering*/){ + /* + int devId = matrices.GetDeviceId(); + matrices.TransferFromDeviceToDevice(devId, -1); + */ + + int32_t cur_index = 0; + + for (int c = 0; c < numToRead; c++,cur++) + { + //int64_t cur_offset = offsets[ordering[cur]]; + int64_t cur_offset = offsets[cur]; + //int64_t cur_offset = offsets[ordering[c]]; + //int32_t nnz; + colIndices[c] = cur_index; + int32_t nnz = *(int32_t*)((char*)data_buffer + cur_offset); + //memcpy(&nnz, (char*)data_buffer + cur_offset, sizeof(int32_t)); + memcpy(values+cur_index, (char*)data_buffer + cur_offset + sizeof(int32_t), sizeof(ElemType)*nnz); + memcpy(rowIndices+cur_index, (char*)data_buffer + cur_offset + sizeof(int32_t)+sizeof(ElemType)*nnz, sizeof(int32_t)*nnz); + /** + fprintf(stderr, "%4d (%3d, %6d): ", c, nnz, cur_index + nnz); + for (int i = 0; i < nnz; i++) + { + fprintf(stderr, "%d:%.f ", rowIndices[cur_index+i], values[cur_index+i]); + //matrices.SetValue(rowIndices[cur_index + i], c, values[cur_index + i]); + } + fprintf(stderr, "\n"); + **/ + + cur_index += nnz; + } + colIndices[numToRead] = cur_index; + /* + int col = 0; + for (int c = 0; c < cur_index; c++) + { + if (colIndices[col] == c) + { + fprintf(stderr, "\n%4d: ", col); + col++; + } + fprintf(stderr, "%d:%.f ", rowIndices[c], values[c]); + } + */ + /* + fprintf(stderr, "\nXXXX nnz: %d\n", cur_index); + fprintf(stderr, "XXXX max values read: %d vs %d\n", sizeof(ElemType)*cur_index, sizeof(ElemType)*MAX_BUFFER*numToRead); + fprintf(stderr, "XXXX max indices read: %d vs %d\n", sizeof(int32_t)*cur_index, sizeof(int32_t)*MAX_BUFFER*numToRead); + fprintf(stderr, "XXXX sizeof(int32_t) = %d, sizeof(int) = %d\n", sizeof(int32_t), sizeof(int)); + */ + /* + values = (ElemType*)malloc(sizeof(ElemType)*MAX_BUFFER*minibatchSize); + colIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*(minibatchSize+1)); + rowIndices = (int32_t*)malloc(sizeof(int32_t)*MAX_BUFFER*minibatchSize); + */ + + matrices.SetMatrixFromCSCFormat(colIndices, rowIndices, values, cur_index, m_dim, numToRead); + //matrices.Print("actual values"); + //exit(1); + /* + matrices.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense); + matrices.Print("featuresQ"); + exit(1); + matrices.TransferFromDeviceToDevice(-1,devId); + */ + return true; +} + +template +void DSSM_BinaryInput::Dispose(){ + if (offsets_orig != NULL){ + UnmapViewOfFile(offsets_orig); + } + if (data_orig != NULL) + { + UnmapViewOfFile(data_orig); + } + + if (offsets!= NULL) + { + free(offsets);// = (ElemType*)malloc(sizeof(float)* 230 * 1024); + } + if (values != NULL) + { + free(values);// = (ElemType*)malloc(sizeof(float)* 230 * 1024); + } + if (rowIndices != NULL){ + free(rowIndices);// = (int*)malloc(sizeof(float)* 230 * 1024); + } + if (colIndices != NULL){ + free(colIndices);// = (int*)malloc(sizeof(float)* 230 * 1024); + } +} + +template +bool DSSMReader::GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart) +{ + if (m_cachingReader) + { + return m_cachingReader->GetData(sectionName, numRecords, data, dataBufferSize, recordStart); + } + RuntimeError("GetData not supported in DSSMReader"); +} + +// instantiate all the combinations we expect to be used +template class DSSMReader; +template class DSSMReader; +}}} diff --git a/Source/Readers/DSSMReader/DSSMReader.h b/Source/Readers/DSSMReader/DSSMReader.h index 7f0a2dc5256c..136506aa6e8e 100644 --- a/Source/Readers/DSSMReader/DSSMReader.h +++ b/Source/Readers/DSSMReader/DSSMReader.h @@ -1,159 +1,159 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// DSSMReader.h - Include file for the MTK and MLF format of features and samples -#pragma once -#include "DataReader.h" -#include "DataWriter.h" -#include "Config.h" -#include "RandomOrdering.h" -#include -#include -#include - -namespace Microsoft { namespace MSR { namespace CNTK { - -enum LabelKind -{ - labelNone = 0, // no labels to worry about - labelCategory = 1, // category labels, creates mapping tables - labelRegression = 2, // regression labels - labelOther = 3, // some other type of label -}; - - -template -class DSSM_BinaryInput { -private: - HANDLE m_hndl; - HANDLE m_filemap; - HANDLE m_header; - HANDLE m_offsets; - HANDLE m_data; - - //void* header_orig; // Don't need this since the header is at the start of the file - void* offsets_orig; - void* data_orig; - - void* header_buffer; - void* offsets_buffer; - void* data_buffer; - - size_t m_dim; - size_t mbSize; - size_t MAX_BUFFER = 300; - - ElemType* values; // = (ElemType*)malloc(sizeof(float)* 230 * 1024); - int64_t* offsets; // = (int*)malloc(sizeof(int)* 230 * 1024); - int32_t* colIndices; // = (int*)malloc(sizeof(int)* (batchsize + 1)); - int32_t* rowIndices; // = (int*)malloc(sizeof(int)* MAX_BUFFER * batchsize); - -public: - int64_t numRows; - int32_t numCols; - int64_t totalNNz; - - DSSM_BinaryInput(); - ~DSSM_BinaryInput(); - void Init(std::wstring fileName, size_t dim); - bool SetupEpoch( size_t minibatchSize); - bool Next_Batch(Matrix& matrices, size_t cur, size_t numToRead, int* ordering); - void Dispose(); -}; - -template -class DSSMReader : public IDataReader -{ -//public: -// typedef std::string LabelType; -// typedef unsigned LabelIdType; -private: - int* read_order; // array to shuffle to reorder the dataset - std::wstring m_featuresNameQuery; - std::wstring m_featuresNameDoc; - size_t m_featuresDimQuery; - size_t m_featuresDimDoc; - DSSM_BinaryInput dssm_queryInput; - DSSM_BinaryInput dssm_docInput; - - size_t m_mbSize; // size of minibatch requested - LabelIdType m_labelIdMax; // maximum label ID we have encountered so far - LabelIdType m_labelDim; // maximum label ID we will ever see (used for array dimensions) - size_t m_mbStartSample; // starting sample # of the next minibatch - size_t m_epochSize; // size of an epoch - size_t m_epoch; // which epoch are we on - size_t m_epochStartSample; // the starting sample for the epoch - size_t m_totalSamples; // number of samples in the dataset - size_t m_randomizeRange; // randomization range - size_t m_featureCount; // feature count - size_t m_readNextSample; // next sample to read - bool m_labelFirst; // the label is the first element in a line - bool m_partialMinibatch; // a partial minibatch is allowed - LabelKind m_labelType; // labels are categories, create mapping table - RandomOrdering m_randomordering; // randomizing class - MBLayoutPtr m_pMBLayout; - - std::wstring m_labelsName; - std::wstring m_featuresName; - std::wstring m_labelsCategoryName; - std::wstring m_labelsMapName; - ElemType* m_qfeaturesBuffer; - ElemType* m_dfeaturesBuffer; - ElemType* m_labelsBuffer; - LabelIdType* m_labelsIdBuffer; - std::wstring m_labelFileToWrite; // set to the path if we need to write out the label file - - bool m_endReached; - int m_traceLevel; - - // feature and label data are parallel arrays - std::vector m_featureData; - std::vector m_labelIdData; - std::vector m_labelData; - - // map is from ElemType to LabelType - // For DSSM, we really only need an int for label data, but we have to transmit in Matrix, so use ElemType instead - std::map m_mapIdToLabel; - std::map m_mapLabelToId; - - // caching support - DataReader* m_cachingReader; - DataWriter* m_cachingWriter; - ConfigParameters m_readerConfig; - - size_t RandomizeSweep(size_t epochSample); - //bool Randomize() {return m_randomizeRange != randomizeNone;} - bool Randomize() { return false; } - void SetupEpoch(); - void StoreLabel(ElemType& labelStore, const LabelType& labelValue); - size_t RecordsToRead(size_t mbStartSample, bool tail=false); - void ReleaseMemory(); - void WriteLabelFile(); - - - virtual bool ReadRecord(size_t readSample); -public: - template void InitFromConfig(const ConfigRecordType &); - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } - virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } - virtual void Destroy(); - DSSMReader() : m_pMBLayout(make_shared()) { m_qfeaturesBuffer = NULL; m_dfeaturesBuffer = NULL; m_labelsBuffer = NULL; } - virtual ~DSSMReader(); - virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples=requestDataSize); - virtual bool GetMinibatch(std::map*>& matrices); - - size_t GetNumParallelSequences() { return 1 ;} - void SetNumParallelSequences(const size_t) { }; - void CopyMBLayoutTo(MBLayoutPtr pMBLayout) { pMBLayout->CopyFrom(m_pMBLayout); NOT_IMPLEMENTED; } - - virtual const std::map& GetLabelMapping(const std::wstring& sectionName); - virtual void SetLabelMapping(const std::wstring& sectionName, const std::map& labelMapping); - virtual bool GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart=0); - - virtual bool DataEnd(EndDataType endDataType); - - void SetRandomSeed(int){ NOT_IMPLEMENTED; } -}; -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// DSSMReader.h - Include file for the MTK and MLF format of features and samples +#pragma once +#include "DataReader.h" +#include "DataWriter.h" +#include "Config.h" +#include "RandomOrdering.h" +#include +#include +#include + +namespace Microsoft { namespace MSR { namespace CNTK { + +enum LabelKind +{ + labelNone = 0, // no labels to worry about + labelCategory = 1, // category labels, creates mapping tables + labelRegression = 2, // regression labels + labelOther = 3, // some other type of label +}; + + +template +class DSSM_BinaryInput { +private: + HANDLE m_hndl; + HANDLE m_filemap; + HANDLE m_header; + HANDLE m_offsets; + HANDLE m_data; + + //void* header_orig; // Don't need this since the header is at the start of the file + void* offsets_orig; + void* data_orig; + + void* header_buffer; + void* offsets_buffer; + void* data_buffer; + + size_t m_dim; + size_t mbSize; + size_t MAX_BUFFER = 300; + + ElemType* values; // = (ElemType*)malloc(sizeof(float)* 230 * 1024); + int64_t* offsets; // = (int*)malloc(sizeof(int)* 230 * 1024); + int32_t* colIndices; // = (int*)malloc(sizeof(int)* (batchsize + 1)); + int32_t* rowIndices; // = (int*)malloc(sizeof(int)* MAX_BUFFER * batchsize); + +public: + int64_t numRows; + int32_t numCols; + int64_t totalNNz; + + DSSM_BinaryInput(); + ~DSSM_BinaryInput(); + void Init(std::wstring fileName, size_t dim); + bool SetupEpoch( size_t minibatchSize); + bool Next_Batch(Matrix& matrices, size_t cur, size_t numToRead, int* ordering); + void Dispose(); +}; + +template +class DSSMReader : public IDataReader +{ +//public: +// typedef std::string LabelType; +// typedef unsigned LabelIdType; +private: + int* read_order; // array to shuffle to reorder the dataset + std::wstring m_featuresNameQuery; + std::wstring m_featuresNameDoc; + size_t m_featuresDimQuery; + size_t m_featuresDimDoc; + DSSM_BinaryInput dssm_queryInput; + DSSM_BinaryInput dssm_docInput; + + size_t m_mbSize; // size of minibatch requested + LabelIdType m_labelIdMax; // maximum label ID we have encountered so far + LabelIdType m_labelDim; // maximum label ID we will ever see (used for array dimensions) + size_t m_mbStartSample; // starting sample # of the next minibatch + size_t m_epochSize; // size of an epoch + size_t m_epoch; // which epoch are we on + size_t m_epochStartSample; // the starting sample for the epoch + size_t m_totalSamples; // number of samples in the dataset + size_t m_randomizeRange; // randomization range + size_t m_featureCount; // feature count + size_t m_readNextSample; // next sample to read + bool m_labelFirst; // the label is the first element in a line + bool m_partialMinibatch; // a partial minibatch is allowed + LabelKind m_labelType; // labels are categories, create mapping table + RandomOrdering m_randomordering; // randomizing class + MBLayoutPtr m_pMBLayout; + + std::wstring m_labelsName; + std::wstring m_featuresName; + std::wstring m_labelsCategoryName; + std::wstring m_labelsMapName; + ElemType* m_qfeaturesBuffer; + ElemType* m_dfeaturesBuffer; + ElemType* m_labelsBuffer; + LabelIdType* m_labelsIdBuffer; + std::wstring m_labelFileToWrite; // set to the path if we need to write out the label file + + bool m_endReached; + int m_traceLevel; + + // feature and label data are parallel arrays + std::vector m_featureData; + std::vector m_labelIdData; + std::vector m_labelData; + + // map is from ElemType to LabelType + // For DSSM, we really only need an int for label data, but we have to transmit in Matrix, so use ElemType instead + std::map m_mapIdToLabel; + std::map m_mapLabelToId; + + // caching support + DataReader* m_cachingReader; + DataWriter* m_cachingWriter; + ConfigParameters m_readerConfig; + + size_t RandomizeSweep(size_t epochSample); + //bool Randomize() {return m_randomizeRange != randomizeNone;} + bool Randomize() { return false; } + void SetupEpoch(); + void StoreLabel(ElemType& labelStore, const LabelType& labelValue); + size_t RecordsToRead(size_t mbStartSample, bool tail=false); + void ReleaseMemory(); + void WriteLabelFile(); + + + virtual bool ReadRecord(size_t readSample); +public: + template void InitFromConfig(const ConfigRecordType &); + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } + virtual void Destroy(); + DSSMReader() : m_pMBLayout(make_shared()) { m_qfeaturesBuffer = NULL; m_dfeaturesBuffer = NULL; m_labelsBuffer = NULL; } + virtual ~DSSMReader(); + virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples=requestDataSize); + virtual bool GetMinibatch(std::map*>& matrices); + + size_t GetNumParallelSequences() { return 1 ;} + void SetNumParallelSequences(const size_t) { }; + void CopyMBLayoutTo(MBLayoutPtr pMBLayout) { pMBLayout->CopyFrom(m_pMBLayout); NOT_IMPLEMENTED; } + + virtual const std::map& GetLabelMapping(const std::wstring& sectionName); + virtual void SetLabelMapping(const std::wstring& sectionName, const std::map& labelMapping); + virtual bool GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart=0); + + virtual bool DataEnd(EndDataType endDataType); + + void SetRandomSeed(int){ NOT_IMPLEMENTED; } +}; +}}} diff --git a/Source/Readers/DSSMReader/DSSMReader.vcxproj b/Source/Readers/DSSMReader/DSSMReader.vcxproj index d607a7c9fc38..1427cdf815d4 100644 --- a/Source/Readers/DSSMReader/DSSMReader.vcxproj +++ b/Source/Readers/DSSMReader/DSSMReader.vcxproj @@ -1,150 +1,150 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {014DA766-B37B-4581-BC26-963EA5507931} - - - - - - - - - Win32Proj - DSSMReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - NotUsing - Level4 - Disabled - _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - /bigobj %(AdditionalOptions) - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - NotUsing - MaxSpeed - true - true - _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - false - - - false - - - false - - - false - - - - - - - - - - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {014DA766-B37B-4581-BC26-963EA5507931} + + + + + + + + + Win32Proj + DSSMReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + NotUsing + Level4 + Disabled + _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + /bigobj %(AdditionalOptions) + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + NotUsing + MaxSpeed + true + true + _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + false + + + false + + + false + + + false + + + + + + + + + + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + + + + + + + \ No newline at end of file diff --git a/Source/Readers/DSSMReader/DSSMReader.vcxproj.filters b/Source/Readers/DSSMReader/DSSMReader.vcxproj.filters index edd0fc7c8eb0..d80fe365ffb4 100644 --- a/Source/Readers/DSSMReader/DSSMReader.vcxproj.filters +++ b/Source/Readers/DSSMReader/DSSMReader.vcxproj.filters @@ -1,80 +1,80 @@ - - - - - {4FC737F1-C7A5-4376-A066-2A32D752A2FF} - cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx - - - {93995380-89BD-4b04-88EB-625FBE52EBFB} - h;hh;hpp;hxx;hm;inl;inc;xsd - - - {b032f325-ad9c-49b8-8607-4ad5b4130f4f} - - - {28ed042e-a64c-4248-ad9c-2b9e82bef354} - - - - - Common - - - Common - - - Common - - - Common - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Header Files - - - Source Files - - - Header Files - - - Header Files - - - Common\Include - - + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;hm;inl;inc;xsd + + + {b032f325-ad9c-49b8-8607-4ad5b4130f4f} + + + {28ed042e-a64c-4248-ad9c-2b9e82bef354} + + + + + Common + + + Common + + + Common + + + Common + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Header Files + + + Source Files + + + Header Files + + + Header Files + + + Common\Include + + \ No newline at end of file diff --git a/Source/Readers/DSSMReader/Exports.cpp b/Source/Readers/DSSMReader/Exports.cpp index 3514588b961f..c9b4da1ab2f2 100644 --- a/Source/Readers/DSSMReader/Exports.cpp +++ b/Source/Readers/DSSMReader/Exports.cpp @@ -1,31 +1,31 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// Exports.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#define DATAREADER_EXPORTS -#include "DataReader.h" -#include "DSSMReader.h" - -namespace Microsoft { namespace MSR { namespace CNTK { - -template -void DATAREADER_API GetReader(IDataReader** preader) -{ - *preader = new DSSMReader(); -} - -extern "C" DATAREADER_API void GetReaderF(IDataReader** preader) -{ - GetReader(preader); -} -extern "C" DATAREADER_API void GetReaderD(IDataReader** preader) -{ - GetReader(preader); -} - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// Exports.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#define DATAREADER_EXPORTS +#include "DataReader.h" +#include "DSSMReader.h" + +namespace Microsoft { namespace MSR { namespace CNTK { + +template +void DATAREADER_API GetReader(IDataReader** preader) +{ + *preader = new DSSMReader(); +} + +extern "C" DATAREADER_API void GetReaderF(IDataReader** preader) +{ + GetReader(preader); +} +extern "C" DATAREADER_API void GetReaderD(IDataReader** preader) +{ + GetReader(preader); +} + +}}} diff --git a/Source/Readers/DSSMReader/dllmain.cpp b/Source/Readers/DSSMReader/dllmain.cpp index 6b7cdabe4cdd..aeaef9e3ca22 100644 --- a/Source/Readers/DSSMReader/dllmain.cpp +++ b/Source/Readers/DSSMReader/dllmain.cpp @@ -1,19 +1,19 @@ -// dllmain.cpp : Defines the entry point for the DLL application. -#include "stdafx.h" - -BOOL APIENTRY DllMain( HMODULE /*hModule*/, - DWORD ul_reason_for_call, - LPVOID /*lpReserved*/ - ) -{ - switch (ul_reason_for_call) - { - case DLL_PROCESS_ATTACH: - case DLL_THREAD_ATTACH: - case DLL_THREAD_DETACH: - case DLL_PROCESS_DETACH: - break; - } - return TRUE; -} - +// dllmain.cpp : Defines the entry point for the DLL application. +#include "stdafx.h" + +BOOL APIENTRY DllMain( HMODULE /*hModule*/, + DWORD ul_reason_for_call, + LPVOID /*lpReserved*/ + ) +{ + switch (ul_reason_for_call) + { + case DLL_PROCESS_ATTACH: + case DLL_THREAD_ATTACH: + case DLL_THREAD_DETACH: + case DLL_PROCESS_DETACH: + break; + } + return TRUE; +} + diff --git a/Source/Readers/DSSMReader/stdafx.cpp b/Source/Readers/DSSMReader/stdafx.cpp index bb1431c178a3..1eb86834a664 100644 --- a/Source/Readers/DSSMReader/stdafx.cpp +++ b/Source/Readers/DSSMReader/stdafx.cpp @@ -1,8 +1,8 @@ -// stdafx.cpp : source file that includes just the standard includes -// DSSMReader.pch will be the pre-compiled header -// stdafx.obj will contain the pre-compiled type information - -#include "stdafx.h" - -// TODO: reference any additional headers you need in STDAFX.H -// and not in this file +// stdafx.cpp : source file that includes just the standard includes +// DSSMReader.pch will be the pre-compiled header +// stdafx.obj will contain the pre-compiled type information + +#include "stdafx.h" + +// TODO: reference any additional headers you need in STDAFX.H +// and not in this file diff --git a/Source/Readers/DSSMReader/stdafx.h b/Source/Readers/DSSMReader/stdafx.h index 28eded9eb008..f9771997642c 100644 --- a/Source/Readers/DSSMReader/stdafx.h +++ b/Source/Readers/DSSMReader/stdafx.h @@ -1,20 +1,20 @@ -// stdafx.h : include file for standard system include files, -// or project specific include files that are used frequently, but -// are changed infrequently -// - -#pragma once - -#include "targetver.h" - +// stdafx.h : include file for standard system include files, +// or project specific include files that are used frequently, but +// are changed infrequently +// + +#pragma once + +#include "targetver.h" + #ifndef _CRT_SECURE_NO_WARNINGS #define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings #endif - -#define WIN32_LEAN_AND_MEAN // Exclude rarely-used stuff from Windows headers -// Windows Header Files: -#include - - - -// TODO: reference additional headers your program requires here + +#define WIN32_LEAN_AND_MEAN // Exclude rarely-used stuff from Windows headers +// Windows Header Files: +#include + + + +// TODO: reference additional headers your program requires here diff --git a/Source/Readers/DSSMReader/targetver.h b/Source/Readers/DSSMReader/targetver.h index 90e767bfce74..87c0086de751 100644 --- a/Source/Readers/DSSMReader/targetver.h +++ b/Source/Readers/DSSMReader/targetver.h @@ -1,8 +1,8 @@ -#pragma once - -// Including SDKDDKVer.h defines the highest available Windows platform. - -// If you wish to build your application for a previous Windows platform, include WinSDKVer.h and -// set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h. - -#include +#pragma once + +// Including SDKDDKVer.h defines the highest available Windows platform. + +// If you wish to build your application for a previous Windows platform, include WinSDKVer.h and +// set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h. + +#include diff --git a/Source/Readers/HTKMLFReader/HTKMLFReader.cpp b/Source/Readers/HTKMLFReader/HTKMLFReader.cpp index ecc6283f615d..a88d8304e7c3 100644 --- a/Source/Readers/HTKMLFReader/HTKMLFReader.cpp +++ b/Source/Readers/HTKMLFReader/HTKMLFReader.cpp @@ -1,1932 +1,1932 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// HTKMLFReader.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#ifdef _WIN32 -#include -#endif -#include "Basics.h" - -#include "htkfeatio.h" // for reading HTK features -#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) -#include "simplesenonehmm.h" // for MMI scoring -#include "msra_mgram.h" // for unigram scores of ground-truth path in sequence training - -#include "rollingwindowsource.h" // minibatch sources -#include "utterancesourcemulti.h" -#include "chunkevalsource.h" -#include "minibatchiterator.h" -#define DATAREADER_EXPORTS // creating the exports here -#include "DataReader.h" -#include "Config.h" -#include "ScriptableObjects.h" -#include "HTKMLFReader.h" -#include "TimerUtility.h" -#ifdef LEAKDETECT -#include // for memory leak detection -#endif - -#ifdef __unix__ -#include -typedef unsigned long DWORD; -typedef unsigned short WORD; -typedef unsigned int UNINT32; -#endif -#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this - -#ifdef _WIN32 -int msra::numa::node_override = -1; // for numahelpers.h -#endif - -namespace msra { namespace lm { -/*static*/ const mgram_map::index_t mgram_map::nindex = (mgram_map::index_t) -1; // invalid index -}} - -namespace Microsoft { namespace MSR { namespace CNTK { - - // Create a Data Reader - //DATAREADER_API IDataReader* DataReaderFactory(void) - - template template - void HTKMLFReader::InitFromConfig(const ConfigRecordType & readerConfig) - { - m_truncated = readerConfig(L"truncated", false); - m_convertLabelsToTargets = false; - - intargvector numberOfuttsPerMinibatchForAllEpochs = readerConfig(L"nbruttsineachrecurrentiter", ConfigRecordType::Array(intargvector(vector{ 1 }))); - m_numSeqsPerMBForAllEpochs = numberOfuttsPerMinibatchForAllEpochs; - - for (int i = 0; i < m_numSeqsPerMBForAllEpochs.size(); i++) - { - if (m_numSeqsPerMBForAllEpochs[i] < 1) - { - LogicError("nbrUttsInEachRecurrentIter cannot be less than 1."); - } - } - - m_numSeqsPerMB = m_numSeqsPerMBForAllEpochs[0]; - m_pMBLayout->Init(m_numSeqsPerMB, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) - - m_noData = false; - - wstring command(readerConfig(L"action", L"")); //look up in the config for the master command to determine whether we're writing output (inputs only) or training/evaluating (inputs and outputs) - - if (readerConfig.Exists(L"legacyMode")) - RuntimeError("legacy mode has been deprecated\n"); - - if (command == L"write") - { - m_trainOrTest = false; - PrepareForWriting(readerConfig); - } - else - { - m_trainOrTest = true; - PrepareForTrainingOrTesting(readerConfig); - } - } - - // Load all input and output data. - // Note that the terms features imply be real-valued quanities and - // labels imply categorical quantities, irrespective of whether they - // are inputs or targets for the network - template template - void HTKMLFReader::PrepareForTrainingOrTesting(const ConfigRecordType& readerConfig) - { - vector scriptpaths; - vector RootPathInScripts; - wstring RootPathInLatticeTocs; - vector mlfpaths; - vector>mlfpathsmulti; - size_t firstfilesonly = SIZE_MAX; // set to a lower value for testing - vector> infilesmulti; - size_t numFiles; - wstring unigrampath(L""); - //wstring statelistpath(L""); - size_t randomize = randomizeAuto; - size_t iFeat, iLabel; - iFeat = iLabel = 0; - vector statelistpaths; - vector numContextLeft; - vector numContextRight; - - std::vector featureNames; - std::vector labelNames; - // for hmm and lattice - std::vector hmmNames; - std::vector latticeNames; - GetDataNamesFromConfig(readerConfig, featureNames, labelNames, hmmNames, latticeNames); - if (featureNames.size() + labelNames.size() <= 1) - { - InvalidArgument("network needs at least 1 input and 1 output specified!"); - } - - //load data for all real-valued inputs (features) - foreach_index(i, featureNames) - { - const ConfigRecordType & thisFeature = readerConfig(featureNames[i]); - m_featDims.push_back(thisFeature(L"dim")); - intargvector contextWindow = thisFeature(L"contextWindow", ConfigRecordType::Array(intargvector(vector{ 1 }))); - if (contextWindow.size() == 1) // symmetric - { - size_t windowFrames = contextWindow[0]; - if (windowFrames % 2 == 0 ) - InvalidArgument("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowFrames); - size_t context = windowFrames / 2; // extend each side by this - numContextLeft.push_back(context); - numContextRight.push_back(context); - - } - else if (contextWindow.size() == 2) // left context, right context - { - numContextLeft.push_back(contextWindow[0]); - numContextRight.push_back(contextWindow[1]); - } - else - { - InvalidArgument("contextFrames must have 1 or 2 values specified, found %d", (int)contextWindow.size()); - } - // update m_featDims to reflect the total input dimension (featDim x contextWindow), not the native feature dimension - // that is what the lower level feature readers expect - m_featDims[i] = m_featDims[i] * (1 + numContextLeft[i] + numContextRight[i]); - - wstring type = thisFeature(L"type", L"real"); - if (!_wcsicmp(type.c_str(), L"real")) - { - m_nameToTypeMap[featureNames[i]] = InputOutputTypes::real; - } - else - { - InvalidArgument("feature type must be 'real'"); - } - - m_featureNameToIdMap[featureNames[i]]= iFeat; - scriptpaths.push_back(thisFeature(L"scpFile")); - RootPathInScripts.push_back(thisFeature(L"prefixPathInSCP", L"")); - m_featureNameToDimMap[featureNames[i]] = m_featDims[i]; - - m_featuresBufferMultiIO.push_back(nullptr); - m_featuresBufferAllocatedMultiIO.push_back(0); - - iFeat++; - } - - foreach_index(i, labelNames) - { - const ConfigRecordType & thisLabel = readerConfig(labelNames[i]); - if (thisLabel.Exists(L"labelDim")) - m_labelDims.push_back(thisLabel(L"labelDim")); - else if (thisLabel.Exists(L"dim")) - m_labelDims.push_back(thisLabel(L"dim")); - else - InvalidArgument("labels must specify dim or labelDim"); - - wstring type; - if (thisLabel.Exists(L"labelType")) - type = (const wstring &)thisLabel(L"labelType"); // let's deprecate this eventually and just use "type"... - else - type = (const wstring &)thisLabel(L"type", L"category"); // outputs should default to category - - if (!_wcsicmp(type.c_str(), L"category")) - m_nameToTypeMap[labelNames[i]] = InputOutputTypes::category; - else - InvalidArgument("label type must be 'category'"); - - statelistpaths.push_back(thisLabel(L"labelMappingFile", L"")); - - m_labelNameToIdMap[labelNames[i]]=iLabel; - m_labelNameToDimMap[labelNames[i]]=m_labelDims[i]; - mlfpaths.clear(); - if (thisLabel.ExistsCurrent(L"mlfFile")) - { - mlfpaths.push_back(thisLabel(L"mlfFile")); - } - else - { - if (!thisLabel.ExistsCurrent(L"mlfFileList")) - { - InvalidArgument("Either mlfFile or mlfFileList must exist in HTKMLFReder"); - } - wstring list = thisLabel(L"mlfFileList"); - for (msra::files::textreader r(list); r;) - { - mlfpaths.push_back(r.wgetline()); - } - } - mlfpathsmulti.push_back(mlfpaths); - - m_labelsBufferMultiIO.push_back(nullptr); - m_labelsBufferAllocatedMultiIO.push_back(0); - - iLabel++; - - wstring labelToTargetMappingFile(thisLabel(L"labelToTargetMappingFile", L"")); - if (labelToTargetMappingFile != L"") - { - std::vector> labelToTargetMap; - m_convertLabelsToTargetsMultiIO.push_back(true); - if (thisLabel.Exists(L"targetDim")) - { - m_labelNameToDimMap[labelNames[i]] = m_labelDims[i] = thisLabel(L"targetDim"); - } - else - RuntimeError("output must specify targetDim if labelToTargetMappingFile specified!"); - size_t targetDim = ReadLabelToTargetMappingFile (labelToTargetMappingFile,statelistpaths[i], labelToTargetMap); - if (targetDim!=m_labelDims[i]) - RuntimeError("mismatch between targetDim and dim found in labelToTargetMappingFile"); - m_labelToTargetMapMultiIO.push_back(labelToTargetMap); - } - else - { - m_convertLabelsToTargetsMultiIO.push_back(false); - m_labelToTargetMapMultiIO.push_back(std::vector>()); - } - } - - //get lattice toc file names - std::pair, std::vector> latticetocs; - foreach_index(i, latticeNames) //only support one set of lattice now - { - const ConfigRecordType & thisLattice = readerConfig(latticeNames[i]); - - vector paths; - expand_wildcards(thisLattice(L"denLatTocFile"), paths); - latticetocs.second.insert(latticetocs.second.end(), paths.begin(), paths.end()); - - if (thisLattice.Exists(L"numLatTocFile")) - { - paths.clear(); - expand_wildcards(thisLattice(L"numLatTocFile"), paths); - latticetocs.first.insert(latticetocs.first.end(), paths.begin(), paths.end()); - } - RootPathInLatticeTocs =(wstring) thisLattice(L"prefixPathInToc",L""); - } - - //get HMM related file names - vector cdphonetyingpaths, transPspaths; - foreach_index(i, hmmNames) - { - const ConfigRecordType & thisHMM = readerConfig(hmmNames[i]); - - cdphonetyingpaths.push_back(thisHMM(L"phoneFile")); - transPspaths.push_back(thisHMM(L"transPFile", L"")); - } - - // mmf files - //only support one set now - if (cdphonetyingpaths.size() > 0 && statelistpaths.size() > 0 && transPspaths.size() > 0) - m_hset.loadfromfile(cdphonetyingpaths[0], statelistpaths[0], transPspaths[0]); - if (iFeat!=scriptpaths.size() || iLabel!=mlfpathsmulti.size()) - RuntimeError("# of inputs files vs. # of inputs or # of output files vs # of outputs inconsistent\n"); - - if (readerConfig.Exists(L"randomize")) - { - wstring randomizeString = readerConfig.CanBeString(L"randomize") ? readerConfig(L"randomize") : wstring(); - if (!_wcsicmp(randomizeString.c_str(), L"none")) - randomize = randomizeNone; - else if (!_wcsicmp(randomizeString.c_str(), L"auto")) - randomize = randomizeAuto; - else - randomize = readerConfig(L"randomize"); - } - - m_frameMode = readerConfig(L"frameMode", true); - m_verbosity = readerConfig(L"verbosity", 2); - - // determine if we partial minibatches are desired - wstring minibatchMode(readerConfig(L"minibatchMode", L"partial")); - m_partialMinibatch = !_wcsicmp(minibatchMode.c_str(), L"partial"); - - // get the read method, defaults to "blockRandomize" other option is "rollingWindow" - wstring readMethod(readerConfig(L"readMethod",L"blockRandomize")); - - if (readMethod == L"blockRandomize" && randomize == randomizeNone) - InvalidArgument("'randomize' cannot be 'none' when 'readMethod' is 'blockRandomize'."); - - // read all input files (from multiple inputs) - // TO DO: check for consistency (same number of files in each script file) - numFiles=0; - foreach_index (i, scriptpaths) - { - vector filelist; - std::wstring scriptpath = scriptpaths[i]; - fprintf(stderr, "reading script file %ls ...", scriptpath.c_str()); - size_t n = 0; - for (msra::files::textreader reader(scriptpath); reader && filelist.size() <= firstfilesonly/*optimization*/; ) - { - filelist.push_back (reader.wgetline()); - n++; - } - - fprintf (stderr, " %lu entries\n", n); - - if (i==0) - numFiles=n; - else - if (n!=numFiles) - RuntimeError("number of files in each scriptfile inconsistent (%d vs. %d)", (int)numFiles, (int)n); - - // post processing file list : - // - if users specified PrefixPath, add the prefix to each of path in filelist - // - else do the dotdotdot expansion if necessary - wstring rootpath = RootPathInScripts[i]; - if (!rootpath.empty()) // use has specified a path prefix for this feature - { - // first make slash consistent (sorry for linux users:this is not necessary for you) - std::replace(rootpath.begin(), rootpath.end(), L'\\', L'/'); - // second, remove trailling slash if there is any - std::wregex trailer(L"/+$"); - rootpath = std::regex_replace(rootpath, trailer, wstring()); - // third, join the rootpath with each entry in filelist - if (!rootpath.empty()) - { - for (wstring & path : filelist) - { - if (path.find_first_of(L'=') != wstring::npos) - { - vector strarr = msra::strfun::split(path, L"="); -#ifdef WIN32 - replace(strarr[1].begin(), strarr[1].end(), L'\\', L'/'); -#endif - - path = strarr[0] + L"=" + rootpath + L"/" + strarr[1]; - } - else - { -#ifdef WIN32 - replace(path.begin(), path.end(), L'\\', L'/'); -#endif - path = rootpath + L"/" + path; - } - } - } - } - else - { - /* - do "..." expansion if SCP uses relative path names - "..." in the SCP means full path is the same as the SCP file - for example, if scp file is "//aaa/bbb/ccc/ddd.scp" - and contains entry like - .../file1.feat - .../file2.feat - etc. - the features will be read from - //aaa/bbb/ccc/file1.feat - //aaa/bbb/ccc/file2.feat - etc. - This works well if you store the scp file with the features but - do not want different scp files everytime you move or create new features - */ - wstring scpdircached; - for (auto & entry : filelist) - ExpandDotDotDot(entry, scriptpath, scpdircached); - } - - infilesmulti.push_back(std::move(filelist)); - } - - if (readerConfig.Exists(L"unigram")) - unigrampath = (const wstring &)readerConfig(L"unigram"); - - // load a unigram if needed (this is used for MMI training) - msra::lm::CSymbolSet unigramsymbols; - std::unique_ptr unigram; - size_t silencewordid = SIZE_MAX; - size_t startwordid = SIZE_MAX; - size_t endwordid = SIZE_MAX; - if (unigrampath != L"") - { - unigram.reset (new msra::lm::CMGramLM()); - unigram->read (unigrampath, unigramsymbols, false/*filterVocabulary--false will build the symbol map*/, 1/*maxM--unigram only*/); - silencewordid = unigramsymbols["!silence"]; // give this an id (even if not in the LM vocabulary) - startwordid = unigramsymbols[""]; - endwordid = unigramsymbols[""]; - } - - if (!unigram && latticetocs.second.size() > 0) - fprintf (stderr, "trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion\n"); - - // currently assumes all mlfs will have same root name (key) - set restrictmlftokeys; // restrict MLF reader to these files--will make stuff much faster without having to use shortened input files - if (infilesmulti[0].size() <= 100) - { - foreach_index (i, infilesmulti[0]) - { - msra::asr::htkfeatreader::parsedpath ppath (infilesmulti[0][i]); - const wstring key = regex_replace ((wstring)ppath, wregex (L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) - restrictmlftokeys.insert (key); - } - } - // get labels - - //if (readerConfig.Exists(L"statelist")) - // statelistpath = readerConfig(L"statelist"); - - double htktimetoframe = 100000.0; // default is 10ms - //std::vector> labelsmulti; - std::vector>> labelsmulti; - //std::vector pagepath; - foreach_index(i, mlfpathsmulti) - { - const msra::lm::CSymbolSet* wordmap = unigram ? &unigramsymbols : NULL; - msra::asr::htkmlfreader - labels(mlfpathsmulti[i], restrictmlftokeys, statelistpaths[i], wordmap, (map*) NULL, htktimetoframe); // label MLF - // get the temp file name for the page file - - // Make sure 'msra::asr::htkmlfreader' type has a move constructor - static_assert(std::is_move_constructible>::value, - "Type 'msra::asr::htkmlfreader' should be move constructible!"); - - labelsmulti.push_back(std::move(labels)); - } - - if (!_wcsicmp(readMethod.c_str(), L"blockRandomize")) - { - // construct all the parameters we don't need, but need to be passed to the constructor... - - m_lattices.reset(new msra::dbn::latticesource(latticetocs, m_hset.getsymmap(), RootPathInLatticeTocs)); - m_lattices->setverbosity(m_verbosity); - - // now get the frame source. This has better randomization and doesn't create temp files - m_frameSource.reset(new msra::dbn::minibatchutterancesourcemulti(infilesmulti, labelsmulti, m_featDims, m_labelDims, numContextLeft, numContextRight, randomize, *m_lattices, m_latticeMap, m_frameMode)); - m_frameSource->setverbosity(m_verbosity); - } - else if (!_wcsicmp(readMethod.c_str(), L"rollingWindow")) - { - std::wstring pageFilePath; - std::vector pagePaths; - if (readerConfig.Exists(L"pageFilePath")) - { - pageFilePath = (const wstring &)readerConfig(L"pageFilePath"); - - // replace any '/' with '\' for compat with default path - std::replace(pageFilePath.begin(), pageFilePath.end(), '/','\\'); -#ifdef _WIN32 - // verify path exists - DWORD attrib = GetFileAttributes(pageFilePath.c_str()); - if (attrib==INVALID_FILE_ATTRIBUTES || !(attrib & FILE_ATTRIBUTE_DIRECTORY)) - RuntimeError("pageFilePath does not exist"); -#endif -#ifdef __unix__ - struct stat statbuf; - if (stat(wtocharpath(pageFilePath).c_str(), &statbuf)==-1) - { - RuntimeError("pageFilePath does not exist"); - } -#endif - } - else // using default temporary path - { -#ifdef _WIN32 - pageFilePath.reserve(MAX_PATH); - GetTempPath(MAX_PATH, &pageFilePath[0]); -#endif -#ifdef __unix__ - pageFilePath = L"/tmp/temp.CNTK.XXXXXX"; -#endif - } - -#ifdef _WIN32 - if (pageFilePath.size()>MAX_PATH-14) // max length of input to GetTempFileName is MAX_PATH-14 - RuntimeError("pageFilePath must be less than %d characters", MAX_PATH-14); -#else - if (pageFilePath.size()>PATH_MAX-14) // max length of input to GetTempFileName is PATH_MAX-14 - RuntimeError("pageFilePath must be less than %d characters", PATH_MAX-14); -#endif - foreach_index(i, infilesmulti) - { -#ifdef _WIN32 - wchar_t tempFile[MAX_PATH]; - GetTempFileName(pageFilePath.c_str(), L"CNTK", 0, tempFile); - pagePaths.push_back(tempFile); -#endif -#ifdef __unix__ - char tempFile[PATH_MAX]; - strcpy(tempFile, msra::strfun::utf8(pageFilePath).c_str()); - int fid = mkstemp(tempFile); - unlink (tempFile); - close (fid); - pagePaths.push_back(GetWC(tempFile)); -#endif - } - - const bool mayhavenoframe=false; - int addEnergy = 0; - - m_frameSource.reset(new msra::dbn::minibatchframesourcemulti(infilesmulti, labelsmulti, m_featDims, m_labelDims, numContextLeft, numContextRight, randomize, pagePaths, mayhavenoframe, addEnergy)); - m_frameSource->setverbosity(m_verbosity); - } - else - { - RuntimeError("readMethod must be 'rollingWindow' or 'blockRandomize'"); - } - } - - // Load all input and output data. - // Note that the terms features imply be real-valued quanities and - // labels imply categorical quantities, irrespective of whether they - // are inputs or targets for the network - // TODO: lots of code dup with the other Prepare function - template template - void HTKMLFReader::PrepareForWriting(const ConfigRecordType& readerConfig) - { - vector scriptpaths; - size_t numFiles; - size_t firstfilesonly = SIZE_MAX; // set to a lower value for testing - size_t evalchunksize = 2048; - vector realDims; - size_t iFeat = 0; - vector numContextLeft; - vector numContextRight; - - std::vector featureNames; - std::vector labelNames; - //lattice and hmm - std::vector hmmNames; - std::vector latticeNames; - - GetDataNamesFromConfig(readerConfig, featureNames, labelNames, hmmNames, latticeNames); - - foreach_index(i, featureNames) - { - const ConfigRecordType & thisFeature = readerConfig(featureNames[i]); - realDims.push_back(thisFeature(L"dim")); - - intargvector contextWindow = thisFeature(L"contextWindow", ConfigRecordType::Array(intargvector(vector{ 1 }))); - if (contextWindow.size() == 1) // symmetric - { - size_t windowFrames = contextWindow[0]; - if (windowFrames % 2 == 0) - RuntimeError("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowFrames); - size_t context = windowFrames / 2; // extend each side by this - numContextLeft.push_back(context); - numContextRight.push_back(context); - - } - else if (contextWindow.size() == 2) // left context, right context - { - numContextLeft.push_back(contextWindow[0]); - numContextRight.push_back(contextWindow[1]); - } - else - { - RuntimeError("contextFrames must have 1 or 2 values specified, found %d", (int)contextWindow.size()); - } - // update m_featDims to reflect the total input dimension (featDim x contextWindow), not the native feature dimension - // that is what the lower level feature readers expect - realDims[i] = realDims[i] * (1 + numContextLeft[i] + numContextRight[i]); - - wstring type = thisFeature(L"type", L"real"); - if (!_wcsicmp(type.c_str(), L"real")) - { - m_nameToTypeMap[featureNames[i]] = InputOutputTypes::real; - } - else{ - RuntimeError("feature type must be 'real'"); - } - - m_featureNameToIdMap[featureNames[i]]= iFeat; - scriptpaths.push_back(thisFeature(L"scpFile")); - m_featureNameToDimMap[featureNames[i]] = realDims[i]; - - m_featuresBufferMultiIO.push_back(nullptr); - m_featuresBufferAllocatedMultiIO.push_back(0); - iFeat++; - } - - if (labelNames.size()>0) - RuntimeError("writer mode does not support labels as inputs, only features"); - - numFiles=0; - foreach_index(i,scriptpaths) - { - vector filelist; - std::wstring scriptpath = scriptpaths[i]; - fprintf(stderr, "reading script file %ls ...", scriptpath.c_str()); - size_t n = 0; - for (msra::files::textreader reader(scriptpath); reader && filelist.size() <= firstfilesonly/*optimization*/; ) - { - filelist.push_back (reader.wgetline()); - n++; - } - - fprintf (stderr, " %d entries\n", (int)n); - - if (i==0) - numFiles=n; - else - if (n!=numFiles) - RuntimeError("HTKMLFReader::InitEvalReader: number of files in each scriptfile inconsistent (%d vs. %d)", (int)numFiles, (int)n); - - m_inputFilesMultiIO.push_back(std::move(filelist)); - } - - m_fileEvalSource.reset(new msra::dbn::FileEvalSource(realDims, numContextLeft, numContextRight, evalchunksize)); - } - - //StartMinibatchLoop - Startup a minibatch loop - // requestedMBSize - [in] size of the minibatch (number of frames, etc.) - // epoch - [in] epoch number for this loop - // requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset - template - void HTKMLFReader::StartDistributedMinibatchLoop(size_t requestedMBSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples /*= requestDataSize*/) - { - assert(subsetNum < numSubsets); - assert(((subsetNum == 0) && (numSubsets == 1)) || this->SupportsDistributedMBRead()); - - m_mbNumTimeSteps = requestedMBSize; // note: ignored in frame mode and full-sequence mode - - m_numSeqsPerMB = m_numSeqsPerMBForAllEpochs[epoch]; - - // For distributed reading under utterance mode, we distribute the utterances per minibatch among all the subsets - if (m_trainOrTest && !m_frameMode) - { - if ((numSubsets > 1) && (m_numSeqsPerMB < numSubsets)) - { - LogicError("Insufficient value of 'nbruttsineachrecurrentiter'=%d for distributed reading with %d subsets", (int)m_numSeqsPerMB, (int)numSubsets); - } - - m_numSeqsPerMB = (m_numSeqsPerMB / numSubsets) + ((subsetNum < (m_numSeqsPerMB % numSubsets)) ? 1 : 0); - } - - m_pMBLayout->Init(m_numSeqsPerMB, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) - - // resize the arrays - // These are sized to the requested number. If not all can be filled, it will still return this many, just with gaps. - // In frame mode, m_numSeqsPerMB must be 1. However, the returned layout has one 1-frame sequence per frame. - m_sentenceEnd.assign(m_numSeqsPerMB, true); - m_processedFrame.assign(m_numSeqsPerMB, 0); - m_numFramesToProcess.assign(m_numSeqsPerMB, 0); - m_switchFrame.assign(m_numSeqsPerMB, 0); - m_numValidFrames.assign(m_numSeqsPerMB, 0); - - if (m_trainOrTest) - { - // for the multi-utterance process - m_featuresBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); - m_featuresBufferAllocatedMultiUtt.assign(m_numSeqsPerMB, 0); - m_labelsBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); - m_labelsBufferAllocatedMultiUtt.assign(m_numSeqsPerMB, 0); - - // for the multi-utterance process for lattice and phone boundary - m_latticeBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); - m_labelsIDBufferMultiUtt.resize(m_numSeqsPerMB); - m_phoneboundaryIDBufferMultiUtt.resize(m_numSeqsPerMB); - - if (m_frameMode && (m_numSeqsPerMB > 1)) - { - LogicError("nbrUttsInEachRecurrentIter cannot be more than 1 in frame mode reading."); - } - - // BUGBUG: in BPTT and sequence mode, we should pass 1 or 2 instead of requestedMBSize to ensure we only get one utterance back at a time - StartMinibatchLoopToTrainOrTest(requestedMBSize, epoch, subsetNum, numSubsets, requestedEpochSamples); - } - else - { - // No distributed reading of mini-batches for write - if ((subsetNum != 0) || (numSubsets != 1)) - { - LogicError("Distributed reading of mini-batches is only supported for training or testing"); - } - m_pMBLayout->Init(requestedMBSize, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) - - StartMinibatchLoopToWrite(requestedMBSize,epoch,requestedEpochSamples); - } - m_checkDictionaryKeys=true; - } - - template - void HTKMLFReader::StartMinibatchLoopToTrainOrTest(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples) - { - size_t datapasses=1; - size_t totalFrames = m_frameSource->totalframes(); - - size_t extraFrames = totalFrames%mbSize; - size_t minibatches = totalFrames/mbSize; - - // if we are allowing partial minibatches, do nothing, and let it go through - if (!m_partialMinibatch) - { - // we don't want any partial frames, so round total frames to be an even multiple of our mbSize - if (totalFrames > mbSize) - totalFrames -= extraFrames; - - if (requestedEpochSamples == requestDataSize) - { - requestedEpochSamples = totalFrames; - } - else if (minibatches > 0) // if we have any full minibatches - { - // since we skip the extraFrames, we need to add them to the total to get the actual number of frames requested - size_t sweeps = (requestedEpochSamples-1)/totalFrames; // want the number of sweeps we will skip the extra, so subtract 1 and divide - requestedEpochSamples += extraFrames*sweeps; - } - } - else if (requestedEpochSamples == requestDataSize) - { - requestedEpochSamples = totalFrames; - } - - m_mbiter.reset(new msra::dbn::minibatchiterator(*m_frameSource, epoch, requestedEpochSamples, mbSize, subsetNum, numSubsets, datapasses)); - // Advance the MB iterator until we find some data or reach the end of epoch - while ((m_mbiter->currentmbframes() == 0) && *m_mbiter) - { - (*m_mbiter)++; - } - - m_noData = false; - if (!(*m_mbiter)) - m_noData = true; - - if (!m_featuresBufferMultiIO.empty()) - { - if (m_featuresBufferMultiIO[0] != nullptr) // check first feature, if it isn't NULL, safe to assume all are not NULL? - { - foreach_index(i, m_featuresBufferMultiIO) - { - m_featuresBufferMultiIO[i] = nullptr; - m_featuresBufferAllocatedMultiIO[i]=0; - } - } - - m_featuresStartIndexMultiUtt.assign(m_featuresBufferMultiIO.size()*m_numSeqsPerMB, 0); - - } - - if (!m_labelsBufferMultiIO.empty()) - { - if (m_labelsBufferMultiIO[0] != nullptr) - { - foreach_index(i, m_labelsBufferMultiIO) - { - m_labelsBufferMultiIO[i] = nullptr; - m_labelsBufferAllocatedMultiIO[i]=0; - } - } - - m_labelsStartIndexMultiUtt.assign(m_labelsBufferMultiIO.size()*m_numSeqsPerMB, 0); - } - - for (size_t u = 0; u < m_numSeqsPerMB; u ++) - { - if (m_featuresBufferMultiUtt[u] != NULL) - { - m_featuresBufferMultiUtt[u] = NULL; - m_featuresBufferAllocatedMultiUtt[u] = 0; - } - - if (m_labelsBufferMultiUtt[u] != NULL) - { - m_labelsBufferMultiUtt[u] = NULL; - m_labelsBufferAllocatedMultiUtt[u] = 0; - } - - if (m_latticeBufferMultiUtt[u] != NULL) - { - m_latticeBufferMultiUtt[u].reset(); - } - - ReNewBufferForMultiIO(u); - } - } - - template - void HTKMLFReader::StartMinibatchLoopToWrite(size_t mbSize, size_t /*epoch*/, size_t /*requestedEpochSamples*/) - { - m_fileEvalSource->Reset(); - m_fileEvalSource->SetMinibatchSize(mbSize); - m_inputFileIndex=0; - - if (m_featuresBufferMultiIO[0] != nullptr) // check first feature, if it isn't NULL, safe to assume all are not NULL? - { - foreach_index(i, m_featuresBufferMultiIO) - { - m_featuresBufferMultiIO[i] = nullptr; - m_featuresBufferAllocatedMultiIO[i]=0; - } - } - } - - template - bool HTKMLFReader::GetMinibatch4SE(std::vector> & latticeinput, - vector &uids, vector &boundaries, vector &extrauttmap) - { - if (m_trainOrTest) - { - return GetMinibatch4SEToTrainOrTest(latticeinput, uids, boundaries, extrauttmap); - } - else - { - return true; - } - } - template - bool HTKMLFReader::GetMinibatch4SEToTrainOrTest(std::vector> & latticeinput, - std::vector &uids, std::vector &boundaries, std::vector &extrauttmap) - { - latticeinput.clear(); - uids.clear(); - boundaries.clear(); - extrauttmap.clear(); - for (size_t i = 0; i < m_extraSeqsPerMB.size(); i++) - { - latticeinput.push_back(m_extraLatticeBufferMultiUtt[i]); - uids.insert(uids.end(), m_extraLabelsIDBufferMultiUtt[i].begin(), m_extraLabelsIDBufferMultiUtt[i].end()); - boundaries.insert(boundaries.end(), m_extraPhoneboundaryIDBufferMultiUtt[i].begin(), m_extraPhoneboundaryIDBufferMultiUtt[i].end()); - } - - extrauttmap.insert(extrauttmap.end(), m_extraSeqsPerMB.begin(), m_extraSeqsPerMB.end()); - return true; - } - - template - bool HTKMLFReader::GetHmmData(msra::asr::simplesenonehmm * hmm) - { - *hmm = m_hset; - return true; - } - // GetMinibatch - Get the next minibatch (features and labels) - // matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, - // [out] each matrix resized if necessary containing data. - // returns - true if there are more minibatches, false if no more minibatchs remain - // TODO: Why do we have two read functions? Is one not a superset of the other? - template - bool HTKMLFReader::GetMinibatch(std::map*>& matrices) - { - if (m_trainOrTest) - { - return GetMinibatchToTrainOrTest(matrices); - } - else - { - return GetMinibatchToWrite(matrices); - } - } - - template - bool HTKMLFReader::GetMinibatchToTrainOrTest(std::map*>& matrices) - { - size_t id; - size_t dim; - bool skip = false; - - // on first minibatch, make sure we can supply data for requested nodes - std::map::iterator iter; - if (m_checkDictionaryKeys) - { - for (auto iter=matrices.begin();iter!=matrices.end();iter++) - { - if (m_nameToTypeMap.find(iter->first)==m_nameToTypeMap.end()) - RuntimeError("minibatch requested for input node %ls not found in reader - cannot generate input\n", iter->first.c_str()); - } - m_checkDictionaryKeys=false; - } - - Timer aggregateTimer; - if (m_verbosity > 2) - aggregateTimer.Start(); - - do - { - if (!m_truncated) - { - // ------------------------------------------------------- - // frame mode or whole utterances - // ------------------------------------------------------- - - m_extraLatticeBufferMultiUtt.clear(); - m_extraLabelsIDBufferMultiUtt.clear(); - m_extraPhoneboundaryIDBufferMultiUtt.clear(); - m_extraSeqsPerMB.clear(); - if (m_noData && m_numFramesToProcess[0] == 0) //no data left for the first channel of this minibatch, - { - return false; - } - - // BUGBUG: We should decide how many utterances we are going to take, until the desired number of frames has been filled. - // Currently it seems to fill a fixed number of utterances, regardless of their length. - - // decide the m_mbNumTimeSteps - // The number of columns is determined by the longest utterance amongst the desired set. - // I.e. whatever is user-specified as the MB size, will be ignored here (that value is, however, passed down to the underlying reader). BUGBUG: That is even more wrong. - // BUGBUG: We should honor the mbSize parameter and fill up to the requested number of samples, using the requested #parallel sequences. - // m_mbNumTimeSteps = max (m_numFramesToProcess[.]) - m_mbNumTimeSteps = m_numFramesToProcess[0]; - for (size_t i = 1; i < m_numSeqsPerMB; i++) - { - if (m_mbNumTimeSteps < m_numFramesToProcess[i]) - m_mbNumTimeSteps = m_numFramesToProcess[i]; - } - - if (m_frameMode) - { - assert(m_numSeqsPerMB == 1); // user must not request parallel sequences - m_pMBLayout->InitAsFrameMode(m_mbNumTimeSteps); - } - else - { - m_pMBLayout->Init(m_numSeqsPerMB, m_mbNumTimeSteps); - } - - // create a MB with the desired utterances - // First fill each parallel sequence with one utterance. No packing yet. - // Note that the code below is a little misleading for frame mode. - // In frame mode, this reader thinks it has only one parallel sequence (m_numSeqsPerMB == 1), - // but it reports it to the outside as N parallel sequences of one frame each. - skip = (m_frameMode && !m_partialMinibatch && (m_mbiter->requestedframes() != m_mbNumTimeSteps) && (m_frameSource->totalframes() > m_mbNumTimeSteps)); - for (size_t i = 0; i < m_numSeqsPerMB; i++) - { - if (!skip) - { - // a stopgap - if (m_numFramesToProcess[i] > 0 && m_latticeBufferMultiUtt[i] && m_latticeBufferMultiUtt[i]->getnumframes() != m_numFramesToProcess[i]) - { - // BUGBUG: we just found that (due to some bugs yet to be tracked down), - // the filled number of frames is inconsistent with the number frames in lattices (though it rarely occurs) - // This is just a stopgap, to be removed after the bugs are found and fixed - bool needRenew = true; - while (needRenew) - { - size_t framenum = m_numFramesToProcess[i]; - fprintf(stderr, "WARNING: mismatched number of frames filled in the reader: %d in data vs %d in lattices. Ignoring this utterance %ls\n", - (int)framenum, (int)m_latticeBufferMultiUtt[i]->getnumframes(), m_latticeBufferMultiUtt[i]->getkey().c_str()); - ReNewBufferForMultiIO(i); - needRenew = m_numFramesToProcess[i] > 0 && m_latticeBufferMultiUtt[i] && m_latticeBufferMultiUtt[i]->getnumframes() != m_numFramesToProcess[i]; - } - - } - m_numValidFrames[i] = m_numFramesToProcess[i]; - if (m_numValidFrames[i] > 0) - { - if (m_frameMode) - { - // the layout has already been initialized as entirely frame mode above - assert(i == 0); // this reader thinks there is only one parallel sequence - for (size_t s = 0; s < m_pMBLayout->GetNumParallelSequences(); s++) - assert(s < m_numValidFrames[i]); // MB is already set to only include the valid frames (no need for gaps) - } - else - m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, 0, m_numValidFrames[i]); - - m_extraSeqsPerMB.push_back(i); - fillOneUttDataforParallelmode(matrices, 0, m_numValidFrames[i], i, i); - if (m_latticeBufferMultiUtt[i] != nullptr) - { - m_extraLatticeBufferMultiUtt.push_back(m_latticeBufferMultiUtt[i]); - m_extraLabelsIDBufferMultiUtt.push_back(m_labelsIDBufferMultiUtt[i]); - m_extraPhoneboundaryIDBufferMultiUtt.push_back(m_phoneboundaryIDBufferMultiUtt[i]); - } - } - } - ReNewBufferForMultiIO(i); - } - - if (!skip) - { - m_extraNumSeqs = 0; - if (!m_frameMode) - { - for (size_t src = 0; src < m_numSeqsPerMB; ) - { - size_t framenum = m_numFramesToProcess[src]; - if (framenum == 0) - { - src++; - continue; - } - if (m_latticeBufferMultiUtt[src]!=nullptr && m_latticeBufferMultiUtt[src]->getnumframes()!=framenum) - { - // BUGBUG: we just found that (due to some bugs yet to be tracked down), - // the filled number of frames is inconsistent with the number frames in lattices (though it rarely occurs) - // This is just a stopgap, to be removed after the bugs are found and fixed - fprintf(stderr, "WARNING: mismatched number of frames filled in the reader: %d in data vs %d in lattices. Ignoring this utterance %ls\n", - (int)framenum, (int)m_latticeBufferMultiUtt[src]->getnumframes(), m_latticeBufferMultiUtt[src]->getkey().c_str()); - src++; - continue; - } - - bool slotFound = false; - for (size_t des = 0; des < m_numSeqsPerMB; des++) // try to found a slot - { - if (framenum + m_numValidFrames[des] < m_mbNumTimeSteps) - { // found ! - m_extraSeqsPerMB.push_back(des); - if (m_latticeBufferMultiUtt[src] != nullptr) - { - m_extraLatticeBufferMultiUtt.push_back(m_latticeBufferMultiUtt[src]); - m_extraLabelsIDBufferMultiUtt.push_back(m_labelsIDBufferMultiUtt[src]); - m_extraPhoneboundaryIDBufferMultiUtt.push_back(m_phoneboundaryIDBufferMultiUtt[src]); - } - fillOneUttDataforParallelmode(matrices, m_numValidFrames[des], framenum, des, src); - m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, des, m_numValidFrames[des], m_numValidFrames[des] + framenum); - - ReNewBufferForMultiIO(src); - m_numValidFrames[des] += framenum; - m_extraNumSeqs++; - slotFound = true; - break; - } - } - if (!slotFound) - { - src++; // done with this source; try next source; - } - } - - // and declare the remaining gaps as such - for (size_t i = 0; i < m_numSeqsPerMB; i++) - m_pMBLayout->AddGap(i, m_numValidFrames[i], m_mbNumTimeSteps); - } // if (!frameMode) - - for (auto iter = matrices.begin(); iter != matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - Matrix& data = *matrices[iter->first]; // can be features or labels - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_labelsBufferMultiIO[id].get(), matrixFlagNormal); - } - } - } - } - else // if m_truncated - { - // ------------------------------------------------------- - // truncated BPTT - // ------------------------------------------------------- - - // In truncated BPTT mode, a minibatch only consists of the truncation length, e.g. 20 frames. - // The reader maintains a set of current utterances, and each next minibatch contains the next 20 frames. - // When the end of an utterance is reached, the next available utterance is begin in the same slot. - if (m_noData) // we are returning the last utterances for this epoch - { - // return false if all cursors for all parallel sequences have reached the end - bool endEpoch = true; - for (size_t i = 0; i < m_numSeqsPerMB; i++) - { - if (m_processedFrame[i] != m_numFramesToProcess[i]) - endEpoch = false; - } - if (endEpoch) - return false; - } - - size_t numOfFea = m_featuresBufferMultiIO.size(); - size_t numOfLabel = m_labelsBufferMultiIO.size(); - - // create the feature matrix - m_pMBLayout->Init(m_numSeqsPerMB, m_mbNumTimeSteps); - - vector actualmbsize(m_numSeqsPerMB,0); - for (size_t i = 0; i < m_numSeqsPerMB; i++) - { - // fill one parallel-sequence slot - const size_t startFr = m_processedFrame[i]; // start frame (cursor) inside the utterance that corresponds to time step [0] - - // add utterance to MBLayout - assert(m_numFramesToProcess[i] > startFr || (m_noData && m_numFramesToProcess[i] == startFr)); - if (m_numFramesToProcess[i] > startFr) // in an edge case (m_noData), startFr is at end - m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, -(ptrdiff_t)startFr, m_numFramesToProcess[i] - startFr); - - if (startFr + m_mbNumTimeSteps < m_numFramesToProcess[i]) // end of this minibatch does not reach until end of utterance - { - // we return the next 'm_mbNumTimeSteps' frames, filling all time steps - if (startFr > 0) // not the beginning of the utterance - { - m_sentenceEnd[i] = false; - m_switchFrame[i] = m_mbNumTimeSteps+1; - } - else // beginning of the utterance - { - m_sentenceEnd[i] = true; - m_switchFrame[i] = 0; - } - actualmbsize[i] = m_mbNumTimeSteps; - const size_t endFr = startFr + actualmbsize[i]; // actual end frame index of this segment - for (auto iter = matrices.begin(); iter != matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - Matrix& data = *matrices[iter->first]; // can be features or labels - - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - - if ((m_featuresBufferMultiIO[id] == nullptr) || - (m_featuresBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB)) /*buffer size changed. can be partial minibatch*/) - { - m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); - m_featuresBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; - } - - if (sizeof(ElemType) == sizeof(float)) - { - for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns - memcpy_s(&m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[j * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); - } - } - else // double: must type-cast, cannot memcpy() - { - for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns in outside loop - { - for (int d = 0; d < dim; d++) - m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[j * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; - } - } - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - if ((m_labelsBufferMultiIO[id] == nullptr) || - (m_labelsBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB))) - { - m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); - m_labelsBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; - } - - for (size_t j = startFr,k=0; j < endFr; j++,k++) - { - for (int d = 0; d < dim; d++) - m_labelsBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[j * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; - } - } - } - m_processedFrame[i] += m_mbNumTimeSteps; - } - else // if (startFr + m_mbNumTimeSteps < m_numFramesToProcess[i]) (in this else branch, utterance ends inside this minibatch) - { - // utterance ends: first copy this segment (later, we will pack more utterances in) - assert(startFr == m_processedFrame[i]); - actualmbsize[i] = m_numFramesToProcess[i] - startFr; // parallel sequence is used up to this point - const size_t endFr = startFr + actualmbsize[i]; // end frame in sequence - assert(endFr == m_numFramesToProcess[i]); // we are at the end - - // fill frames for the tail of this utterance - for (auto iter = matrices.begin(); iter != matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - Matrix& data = *matrices[iter->first]; // can be features or labels - - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - - if ((m_featuresBufferMultiIO[id] == nullptr) || - (m_featuresBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB)) /*buffer size changed. can be partial minibatch*/) - { - m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); - m_featuresBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; - } - - if (sizeof(ElemType) == sizeof(float)) - { - for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns - memcpy_s(&m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[j * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); - } - } - else - { - for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns in outside loop - { - for (int d = 0; d < dim; d++) - { - m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[j * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; - } - } - } - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - if ((m_labelsBufferMultiIO[id] == nullptr) || - (m_labelsBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB))) - { - m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); - m_labelsBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; - } - for (size_t j = startFr,k=0; j < endFr; j++,k++) - { - for (int d = 0; d < dim; d++) - m_labelsBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[j * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; - } - } - } - m_processedFrame[i] += (endFr - startFr); // advance the cursor - assert(m_processedFrame[i] == m_numFramesToProcess[i]); // we must be at the end - m_switchFrame[i] = actualmbsize[i]; - //if (actualmbsize[i] != 0) - // m_pMBLayout->Set(i, actualmbsize[i] - 1, MinibatchPackingFlags::SequenceEnd); // NOTE: this ORs, while original code overwrote in matrix but ORed into vector - // at this point, we completed an utterance--fill the rest with the next utterance - - // BUGBUG: We should fill in a loop until we fill the minibatch for the case where just one ReNew is not sufficient - // to fill up the remaining slots in the minibatch - bool reNewSucc = ReNewBufferForMultiIO(i); - if (actualmbsize[i] < m_mbNumTimeSteps) // we actually have space - { - if (reNewSucc) // we actually have another utterance to start here - { - const size_t startT = m_switchFrame[i]; - const size_t endT = m_mbNumTimeSteps; - // Note: Don't confuse startT/endT with startFr/endFr above. - - // add sequence to MBLayout - m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, startT, startT + m_numFramesToProcess[i]); - - // copy the data - for (auto iter = matrices.begin(); iter != matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - //Matrix& data = *matrices[iter->first]; // can be features or labels - - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - if (sizeof(ElemType) == sizeof(float)) - { - for (size_t t = startT, fr = 0; t < endT; t++, fr++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns (for SSE alignment) - memcpy_s(&m_featuresBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[fr * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); - } - } - else - { - for (size_t t = startT, fr = 0; t < endT; t++, fr++) // column major, so iterate columns in outside loop - { - for (int d = 0; d < dim; d++) - m_featuresBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[fr * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; - } - } - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - for (size_t t = startT, fr = 0; t < endT; t++, fr++) - { - for (int d = 0; d < dim; d++) - m_labelsBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[fr * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; - } - } - } - - m_processedFrame[i] += (endT - startT); - - // BUGBUG: since we currently cannot fill >1 utterances, at least let's check - size_t a = actualmbsize[i] + (endT - startT); - // actualmbsize[i] += (endT - startT); // BUGBUG: don't we need something like this? - if (a < m_mbNumTimeSteps) - { - fprintf(stderr, "GetMinibatchToTrainOrTest(): WARNING: Packing a second utterance did still not fill all time slots; filling slots from %d on as gaps.\n", (int)a); - // declare the rest as a gap - m_pMBLayout->AddGap(i, a, m_mbNumTimeSteps); - } - } - else // we did have space for more, but no more data is available. BUGBUG: we should update actualmbsize[i] above and re-test here - { - // declare the rest as a gap - m_pMBLayout->AddGap(i, actualmbsize[i], m_mbNumTimeSteps); - } - } // if (actualmbsize[i] < m_mbNumTimeSteps) // we actually have space - } - } // for (size_t i = 0; i < m_numSeqsPerMB; i++) - // we are done filling all parallel sequences - - for (auto iter = matrices.begin();iter!=matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - Matrix& data = *matrices[iter->first]; // can be features or labels - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_labelsBufferMultiIO[id].get(), matrixFlagNormal); - } - } - skip = false; - } // if truncated then else - } - while(skip); // keep going if we didn't get the right size minibatch - - if (m_verbosity > 2) - { - aggregateTimer.Stop(); - double totalMBReadTime = aggregateTimer.ElapsedSeconds(); - fprintf(stderr, "Total Minibatch read time = %.8g\n", totalMBReadTime); - } - - return true; - } - - // copy an utterance into the minibatch given a location (parallel-sequence index, start frame) - // TODO: This should use DataFor(). But for that, DataFor() will have to move out from ComputationNode. Ah, it has! - template - void HTKMLFReader::fillOneUttDataforParallelmode(std::map*>& matrices, size_t startFr, - size_t framenum, size_t channelIndex, size_t sourceChannelIndex) - { - size_t id; - size_t dim; - size_t numOfFea = m_featuresBufferMultiIO.size(); - size_t numOfLabel = m_labelsBufferMultiIO.size(); - - typename std::map*>::iterator iter; - for (iter = matrices.begin(); iter != matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - Matrix& data = *matrices[iter->first]; // can be features or labels - - if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - id = m_featureNameToIdMap[iter->first]; - dim = m_featureNameToDimMap[iter->first]; - - if (m_featuresBufferMultiIO[id] == nullptr || m_featuresBufferAllocatedMultiIO[id] < dim*m_mbNumTimeSteps*m_numSeqsPerMB) - { - m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim*m_mbNumTimeSteps*m_numSeqsPerMB); - memset(m_featuresBufferMultiIO[id].get(), 0, sizeof(ElemType)*dim*m_mbNumTimeSteps*m_numSeqsPerMB); - m_featuresBufferAllocatedMultiIO[id] = dim*m_mbNumTimeSteps*m_numSeqsPerMB; - } - - if (sizeof(ElemType) == sizeof(float)) - { - for (size_t j = 0, k = startFr; j < framenum; j++, k++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns - memcpy_s(&m_featuresBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim], sizeof(ElemType)*dim, &m_featuresBufferMultiUtt[sourceChannelIndex].get()[j*dim + m_featuresStartIndexMultiUtt[id + sourceChannelIndex*numOfFea]], sizeof(ElemType)*dim); - } - } - else - { - for (size_t j = 0, k = startFr; j < framenum; j++, k++) // column major, so iterate columns in outside loop - { - for (int d = 0; d < dim; d++) - { - m_featuresBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim + d] = m_featuresBufferMultiUtt[sourceChannelIndex].get()[j*dim + d + m_featuresStartIndexMultiUtt[id + sourceChannelIndex*numOfFea]]; - } - } - } - } - else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) - { - id = m_labelNameToIdMap[iter->first]; - dim = m_labelNameToDimMap[iter->first]; - if (m_labelsBufferMultiIO[id] == nullptr || m_labelsBufferAllocatedMultiIO[id] < dim*m_mbNumTimeSteps*m_numSeqsPerMB) - { - m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim*m_mbNumTimeSteps*m_numSeqsPerMB); - memset(m_labelsBufferMultiIO[id].get(), 0, sizeof(ElemType)*dim*m_mbNumTimeSteps*m_numSeqsPerMB); - m_labelsBufferAllocatedMultiIO[id] = dim*m_mbNumTimeSteps*m_numSeqsPerMB; - } - - for (size_t j = 0, k = startFr; j < framenum; j++, k++) - { - for (int d = 0; d < dim; d++) - { - m_labelsBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim + d] = m_labelsBufferMultiUtt[sourceChannelIndex].get()[j*dim + d + m_labelsStartIndexMultiUtt[id + sourceChannelIndex*numOfLabel]]; - } - } - } - } - } - - template - bool HTKMLFReader::GetMinibatchToWrite(std::map*>& matrices) - { - std::map::iterator iter; - if (m_checkDictionaryKeys) - { - for (auto iter=m_featureNameToIdMap.begin();iter!=m_featureNameToIdMap.end();iter++) - { - if (matrices.find(iter->first)==matrices.end()) - { - fprintf(stderr,"GetMinibatchToWrite: feature node %ls specified in reader not found in the network\n", iter->first.c_str()); - RuntimeError("GetMinibatchToWrite: feature node specified in reader not found in the network."); - } - } - /* - for (auto iter=matrices.begin();iter!=matrices.end();iter++) - { - if (m_featureNameToIdMap.find(iter->first)==m_featureNameToIdMap.end()) - RuntimeError(msra::strfun::strprintf("minibatch requested for input node %ws not found in reader - cannot generate input\n",iter->first.c_str())); - } - */ - m_checkDictionaryKeys=false; - } - - if (m_inputFileIndexReset(); - - // load next file (or set of files) - foreach_index(i, m_inputFilesMultiIO) - { - msra::asr::htkfeatreader reader; - - const auto path = reader.parse(m_inputFilesMultiIO[i][m_inputFileIndex]); - // read file - msra::dbn::matrix feat; - string featkind; - unsigned int sampperiod; - msra::util::attempt (5, [&]() - { - reader.read (path, featkind, sampperiod, feat); // whole file read as columns of feature vectors - }); - fprintf (stderr, "evaluate: reading %d frames of %ls\n", (int)feat.cols(), ((wstring)path).c_str()); - m_fileEvalSource->AddFile(feat, featkind, sampperiod, i); - } - m_inputFileIndex++; - - // turn frames into minibatch (augment neighbors, etc) - m_fileEvalSource->CreateEvalMinibatch(); - - // populate input matrices - bool first = true; - typename std::map*>::iterator iter; - for (iter = matrices.begin();iter!=matrices.end(); iter++) - { - // dereference matrix that corresponds to key (input/output name) and - // populate based on whether its a feature or a label - - if (m_nameToTypeMap.find(iter->first) != m_nameToTypeMap.end() && m_nameToTypeMap[iter->first] == InputOutputTypes::real) - { - Matrix& data = *matrices[iter->first]; // can be features or labels (TODO: Really? Didn't we just ^^^ check that it is 'real'?) - size_t id = m_featureNameToIdMap[iter->first]; - size_t dim = m_featureNameToDimMap[iter->first]; - - const msra::dbn::matrix feat = m_fileEvalSource->ChunkOfFrames(id); - - // update the MBLayout - if (first) - { - m_pMBLayout->Init(1, feat.cols()); - m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, 0, 0, feat.cols()); // feat.cols() == number of time steps here since we only have one parallel sequence - //m_pMBLayout->Set(0, 0, MinibatchPackingFlags::SequenceStart); - //m_pMBLayout->SetWithoutOr(0, feat.cols() - 1, MinibatchPackingFlags::SequenceEnd); // BUGBUG: using SetWithoutOr() because original code did; but that seems inconsistent - first = false; - } - - // copy the features over to our array type - assert(feat.rows()==dim); dim; // check feature dimension matches what's expected - - if ((m_featuresBufferMultiIO[id] == nullptr) || - (m_featuresBufferAllocatedMultiIO[id] < (feat.rows() * feat.cols())) /*buffer size changed. can be partial minibatch*/) - { - m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), feat.rows() * feat.cols()); - m_featuresBufferAllocatedMultiIO[id] = feat.rows() * feat.cols(); - } - - if (sizeof(ElemType) == sizeof(float)) - { - for (int j = 0; j < feat.cols(); j++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns - memcpy_s(&m_featuresBufferMultiIO[id].get()[j * feat.rows()], sizeof(ElemType) * feat.rows(), &feat(0, j), sizeof(ElemType) * feat.rows()); - } - } - else - { - for (int j = 0; j < feat.cols(); j++) // column major, so iterate columns in outside loop - { - for (int i = 0; i < feat.rows(); i++) - { - m_featuresBufferMultiIO[id].get()[j * feat.rows() + i] = feat(i, j); - } - } - } - data.SetValue(feat.rows(), feat.cols(), data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); - } - } - return true; - } - else - { - return false; - } - } - - - template - bool HTKMLFReader::ReNewBufferForMultiIO(size_t i) - { - if (m_noData) - { - if ((i == 0) && !m_truncated) - m_numFramesToProcess[i] = 0; - return false; - } - - size_t numOfFea = m_featuresBufferMultiIO.size(); - size_t numOfLabel = m_labelsBufferMultiIO.size(); - - size_t totalFeatNum = 0; - foreach_index(id, m_featuresBufferAllocatedMultiIO) - { - const msra::dbn::matrixstripe featOri = m_mbiter->frames(id); - size_t fdim = featOri.rows(); - const size_t actualmbsizeOri = featOri.cols(); - m_featuresStartIndexMultiUtt[id+i*numOfFea] = totalFeatNum; - totalFeatNum = fdim * actualmbsizeOri + m_featuresStartIndexMultiUtt[id+i*numOfFea]; - } - if ((m_featuresBufferMultiUtt[i] == NULL) || (m_featuresBufferAllocatedMultiUtt[i] < totalFeatNum)) - { - m_featuresBufferMultiUtt[i] = AllocateIntermediateBuffer(-1 /*CPU*/, totalFeatNum); - m_featuresBufferAllocatedMultiUtt[i] = totalFeatNum; - } - - size_t totalLabelsNum = 0; - for (auto it = m_labelNameToIdMap.begin(); it != m_labelNameToIdMap.end(); ++it) - { - size_t id = m_labelNameToIdMap[it->first]; - size_t dim = m_labelNameToDimMap[it->first]; - - const vector & uids = m_mbiter->labels(id); - size_t actualmbsizeOri = uids.size(); - m_labelsStartIndexMultiUtt[id+i*numOfLabel] = totalLabelsNum; - totalLabelsNum = m_labelsStartIndexMultiUtt[id+i*numOfLabel] + dim * actualmbsizeOri; - } - - if ((m_labelsBufferMultiUtt[i] == NULL) || (m_labelsBufferAllocatedMultiUtt[i] < totalLabelsNum)) - { - m_labelsBufferMultiUtt[i] = AllocateIntermediateBuffer(-1 /*CPU */, totalLabelsNum); - m_labelsBufferAllocatedMultiUtt[i] = totalLabelsNum; - } - - memset(m_labelsBufferMultiUtt[i].get(), 0, sizeof(ElemType)*totalLabelsNum); - - bool first = true; - foreach_index(id, m_featuresBufferMultiIO) - { - const msra::dbn::matrixstripe featOri = m_mbiter->frames(id); - const size_t actualmbsizeOri = featOri.cols(); - size_t fdim = featOri.rows(); - if (first) - { - m_numFramesToProcess[i] = actualmbsizeOri; - first = false; - } - else - { - if (m_numFramesToProcess[i] != actualmbsizeOri) - { - RuntimeError("The multi-IO features has inconsistent number of frames!"); - } - } - assert (actualmbsizeOri == m_mbiter->currentmbframes()); - - if (sizeof(ElemType) == sizeof(float)) - { - for (int k = 0; k < actualmbsizeOri; k++) // column major, so iterate columns - { - // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns - memcpy_s(&m_featuresBufferMultiUtt[i].get()[k*fdim + m_featuresStartIndexMultiUtt[id + i*numOfFea]], sizeof(ElemType)*fdim, &featOri(0, k), sizeof(ElemType)*fdim); - } - } - else - { - for (int k=0; k < actualmbsizeOri; k++) // column major, so iterate columns in outside loop - { - for (int d = 0; d < featOri.rows(); d++) - { - m_featuresBufferMultiUtt[i].get()[k*featOri.rows() + d + m_featuresStartIndexMultiUtt[id + i*numOfFea]] = featOri(d, k); - } - } - } - } - - for (auto it = m_labelNameToIdMap.begin(); it != m_labelNameToIdMap.end(); ++it) - { - size_t id = m_labelNameToIdMap[it->first]; - size_t dim = m_labelNameToDimMap[it->first]; - - const vector & uids = m_mbiter->labels(id); - size_t actualmbsizeOri = uids.size(); - - if (m_convertLabelsToTargetsMultiIO[id]) - { - size_t labelDim = m_labelToTargetMapMultiIO[id].size(); - for (int k=0; k < actualmbsizeOri; k++) - { - assert(uids[k] < labelDim); labelDim; - size_t labelId = uids[k]; - for (int j = 0; j < dim; j++) - { - m_labelsBufferMultiUtt[i].get()[k*dim + j + m_labelsStartIndexMultiUtt[id + i*numOfLabel]] = m_labelToTargetMapMultiIO[id][labelId][j]; - } - } - } - else - { - // loop through the columns and set one value to 1 - // in the future we want to use a sparse matrix here - for (int k=0; k < actualmbsizeOri; k++) - { - assert(uids[k] < dim); - m_labelsBufferMultiUtt[i].get()[k*dim + uids[k] + m_labelsStartIndexMultiUtt[id + i*numOfLabel]] = (ElemType)1; - } - } - } - //lattice - if (m_latticeBufferMultiUtt[i] != NULL) - { - m_latticeBufferMultiUtt[i].reset(); - } - - if (m_mbiter->haslattice()) - { - m_latticeBufferMultiUtt[i] = std::move(m_mbiter->lattice(0)); - m_phoneboundaryIDBufferMultiUtt[i].clear(); - m_phoneboundaryIDBufferMultiUtt[i] = m_mbiter->bounds(); - m_labelsIDBufferMultiUtt[i].clear(); - m_labelsIDBufferMultiUtt[i] = m_mbiter->labels(); - } - - m_processedFrame[i] = 0; - - Timer mbIterAdvancementTimer; - if (m_verbosity > 2) - mbIterAdvancementTimer.Start(); - - // Advance the MB iterator until we find some data or reach the end of epoch - do - { - (*m_mbiter)++; - } while ((m_mbiter->currentmbframes() == 0) && *m_mbiter); - - if (m_verbosity > 2) - { - mbIterAdvancementTimer.Stop(); - double advancementTime = mbIterAdvancementTimer.ElapsedSeconds(); - fprintf(stderr, "Time to advance mbiter = %.8g\n", advancementTime); - } - - if (!(*m_mbiter)) - m_noData = true; - - return true; - } - - // GetLabelMapping - Gets the label mapping from integer to type in file - // mappingTable - a map from numeric datatype to native label type stored as a string - template - const std::map::LabelIdType, typename IDataReader::LabelType>& HTKMLFReader::GetLabelMapping(const std::wstring& /*sectionName*/) - { - return m_idToLabelMap; - } - - // SetLabelMapping - Sets the label mapping from integer index to label - // labelMapping - mapping table from label values to IDs (must be 0-n) - // note: for tasks with labels, the mapping table must be the same between a training run and a testing run - template - void HTKMLFReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, typename IDataReader::LabelType>& labelMapping) - { - m_idToLabelMap = labelMapping; - } - - template - size_t HTKMLFReader::ReadLabelToTargetMappingFile (const std::wstring& labelToTargetMappingFile, const std::wstring& labelListFile, std::vector>& labelToTargetMap) - { - if (labelListFile==L"") - RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): cannot read labelToTargetMappingFile without a labelMappingFile!"); - - vector labelList; - size_t count, numLabels; - count=0; - // read statelist first - msra::files::textreader labelReader(labelListFile); - while(labelReader) - { - labelList.push_back(labelReader.wgetline()); - count++; - } - numLabels=count; - count=0; - msra::files::textreader mapReader(labelToTargetMappingFile); - size_t targetDim = 0; - while(mapReader) - { - std::wstring line(mapReader.wgetline()); - // find white space as a demarcation - std::wstring::size_type pos = line.find(L" "); - std::wstring token = line.substr(0,pos); - std::wstring targetstring = line.substr(pos+1); - - if (labelList[count]!=token) - RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): mismatch between labelMappingFile and labelToTargetMappingFile"); - - if (count==0) - targetDim = targetstring.length(); - else if (targetDim!=targetstring.length()) - RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): inconsistent target length among records"); - - std::vector targetVector(targetstring.length(),(ElemType)0.0); - foreach_index(i, targetstring) - { - if (targetstring.compare(i,1,L"1")==0) - targetVector[i] = (ElemType)1.0; - else if (targetstring.compare(i,1,L"0")!=0) - RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): expecting label2target mapping to contain only 1's or 0's"); - } - labelToTargetMap.push_back(targetVector); - count++; - } - - // verify that statelist and label2target mapping file are in same order (to match up with reader) while reading mapping - if (count!=labelList.size()) - RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): mismatch between lengths of labelMappingFile vs labelToTargetMappingFile"); - - return targetDim; - } - - // GetData - Gets metadata from the specified section (into CPU memory) - // sectionName - section name to retrieve data from - // numRecords - number of records to read - // data - pointer to data buffer, if NULL, dataBufferSize will be set to size of required buffer to accomidate request - // dataBufferSize - [in] size of the databuffer in bytes - // [out] size of buffer filled with data - // recordStart - record to start reading from, defaults to zero (start of data) - // returns: true if data remains to be read, false if the end of data was reached - template - bool HTKMLFReader::GetData(const std::wstring& /*sectionName*/, size_t /*numRecords*/, void* /*data*/, size_t& /*dataBufferSize*/, size_t /*recordStart*/) - { - RuntimeError("GetData not supported in HTKMLFReader"); - } - - - template - bool HTKMLFReader::DataEnd(EndDataType endDataType) - { - // each minibatch is considered a "sentence" - // other datatypes not really supported... - // assert(endDataType == endDataSentence); - // for the truncated BPTT, we need to support check wether it's the end of data - bool ret = false; - switch (endDataType) - { - case endDataNull: - case endDataEpoch: - case endDataSet: - LogicError("DataEnd: does not support endDataTypes: endDataNull, endDataEpoch and endDataSet"); - break; - case endDataSentence: - if (m_truncated) - ret = m_sentenceEnd[0]; - else - ret = true; // useless in current condition - break; - } - return ret; - } - - template - void HTKMLFReader::SetSentenceEndInBatch(vector &sentenceEnd) - { - sentenceEnd.resize(m_switchFrame.size()); - for (size_t i = 0; i < m_switchFrame.size() ; i++) - sentenceEnd[i] = m_switchFrame[i]; - } - - template - void HTKMLFReader::CopyMBLayoutTo(MBLayoutPtr pMBLayout) - { - pMBLayout->CopyFrom(m_pMBLayout); - } - - template - size_t HTKMLFReader::GetNumParallelSequences() - { - if (!m_frameMode) - if (m_numSeqsPerMB != m_pMBLayout->GetNumParallelSequences()) - LogicError("HTKMLFReader: Number of parallel sequences in m_pMBLayout did not get set to m_numSeqsPerMB."); - return m_pMBLayout->GetNumParallelSequences(); // (this function is only used for validation anyway) - } - - // GetFileConfigNames - determine the names of the features and labels sections in the config file - // features - [in,out] a vector of feature name strings - // labels - [in,out] a vector of label name strings - template template - void HTKMLFReader::GetDataNamesFromConfig(const ConfigRecordType & readerConfig, std::vector& features, std::vector& labels, - std::vector& hmms, std::vector& lattices) - { - for (const auto & id : readerConfig.GetMemberIds()) - { +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// HTKMLFReader.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#ifdef _WIN32 +#include +#endif +#include "Basics.h" + +#include "htkfeatio.h" // for reading HTK features +#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) +#include "simplesenonehmm.h" // for MMI scoring +#include "msra_mgram.h" // for unigram scores of ground-truth path in sequence training + +#include "rollingwindowsource.h" // minibatch sources +#include "utterancesourcemulti.h" +#include "chunkevalsource.h" +#include "minibatchiterator.h" +#define DATAREADER_EXPORTS // creating the exports here +#include "DataReader.h" +#include "Config.h" +#include "ScriptableObjects.h" +#include "HTKMLFReader.h" +#include "TimerUtility.h" +#ifdef LEAKDETECT +#include // for memory leak detection +#endif + +#ifdef __unix__ +#include +typedef unsigned long DWORD; +typedef unsigned short WORD; +typedef unsigned int UNINT32; +#endif +#pragma warning (disable: 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this + +#ifdef _WIN32 +int msra::numa::node_override = -1; // for numahelpers.h +#endif + +namespace msra { namespace lm { +/*static*/ const mgram_map::index_t mgram_map::nindex = (mgram_map::index_t) -1; // invalid index +}} + +namespace Microsoft { namespace MSR { namespace CNTK { + + // Create a Data Reader + //DATAREADER_API IDataReader* DataReaderFactory(void) + + template template + void HTKMLFReader::InitFromConfig(const ConfigRecordType & readerConfig) + { + m_truncated = readerConfig(L"truncated", false); + m_convertLabelsToTargets = false; + + intargvector numberOfuttsPerMinibatchForAllEpochs = readerConfig(L"nbruttsineachrecurrentiter", ConfigRecordType::Array(intargvector(vector{ 1 }))); + m_numSeqsPerMBForAllEpochs = numberOfuttsPerMinibatchForAllEpochs; + + for (int i = 0; i < m_numSeqsPerMBForAllEpochs.size(); i++) + { + if (m_numSeqsPerMBForAllEpochs[i] < 1) + { + LogicError("nbrUttsInEachRecurrentIter cannot be less than 1."); + } + } + + m_numSeqsPerMB = m_numSeqsPerMBForAllEpochs[0]; + m_pMBLayout->Init(m_numSeqsPerMB, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) + + m_noData = false; + + wstring command(readerConfig(L"action", L"")); //look up in the config for the master command to determine whether we're writing output (inputs only) or training/evaluating (inputs and outputs) + + if (readerConfig.Exists(L"legacyMode")) + RuntimeError("legacy mode has been deprecated\n"); + + if (command == L"write") + { + m_trainOrTest = false; + PrepareForWriting(readerConfig); + } + else + { + m_trainOrTest = true; + PrepareForTrainingOrTesting(readerConfig); + } + } + + // Load all input and output data. + // Note that the terms features imply be real-valued quanities and + // labels imply categorical quantities, irrespective of whether they + // are inputs or targets for the network + template template + void HTKMLFReader::PrepareForTrainingOrTesting(const ConfigRecordType& readerConfig) + { + vector scriptpaths; + vector RootPathInScripts; + wstring RootPathInLatticeTocs; + vector mlfpaths; + vector>mlfpathsmulti; + size_t firstfilesonly = SIZE_MAX; // set to a lower value for testing + vector> infilesmulti; + size_t numFiles; + wstring unigrampath(L""); + //wstring statelistpath(L""); + size_t randomize = randomizeAuto; + size_t iFeat, iLabel; + iFeat = iLabel = 0; + vector statelistpaths; + vector numContextLeft; + vector numContextRight; + + std::vector featureNames; + std::vector labelNames; + // for hmm and lattice + std::vector hmmNames; + std::vector latticeNames; + GetDataNamesFromConfig(readerConfig, featureNames, labelNames, hmmNames, latticeNames); + if (featureNames.size() + labelNames.size() <= 1) + { + InvalidArgument("network needs at least 1 input and 1 output specified!"); + } + + //load data for all real-valued inputs (features) + foreach_index(i, featureNames) + { + const ConfigRecordType & thisFeature = readerConfig(featureNames[i]); + m_featDims.push_back(thisFeature(L"dim")); + intargvector contextWindow = thisFeature(L"contextWindow", ConfigRecordType::Array(intargvector(vector{ 1 }))); + if (contextWindow.size() == 1) // symmetric + { + size_t windowFrames = contextWindow[0]; + if (windowFrames % 2 == 0 ) + InvalidArgument("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowFrames); + size_t context = windowFrames / 2; // extend each side by this + numContextLeft.push_back(context); + numContextRight.push_back(context); + + } + else if (contextWindow.size() == 2) // left context, right context + { + numContextLeft.push_back(contextWindow[0]); + numContextRight.push_back(contextWindow[1]); + } + else + { + InvalidArgument("contextFrames must have 1 or 2 values specified, found %d", (int)contextWindow.size()); + } + // update m_featDims to reflect the total input dimension (featDim x contextWindow), not the native feature dimension + // that is what the lower level feature readers expect + m_featDims[i] = m_featDims[i] * (1 + numContextLeft[i] + numContextRight[i]); + + wstring type = thisFeature(L"type", L"real"); + if (!_wcsicmp(type.c_str(), L"real")) + { + m_nameToTypeMap[featureNames[i]] = InputOutputTypes::real; + } + else + { + InvalidArgument("feature type must be 'real'"); + } + + m_featureNameToIdMap[featureNames[i]]= iFeat; + scriptpaths.push_back(thisFeature(L"scpFile")); + RootPathInScripts.push_back(thisFeature(L"prefixPathInSCP", L"")); + m_featureNameToDimMap[featureNames[i]] = m_featDims[i]; + + m_featuresBufferMultiIO.push_back(nullptr); + m_featuresBufferAllocatedMultiIO.push_back(0); + + iFeat++; + } + + foreach_index(i, labelNames) + { + const ConfigRecordType & thisLabel = readerConfig(labelNames[i]); + if (thisLabel.Exists(L"labelDim")) + m_labelDims.push_back(thisLabel(L"labelDim")); + else if (thisLabel.Exists(L"dim")) + m_labelDims.push_back(thisLabel(L"dim")); + else + InvalidArgument("labels must specify dim or labelDim"); + + wstring type; + if (thisLabel.Exists(L"labelType")) + type = (const wstring &)thisLabel(L"labelType"); // let's deprecate this eventually and just use "type"... + else + type = (const wstring &)thisLabel(L"type", L"category"); // outputs should default to category + + if (!_wcsicmp(type.c_str(), L"category")) + m_nameToTypeMap[labelNames[i]] = InputOutputTypes::category; + else + InvalidArgument("label type must be 'category'"); + + statelistpaths.push_back(thisLabel(L"labelMappingFile", L"")); + + m_labelNameToIdMap[labelNames[i]]=iLabel; + m_labelNameToDimMap[labelNames[i]]=m_labelDims[i]; + mlfpaths.clear(); + if (thisLabel.ExistsCurrent(L"mlfFile")) + { + mlfpaths.push_back(thisLabel(L"mlfFile")); + } + else + { + if (!thisLabel.ExistsCurrent(L"mlfFileList")) + { + InvalidArgument("Either mlfFile or mlfFileList must exist in HTKMLFReder"); + } + wstring list = thisLabel(L"mlfFileList"); + for (msra::files::textreader r(list); r;) + { + mlfpaths.push_back(r.wgetline()); + } + } + mlfpathsmulti.push_back(mlfpaths); + + m_labelsBufferMultiIO.push_back(nullptr); + m_labelsBufferAllocatedMultiIO.push_back(0); + + iLabel++; + + wstring labelToTargetMappingFile(thisLabel(L"labelToTargetMappingFile", L"")); + if (labelToTargetMappingFile != L"") + { + std::vector> labelToTargetMap; + m_convertLabelsToTargetsMultiIO.push_back(true); + if (thisLabel.Exists(L"targetDim")) + { + m_labelNameToDimMap[labelNames[i]] = m_labelDims[i] = thisLabel(L"targetDim"); + } + else + RuntimeError("output must specify targetDim if labelToTargetMappingFile specified!"); + size_t targetDim = ReadLabelToTargetMappingFile (labelToTargetMappingFile,statelistpaths[i], labelToTargetMap); + if (targetDim!=m_labelDims[i]) + RuntimeError("mismatch between targetDim and dim found in labelToTargetMappingFile"); + m_labelToTargetMapMultiIO.push_back(labelToTargetMap); + } + else + { + m_convertLabelsToTargetsMultiIO.push_back(false); + m_labelToTargetMapMultiIO.push_back(std::vector>()); + } + } + + //get lattice toc file names + std::pair, std::vector> latticetocs; + foreach_index(i, latticeNames) //only support one set of lattice now + { + const ConfigRecordType & thisLattice = readerConfig(latticeNames[i]); + + vector paths; + expand_wildcards(thisLattice(L"denLatTocFile"), paths); + latticetocs.second.insert(latticetocs.second.end(), paths.begin(), paths.end()); + + if (thisLattice.Exists(L"numLatTocFile")) + { + paths.clear(); + expand_wildcards(thisLattice(L"numLatTocFile"), paths); + latticetocs.first.insert(latticetocs.first.end(), paths.begin(), paths.end()); + } + RootPathInLatticeTocs =(wstring) thisLattice(L"prefixPathInToc",L""); + } + + //get HMM related file names + vector cdphonetyingpaths, transPspaths; + foreach_index(i, hmmNames) + { + const ConfigRecordType & thisHMM = readerConfig(hmmNames[i]); + + cdphonetyingpaths.push_back(thisHMM(L"phoneFile")); + transPspaths.push_back(thisHMM(L"transPFile", L"")); + } + + // mmf files + //only support one set now + if (cdphonetyingpaths.size() > 0 && statelistpaths.size() > 0 && transPspaths.size() > 0) + m_hset.loadfromfile(cdphonetyingpaths[0], statelistpaths[0], transPspaths[0]); + if (iFeat!=scriptpaths.size() || iLabel!=mlfpathsmulti.size()) + RuntimeError("# of inputs files vs. # of inputs or # of output files vs # of outputs inconsistent\n"); + + if (readerConfig.Exists(L"randomize")) + { + wstring randomizeString = readerConfig.CanBeString(L"randomize") ? readerConfig(L"randomize") : wstring(); + if (!_wcsicmp(randomizeString.c_str(), L"none")) + randomize = randomizeNone; + else if (!_wcsicmp(randomizeString.c_str(), L"auto")) + randomize = randomizeAuto; + else + randomize = readerConfig(L"randomize"); + } + + m_frameMode = readerConfig(L"frameMode", true); + m_verbosity = readerConfig(L"verbosity", 2); + + // determine if we partial minibatches are desired + wstring minibatchMode(readerConfig(L"minibatchMode", L"partial")); + m_partialMinibatch = !_wcsicmp(minibatchMode.c_str(), L"partial"); + + // get the read method, defaults to "blockRandomize" other option is "rollingWindow" + wstring readMethod(readerConfig(L"readMethod",L"blockRandomize")); + + if (readMethod == L"blockRandomize" && randomize == randomizeNone) + InvalidArgument("'randomize' cannot be 'none' when 'readMethod' is 'blockRandomize'."); + + // read all input files (from multiple inputs) + // TO DO: check for consistency (same number of files in each script file) + numFiles=0; + foreach_index (i, scriptpaths) + { + vector filelist; + std::wstring scriptpath = scriptpaths[i]; + fprintf(stderr, "reading script file %ls ...", scriptpath.c_str()); + size_t n = 0; + for (msra::files::textreader reader(scriptpath); reader && filelist.size() <= firstfilesonly/*optimization*/; ) + { + filelist.push_back (reader.wgetline()); + n++; + } + + fprintf (stderr, " %lu entries\n", n); + + if (i==0) + numFiles=n; + else + if (n!=numFiles) + RuntimeError("number of files in each scriptfile inconsistent (%d vs. %d)", (int)numFiles, (int)n); + + // post processing file list : + // - if users specified PrefixPath, add the prefix to each of path in filelist + // - else do the dotdotdot expansion if necessary + wstring rootpath = RootPathInScripts[i]; + if (!rootpath.empty()) // use has specified a path prefix for this feature + { + // first make slash consistent (sorry for linux users:this is not necessary for you) + std::replace(rootpath.begin(), rootpath.end(), L'\\', L'/'); + // second, remove trailling slash if there is any + std::wregex trailer(L"/+$"); + rootpath = std::regex_replace(rootpath, trailer, wstring()); + // third, join the rootpath with each entry in filelist + if (!rootpath.empty()) + { + for (wstring & path : filelist) + { + if (path.find_first_of(L'=') != wstring::npos) + { + vector strarr = msra::strfun::split(path, L"="); +#ifdef WIN32 + replace(strarr[1].begin(), strarr[1].end(), L'\\', L'/'); +#endif + + path = strarr[0] + L"=" + rootpath + L"/" + strarr[1]; + } + else + { +#ifdef WIN32 + replace(path.begin(), path.end(), L'\\', L'/'); +#endif + path = rootpath + L"/" + path; + } + } + } + } + else + { + /* + do "..." expansion if SCP uses relative path names + "..." in the SCP means full path is the same as the SCP file + for example, if scp file is "//aaa/bbb/ccc/ddd.scp" + and contains entry like + .../file1.feat + .../file2.feat + etc. + the features will be read from + //aaa/bbb/ccc/file1.feat + //aaa/bbb/ccc/file2.feat + etc. + This works well if you store the scp file with the features but + do not want different scp files everytime you move or create new features + */ + wstring scpdircached; + for (auto & entry : filelist) + ExpandDotDotDot(entry, scriptpath, scpdircached); + } + + infilesmulti.push_back(std::move(filelist)); + } + + if (readerConfig.Exists(L"unigram")) + unigrampath = (const wstring &)readerConfig(L"unigram"); + + // load a unigram if needed (this is used for MMI training) + msra::lm::CSymbolSet unigramsymbols; + std::unique_ptr unigram; + size_t silencewordid = SIZE_MAX; + size_t startwordid = SIZE_MAX; + size_t endwordid = SIZE_MAX; + if (unigrampath != L"") + { + unigram.reset (new msra::lm::CMGramLM()); + unigram->read (unigrampath, unigramsymbols, false/*filterVocabulary--false will build the symbol map*/, 1/*maxM--unigram only*/); + silencewordid = unigramsymbols["!silence"]; // give this an id (even if not in the LM vocabulary) + startwordid = unigramsymbols[""]; + endwordid = unigramsymbols[""]; + } + + if (!unigram && latticetocs.second.size() > 0) + fprintf (stderr, "trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion\n"); + + // currently assumes all mlfs will have same root name (key) + set restrictmlftokeys; // restrict MLF reader to these files--will make stuff much faster without having to use shortened input files + if (infilesmulti[0].size() <= 100) + { + foreach_index (i, infilesmulti[0]) + { + msra::asr::htkfeatreader::parsedpath ppath (infilesmulti[0][i]); + const wstring key = regex_replace ((wstring)ppath, wregex (L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) + restrictmlftokeys.insert (key); + } + } + // get labels + + //if (readerConfig.Exists(L"statelist")) + // statelistpath = readerConfig(L"statelist"); + + double htktimetoframe = 100000.0; // default is 10ms + //std::vector> labelsmulti; + std::vector>> labelsmulti; + //std::vector pagepath; + foreach_index(i, mlfpathsmulti) + { + const msra::lm::CSymbolSet* wordmap = unigram ? &unigramsymbols : NULL; + msra::asr::htkmlfreader + labels(mlfpathsmulti[i], restrictmlftokeys, statelistpaths[i], wordmap, (map*) NULL, htktimetoframe); // label MLF + // get the temp file name for the page file + + // Make sure 'msra::asr::htkmlfreader' type has a move constructor + static_assert(std::is_move_constructible>::value, + "Type 'msra::asr::htkmlfreader' should be move constructible!"); + + labelsmulti.push_back(std::move(labels)); + } + + if (!_wcsicmp(readMethod.c_str(), L"blockRandomize")) + { + // construct all the parameters we don't need, but need to be passed to the constructor... + + m_lattices.reset(new msra::dbn::latticesource(latticetocs, m_hset.getsymmap(), RootPathInLatticeTocs)); + m_lattices->setverbosity(m_verbosity); + + // now get the frame source. This has better randomization and doesn't create temp files + m_frameSource.reset(new msra::dbn::minibatchutterancesourcemulti(infilesmulti, labelsmulti, m_featDims, m_labelDims, numContextLeft, numContextRight, randomize, *m_lattices, m_latticeMap, m_frameMode)); + m_frameSource->setverbosity(m_verbosity); + } + else if (!_wcsicmp(readMethod.c_str(), L"rollingWindow")) + { + std::wstring pageFilePath; + std::vector pagePaths; + if (readerConfig.Exists(L"pageFilePath")) + { + pageFilePath = (const wstring &)readerConfig(L"pageFilePath"); + + // replace any '/' with '\' for compat with default path + std::replace(pageFilePath.begin(), pageFilePath.end(), '/','\\'); +#ifdef _WIN32 + // verify path exists + DWORD attrib = GetFileAttributes(pageFilePath.c_str()); + if (attrib==INVALID_FILE_ATTRIBUTES || !(attrib & FILE_ATTRIBUTE_DIRECTORY)) + RuntimeError("pageFilePath does not exist"); +#endif +#ifdef __unix__ + struct stat statbuf; + if (stat(wtocharpath(pageFilePath).c_str(), &statbuf)==-1) + { + RuntimeError("pageFilePath does not exist"); + } +#endif + } + else // using default temporary path + { +#ifdef _WIN32 + pageFilePath.reserve(MAX_PATH); + GetTempPath(MAX_PATH, &pageFilePath[0]); +#endif +#ifdef __unix__ + pageFilePath = L"/tmp/temp.CNTK.XXXXXX"; +#endif + } + +#ifdef _WIN32 + if (pageFilePath.size()>MAX_PATH-14) // max length of input to GetTempFileName is MAX_PATH-14 + RuntimeError("pageFilePath must be less than %d characters", MAX_PATH-14); +#else + if (pageFilePath.size()>PATH_MAX-14) // max length of input to GetTempFileName is PATH_MAX-14 + RuntimeError("pageFilePath must be less than %d characters", PATH_MAX-14); +#endif + foreach_index(i, infilesmulti) + { +#ifdef _WIN32 + wchar_t tempFile[MAX_PATH]; + GetTempFileName(pageFilePath.c_str(), L"CNTK", 0, tempFile); + pagePaths.push_back(tempFile); +#endif +#ifdef __unix__ + char tempFile[PATH_MAX]; + strcpy(tempFile, msra::strfun::utf8(pageFilePath).c_str()); + int fid = mkstemp(tempFile); + unlink (tempFile); + close (fid); + pagePaths.push_back(GetWC(tempFile)); +#endif + } + + const bool mayhavenoframe=false; + int addEnergy = 0; + + m_frameSource.reset(new msra::dbn::minibatchframesourcemulti(infilesmulti, labelsmulti, m_featDims, m_labelDims, numContextLeft, numContextRight, randomize, pagePaths, mayhavenoframe, addEnergy)); + m_frameSource->setverbosity(m_verbosity); + } + else + { + RuntimeError("readMethod must be 'rollingWindow' or 'blockRandomize'"); + } + } + + // Load all input and output data. + // Note that the terms features imply be real-valued quanities and + // labels imply categorical quantities, irrespective of whether they + // are inputs or targets for the network + // TODO: lots of code dup with the other Prepare function + template template + void HTKMLFReader::PrepareForWriting(const ConfigRecordType& readerConfig) + { + vector scriptpaths; + size_t numFiles; + size_t firstfilesonly = SIZE_MAX; // set to a lower value for testing + size_t evalchunksize = 2048; + vector realDims; + size_t iFeat = 0; + vector numContextLeft; + vector numContextRight; + + std::vector featureNames; + std::vector labelNames; + //lattice and hmm + std::vector hmmNames; + std::vector latticeNames; + + GetDataNamesFromConfig(readerConfig, featureNames, labelNames, hmmNames, latticeNames); + + foreach_index(i, featureNames) + { + const ConfigRecordType & thisFeature = readerConfig(featureNames[i]); + realDims.push_back(thisFeature(L"dim")); + + intargvector contextWindow = thisFeature(L"contextWindow", ConfigRecordType::Array(intargvector(vector{ 1 }))); + if (contextWindow.size() == 1) // symmetric + { + size_t windowFrames = contextWindow[0]; + if (windowFrames % 2 == 0) + RuntimeError("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowFrames); + size_t context = windowFrames / 2; // extend each side by this + numContextLeft.push_back(context); + numContextRight.push_back(context); + + } + else if (contextWindow.size() == 2) // left context, right context + { + numContextLeft.push_back(contextWindow[0]); + numContextRight.push_back(contextWindow[1]); + } + else + { + RuntimeError("contextFrames must have 1 or 2 values specified, found %d", (int)contextWindow.size()); + } + // update m_featDims to reflect the total input dimension (featDim x contextWindow), not the native feature dimension + // that is what the lower level feature readers expect + realDims[i] = realDims[i] * (1 + numContextLeft[i] + numContextRight[i]); + + wstring type = thisFeature(L"type", L"real"); + if (!_wcsicmp(type.c_str(), L"real")) + { + m_nameToTypeMap[featureNames[i]] = InputOutputTypes::real; + } + else{ + RuntimeError("feature type must be 'real'"); + } + + m_featureNameToIdMap[featureNames[i]]= iFeat; + scriptpaths.push_back(thisFeature(L"scpFile")); + m_featureNameToDimMap[featureNames[i]] = realDims[i]; + + m_featuresBufferMultiIO.push_back(nullptr); + m_featuresBufferAllocatedMultiIO.push_back(0); + iFeat++; + } + + if (labelNames.size()>0) + RuntimeError("writer mode does not support labels as inputs, only features"); + + numFiles=0; + foreach_index(i,scriptpaths) + { + vector filelist; + std::wstring scriptpath = scriptpaths[i]; + fprintf(stderr, "reading script file %ls ...", scriptpath.c_str()); + size_t n = 0; + for (msra::files::textreader reader(scriptpath); reader && filelist.size() <= firstfilesonly/*optimization*/; ) + { + filelist.push_back (reader.wgetline()); + n++; + } + + fprintf (stderr, " %d entries\n", (int)n); + + if (i==0) + numFiles=n; + else + if (n!=numFiles) + RuntimeError("HTKMLFReader::InitEvalReader: number of files in each scriptfile inconsistent (%d vs. %d)", (int)numFiles, (int)n); + + m_inputFilesMultiIO.push_back(std::move(filelist)); + } + + m_fileEvalSource.reset(new msra::dbn::FileEvalSource(realDims, numContextLeft, numContextRight, evalchunksize)); + } + + //StartMinibatchLoop - Startup a minibatch loop + // requestedMBSize - [in] size of the minibatch (number of frames, etc.) + // epoch - [in] epoch number for this loop + // requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset + template + void HTKMLFReader::StartDistributedMinibatchLoop(size_t requestedMBSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples /*= requestDataSize*/) + { + assert(subsetNum < numSubsets); + assert(((subsetNum == 0) && (numSubsets == 1)) || this->SupportsDistributedMBRead()); + + m_mbNumTimeSteps = requestedMBSize; // note: ignored in frame mode and full-sequence mode + + m_numSeqsPerMB = m_numSeqsPerMBForAllEpochs[epoch]; + + // For distributed reading under utterance mode, we distribute the utterances per minibatch among all the subsets + if (m_trainOrTest && !m_frameMode) + { + if ((numSubsets > 1) && (m_numSeqsPerMB < numSubsets)) + { + LogicError("Insufficient value of 'nbruttsineachrecurrentiter'=%d for distributed reading with %d subsets", (int)m_numSeqsPerMB, (int)numSubsets); + } + + m_numSeqsPerMB = (m_numSeqsPerMB / numSubsets) + ((subsetNum < (m_numSeqsPerMB % numSubsets)) ? 1 : 0); + } + + m_pMBLayout->Init(m_numSeqsPerMB, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) + + // resize the arrays + // These are sized to the requested number. If not all can be filled, it will still return this many, just with gaps. + // In frame mode, m_numSeqsPerMB must be 1. However, the returned layout has one 1-frame sequence per frame. + m_sentenceEnd.assign(m_numSeqsPerMB, true); + m_processedFrame.assign(m_numSeqsPerMB, 0); + m_numFramesToProcess.assign(m_numSeqsPerMB, 0); + m_switchFrame.assign(m_numSeqsPerMB, 0); + m_numValidFrames.assign(m_numSeqsPerMB, 0); + + if (m_trainOrTest) + { + // for the multi-utterance process + m_featuresBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); + m_featuresBufferAllocatedMultiUtt.assign(m_numSeqsPerMB, 0); + m_labelsBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); + m_labelsBufferAllocatedMultiUtt.assign(m_numSeqsPerMB, 0); + + // for the multi-utterance process for lattice and phone boundary + m_latticeBufferMultiUtt.assign(m_numSeqsPerMB, nullptr); + m_labelsIDBufferMultiUtt.resize(m_numSeqsPerMB); + m_phoneboundaryIDBufferMultiUtt.resize(m_numSeqsPerMB); + + if (m_frameMode && (m_numSeqsPerMB > 1)) + { + LogicError("nbrUttsInEachRecurrentIter cannot be more than 1 in frame mode reading."); + } + + // BUGBUG: in BPTT and sequence mode, we should pass 1 or 2 instead of requestedMBSize to ensure we only get one utterance back at a time + StartMinibatchLoopToTrainOrTest(requestedMBSize, epoch, subsetNum, numSubsets, requestedEpochSamples); + } + else + { + // No distributed reading of mini-batches for write + if ((subsetNum != 0) || (numSubsets != 1)) + { + LogicError("Distributed reading of mini-batches is only supported for training or testing"); + } + m_pMBLayout->Init(requestedMBSize, 0); // (SGD will ask before entering actual reading --TODO: This is hacky.) + + StartMinibatchLoopToWrite(requestedMBSize,epoch,requestedEpochSamples); + } + m_checkDictionaryKeys=true; + } + + template + void HTKMLFReader::StartMinibatchLoopToTrainOrTest(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples) + { + size_t datapasses=1; + size_t totalFrames = m_frameSource->totalframes(); + + size_t extraFrames = totalFrames%mbSize; + size_t minibatches = totalFrames/mbSize; + + // if we are allowing partial minibatches, do nothing, and let it go through + if (!m_partialMinibatch) + { + // we don't want any partial frames, so round total frames to be an even multiple of our mbSize + if (totalFrames > mbSize) + totalFrames -= extraFrames; + + if (requestedEpochSamples == requestDataSize) + { + requestedEpochSamples = totalFrames; + } + else if (minibatches > 0) // if we have any full minibatches + { + // since we skip the extraFrames, we need to add them to the total to get the actual number of frames requested + size_t sweeps = (requestedEpochSamples-1)/totalFrames; // want the number of sweeps we will skip the extra, so subtract 1 and divide + requestedEpochSamples += extraFrames*sweeps; + } + } + else if (requestedEpochSamples == requestDataSize) + { + requestedEpochSamples = totalFrames; + } + + m_mbiter.reset(new msra::dbn::minibatchiterator(*m_frameSource, epoch, requestedEpochSamples, mbSize, subsetNum, numSubsets, datapasses)); + // Advance the MB iterator until we find some data or reach the end of epoch + while ((m_mbiter->currentmbframes() == 0) && *m_mbiter) + { + (*m_mbiter)++; + } + + m_noData = false; + if (!(*m_mbiter)) + m_noData = true; + + if (!m_featuresBufferMultiIO.empty()) + { + if (m_featuresBufferMultiIO[0] != nullptr) // check first feature, if it isn't NULL, safe to assume all are not NULL? + { + foreach_index(i, m_featuresBufferMultiIO) + { + m_featuresBufferMultiIO[i] = nullptr; + m_featuresBufferAllocatedMultiIO[i]=0; + } + } + + m_featuresStartIndexMultiUtt.assign(m_featuresBufferMultiIO.size()*m_numSeqsPerMB, 0); + + } + + if (!m_labelsBufferMultiIO.empty()) + { + if (m_labelsBufferMultiIO[0] != nullptr) + { + foreach_index(i, m_labelsBufferMultiIO) + { + m_labelsBufferMultiIO[i] = nullptr; + m_labelsBufferAllocatedMultiIO[i]=0; + } + } + + m_labelsStartIndexMultiUtt.assign(m_labelsBufferMultiIO.size()*m_numSeqsPerMB, 0); + } + + for (size_t u = 0; u < m_numSeqsPerMB; u ++) + { + if (m_featuresBufferMultiUtt[u] != NULL) + { + m_featuresBufferMultiUtt[u] = NULL; + m_featuresBufferAllocatedMultiUtt[u] = 0; + } + + if (m_labelsBufferMultiUtt[u] != NULL) + { + m_labelsBufferMultiUtt[u] = NULL; + m_labelsBufferAllocatedMultiUtt[u] = 0; + } + + if (m_latticeBufferMultiUtt[u] != NULL) + { + m_latticeBufferMultiUtt[u].reset(); + } + + ReNewBufferForMultiIO(u); + } + } + + template + void HTKMLFReader::StartMinibatchLoopToWrite(size_t mbSize, size_t /*epoch*/, size_t /*requestedEpochSamples*/) + { + m_fileEvalSource->Reset(); + m_fileEvalSource->SetMinibatchSize(mbSize); + m_inputFileIndex=0; + + if (m_featuresBufferMultiIO[0] != nullptr) // check first feature, if it isn't NULL, safe to assume all are not NULL? + { + foreach_index(i, m_featuresBufferMultiIO) + { + m_featuresBufferMultiIO[i] = nullptr; + m_featuresBufferAllocatedMultiIO[i]=0; + } + } + } + + template + bool HTKMLFReader::GetMinibatch4SE(std::vector> & latticeinput, + vector &uids, vector &boundaries, vector &extrauttmap) + { + if (m_trainOrTest) + { + return GetMinibatch4SEToTrainOrTest(latticeinput, uids, boundaries, extrauttmap); + } + else + { + return true; + } + } + template + bool HTKMLFReader::GetMinibatch4SEToTrainOrTest(std::vector> & latticeinput, + std::vector &uids, std::vector &boundaries, std::vector &extrauttmap) + { + latticeinput.clear(); + uids.clear(); + boundaries.clear(); + extrauttmap.clear(); + for (size_t i = 0; i < m_extraSeqsPerMB.size(); i++) + { + latticeinput.push_back(m_extraLatticeBufferMultiUtt[i]); + uids.insert(uids.end(), m_extraLabelsIDBufferMultiUtt[i].begin(), m_extraLabelsIDBufferMultiUtt[i].end()); + boundaries.insert(boundaries.end(), m_extraPhoneboundaryIDBufferMultiUtt[i].begin(), m_extraPhoneboundaryIDBufferMultiUtt[i].end()); + } + + extrauttmap.insert(extrauttmap.end(), m_extraSeqsPerMB.begin(), m_extraSeqsPerMB.end()); + return true; + } + + template + bool HTKMLFReader::GetHmmData(msra::asr::simplesenonehmm * hmm) + { + *hmm = m_hset; + return true; + } + // GetMinibatch - Get the next minibatch (features and labels) + // matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, + // [out] each matrix resized if necessary containing data. + // returns - true if there are more minibatches, false if no more minibatchs remain + // TODO: Why do we have two read functions? Is one not a superset of the other? + template + bool HTKMLFReader::GetMinibatch(std::map*>& matrices) + { + if (m_trainOrTest) + { + return GetMinibatchToTrainOrTest(matrices); + } + else + { + return GetMinibatchToWrite(matrices); + } + } + + template + bool HTKMLFReader::GetMinibatchToTrainOrTest(std::map*>& matrices) + { + size_t id; + size_t dim; + bool skip = false; + + // on first minibatch, make sure we can supply data for requested nodes + std::map::iterator iter; + if (m_checkDictionaryKeys) + { + for (auto iter=matrices.begin();iter!=matrices.end();iter++) + { + if (m_nameToTypeMap.find(iter->first)==m_nameToTypeMap.end()) + RuntimeError("minibatch requested for input node %ls not found in reader - cannot generate input\n", iter->first.c_str()); + } + m_checkDictionaryKeys=false; + } + + Timer aggregateTimer; + if (m_verbosity > 2) + aggregateTimer.Start(); + + do + { + if (!m_truncated) + { + // ------------------------------------------------------- + // frame mode or whole utterances + // ------------------------------------------------------- + + m_extraLatticeBufferMultiUtt.clear(); + m_extraLabelsIDBufferMultiUtt.clear(); + m_extraPhoneboundaryIDBufferMultiUtt.clear(); + m_extraSeqsPerMB.clear(); + if (m_noData && m_numFramesToProcess[0] == 0) //no data left for the first channel of this minibatch, + { + return false; + } + + // BUGBUG: We should decide how many utterances we are going to take, until the desired number of frames has been filled. + // Currently it seems to fill a fixed number of utterances, regardless of their length. + + // decide the m_mbNumTimeSteps + // The number of columns is determined by the longest utterance amongst the desired set. + // I.e. whatever is user-specified as the MB size, will be ignored here (that value is, however, passed down to the underlying reader). BUGBUG: That is even more wrong. + // BUGBUG: We should honor the mbSize parameter and fill up to the requested number of samples, using the requested #parallel sequences. + // m_mbNumTimeSteps = max (m_numFramesToProcess[.]) + m_mbNumTimeSteps = m_numFramesToProcess[0]; + for (size_t i = 1; i < m_numSeqsPerMB; i++) + { + if (m_mbNumTimeSteps < m_numFramesToProcess[i]) + m_mbNumTimeSteps = m_numFramesToProcess[i]; + } + + if (m_frameMode) + { + assert(m_numSeqsPerMB == 1); // user must not request parallel sequences + m_pMBLayout->InitAsFrameMode(m_mbNumTimeSteps); + } + else + { + m_pMBLayout->Init(m_numSeqsPerMB, m_mbNumTimeSteps); + } + + // create a MB with the desired utterances + // First fill each parallel sequence with one utterance. No packing yet. + // Note that the code below is a little misleading for frame mode. + // In frame mode, this reader thinks it has only one parallel sequence (m_numSeqsPerMB == 1), + // but it reports it to the outside as N parallel sequences of one frame each. + skip = (m_frameMode && !m_partialMinibatch && (m_mbiter->requestedframes() != m_mbNumTimeSteps) && (m_frameSource->totalframes() > m_mbNumTimeSteps)); + for (size_t i = 0; i < m_numSeqsPerMB; i++) + { + if (!skip) + { + // a stopgap + if (m_numFramesToProcess[i] > 0 && m_latticeBufferMultiUtt[i] && m_latticeBufferMultiUtt[i]->getnumframes() != m_numFramesToProcess[i]) + { + // BUGBUG: we just found that (due to some bugs yet to be tracked down), + // the filled number of frames is inconsistent with the number frames in lattices (though it rarely occurs) + // This is just a stopgap, to be removed after the bugs are found and fixed + bool needRenew = true; + while (needRenew) + { + size_t framenum = m_numFramesToProcess[i]; + fprintf(stderr, "WARNING: mismatched number of frames filled in the reader: %d in data vs %d in lattices. Ignoring this utterance %ls\n", + (int)framenum, (int)m_latticeBufferMultiUtt[i]->getnumframes(), m_latticeBufferMultiUtt[i]->getkey().c_str()); + ReNewBufferForMultiIO(i); + needRenew = m_numFramesToProcess[i] > 0 && m_latticeBufferMultiUtt[i] && m_latticeBufferMultiUtt[i]->getnumframes() != m_numFramesToProcess[i]; + } + + } + m_numValidFrames[i] = m_numFramesToProcess[i]; + if (m_numValidFrames[i] > 0) + { + if (m_frameMode) + { + // the layout has already been initialized as entirely frame mode above + assert(i == 0); // this reader thinks there is only one parallel sequence + for (size_t s = 0; s < m_pMBLayout->GetNumParallelSequences(); s++) + assert(s < m_numValidFrames[i]); // MB is already set to only include the valid frames (no need for gaps) + } + else + m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, 0, m_numValidFrames[i]); + + m_extraSeqsPerMB.push_back(i); + fillOneUttDataforParallelmode(matrices, 0, m_numValidFrames[i], i, i); + if (m_latticeBufferMultiUtt[i] != nullptr) + { + m_extraLatticeBufferMultiUtt.push_back(m_latticeBufferMultiUtt[i]); + m_extraLabelsIDBufferMultiUtt.push_back(m_labelsIDBufferMultiUtt[i]); + m_extraPhoneboundaryIDBufferMultiUtt.push_back(m_phoneboundaryIDBufferMultiUtt[i]); + } + } + } + ReNewBufferForMultiIO(i); + } + + if (!skip) + { + m_extraNumSeqs = 0; + if (!m_frameMode) + { + for (size_t src = 0; src < m_numSeqsPerMB; ) + { + size_t framenum = m_numFramesToProcess[src]; + if (framenum == 0) + { + src++; + continue; + } + if (m_latticeBufferMultiUtt[src]!=nullptr && m_latticeBufferMultiUtt[src]->getnumframes()!=framenum) + { + // BUGBUG: we just found that (due to some bugs yet to be tracked down), + // the filled number of frames is inconsistent with the number frames in lattices (though it rarely occurs) + // This is just a stopgap, to be removed after the bugs are found and fixed + fprintf(stderr, "WARNING: mismatched number of frames filled in the reader: %d in data vs %d in lattices. Ignoring this utterance %ls\n", + (int)framenum, (int)m_latticeBufferMultiUtt[src]->getnumframes(), m_latticeBufferMultiUtt[src]->getkey().c_str()); + src++; + continue; + } + + bool slotFound = false; + for (size_t des = 0; des < m_numSeqsPerMB; des++) // try to found a slot + { + if (framenum + m_numValidFrames[des] < m_mbNumTimeSteps) + { // found ! + m_extraSeqsPerMB.push_back(des); + if (m_latticeBufferMultiUtt[src] != nullptr) + { + m_extraLatticeBufferMultiUtt.push_back(m_latticeBufferMultiUtt[src]); + m_extraLabelsIDBufferMultiUtt.push_back(m_labelsIDBufferMultiUtt[src]); + m_extraPhoneboundaryIDBufferMultiUtt.push_back(m_phoneboundaryIDBufferMultiUtt[src]); + } + fillOneUttDataforParallelmode(matrices, m_numValidFrames[des], framenum, des, src); + m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, des, m_numValidFrames[des], m_numValidFrames[des] + framenum); + + ReNewBufferForMultiIO(src); + m_numValidFrames[des] += framenum; + m_extraNumSeqs++; + slotFound = true; + break; + } + } + if (!slotFound) + { + src++; // done with this source; try next source; + } + } + + // and declare the remaining gaps as such + for (size_t i = 0; i < m_numSeqsPerMB; i++) + m_pMBLayout->AddGap(i, m_numValidFrames[i], m_mbNumTimeSteps); + } // if (!frameMode) + + for (auto iter = matrices.begin(); iter != matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + Matrix& data = *matrices[iter->first]; // can be features or labels + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_labelsBufferMultiIO[id].get(), matrixFlagNormal); + } + } + } + } + else // if m_truncated + { + // ------------------------------------------------------- + // truncated BPTT + // ------------------------------------------------------- + + // In truncated BPTT mode, a minibatch only consists of the truncation length, e.g. 20 frames. + // The reader maintains a set of current utterances, and each next minibatch contains the next 20 frames. + // When the end of an utterance is reached, the next available utterance is begin in the same slot. + if (m_noData) // we are returning the last utterances for this epoch + { + // return false if all cursors for all parallel sequences have reached the end + bool endEpoch = true; + for (size_t i = 0; i < m_numSeqsPerMB; i++) + { + if (m_processedFrame[i] != m_numFramesToProcess[i]) + endEpoch = false; + } + if (endEpoch) + return false; + } + + size_t numOfFea = m_featuresBufferMultiIO.size(); + size_t numOfLabel = m_labelsBufferMultiIO.size(); + + // create the feature matrix + m_pMBLayout->Init(m_numSeqsPerMB, m_mbNumTimeSteps); + + vector actualmbsize(m_numSeqsPerMB,0); + for (size_t i = 0; i < m_numSeqsPerMB; i++) + { + // fill one parallel-sequence slot + const size_t startFr = m_processedFrame[i]; // start frame (cursor) inside the utterance that corresponds to time step [0] + + // add utterance to MBLayout + assert(m_numFramesToProcess[i] > startFr || (m_noData && m_numFramesToProcess[i] == startFr)); + if (m_numFramesToProcess[i] > startFr) // in an edge case (m_noData), startFr is at end + m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, -(ptrdiff_t)startFr, m_numFramesToProcess[i] - startFr); + + if (startFr + m_mbNumTimeSteps < m_numFramesToProcess[i]) // end of this minibatch does not reach until end of utterance + { + // we return the next 'm_mbNumTimeSteps' frames, filling all time steps + if (startFr > 0) // not the beginning of the utterance + { + m_sentenceEnd[i] = false; + m_switchFrame[i] = m_mbNumTimeSteps+1; + } + else // beginning of the utterance + { + m_sentenceEnd[i] = true; + m_switchFrame[i] = 0; + } + actualmbsize[i] = m_mbNumTimeSteps; + const size_t endFr = startFr + actualmbsize[i]; // actual end frame index of this segment + for (auto iter = matrices.begin(); iter != matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + Matrix& data = *matrices[iter->first]; // can be features or labels + + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + + if ((m_featuresBufferMultiIO[id] == nullptr) || + (m_featuresBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB)) /*buffer size changed. can be partial minibatch*/) + { + m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); + m_featuresBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; + } + + if (sizeof(ElemType) == sizeof(float)) + { + for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns + memcpy_s(&m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[j * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); + } + } + else // double: must type-cast, cannot memcpy() + { + for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns in outside loop + { + for (int d = 0; d < dim; d++) + m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[j * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; + } + } + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + if ((m_labelsBufferMultiIO[id] == nullptr) || + (m_labelsBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB))) + { + m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); + m_labelsBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; + } + + for (size_t j = startFr,k=0; j < endFr; j++,k++) + { + for (int d = 0; d < dim; d++) + m_labelsBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[j * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; + } + } + } + m_processedFrame[i] += m_mbNumTimeSteps; + } + else // if (startFr + m_mbNumTimeSteps < m_numFramesToProcess[i]) (in this else branch, utterance ends inside this minibatch) + { + // utterance ends: first copy this segment (later, we will pack more utterances in) + assert(startFr == m_processedFrame[i]); + actualmbsize[i] = m_numFramesToProcess[i] - startFr; // parallel sequence is used up to this point + const size_t endFr = startFr + actualmbsize[i]; // end frame in sequence + assert(endFr == m_numFramesToProcess[i]); // we are at the end + + // fill frames for the tail of this utterance + for (auto iter = matrices.begin(); iter != matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + Matrix& data = *matrices[iter->first]; // can be features or labels + + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + + if ((m_featuresBufferMultiIO[id] == nullptr) || + (m_featuresBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB)) /*buffer size changed. can be partial minibatch*/) + { + m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); + m_featuresBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; + } + + if (sizeof(ElemType) == sizeof(float)) + { + for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns + memcpy_s(&m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[j * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); + } + } + else + { + for (size_t j = startFr,k = 0; j < endFr; j++,k++) // column major, so iterate columns in outside loop + { + for (int d = 0; d < dim; d++) + { + m_featuresBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[j * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; + } + } + } + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + if ((m_labelsBufferMultiIO[id] == nullptr) || + (m_labelsBufferAllocatedMultiIO[id] < (dim * m_mbNumTimeSteps * m_numSeqsPerMB))) + { + m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim * m_mbNumTimeSteps * m_numSeqsPerMB); + m_labelsBufferAllocatedMultiIO[id] = dim * m_mbNumTimeSteps * m_numSeqsPerMB; + } + for (size_t j = startFr,k=0; j < endFr; j++,k++) + { + for (int d = 0; d < dim; d++) + m_labelsBufferMultiIO[id].get()[(k * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[j * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; + } + } + } + m_processedFrame[i] += (endFr - startFr); // advance the cursor + assert(m_processedFrame[i] == m_numFramesToProcess[i]); // we must be at the end + m_switchFrame[i] = actualmbsize[i]; + //if (actualmbsize[i] != 0) + // m_pMBLayout->Set(i, actualmbsize[i] - 1, MinibatchPackingFlags::SequenceEnd); // NOTE: this ORs, while original code overwrote in matrix but ORed into vector + // at this point, we completed an utterance--fill the rest with the next utterance + + // BUGBUG: We should fill in a loop until we fill the minibatch for the case where just one ReNew is not sufficient + // to fill up the remaining slots in the minibatch + bool reNewSucc = ReNewBufferForMultiIO(i); + if (actualmbsize[i] < m_mbNumTimeSteps) // we actually have space + { + if (reNewSucc) // we actually have another utterance to start here + { + const size_t startT = m_switchFrame[i]; + const size_t endT = m_mbNumTimeSteps; + // Note: Don't confuse startT/endT with startFr/endFr above. + + // add sequence to MBLayout + m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, i, startT, startT + m_numFramesToProcess[i]); + + // copy the data + for (auto iter = matrices.begin(); iter != matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + //Matrix& data = *matrices[iter->first]; // can be features or labels + + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + if (sizeof(ElemType) == sizeof(float)) + { + for (size_t t = startT, fr = 0; t < endT; t++, fr++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns (for SSE alignment) + memcpy_s(&m_featuresBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim], sizeof(ElemType) * dim, &m_featuresBufferMultiUtt[i].get()[fr * dim + m_featuresStartIndexMultiUtt[id + i * numOfFea]], sizeof(ElemType) * dim); + } + } + else + { + for (size_t t = startT, fr = 0; t < endT; t++, fr++) // column major, so iterate columns in outside loop + { + for (int d = 0; d < dim; d++) + m_featuresBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim + d] = m_featuresBufferMultiUtt[i].get()[fr * dim + d + m_featuresStartIndexMultiUtt[id + i * numOfFea]]; + } + } + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + for (size_t t = startT, fr = 0; t < endT; t++, fr++) + { + for (int d = 0; d < dim; d++) + m_labelsBufferMultiIO[id].get()[(t * m_numSeqsPerMB + i) * dim + d] = m_labelsBufferMultiUtt[i].get()[fr * dim + d + m_labelsStartIndexMultiUtt[id + i * numOfLabel]]; + } + } + } + + m_processedFrame[i] += (endT - startT); + + // BUGBUG: since we currently cannot fill >1 utterances, at least let's check + size_t a = actualmbsize[i] + (endT - startT); + // actualmbsize[i] += (endT - startT); // BUGBUG: don't we need something like this? + if (a < m_mbNumTimeSteps) + { + fprintf(stderr, "GetMinibatchToTrainOrTest(): WARNING: Packing a second utterance did still not fill all time slots; filling slots from %d on as gaps.\n", (int)a); + // declare the rest as a gap + m_pMBLayout->AddGap(i, a, m_mbNumTimeSteps); + } + } + else // we did have space for more, but no more data is available. BUGBUG: we should update actualmbsize[i] above and re-test here + { + // declare the rest as a gap + m_pMBLayout->AddGap(i, actualmbsize[i], m_mbNumTimeSteps); + } + } // if (actualmbsize[i] < m_mbNumTimeSteps) // we actually have space + } + } // for (size_t i = 0; i < m_numSeqsPerMB; i++) + // we are done filling all parallel sequences + + for (auto iter = matrices.begin();iter!=matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + Matrix& data = *matrices[iter->first]; // can be features or labels + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + data.SetValue(dim, m_mbNumTimeSteps*m_numSeqsPerMB, data.GetDeviceId(), m_labelsBufferMultiIO[id].get(), matrixFlagNormal); + } + } + skip = false; + } // if truncated then else + } + while(skip); // keep going if we didn't get the right size minibatch + + if (m_verbosity > 2) + { + aggregateTimer.Stop(); + double totalMBReadTime = aggregateTimer.ElapsedSeconds(); + fprintf(stderr, "Total Minibatch read time = %.8g\n", totalMBReadTime); + } + + return true; + } + + // copy an utterance into the minibatch given a location (parallel-sequence index, start frame) + // TODO: This should use DataFor(). But for that, DataFor() will have to move out from ComputationNode. Ah, it has! + template + void HTKMLFReader::fillOneUttDataforParallelmode(std::map*>& matrices, size_t startFr, + size_t framenum, size_t channelIndex, size_t sourceChannelIndex) + { + size_t id; + size_t dim; + size_t numOfFea = m_featuresBufferMultiIO.size(); + size_t numOfLabel = m_labelsBufferMultiIO.size(); + + typename std::map*>::iterator iter; + for (iter = matrices.begin(); iter != matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + Matrix& data = *matrices[iter->first]; // can be features or labels + + if (m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + id = m_featureNameToIdMap[iter->first]; + dim = m_featureNameToDimMap[iter->first]; + + if (m_featuresBufferMultiIO[id] == nullptr || m_featuresBufferAllocatedMultiIO[id] < dim*m_mbNumTimeSteps*m_numSeqsPerMB) + { + m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim*m_mbNumTimeSteps*m_numSeqsPerMB); + memset(m_featuresBufferMultiIO[id].get(), 0, sizeof(ElemType)*dim*m_mbNumTimeSteps*m_numSeqsPerMB); + m_featuresBufferAllocatedMultiIO[id] = dim*m_mbNumTimeSteps*m_numSeqsPerMB; + } + + if (sizeof(ElemType) == sizeof(float)) + { + for (size_t j = 0, k = startFr; j < framenum; j++, k++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns + memcpy_s(&m_featuresBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim], sizeof(ElemType)*dim, &m_featuresBufferMultiUtt[sourceChannelIndex].get()[j*dim + m_featuresStartIndexMultiUtt[id + sourceChannelIndex*numOfFea]], sizeof(ElemType)*dim); + } + } + else + { + for (size_t j = 0, k = startFr; j < framenum; j++, k++) // column major, so iterate columns in outside loop + { + for (int d = 0; d < dim; d++) + { + m_featuresBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim + d] = m_featuresBufferMultiUtt[sourceChannelIndex].get()[j*dim + d + m_featuresStartIndexMultiUtt[id + sourceChannelIndex*numOfFea]]; + } + } + } + } + else if (m_nameToTypeMap[iter->first] == InputOutputTypes::category) + { + id = m_labelNameToIdMap[iter->first]; + dim = m_labelNameToDimMap[iter->first]; + if (m_labelsBufferMultiIO[id] == nullptr || m_labelsBufferAllocatedMultiIO[id] < dim*m_mbNumTimeSteps*m_numSeqsPerMB) + { + m_labelsBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), dim*m_mbNumTimeSteps*m_numSeqsPerMB); + memset(m_labelsBufferMultiIO[id].get(), 0, sizeof(ElemType)*dim*m_mbNumTimeSteps*m_numSeqsPerMB); + m_labelsBufferAllocatedMultiIO[id] = dim*m_mbNumTimeSteps*m_numSeqsPerMB; + } + + for (size_t j = 0, k = startFr; j < framenum; j++, k++) + { + for (int d = 0; d < dim; d++) + { + m_labelsBufferMultiIO[id].get()[(k*m_numSeqsPerMB + channelIndex)*dim + d] = m_labelsBufferMultiUtt[sourceChannelIndex].get()[j*dim + d + m_labelsStartIndexMultiUtt[id + sourceChannelIndex*numOfLabel]]; + } + } + } + } + } + + template + bool HTKMLFReader::GetMinibatchToWrite(std::map*>& matrices) + { + std::map::iterator iter; + if (m_checkDictionaryKeys) + { + for (auto iter=m_featureNameToIdMap.begin();iter!=m_featureNameToIdMap.end();iter++) + { + if (matrices.find(iter->first)==matrices.end()) + { + fprintf(stderr,"GetMinibatchToWrite: feature node %ls specified in reader not found in the network\n", iter->first.c_str()); + RuntimeError("GetMinibatchToWrite: feature node specified in reader not found in the network."); + } + } + /* + for (auto iter=matrices.begin();iter!=matrices.end();iter++) + { + if (m_featureNameToIdMap.find(iter->first)==m_featureNameToIdMap.end()) + RuntimeError(msra::strfun::strprintf("minibatch requested for input node %ws not found in reader - cannot generate input\n",iter->first.c_str())); + } + */ + m_checkDictionaryKeys=false; + } + + if (m_inputFileIndexReset(); + + // load next file (or set of files) + foreach_index(i, m_inputFilesMultiIO) + { + msra::asr::htkfeatreader reader; + + const auto path = reader.parse(m_inputFilesMultiIO[i][m_inputFileIndex]); + // read file + msra::dbn::matrix feat; + string featkind; + unsigned int sampperiod; + msra::util::attempt (5, [&]() + { + reader.read (path, featkind, sampperiod, feat); // whole file read as columns of feature vectors + }); + fprintf (stderr, "evaluate: reading %d frames of %ls\n", (int)feat.cols(), ((wstring)path).c_str()); + m_fileEvalSource->AddFile(feat, featkind, sampperiod, i); + } + m_inputFileIndex++; + + // turn frames into minibatch (augment neighbors, etc) + m_fileEvalSource->CreateEvalMinibatch(); + + // populate input matrices + bool first = true; + typename std::map*>::iterator iter; + for (iter = matrices.begin();iter!=matrices.end(); iter++) + { + // dereference matrix that corresponds to key (input/output name) and + // populate based on whether its a feature or a label + + if (m_nameToTypeMap.find(iter->first) != m_nameToTypeMap.end() && m_nameToTypeMap[iter->first] == InputOutputTypes::real) + { + Matrix& data = *matrices[iter->first]; // can be features or labels (TODO: Really? Didn't we just ^^^ check that it is 'real'?) + size_t id = m_featureNameToIdMap[iter->first]; + size_t dim = m_featureNameToDimMap[iter->first]; + + const msra::dbn::matrix feat = m_fileEvalSource->ChunkOfFrames(id); + + // update the MBLayout + if (first) + { + m_pMBLayout->Init(1, feat.cols()); + m_pMBLayout->AddSequence(NEW_SEQUENCE_ID, 0, 0, feat.cols()); // feat.cols() == number of time steps here since we only have one parallel sequence + //m_pMBLayout->Set(0, 0, MinibatchPackingFlags::SequenceStart); + //m_pMBLayout->SetWithoutOr(0, feat.cols() - 1, MinibatchPackingFlags::SequenceEnd); // BUGBUG: using SetWithoutOr() because original code did; but that seems inconsistent + first = false; + } + + // copy the features over to our array type + assert(feat.rows()==dim); dim; // check feature dimension matches what's expected + + if ((m_featuresBufferMultiIO[id] == nullptr) || + (m_featuresBufferAllocatedMultiIO[id] < (feat.rows() * feat.cols())) /*buffer size changed. can be partial minibatch*/) + { + m_featuresBufferMultiIO[id] = AllocateIntermediateBuffer(data.GetDeviceId(), feat.rows() * feat.cols()); + m_featuresBufferAllocatedMultiIO[id] = feat.rows() * feat.cols(); + } + + if (sizeof(ElemType) == sizeof(float)) + { + for (int j = 0; j < feat.cols(); j++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns + memcpy_s(&m_featuresBufferMultiIO[id].get()[j * feat.rows()], sizeof(ElemType) * feat.rows(), &feat(0, j), sizeof(ElemType) * feat.rows()); + } + } + else + { + for (int j = 0; j < feat.cols(); j++) // column major, so iterate columns in outside loop + { + for (int i = 0; i < feat.rows(); i++) + { + m_featuresBufferMultiIO[id].get()[j * feat.rows() + i] = feat(i, j); + } + } + } + data.SetValue(feat.rows(), feat.cols(), data.GetDeviceId(), m_featuresBufferMultiIO[id].get(), matrixFlagNormal); + } + } + return true; + } + else + { + return false; + } + } + + + template + bool HTKMLFReader::ReNewBufferForMultiIO(size_t i) + { + if (m_noData) + { + if ((i == 0) && !m_truncated) + m_numFramesToProcess[i] = 0; + return false; + } + + size_t numOfFea = m_featuresBufferMultiIO.size(); + size_t numOfLabel = m_labelsBufferMultiIO.size(); + + size_t totalFeatNum = 0; + foreach_index(id, m_featuresBufferAllocatedMultiIO) + { + const msra::dbn::matrixstripe featOri = m_mbiter->frames(id); + size_t fdim = featOri.rows(); + const size_t actualmbsizeOri = featOri.cols(); + m_featuresStartIndexMultiUtt[id+i*numOfFea] = totalFeatNum; + totalFeatNum = fdim * actualmbsizeOri + m_featuresStartIndexMultiUtt[id+i*numOfFea]; + } + if ((m_featuresBufferMultiUtt[i] == NULL) || (m_featuresBufferAllocatedMultiUtt[i] < totalFeatNum)) + { + m_featuresBufferMultiUtt[i] = AllocateIntermediateBuffer(-1 /*CPU*/, totalFeatNum); + m_featuresBufferAllocatedMultiUtt[i] = totalFeatNum; + } + + size_t totalLabelsNum = 0; + for (auto it = m_labelNameToIdMap.begin(); it != m_labelNameToIdMap.end(); ++it) + { + size_t id = m_labelNameToIdMap[it->first]; + size_t dim = m_labelNameToDimMap[it->first]; + + const vector & uids = m_mbiter->labels(id); + size_t actualmbsizeOri = uids.size(); + m_labelsStartIndexMultiUtt[id+i*numOfLabel] = totalLabelsNum; + totalLabelsNum = m_labelsStartIndexMultiUtt[id+i*numOfLabel] + dim * actualmbsizeOri; + } + + if ((m_labelsBufferMultiUtt[i] == NULL) || (m_labelsBufferAllocatedMultiUtt[i] < totalLabelsNum)) + { + m_labelsBufferMultiUtt[i] = AllocateIntermediateBuffer(-1 /*CPU */, totalLabelsNum); + m_labelsBufferAllocatedMultiUtt[i] = totalLabelsNum; + } + + memset(m_labelsBufferMultiUtt[i].get(), 0, sizeof(ElemType)*totalLabelsNum); + + bool first = true; + foreach_index(id, m_featuresBufferMultiIO) + { + const msra::dbn::matrixstripe featOri = m_mbiter->frames(id); + const size_t actualmbsizeOri = featOri.cols(); + size_t fdim = featOri.rows(); + if (first) + { + m_numFramesToProcess[i] = actualmbsizeOri; + first = false; + } + else + { + if (m_numFramesToProcess[i] != actualmbsizeOri) + { + RuntimeError("The multi-IO features has inconsistent number of frames!"); + } + } + assert (actualmbsizeOri == m_mbiter->currentmbframes()); + + if (sizeof(ElemType) == sizeof(float)) + { + for (int k = 0; k < actualmbsizeOri; k++) // column major, so iterate columns + { + // copy over the entire column at once, need to do this because SSEMatrix may have gaps at the end of the columns + memcpy_s(&m_featuresBufferMultiUtt[i].get()[k*fdim + m_featuresStartIndexMultiUtt[id + i*numOfFea]], sizeof(ElemType)*fdim, &featOri(0, k), sizeof(ElemType)*fdim); + } + } + else + { + for (int k=0; k < actualmbsizeOri; k++) // column major, so iterate columns in outside loop + { + for (int d = 0; d < featOri.rows(); d++) + { + m_featuresBufferMultiUtt[i].get()[k*featOri.rows() + d + m_featuresStartIndexMultiUtt[id + i*numOfFea]] = featOri(d, k); + } + } + } + } + + for (auto it = m_labelNameToIdMap.begin(); it != m_labelNameToIdMap.end(); ++it) + { + size_t id = m_labelNameToIdMap[it->first]; + size_t dim = m_labelNameToDimMap[it->first]; + + const vector & uids = m_mbiter->labels(id); + size_t actualmbsizeOri = uids.size(); + + if (m_convertLabelsToTargetsMultiIO[id]) + { + size_t labelDim = m_labelToTargetMapMultiIO[id].size(); + for (int k=0; k < actualmbsizeOri; k++) + { + assert(uids[k] < labelDim); labelDim; + size_t labelId = uids[k]; + for (int j = 0; j < dim; j++) + { + m_labelsBufferMultiUtt[i].get()[k*dim + j + m_labelsStartIndexMultiUtt[id + i*numOfLabel]] = m_labelToTargetMapMultiIO[id][labelId][j]; + } + } + } + else + { + // loop through the columns and set one value to 1 + // in the future we want to use a sparse matrix here + for (int k=0; k < actualmbsizeOri; k++) + { + assert(uids[k] < dim); + m_labelsBufferMultiUtt[i].get()[k*dim + uids[k] + m_labelsStartIndexMultiUtt[id + i*numOfLabel]] = (ElemType)1; + } + } + } + //lattice + if (m_latticeBufferMultiUtt[i] != NULL) + { + m_latticeBufferMultiUtt[i].reset(); + } + + if (m_mbiter->haslattice()) + { + m_latticeBufferMultiUtt[i] = std::move(m_mbiter->lattice(0)); + m_phoneboundaryIDBufferMultiUtt[i].clear(); + m_phoneboundaryIDBufferMultiUtt[i] = m_mbiter->bounds(); + m_labelsIDBufferMultiUtt[i].clear(); + m_labelsIDBufferMultiUtt[i] = m_mbiter->labels(); + } + + m_processedFrame[i] = 0; + + Timer mbIterAdvancementTimer; + if (m_verbosity > 2) + mbIterAdvancementTimer.Start(); + + // Advance the MB iterator until we find some data or reach the end of epoch + do + { + (*m_mbiter)++; + } while ((m_mbiter->currentmbframes() == 0) && *m_mbiter); + + if (m_verbosity > 2) + { + mbIterAdvancementTimer.Stop(); + double advancementTime = mbIterAdvancementTimer.ElapsedSeconds(); + fprintf(stderr, "Time to advance mbiter = %.8g\n", advancementTime); + } + + if (!(*m_mbiter)) + m_noData = true; + + return true; + } + + // GetLabelMapping - Gets the label mapping from integer to type in file + // mappingTable - a map from numeric datatype to native label type stored as a string + template + const std::map::LabelIdType, typename IDataReader::LabelType>& HTKMLFReader::GetLabelMapping(const std::wstring& /*sectionName*/) + { + return m_idToLabelMap; + } + + // SetLabelMapping - Sets the label mapping from integer index to label + // labelMapping - mapping table from label values to IDs (must be 0-n) + // note: for tasks with labels, the mapping table must be the same between a training run and a testing run + template + void HTKMLFReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, typename IDataReader::LabelType>& labelMapping) + { + m_idToLabelMap = labelMapping; + } + + template + size_t HTKMLFReader::ReadLabelToTargetMappingFile (const std::wstring& labelToTargetMappingFile, const std::wstring& labelListFile, std::vector>& labelToTargetMap) + { + if (labelListFile==L"") + RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): cannot read labelToTargetMappingFile without a labelMappingFile!"); + + vector labelList; + size_t count, numLabels; + count=0; + // read statelist first + msra::files::textreader labelReader(labelListFile); + while(labelReader) + { + labelList.push_back(labelReader.wgetline()); + count++; + } + numLabels=count; + count=0; + msra::files::textreader mapReader(labelToTargetMappingFile); + size_t targetDim = 0; + while(mapReader) + { + std::wstring line(mapReader.wgetline()); + // find white space as a demarcation + std::wstring::size_type pos = line.find(L" "); + std::wstring token = line.substr(0,pos); + std::wstring targetstring = line.substr(pos+1); + + if (labelList[count]!=token) + RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): mismatch between labelMappingFile and labelToTargetMappingFile"); + + if (count==0) + targetDim = targetstring.length(); + else if (targetDim!=targetstring.length()) + RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): inconsistent target length among records"); + + std::vector targetVector(targetstring.length(),(ElemType)0.0); + foreach_index(i, targetstring) + { + if (targetstring.compare(i,1,L"1")==0) + targetVector[i] = (ElemType)1.0; + else if (targetstring.compare(i,1,L"0")!=0) + RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): expecting label2target mapping to contain only 1's or 0's"); + } + labelToTargetMap.push_back(targetVector); + count++; + } + + // verify that statelist and label2target mapping file are in same order (to match up with reader) while reading mapping + if (count!=labelList.size()) + RuntimeError("HTKMLFReader::ReadLabelToTargetMappingFile(): mismatch between lengths of labelMappingFile vs labelToTargetMappingFile"); + + return targetDim; + } + + // GetData - Gets metadata from the specified section (into CPU memory) + // sectionName - section name to retrieve data from + // numRecords - number of records to read + // data - pointer to data buffer, if NULL, dataBufferSize will be set to size of required buffer to accomidate request + // dataBufferSize - [in] size of the databuffer in bytes + // [out] size of buffer filled with data + // recordStart - record to start reading from, defaults to zero (start of data) + // returns: true if data remains to be read, false if the end of data was reached + template + bool HTKMLFReader::GetData(const std::wstring& /*sectionName*/, size_t /*numRecords*/, void* /*data*/, size_t& /*dataBufferSize*/, size_t /*recordStart*/) + { + RuntimeError("GetData not supported in HTKMLFReader"); + } + + + template + bool HTKMLFReader::DataEnd(EndDataType endDataType) + { + // each minibatch is considered a "sentence" + // other datatypes not really supported... + // assert(endDataType == endDataSentence); + // for the truncated BPTT, we need to support check wether it's the end of data + bool ret = false; + switch (endDataType) + { + case endDataNull: + case endDataEpoch: + case endDataSet: + LogicError("DataEnd: does not support endDataTypes: endDataNull, endDataEpoch and endDataSet"); + break; + case endDataSentence: + if (m_truncated) + ret = m_sentenceEnd[0]; + else + ret = true; // useless in current condition + break; + } + return ret; + } + + template + void HTKMLFReader::SetSentenceEndInBatch(vector &sentenceEnd) + { + sentenceEnd.resize(m_switchFrame.size()); + for (size_t i = 0; i < m_switchFrame.size() ; i++) + sentenceEnd[i] = m_switchFrame[i]; + } + + template + void HTKMLFReader::CopyMBLayoutTo(MBLayoutPtr pMBLayout) + { + pMBLayout->CopyFrom(m_pMBLayout); + } + + template + size_t HTKMLFReader::GetNumParallelSequences() + { + if (!m_frameMode) + if (m_numSeqsPerMB != m_pMBLayout->GetNumParallelSequences()) + LogicError("HTKMLFReader: Number of parallel sequences in m_pMBLayout did not get set to m_numSeqsPerMB."); + return m_pMBLayout->GetNumParallelSequences(); // (this function is only used for validation anyway) + } + + // GetFileConfigNames - determine the names of the features and labels sections in the config file + // features - [in,out] a vector of feature name strings + // labels - [in,out] a vector of label name strings + template template + void HTKMLFReader::GetDataNamesFromConfig(const ConfigRecordType & readerConfig, std::vector& features, std::vector& labels, + std::vector& hmms, std::vector& lattices) + { + for (const auto & id : readerConfig.GetMemberIds()) + { if (!readerConfig.CanBeConfigRecord(id)) continue; - const ConfigRecordType & temp = readerConfig(id); - // see if we have a config parameters that contains a "file" element, it's a sub key, use it - if (temp.ExistsCurrent(L"scpFile")) - { - features.push_back(id); - } - else if (temp.ExistsCurrent(L"mlfFile")|| temp.ExistsCurrent(L"mlfFileList")) - { - labels.push_back(id); - } - else if (temp.ExistsCurrent(L"phoneFile")) - { - hmms.push_back(id); - } - else if (temp.ExistsCurrent(L"denlatTocFile")) - { - lattices.push_back(id); - } - } - } - - template - void HTKMLFReader::ExpandDotDotDot(wstring & featPath, const wstring & scpPath, wstring & scpDirCached) - { - wstring delim = L"/\\"; - - if (scpDirCached.empty()) - { - scpDirCached = scpPath; - wstring tail; - auto pos = scpDirCached.find_last_of(delim); - if (pos != wstring::npos) - { - tail = scpDirCached.substr(pos + 1); - scpDirCached.resize(pos); - } - if (tail.empty()) // nothing was split off: no dir given, 'dir' contains the filename - scpDirCached.swap(tail); - } - size_t pos = featPath.find(L"..."); - if (pos != featPath.npos) - featPath = featPath.substr(0, pos) + scpDirCached + featPath.substr(pos + 3); - } - - template - unique_ptr& HTKMLFReader::GetCUDAAllocator(int deviceID) - { - if (m_cudaAllocator != nullptr) - { - if (m_cudaAllocator->GetDeviceId() != deviceID) - { - m_cudaAllocator.reset(nullptr); - } - } - - if (m_cudaAllocator == nullptr) - { - m_cudaAllocator.reset(new CUDAPageLockedMemAllocator(deviceID)); - } - - return m_cudaAllocator; - } - - template - std::shared_ptr HTKMLFReader::AllocateIntermediateBuffer(int deviceID, size_t numElements) - { - if (deviceID >= 0) - { - // Use pinned memory for GPU devices for better copy performance - size_t totalSize = sizeof(ElemType) * numElements; - return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { - this->GetCUDAAllocator(deviceID)->Free((char*)p); - }); - } - else - { - return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { - delete[] p; - }); - } - } - - template class HTKMLFReader; - template class HTKMLFReader; -}}} + const ConfigRecordType & temp = readerConfig(id); + // see if we have a config parameters that contains a "file" element, it's a sub key, use it + if (temp.ExistsCurrent(L"scpFile")) + { + features.push_back(id); + } + else if (temp.ExistsCurrent(L"mlfFile")|| temp.ExistsCurrent(L"mlfFileList")) + { + labels.push_back(id); + } + else if (temp.ExistsCurrent(L"phoneFile")) + { + hmms.push_back(id); + } + else if (temp.ExistsCurrent(L"denlatTocFile")) + { + lattices.push_back(id); + } + } + } + + template + void HTKMLFReader::ExpandDotDotDot(wstring & featPath, const wstring & scpPath, wstring & scpDirCached) + { + wstring delim = L"/\\"; + + if (scpDirCached.empty()) + { + scpDirCached = scpPath; + wstring tail; + auto pos = scpDirCached.find_last_of(delim); + if (pos != wstring::npos) + { + tail = scpDirCached.substr(pos + 1); + scpDirCached.resize(pos); + } + if (tail.empty()) // nothing was split off: no dir given, 'dir' contains the filename + scpDirCached.swap(tail); + } + size_t pos = featPath.find(L"..."); + if (pos != featPath.npos) + featPath = featPath.substr(0, pos) + scpDirCached + featPath.substr(pos + 3); + } + + template + unique_ptr& HTKMLFReader::GetCUDAAllocator(int deviceID) + { + if (m_cudaAllocator != nullptr) + { + if (m_cudaAllocator->GetDeviceId() != deviceID) + { + m_cudaAllocator.reset(nullptr); + } + } + + if (m_cudaAllocator == nullptr) + { + m_cudaAllocator.reset(new CUDAPageLockedMemAllocator(deviceID)); + } + + return m_cudaAllocator; + } + + template + std::shared_ptr HTKMLFReader::AllocateIntermediateBuffer(int deviceID, size_t numElements) + { + if (deviceID >= 0) + { + // Use pinned memory for GPU devices for better copy performance + size_t totalSize = sizeof(ElemType) * numElements; + return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { + this->GetCUDAAllocator(deviceID)->Free((char*)p); + }); + } + else + { + return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { + delete[] p; + }); + } + } + + template class HTKMLFReader; + template class HTKMLFReader; +}}} diff --git a/Source/Readers/HTKMLFReader/HTKMLFReader.h b/Source/Readers/HTKMLFReader/HTKMLFReader.h index 7e64ee3e8f5d..6843d90b4351 100644 --- a/Source/Readers/HTKMLFReader/HTKMLFReader.h +++ b/Source/Readers/HTKMLFReader/HTKMLFReader.h @@ -1,178 +1,178 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// HTKMLFReader.h - Include file for the MTK and MLF format of features and samples -#pragma once -#include "DataReader.h" -#include "Config.h" // for intargvector -#include "CUDAPageLockedMemAllocator.h" - -namespace Microsoft { namespace MSR { namespace CNTK { - -template -class HTKMLFReader : public IDataReader -{ -private: - - const static size_t m_htkRandomizeAuto = 0; - const static size_t m_htkRandomizeDisable = (size_t)-1; - - unique_ptr m_mbiter; - unique_ptr m_frameSource; - unique_ptr m_fileEvalSource; - unique_ptr m_lattices; - map m_latticeMap; - - vector m_sentenceEnd; - bool m_truncated; - bool m_frameMode; - vector m_processedFrame; // [seq index] (truncated BPTT only) current time step (cursor) - intargvector m_numSeqsPerMBForAllEpochs; - size_t m_numSeqsPerMB; // requested number of parallel sequences - size_t m_mbNumTimeSteps; // number of time steps to fill/filled (note: for frame randomization, this the #frames, and not 1 as later reported) - size_t m_mbMaxNumTimeSteps; // max time steps we take in a MB layout; any setence longer than this max will be discarded (and a warning will be issued ) - // this is used to prevent CUDA out-of memory errors - - vector m_numFramesToProcess; // [seq index] number of frames available (left to return) in each parallel sequence - vector m_switchFrame; /// TODO: something like the position where a new sequence starts; still supported? - vector m_numValidFrames; // [seq index] valid #frames in each parallel sequence. Frames (s, t) with t >= m_numValidFrames[s] are NoInput. - vector m_extraSeqsPerMB; - size_t m_extraNumSeqs; - bool m_noData; - bool m_trainOrTest; // if false, in file writing mode - using LabelType = typename IDataReader::LabelType; - using LabelIdType = typename IDataReader::LabelIdType; - - std::map m_idToLabelMap; - - bool m_partialMinibatch; // allow partial minibatches? - - std::vector> m_featuresBufferMultiUtt; - std::vector m_featuresBufferAllocatedMultiUtt; - std::vector> m_labelsBufferMultiUtt; - std::vector m_labelsBufferAllocatedMultiUtt; - std::vector m_featuresStartIndexMultiUtt; - std::vector m_labelsStartIndexMultiUtt; - - unique_ptr m_cudaAllocator; - std::vector> m_featuresBufferMultiIO; - std::vector m_featuresBufferAllocatedMultiIO; - std::vector> m_labelsBufferMultiIO; - std::vector m_labelsBufferAllocatedMultiIO; - - //for lattice uids and phoneboundaries - std::vector> m_latticeBufferMultiUtt; - std::vector> m_labelsIDBufferMultiUtt; - std::vector> m_phoneboundaryIDBufferMultiUtt; - std::vector> m_extraLatticeBufferMultiUtt; - std::vector> m_extraLabelsIDBufferMultiUtt; - std::vector> m_extraPhoneboundaryIDBufferMultiUtt; - - //hmm - msra::asr::simplesenonehmm m_hset; - - std::map m_featureNameToIdMap; - std::map m_labelNameToIdMap; - std::map m_nameToTypeMap; - std::map m_featureNameToDimMap; - std::map m_labelNameToDimMap; - - // for writing outputs to files (standard single input/output network) - deprecate eventually - bool m_checkDictionaryKeys; - bool m_convertLabelsToTargets; - std::vector m_convertLabelsToTargetsMultiIO; - std::vector> m_inputFilesMultiIO; - - size_t m_inputFileIndex; - std::vector m_featDims; - std::vector m_labelDims; - - std::vector>>m_labelToTargetMapMultiIO; - - int m_verbosity; - - template void PrepareForTrainingOrTesting(const ConfigRecordType & config); - template void PrepareForWriting(const ConfigRecordType & config); - - bool GetMinibatchToTrainOrTest(std::map*>&matrices); - bool GetMinibatch4SEToTrainOrTest(std::vector> & latticeinput, vector &uids, vector &boundaries, std::vector &extrauttmap); - void fillOneUttDataforParallelmode(std::map*>& matrices, size_t startFr, size_t framenum, size_t channelIndex, size_t sourceChannelIndex); - bool GetMinibatchToWrite(std::map*>&matrices); - - void StartMinibatchLoopToTrainOrTest(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize); - void StartMinibatchLoopToWrite(size_t mbSize, size_t epoch, size_t requestedEpochSamples=requestDataSize); - - bool ReNewBufferForMultiIO(size_t i); - - size_t GetNumParallelSequences(); - void SetNumParallelSequences(const size_t) { }; - - template void GetDataNamesFromConfig(const ConfigRecordType & readerConfig, std::vector& features, std::vector& labels, - std::vector& hmms, std::vector& lattices); - - size_t ReadLabelToTargetMappingFile (const std::wstring& labelToTargetMappingFile, const std::wstring& labelListFile, std::vector>& labelToTargetMap); - - void ExpandDotDotDot(wstring & featPath, const wstring & scpPath, wstring & scpDirCached); - - - enum InputOutputTypes - { - real, - category, - }; - -private: - // Helper functions - unique_ptr& GetCUDAAllocator(int deviceID); - std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements); - -public: - MBLayoutPtr m_pMBLayout; - - /// by default it is false - /// if true, reader will set to ((int) MinibatchPackingFlags::None) for time positions that are orignally correspond to ((int) MinibatchPackingFlags::SequenceStart) - /// set to true so that a current minibatch can uses state activities from the previous minibatch. - /// default will have truncated BPTT, which only does BPTT inside a minibatch - bool mIgnoreSentenceBeginTag; - // TODO: this ^^ does not seem to belong here. - - HTKMLFReader() : m_pMBLayout(make_shared()) - { - } - template void InitFromConfig(const ConfigRecordType &); - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } - virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } - virtual void Destroy() { delete this; } - virtual ~HTKMLFReader() { } - - virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize) - { - return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); - } - - virtual bool SupportsDistributedMBRead() const override - { - return m_frameSource && m_frameSource->supportsbatchsubsetting(); - } - - virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize) override; - - virtual bool GetMinibatch(std::map*>& matrices); - virtual const std::map& GetLabelMapping(const std::wstring& sectionName); - virtual void SetLabelMapping(const std::wstring& sectionName, const std::map& labelMapping); - virtual bool GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart=0); - virtual bool GetMinibatch4SE(std::vector> & latticeinput, vector &uids, vector &boundaries, vector &extrauttmap); - virtual bool GetHmmData(msra::asr::simplesenonehmm * hmm); - - virtual bool DataEnd(EndDataType endDataType); - void CopyMBLayoutTo(MBLayoutPtr); - void SetSentenceEndInBatch(vector &/*sentenceEnd*/); - void SetSentenceEnd(int /*actualMbSize*/){}; - void SetRandomSeed(int){ NOT_IMPLEMENTED }; - - bool RequireSentenceSeg() const override { return !m_frameMode; }; -}; - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// HTKMLFReader.h - Include file for the MTK and MLF format of features and samples +#pragma once +#include "DataReader.h" +#include "Config.h" // for intargvector +#include "CUDAPageLockedMemAllocator.h" + +namespace Microsoft { namespace MSR { namespace CNTK { + +template +class HTKMLFReader : public IDataReader +{ +private: + + const static size_t m_htkRandomizeAuto = 0; + const static size_t m_htkRandomizeDisable = (size_t)-1; + + unique_ptr m_mbiter; + unique_ptr m_frameSource; + unique_ptr m_fileEvalSource; + unique_ptr m_lattices; + map m_latticeMap; + + vector m_sentenceEnd; + bool m_truncated; + bool m_frameMode; + vector m_processedFrame; // [seq index] (truncated BPTT only) current time step (cursor) + intargvector m_numSeqsPerMBForAllEpochs; + size_t m_numSeqsPerMB; // requested number of parallel sequences + size_t m_mbNumTimeSteps; // number of time steps to fill/filled (note: for frame randomization, this the #frames, and not 1 as later reported) + size_t m_mbMaxNumTimeSteps; // max time steps we take in a MB layout; any setence longer than this max will be discarded (and a warning will be issued ) + // this is used to prevent CUDA out-of memory errors + + vector m_numFramesToProcess; // [seq index] number of frames available (left to return) in each parallel sequence + vector m_switchFrame; /// TODO: something like the position where a new sequence starts; still supported? + vector m_numValidFrames; // [seq index] valid #frames in each parallel sequence. Frames (s, t) with t >= m_numValidFrames[s] are NoInput. + vector m_extraSeqsPerMB; + size_t m_extraNumSeqs; + bool m_noData; + bool m_trainOrTest; // if false, in file writing mode + using LabelType = typename IDataReader::LabelType; + using LabelIdType = typename IDataReader::LabelIdType; + + std::map m_idToLabelMap; + + bool m_partialMinibatch; // allow partial minibatches? + + std::vector> m_featuresBufferMultiUtt; + std::vector m_featuresBufferAllocatedMultiUtt; + std::vector> m_labelsBufferMultiUtt; + std::vector m_labelsBufferAllocatedMultiUtt; + std::vector m_featuresStartIndexMultiUtt; + std::vector m_labelsStartIndexMultiUtt; + + unique_ptr m_cudaAllocator; + std::vector> m_featuresBufferMultiIO; + std::vector m_featuresBufferAllocatedMultiIO; + std::vector> m_labelsBufferMultiIO; + std::vector m_labelsBufferAllocatedMultiIO; + + //for lattice uids and phoneboundaries + std::vector> m_latticeBufferMultiUtt; + std::vector> m_labelsIDBufferMultiUtt; + std::vector> m_phoneboundaryIDBufferMultiUtt; + std::vector> m_extraLatticeBufferMultiUtt; + std::vector> m_extraLabelsIDBufferMultiUtt; + std::vector> m_extraPhoneboundaryIDBufferMultiUtt; + + //hmm + msra::asr::simplesenonehmm m_hset; + + std::map m_featureNameToIdMap; + std::map m_labelNameToIdMap; + std::map m_nameToTypeMap; + std::map m_featureNameToDimMap; + std::map m_labelNameToDimMap; + + // for writing outputs to files (standard single input/output network) - deprecate eventually + bool m_checkDictionaryKeys; + bool m_convertLabelsToTargets; + std::vector m_convertLabelsToTargetsMultiIO; + std::vector> m_inputFilesMultiIO; + + size_t m_inputFileIndex; + std::vector m_featDims; + std::vector m_labelDims; + + std::vector>>m_labelToTargetMapMultiIO; + + int m_verbosity; + + template void PrepareForTrainingOrTesting(const ConfigRecordType & config); + template void PrepareForWriting(const ConfigRecordType & config); + + bool GetMinibatchToTrainOrTest(std::map*>&matrices); + bool GetMinibatch4SEToTrainOrTest(std::vector> & latticeinput, vector &uids, vector &boundaries, std::vector &extrauttmap); + void fillOneUttDataforParallelmode(std::map*>& matrices, size_t startFr, size_t framenum, size_t channelIndex, size_t sourceChannelIndex); + bool GetMinibatchToWrite(std::map*>&matrices); + + void StartMinibatchLoopToTrainOrTest(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize); + void StartMinibatchLoopToWrite(size_t mbSize, size_t epoch, size_t requestedEpochSamples=requestDataSize); + + bool ReNewBufferForMultiIO(size_t i); + + size_t GetNumParallelSequences(); + void SetNumParallelSequences(const size_t) { }; + + template void GetDataNamesFromConfig(const ConfigRecordType & readerConfig, std::vector& features, std::vector& labels, + std::vector& hmms, std::vector& lattices); + + size_t ReadLabelToTargetMappingFile (const std::wstring& labelToTargetMappingFile, const std::wstring& labelListFile, std::vector>& labelToTargetMap); + + void ExpandDotDotDot(wstring & featPath, const wstring & scpPath, wstring & scpDirCached); + + + enum InputOutputTypes + { + real, + category, + }; + +private: + // Helper functions + unique_ptr& GetCUDAAllocator(int deviceID); + std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements); + +public: + MBLayoutPtr m_pMBLayout; + + /// by default it is false + /// if true, reader will set to ((int) MinibatchPackingFlags::None) for time positions that are orignally correspond to ((int) MinibatchPackingFlags::SequenceStart) + /// set to true so that a current minibatch can uses state activities from the previous minibatch. + /// default will have truncated BPTT, which only does BPTT inside a minibatch + bool mIgnoreSentenceBeginTag; + // TODO: this ^^ does not seem to belong here. + + HTKMLFReader() : m_pMBLayout(make_shared()) + { + } + template void InitFromConfig(const ConfigRecordType &); + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } + virtual void Destroy() { delete this; } + virtual ~HTKMLFReader() { } + + virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize) + { + return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); + } + + virtual bool SupportsDistributedMBRead() const override + { + return m_frameSource && m_frameSource->supportsbatchsubsetting(); + } + + virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize) override; + + virtual bool GetMinibatch(std::map*>& matrices); + virtual const std::map& GetLabelMapping(const std::wstring& sectionName); + virtual void SetLabelMapping(const std::wstring& sectionName, const std::map& labelMapping); + virtual bool GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart=0); + virtual bool GetMinibatch4SE(std::vector> & latticeinput, vector &uids, vector &boundaries, vector &extrauttmap); + virtual bool GetHmmData(msra::asr::simplesenonehmm * hmm); + + virtual bool DataEnd(EndDataType endDataType); + void CopyMBLayoutTo(MBLayoutPtr); + void SetSentenceEndInBatch(vector &/*sentenceEnd*/); + void SetSentenceEnd(int /*actualMbSize*/){}; + void SetRandomSeed(int){ NOT_IMPLEMENTED }; + + bool RequireSentenceSeg() const override { return !m_frameMode; }; +}; + +}}} diff --git a/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj b/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj index fd8f9c343f67..d92d4d26acaf 100644 --- a/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj +++ b/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj @@ -1,155 +1,155 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {33D2FD22-DEF2-4507-A58A-368F641AEBE5} - Win32Proj - HTKMLFReader - - - - - - - - - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;HTKMLFREADER_EXPORTS;%(PreprocessorDefinitions) - true - true - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;HTKMLFREADER_EXPORTS;%(PreprocessorDefinitions) - true - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - true - - - - - - - - - - - - - - - - - - - - - - - - - - - - NotUsing - - - - - false - - - false - - - - - NotUsing - - - - - - - - - - - - Create - Create - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {33D2FD22-DEF2-4507-A58A-368F641AEBE5} + Win32Proj + HTKMLFReader + + + + + + + + + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_WINDOWS;_USRDLL;HTKMLFREADER_EXPORTS;%(PreprocessorDefinitions) + true + true + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_WINDOWS;_USRDLL;HTKMLFREADER_EXPORTS;%(PreprocessorDefinitions) + true + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + NotUsing + + + + + false + + + false + + + + + NotUsing + + + + + + + + + + + + Create + Create + + + + + \ No newline at end of file diff --git a/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj.filters b/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj.filters index a66ca8153a1d..d32054de518d 100644 --- a/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj.filters +++ b/Source/Readers/HTKMLFReader/HTKMLFReader.vcxproj.filters @@ -1,67 +1,67 @@ - - - - - - - - - - - Duplicates to remove - - - - Common - - - Common - - - - - - - - - - - - - - - - - Duplicates to remove - - - Duplicates to remove - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - - - - {9a77edee-47ee-45ec-9b35-d22cc73ea64e} - - - {5e80057c-a54a-4d57-a856-dc1d23a63aea} - - - {812d1247-04eb-4693-a0d2-5399a94e6235} - - + + + + + + + + + + + Duplicates to remove + + + + Common + + + Common + + + + + + + + + + + + + + + + + Duplicates to remove + + + Duplicates to remove + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + + + + {9a77edee-47ee-45ec-9b35-d22cc73ea64e} + + + {5e80057c-a54a-4d57-a856-dc1d23a63aea} + + + {812d1247-04eb-4693-a0d2-5399a94e6235} + + \ No newline at end of file diff --git a/Source/Readers/HTKMLFReader/ReadMe.txt b/Source/Readers/HTKMLFReader/ReadMe.txt index d3398d5817a7..6f5abbd3e760 100644 --- a/Source/Readers/HTKMLFReader/ReadMe.txt +++ b/Source/Readers/HTKMLFReader/ReadMe.txt @@ -1,5 +1,5 @@ -This is the reader for HTK-formatted speech files. - -Large parts of this code deviate from the coding style of the rest of CNTK and also include some unused code and complexity not really needed for CNTK. - -The reason is that this code was ported from another, older tool. We hope to gradually clean this out. +This is the reader for HTK-formatted speech files. + +Large parts of this code deviate from the coding style of the rest of CNTK and also include some unused code and complexity not really needed for CNTK. + +The reason is that this code was ported from another, older tool. We hope to gradually clean this out. diff --git a/Source/Readers/HTKMLFReader/chunkevalsource.h b/Source/Readers/HTKMLFReader/chunkevalsource.h index c3db66f00c60..944cd8d37b34 100644 --- a/Source/Readers/HTKMLFReader/chunkevalsource.h +++ b/Source/Readers/HTKMLFReader/chunkevalsource.h @@ -1,372 +1,372 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - - -//#include -#include "Basics.h" // for attempt() -#include "htkfeatio.h" // for reading HTK features -#include "minibatchsourcehelpers.h" -#ifndef __unix__ -#include "ssematrix.h" // TODO: why can it not be removed for Windows as well? At least needs a comment here. -#endif - -#ifdef LEAKDETECT -#include // for memory leak detection -#endif - -namespace msra { namespace dbn { - - class chunkevalsource // : public numamodelmanager - { - const size_t chunksize; // actual block size to perform computation on - - // data FIFO - msra::dbn::matrix feat; - std::vector> frames; // [t] all feature frames concatenated into a big block - std::vector boundaryflags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) - std::vector numframes; // [k] number of frames for all appended files - std::vector outpaths; // [k] and their pathnames - std::vector sampperiods; // [k] and sample periods (they should really all be the same...) - size_t vdim; // input dimension - size_t udim; // output dimension - bool minibatchready; - void operator=(const chunkevalsource &); - private: - void clear() // empty the FIFO - { - frames.clear(); - boundaryflags.clear(); - numframes.clear(); - outpaths.clear(); - sampperiods.clear(); - minibatchready=false; - } - - - - void saveandflush(msra::dbn::matrix &pred) - { - const size_t framesinblock = frames.size(); - - // write out all files - size_t firstframe = 0; - foreach_index (k, numframes) - { - const wstring & outfile = outpaths[k]; - unsigned int sampperiod = sampperiods[k]; - size_t n = numframes[k]; - msra::files::make_intermediate_dirs (outfile); - fprintf (stderr, "saveandflush: writing %d frames to %ls\n", (int)n, outfile.c_str()); - msra::dbn::matrixstripe thispred (pred, firstframe, n); - // some sanity check for the data we've written - const size_t nansinf = thispred.countnaninf(); - if (nansinf > 0) - fprintf (stderr, "chunkeval: %d NaNs or INF detected in '%ls' (%d frames)\n", (int) nansinf, outfile.c_str(), (int) thispred.cols()); - // save it - msra::util::attempt (5, [&]() - { - msra::asr::htkfeatwriter::write (outfile, "USER", sampperiod, thispred); - }); - firstframe += n; - } - assert (firstframe == framesinblock); framesinblock; - - // and we are done --forget the FIFO content & get ready for next chunk - clear(); - - } - - public: - chunkevalsource (size_t numinput, size_t numoutput, size_t chunksize) - :vdim(numinput),udim(numoutput),chunksize(chunksize) - { - frames.reserve (chunksize * 2); - feat.resize(vdim,chunksize); // initialize to size chunksize - } - - // append data to chunk - template void addfile (const MATRIX & feat, const string & featkind, unsigned int sampperiod, const std::wstring & outpath) - { - // append to frames; also expand neighbor frames - if (feat.cols() < 2) - RuntimeError("evaltofile: utterances < 2 frames not supported"); - foreach_column (t, feat) - { - std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); - frames.push_back (v); - boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); - } - - numframes.push_back (feat.cols()); - outpaths.push_back (outpath); - sampperiods.push_back (sampperiod); - - } - - void createevalminibatch() - { - const size_t framesinblock = frames.size(); - feat.resize(vdim, framesinblock); // input features for whole utt (col vectors) - // augment the features - msra::dbn::augmentneighbors (frames, boundaryflags, 0, framesinblock, feat); - minibatchready=true; - } - - void writetofiles(msra::dbn::matrix &pred){ saveandflush(pred); } - - msra::dbn::matrix chunkofframes() { assert(minibatchready); return feat; } - - bool isminibatchready() { return minibatchready; } - - size_t currentchunksize() { return frames.size(); } - void flushinput(){createevalminibatch();} - void reset() { clear(); } - - }; - - - class chunkevalsourcemulti // : public numamodelmanager - { - const size_t chunksize; // actual block size to perform computation on - - // data FIFO - std::vector feat; - std::vector>> framesmulti; // [t] all feature frames concatenated into a big block - std::vector boundaryflags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) - std::vector numframes; // [k] number of frames for all appended files - std::vector> outpaths; // [k] and their pathnames - std::vector> sampperiods; // [k] and sample periods (they should really all be the same...) - std::vector vdims; // input dimension - std::vector udims; // output dimension - bool minibatchready; - - void operator=(const chunkevalsourcemulti &); - private: - void clear() // empty the FIFO - { - foreach_index(i, vdims) - { - framesmulti[i].clear(); - outpaths[i].clear(); - sampperiods[i].clear(); - } - boundaryflags.clear(); - numframes.clear(); - minibatchready=false; - } - - - - void saveandflush(msra::dbn::matrix &pred, size_t index) - { - const size_t framesinblock = framesmulti[index].size(); - - // write out all files - size_t firstframe = 0; - foreach_index (k, numframes) - { - const wstring & outfile = outpaths[index][k]; - unsigned int sampperiod = sampperiods[index][k]; - size_t n = numframes[k]; - msra::files::make_intermediate_dirs (outfile); - fprintf (stderr, "saveandflush: writing %d frames to %ls\n", (int)n, outfile.c_str()); - msra::dbn::matrixstripe thispred (pred, firstframe, n); - // some sanity check for the data we've written - const size_t nansinf = thispred.countnaninf(); - if (nansinf > 0) - fprintf (stderr, "chunkeval: %d NaNs or INF detected in '%ls' (%d frames)\n", (int) nansinf, outfile.c_str(), (int) thispred.cols()); - // save it - msra::util::attempt (5, [&]() - { - msra::asr::htkfeatwriter::write (outfile, "USER", sampperiod, thispred); - }); - firstframe += n; - } - assert (firstframe == framesinblock); framesinblock; - - // and we are done --forget the FIFO content & get ready for next chunk - - } - - public: - chunkevalsourcemulti (std::vector vdims, std::vector udims, size_t chunksize) - :vdims(vdims),udims(udims),chunksize(chunksize) - { - - foreach_index(i, vdims) - { - msra::dbn::matrix thisfeat; - std::vector> frames; // [t] all feature frames concatenated into a big block - - frames.reserve(chunksize * 2); - framesmulti.push_back(frames); - //framesmulti[i].reserve (chunksize * 2); - - thisfeat.resize(vdims[i], chunksize); - feat.push_back(thisfeat); - - outpaths.push_back(std::vector()); - sampperiods.push_back(std::vector()); - //feat[i].resize(vdims[i],chunksize); // initialize to size chunksize - } - } - - // append data to chunk - template void addfile (const MATRIX & feat, const string & featkind, unsigned int sampperiod, const std::wstring & outpath, size_t index) - { - // append to frames; also expand neighbor frames - if (feat.cols() < 2) - RuntimeError("evaltofile: utterances < 2 frames not supported"); - foreach_column (t, feat) - { - std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); - framesmulti[index].push_back (v); - if (index==0) - boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); - } - if (index==0) - numframes.push_back (feat.cols()); - - outpaths[index].push_back (outpath); - sampperiods[index].push_back (sampperiod); - - } - - void createevalminibatch() - { - foreach_index(i, framesmulti) - { - const size_t framesinblock = framesmulti[i].size(); - feat[i].resize(vdims[i], framesinblock); // input features for whole utt (col vectors) - // augment the features - msra::dbn::augmentneighbors (framesmulti[i], boundaryflags, 0, framesinblock, feat[i]); - } - minibatchready=true; - } - - void writetofiles(msra::dbn::matrix &pred, size_t index){ saveandflush(pred, index); } - - msra::dbn::matrix chunkofframes(size_t index) { assert(minibatchready); assert(index<=feat.size()); return feat[index]; } - - bool isminibatchready() { return minibatchready; } - - size_t currentchunksize() { return framesmulti[0].size(); } - void flushinput(){createevalminibatch();} - void reset() { clear(); } - - }; - - class FileEvalSource // : public numamodelmanager - { - const size_t chunksize; // actual block size to perform computation on - - // data FIFO - std::vector feat; - std::vector>> framesMulti; // [t] all feature frames concatenated into a big block - std::vector boundaryFlags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) - std::vector numFrames; // [k] number of frames for all appended files - std::vector> sampPeriods; // [k] and sample periods (they should really all be the same...) - std::vector vdims; // input dimension - std::vector leftcontext; - std::vector rightcontext; - bool minibatchReady; - size_t minibatchSize; - size_t frameIndex; - - void operator=(const FileEvalSource &); - - private: - void Clear() // empty the FIFO - { - foreach_index(i, vdims) - { - framesMulti[i].clear(); - sampPeriods[i].clear(); - } - boundaryFlags.clear(); - numFrames.clear(); - minibatchReady=false; - frameIndex=0; - } - - public: - FileEvalSource(std::vector vdims, std::vector leftcontext, std::vector rightcontext, size_t chunksize) :vdims(vdims), leftcontext(leftcontext), rightcontext(rightcontext), chunksize(chunksize) - { - foreach_index(i, vdims) - { - msra::dbn::matrix thisfeat; - std::vector> frames; // [t] all feature frames concatenated into a big block - - frames.reserve(chunksize * 2); - framesMulti.push_back(frames); - //framesmulti[i].reserve (chunksize * 2); - - thisfeat.resize(vdims[i], chunksize); - feat.push_back(thisfeat); - - sampPeriods.push_back(std::vector()); - //feat[i].resize(vdims[i],chunksize); // initialize to size chunksize - } - } - - // append data to chunk - template void AddFile (const MATRIX & feat, const string & /*featkind*/, unsigned int sampPeriod, size_t index) - { - // append to frames; also expand neighbor frames - if (feat.cols() < 2) - RuntimeError("evaltofile: utterances < 2 frames not supported"); - foreach_column (t, feat) - { - std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); - framesMulti[index].push_back (v); - if (index==0) - boundaryFlags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); - } - if (index==0) - numFrames.push_back (feat.cols()); - - sampPeriods[index].push_back (sampPeriod); - - } - - void CreateEvalMinibatch() - { - foreach_index(i, framesMulti) - { - const size_t framesInBlock = framesMulti[i].size(); - feat[i].resize(vdims[i], framesInBlock); // input features for whole utt (col vectors) - // augment the features - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(framesMulti[i][0].size(), vdims[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - - //msra::dbn::augmentneighbors(framesMulti[i], boundaryFlags, 0, leftcontext[i], rightcontext[i],) - msra::dbn::augmentneighbors (framesMulti[i], boundaryFlags, leftextent, rightextent, 0, framesInBlock, feat[i]); - } - minibatchReady=true; - } - - void SetMinibatchSize(size_t mbSize){ minibatchSize=mbSize;} - msra::dbn::matrix ChunkOfFrames(size_t index) { assert(minibatchReady); assert(index<=feat.size()); return feat[index]; } - - bool IsMinibatchReady() { return minibatchReady; } - - size_t CurrentFileSize() { return framesMulti[0].size(); } - void FlushInput(){CreateEvalMinibatch();} - void Reset() { Clear(); } - }; - - -};}; +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + + +//#include +#include "Basics.h" // for attempt() +#include "htkfeatio.h" // for reading HTK features +#include "minibatchsourcehelpers.h" +#ifndef __unix__ +#include "ssematrix.h" // TODO: why can it not be removed for Windows as well? At least needs a comment here. +#endif + +#ifdef LEAKDETECT +#include // for memory leak detection +#endif + +namespace msra { namespace dbn { + + class chunkevalsource // : public numamodelmanager + { + const size_t chunksize; // actual block size to perform computation on + + // data FIFO + msra::dbn::matrix feat; + std::vector> frames; // [t] all feature frames concatenated into a big block + std::vector boundaryflags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) + std::vector numframes; // [k] number of frames for all appended files + std::vector outpaths; // [k] and their pathnames + std::vector sampperiods; // [k] and sample periods (they should really all be the same...) + size_t vdim; // input dimension + size_t udim; // output dimension + bool minibatchready; + void operator=(const chunkevalsource &); + private: + void clear() // empty the FIFO + { + frames.clear(); + boundaryflags.clear(); + numframes.clear(); + outpaths.clear(); + sampperiods.clear(); + minibatchready=false; + } + + + + void saveandflush(msra::dbn::matrix &pred) + { + const size_t framesinblock = frames.size(); + + // write out all files + size_t firstframe = 0; + foreach_index (k, numframes) + { + const wstring & outfile = outpaths[k]; + unsigned int sampperiod = sampperiods[k]; + size_t n = numframes[k]; + msra::files::make_intermediate_dirs (outfile); + fprintf (stderr, "saveandflush: writing %d frames to %ls\n", (int)n, outfile.c_str()); + msra::dbn::matrixstripe thispred (pred, firstframe, n); + // some sanity check for the data we've written + const size_t nansinf = thispred.countnaninf(); + if (nansinf > 0) + fprintf (stderr, "chunkeval: %d NaNs or INF detected in '%ls' (%d frames)\n", (int) nansinf, outfile.c_str(), (int) thispred.cols()); + // save it + msra::util::attempt (5, [&]() + { + msra::asr::htkfeatwriter::write (outfile, "USER", sampperiod, thispred); + }); + firstframe += n; + } + assert (firstframe == framesinblock); framesinblock; + + // and we are done --forget the FIFO content & get ready for next chunk + clear(); + + } + + public: + chunkevalsource (size_t numinput, size_t numoutput, size_t chunksize) + :vdim(numinput),udim(numoutput),chunksize(chunksize) + { + frames.reserve (chunksize * 2); + feat.resize(vdim,chunksize); // initialize to size chunksize + } + + // append data to chunk + template void addfile (const MATRIX & feat, const string & featkind, unsigned int sampperiod, const std::wstring & outpath) + { + // append to frames; also expand neighbor frames + if (feat.cols() < 2) + RuntimeError("evaltofile: utterances < 2 frames not supported"); + foreach_column (t, feat) + { + std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); + frames.push_back (v); + boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); + } + + numframes.push_back (feat.cols()); + outpaths.push_back (outpath); + sampperiods.push_back (sampperiod); + + } + + void createevalminibatch() + { + const size_t framesinblock = frames.size(); + feat.resize(vdim, framesinblock); // input features for whole utt (col vectors) + // augment the features + msra::dbn::augmentneighbors (frames, boundaryflags, 0, framesinblock, feat); + minibatchready=true; + } + + void writetofiles(msra::dbn::matrix &pred){ saveandflush(pred); } + + msra::dbn::matrix chunkofframes() { assert(minibatchready); return feat; } + + bool isminibatchready() { return minibatchready; } + + size_t currentchunksize() { return frames.size(); } + void flushinput(){createevalminibatch();} + void reset() { clear(); } + + }; + + + class chunkevalsourcemulti // : public numamodelmanager + { + const size_t chunksize; // actual block size to perform computation on + + // data FIFO + std::vector feat; + std::vector>> framesmulti; // [t] all feature frames concatenated into a big block + std::vector boundaryflags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) + std::vector numframes; // [k] number of frames for all appended files + std::vector> outpaths; // [k] and their pathnames + std::vector> sampperiods; // [k] and sample periods (they should really all be the same...) + std::vector vdims; // input dimension + std::vector udims; // output dimension + bool minibatchready; + + void operator=(const chunkevalsourcemulti &); + private: + void clear() // empty the FIFO + { + foreach_index(i, vdims) + { + framesmulti[i].clear(); + outpaths[i].clear(); + sampperiods[i].clear(); + } + boundaryflags.clear(); + numframes.clear(); + minibatchready=false; + } + + + + void saveandflush(msra::dbn::matrix &pred, size_t index) + { + const size_t framesinblock = framesmulti[index].size(); + + // write out all files + size_t firstframe = 0; + foreach_index (k, numframes) + { + const wstring & outfile = outpaths[index][k]; + unsigned int sampperiod = sampperiods[index][k]; + size_t n = numframes[k]; + msra::files::make_intermediate_dirs (outfile); + fprintf (stderr, "saveandflush: writing %d frames to %ls\n", (int)n, outfile.c_str()); + msra::dbn::matrixstripe thispred (pred, firstframe, n); + // some sanity check for the data we've written + const size_t nansinf = thispred.countnaninf(); + if (nansinf > 0) + fprintf (stderr, "chunkeval: %d NaNs or INF detected in '%ls' (%d frames)\n", (int) nansinf, outfile.c_str(), (int) thispred.cols()); + // save it + msra::util::attempt (5, [&]() + { + msra::asr::htkfeatwriter::write (outfile, "USER", sampperiod, thispred); + }); + firstframe += n; + } + assert (firstframe == framesinblock); framesinblock; + + // and we are done --forget the FIFO content & get ready for next chunk + + } + + public: + chunkevalsourcemulti (std::vector vdims, std::vector udims, size_t chunksize) + :vdims(vdims),udims(udims),chunksize(chunksize) + { + + foreach_index(i, vdims) + { + msra::dbn::matrix thisfeat; + std::vector> frames; // [t] all feature frames concatenated into a big block + + frames.reserve(chunksize * 2); + framesmulti.push_back(frames); + //framesmulti[i].reserve (chunksize * 2); + + thisfeat.resize(vdims[i], chunksize); + feat.push_back(thisfeat); + + outpaths.push_back(std::vector()); + sampperiods.push_back(std::vector()); + //feat[i].resize(vdims[i],chunksize); // initialize to size chunksize + } + } + + // append data to chunk + template void addfile (const MATRIX & feat, const string & featkind, unsigned int sampperiod, const std::wstring & outpath, size_t index) + { + // append to frames; also expand neighbor frames + if (feat.cols() < 2) + RuntimeError("evaltofile: utterances < 2 frames not supported"); + foreach_column (t, feat) + { + std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); + framesmulti[index].push_back (v); + if (index==0) + boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); + } + if (index==0) + numframes.push_back (feat.cols()); + + outpaths[index].push_back (outpath); + sampperiods[index].push_back (sampperiod); + + } + + void createevalminibatch() + { + foreach_index(i, framesmulti) + { + const size_t framesinblock = framesmulti[i].size(); + feat[i].resize(vdims[i], framesinblock); // input features for whole utt (col vectors) + // augment the features + msra::dbn::augmentneighbors (framesmulti[i], boundaryflags, 0, framesinblock, feat[i]); + } + minibatchready=true; + } + + void writetofiles(msra::dbn::matrix &pred, size_t index){ saveandflush(pred, index); } + + msra::dbn::matrix chunkofframes(size_t index) { assert(minibatchready); assert(index<=feat.size()); return feat[index]; } + + bool isminibatchready() { return minibatchready; } + + size_t currentchunksize() { return framesmulti[0].size(); } + void flushinput(){createevalminibatch();} + void reset() { clear(); } + + }; + + class FileEvalSource // : public numamodelmanager + { + const size_t chunksize; // actual block size to perform computation on + + // data FIFO + std::vector feat; + std::vector>> framesMulti; // [t] all feature frames concatenated into a big block + std::vector boundaryFlags; // [t] -1 for first and +1 last frame, 0 else (for augmentneighbors()) + std::vector numFrames; // [k] number of frames for all appended files + std::vector> sampPeriods; // [k] and sample periods (they should really all be the same...) + std::vector vdims; // input dimension + std::vector leftcontext; + std::vector rightcontext; + bool minibatchReady; + size_t minibatchSize; + size_t frameIndex; + + void operator=(const FileEvalSource &); + + private: + void Clear() // empty the FIFO + { + foreach_index(i, vdims) + { + framesMulti[i].clear(); + sampPeriods[i].clear(); + } + boundaryFlags.clear(); + numFrames.clear(); + minibatchReady=false; + frameIndex=0; + } + + public: + FileEvalSource(std::vector vdims, std::vector leftcontext, std::vector rightcontext, size_t chunksize) :vdims(vdims), leftcontext(leftcontext), rightcontext(rightcontext), chunksize(chunksize) + { + foreach_index(i, vdims) + { + msra::dbn::matrix thisfeat; + std::vector> frames; // [t] all feature frames concatenated into a big block + + frames.reserve(chunksize * 2); + framesMulti.push_back(frames); + //framesmulti[i].reserve (chunksize * 2); + + thisfeat.resize(vdims[i], chunksize); + feat.push_back(thisfeat); + + sampPeriods.push_back(std::vector()); + //feat[i].resize(vdims[i],chunksize); // initialize to size chunksize + } + } + + // append data to chunk + template void AddFile (const MATRIX & feat, const string & /*featkind*/, unsigned int sampPeriod, size_t index) + { + // append to frames; also expand neighbor frames + if (feat.cols() < 2) + RuntimeError("evaltofile: utterances < 2 frames not supported"); + foreach_column (t, feat) + { + std::vector v (&feat(0,t), &feat(0,t) + feat.rows()); + framesMulti[index].push_back (v); + if (index==0) + boundaryFlags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); + } + if (index==0) + numFrames.push_back (feat.cols()); + + sampPeriods[index].push_back (sampPeriod); + + } + + void CreateEvalMinibatch() + { + foreach_index(i, framesMulti) + { + const size_t framesInBlock = framesMulti[i].size(); + feat[i].resize(vdims[i], framesInBlock); // input features for whole utt (col vectors) + // augment the features + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(framesMulti[i][0].size(), vdims[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + + //msra::dbn::augmentneighbors(framesMulti[i], boundaryFlags, 0, leftcontext[i], rightcontext[i],) + msra::dbn::augmentneighbors (framesMulti[i], boundaryFlags, leftextent, rightextent, 0, framesInBlock, feat[i]); + } + minibatchReady=true; + } + + void SetMinibatchSize(size_t mbSize){ minibatchSize=mbSize;} + msra::dbn::matrix ChunkOfFrames(size_t index) { assert(minibatchReady); assert(index<=feat.size()); return feat[index]; } + + bool IsMinibatchReady() { return minibatchReady; } + + size_t CurrentFileSize() { return framesMulti[0].size(); } + void FlushInput(){CreateEvalMinibatch();} + void Reset() { Clear(); } + }; + + +};}; diff --git a/Source/Readers/HTKMLFReader/htkfeatio.h b/Source/Readers/HTKMLFReader/htkfeatio.h index 0b99273a2813..bb639ee5e72f 100644 --- a/Source/Readers/HTKMLFReader/htkfeatio.h +++ b/Source/Readers/HTKMLFReader/htkfeatio.h @@ -1,1038 +1,1038 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// htkfeatio.h -- helper for I/O of HTK feature files - -#pragma once - -#include "Basics.h" -#include "basetypes.h" -#include "fileutil.h" -#include "simple_checked_arrays.h" - -#include -#include -#include -#include -#include -#include -#include -#include "simplesenonehmm.h" -#include -#include "minibatchsourcehelpers.h" - -namespace msra { namespace asr { - -using namespace std; - -// =========================================================================== -// htkfeatio -- common base class for reading and writing HTK feature files -// =========================================================================== - -class htkfeatio -{ -protected: - auto_file_ptr f; - wstring physicalpath; // path of this file - bool needbyteswapping; // need to swap the bytes? - - string featkind; // HTK feature-kind string - size_t featdim; // feature dimension - unsigned int featperiod; // sampling period - - // note that by default we assume byte swapping (seems to be HTK default) - htkfeatio() : needbyteswapping (true), featdim (0), featperiod (0) {} - - // set the feature kind variables --if already set then validate that they are the same - // Path is only for error message. - void setkind (string kind, size_t dim, unsigned int period, const wstring & path) - { - if (featkind.empty()) // not set yet: just memorize them - { - assert (featdim == 0 && featperiod == 0); - featkind = kind; - featdim = dim; - featperiod = period; - } - else // set already: check if consistent - { - if (featkind != kind || featdim != dim || featperiod != period) - RuntimeError("setkind: inconsistent feature kind for file '%ls'", path.c_str()); - } - } - - static short swapshort (short v) throw() - { - const unsigned char * b = (const unsigned char *) &v; - return (short) ((b[0] << 8) + b[1]); - } - static int swapint (int v) throw() - { - const unsigned char * b = (const unsigned char *) &v; - return (int) (((((b[0] << 8) + b[1]) << 8) + b[2]) << 8) + b[3]; - } - - struct fileheader - { - int nsamples; - int sampperiod; - short sampsize; - short sampkind; - void read (FILE * f) - { - nsamples = fgetint (f); - sampperiod = fgetint (f); - sampsize = fgetshort (f); - sampkind = fgetshort (f); - } - - // read header of idx feature cach - void idxRead (FILE * f) - { - int magic = swapint(fgetint (f)); - if (magic != 2051) - RuntimeError("reading idx feature cache header: invalid magic"); - nsamples = swapint(fgetint(f)); - sampperiod = 0; - sampkind = (short)9; //user type - int nRows = swapint(fgetint(f)); - int nCols = swapint(fgetint(f)); - sampsize = (short) (nRows * nCols); // features are stored as bytes; - } - - void write (FILE * f) - { - fputint (f, nsamples); - fputint (f, sampperiod); - fputshort (f, sampsize); - fputshort (f, sampkind); - } - void byteswap() - { - nsamples = swapint (nsamples); - sampperiod = swapint (sampperiod); - sampsize = swapshort (sampsize); - sampkind = swapshort (sampkind); - } - }; - - static const int BASEMASK = 077; - static const int PLP = 11; - static const int MFCC = 6; - static const int FBANK = 7; - static const int USER = 9; - static const int FESTREAM = 12; - static const int HASENERGY = 0100; // _E log energy included - static const int HASNULLE = 0200; // _N absolute energy suppressed - static const int HASDELTA = 0400; // _D delta coef appended - static const int HASACCS = 01000; // _A acceleration coefs appended - static const int HASCOMPX = 02000; // _C is compressed - static const int HASZEROM = 04000; // _Z zero meaned - static const int HASCRCC = 010000; // _K has CRC check - static const int HASZEROC = 020000; // _0 0'th Cepstra included - static const int HASVQ = 040000; // _V has VQ index attached - static const int HASTHIRD = 0100000; // _T has Delta-Delta-Delta index attached -}; - -// =========================================================================== -// htkfeatwriter -- write HTK feature file -// This is designed to write a single file only (no archive mode support). -// =========================================================================== - -class htkfeatwriter : protected htkfeatio -{ - size_t curframe; - vector tmp; -public: - short parsekind (const string & str) - { - vector params = msra::strfun::split (str, ";"); - if (params.empty()) - RuntimeError("parsekind: invalid param kind string"); - vector parts = msra::strfun::split (params[0], "_"); - // map base kind - short sampkind; - string basekind = parts[0]; - if (basekind == "PLP") sampkind = PLP; - else if (basekind == "MFCC") sampkind = MFCC; - else if (basekind == "FBANK") sampkind = FBANK; - else if (basekind == "USER") sampkind = USER; - else RuntimeError("parsekind: unsupported param base kind"); - // map qualifiers - for (size_t i = 1; i < parts.size(); i++) - { - string opt = parts[i]; - if (opt.length() != 1) - RuntimeError("parsekind: invalid param kind string"); - switch (opt[0]) - { - case 'E': sampkind |= HASENERGY; break; - case 'D': sampkind |= HASDELTA; break; - case 'N': sampkind |= HASNULLE; break; - case 'A': sampkind |= HASACCS; break; - case 'T': sampkind |= HASTHIRD; break; - case 'Z': sampkind |= HASZEROM; break; - case '0': sampkind |= HASZEROC; break; - default: RuntimeError("parsekind: invalid qualifier in param kind string"); - } - } - return sampkind; - } -public: - // open the file for writing - htkfeatwriter (wstring path, string kind, size_t dim, unsigned int period) - { - setkind (kind, dim, period, path); - // write header - fileheader H; - H.nsamples = 0; // unknown for now, updated in close() - H.sampperiod = period; - const int bytesPerValue = sizeof (float); // we do not support compression for now - H.sampsize = (short) featdim * bytesPerValue; - H.sampkind = parsekind (kind); - if (needbyteswapping) - H.byteswap(); - f = fopenOrDie (path, L"wbS"); - H.write (f); - curframe = 0; - } - // write a frame - void write (const vector & v) - { - if (v.size() != featdim) - LogicError("htkfeatwriter: inconsistent feature dimension"); - if (needbyteswapping) - { - tmp.resize (v.size()); - foreach_index (k, v) tmp[k] = v[k]; - msra::util::byteswap (tmp); - fwriteOrDie (tmp, f); - } - else - fwriteOrDie (v, f); - curframe++; - } - // finish - // This updates the header. - // BUGBUG: need to implement safe-save semantics! Otherwise won't work reliably with -make mode. - // ... e.g. set DeleteOnClose temporarily, and clear at the end? - void close (size_t numframes) - { - if (curframe != numframes) - LogicError("htkfeatwriter: inconsistent number of frames passed to close()"); - fflushOrDie (f); - // now implant the length field; it's at offset 0 - int nSamplesFile = (int) numframes; - if (needbyteswapping) - nSamplesFile = swapint (nSamplesFile); - fseekOrDie (f, 0); - fputint (f, nSamplesFile); - fflushOrDie (f); - f = NULL; // this triggers an fclose() on auto_file_ptr - } - // read an entire utterance into a matrix - // Matrix type needs to have operator(i,j) and resize(n,m). - // We write to a tmp file first to ensure we don't leave broken files that would confuse make mode. - template static void write (const wstring & path, const string & kindstr, unsigned int period, const MATRIX & feat) - { - wstring tmppath = path + L"$$"; // tmp path for make-mode compliant - unlinkOrDie (path); // delete if old file is already there - // write it out - size_t featdim = feat.rows(); - size_t numframes = feat.cols(); - vector v (featdim); - htkfeatwriter W (tmppath, kindstr, feat.rows(), period); -#ifdef SAMPLING_EXPERIMENT - for (size_t i = 0; i < numframes; i++) - { - foreach_index (k, v) - { - float val = feat(k,i) - logf((float) SAMPLING_EXPERIMENT); - if (i % SAMPLING_EXPERIMENT == 0) - v[k] = val; - else - v[k] += (float) (log (1 + exp (val - v[k]))); // log add - } - if (i % SAMPLING_EXPERIMENT == SAMPLING_EXPERIMENT -1) - W.write (v); - } -#else - for (size_t i = 0; i < numframes; i++) - { - foreach_index (k, v) - v[k] = feat(k,i); - W.write (v); - } -#endif -#ifdef SAMPLING_EXPERIMENT - W.close (numframes / SAMPLING_EXPERIMENT); -#else - W.close (numframes); -#endif -#ifdef _WIN32 // BUGBUG: and on Linux?? - // rename to final destination - // (This would only fail in strange circumstances such as accidental multiple processes writing to the same file.) - renameOrDie (tmppath, path); -#endif - } -}; - -// =========================================================================== -// htkfeatreader -- read HTK feature file, with archive support -// -// To support archives, one instance of this can (and is supposed to) be used -// repeatedly. All feat files read on the same instance are validated to have -// the same feature kind. -// -// For archives, this caches the last used file handle, in expectation that most reads -// are sequential anyway. In conjunction with a big buffer, this makes a huge difference. -// =========================================================================== - -class htkfeatreader : protected htkfeatio -{ - // information on current file - // File handle and feature type information is stored in the underlying htkfeatio object. - size_t physicalframes; // total number of frames in physical file - //TODO make this nicer - bool isidxformat; // support reading of features in idxformat as well (it's a hack, but different format's are not supported yet) - uint64_t physicaldatastart; // byte offset of first data byte - size_t vecbytesize; // size of one vector in bytes - - bool addEnergy; // add in energy as data is read (will all have zero values) - bool compressed; // is compressed to 16-bit values - bool hascrcc; // need to skip crcc - vector a, b; // for decompression - vector tmp; // for decompression - vector tmpByteVector; // for decompression of idx files - size_t curframe; // current # samples read so far - size_t numframes; // number of samples for current logical file - size_t energyElements; // how many energy elements to add if addEnergy is true - -public: - - // parser for complex a=b[s,e] syntax - struct parsedpath - { - protected: - friend class htkfeatreader; - wstring logicalpath; // virtual path that this file should be understood to belong to - wstring archivepath; // physical path of archive file - size_t s, e; // first and last frame inside the archive file; (0, INT_MAX) if not given - bool isarchive; // true if archive (range specified) - bool isidxformat; // support reading of features in idxformat as well (it's a hack, but different format's are not supported yet) - void malformed(const wstring& path) const { RuntimeError("parsedpath: malformed path '%ls'", path.c_str()); } - - // consume and return up to 'delim'; remove from 'input' (we try to avoid C++0x here for VS 2008 compat) - wstring consume (wstring & input, const wchar_t * delim) - { - vector parts = msra::strfun::split (input, delim); // (not very efficient, but does not matter here) - if (parts.size() == 1) input.clear(); // not found: consume to end - else input = parts[1]; // found: break at delimiter - return parts[0]; - } - - public: - // constructor parses a=b[s,e] syntax and fills in the file - // Can be used implicitly e.g. by passing a string to open(). - parsedpath(const wstring& pathParam) - { - wstring xpath(pathParam); - - // parse out logical path - logicalpath = consume (xpath, L"="); - isidxformat = false; - if (xpath.empty()) // no '=' detected: pass entire file (it's not an archive) - { - archivepath = logicalpath; - s = 0; - e = INT_MAX; - isarchive = false; - // check for "-ubyte" suffix in path name => it is an idx file - wstring ubyte(L"-ubyte"); - size_t pos = archivepath.size() >= ubyte.size() ? archivepath.size() - ubyte.size() : 0; - wstring suffix = archivepath.substr(pos , ubyte.size()); - isidxformat = ubyte == suffix; - } - else // a=b[s,e] syntax detected - { - archivepath = consume (xpath, L"["); - if (xpath.empty()) // actually it's only a=b - { - s = 0; - e = INT_MAX; - isarchive = false; - } - else - { - s = msra::strfun::toint (consume (xpath, L",")); - if (xpath.empty()) malformed(pathParam); - e = msra::strfun::toint (consume (xpath, L"]")); - if (!xpath.empty()) malformed(pathParam); - isarchive = true; - } - } - } - - // get the physical path for 'make' test - const wstring & physicallocation() const { return archivepath; } - - // casting to wstring yields the logical path - operator const wstring & () const { return logicalpath; } - - // get duration in frames - size_t numframes() const - { - if (!isarchive) - RuntimeError("parsedpath: this mode requires an input script with start and end frames given"); - return e - s + 1; - } - }; - - // Make sure 'parsedpath' type has a move constructor - static_assert(std::is_move_constructible::value, "Type 'parsedpath' should be move constructible!"); - -private: - - // open the physical HTK file - // This is different from the logical (virtual) path name in the case of an archive. - void openphysical (const parsedpath & ppath) - { - wstring physpath = ppath.physicallocation(); - //auto_file_ptr f = fopenOrDie (physpath, L"rbS"); - auto_file_ptr f(fopenOrDie (physpath, L"rb")); // removed 'S' for now, as we mostly run local anyway, and this will speed up debugging - - // read the header (12 bytes for htk feature files) - fileheader H; - isidxformat = ppath.isidxformat; - if (!isidxformat) - H.read (f); - else // read header of idxfile - H.idxRead (f); - - // take a guess as to whether we need byte swapping or not - bool needbyteswapping = ((unsigned int) swapint (H.sampperiod) < (unsigned int) H.sampperiod); - if (needbyteswapping) - H.byteswap(); - - // interpret sampkind - int basekind = H.sampkind & BASEMASK; - string kind; - switch (basekind) - { - case PLP: kind = "PLP"; break; - case MFCC: kind = "MFCC"; break; - case FBANK: kind = "FBANK"; break; - case USER: kind = "USER"; break; - case FESTREAM: kind = "USER"; break; // we return this as USER type (with guid) - default: RuntimeError("htkfeatreader:unsupported feature kind"); - } - // add qualifiers - if (H.sampkind & HASENERGY) kind += "_E"; - if (H.sampkind & HASDELTA) kind += "_D"; - if (H.sampkind & HASNULLE) kind += "_N"; - if (H.sampkind & HASACCS) kind += "_A"; - if (H.sampkind & HASTHIRD) kind += "_T"; - bool compressed = (H.sampkind & HASCOMPX) != 0; - bool hascrcc = (H.sampkind & HASCRCC) != 0; - if (H.sampkind & HASZEROM) kind += "_Z"; - if (H.sampkind & HASZEROC) kind += "_0"; - if (H.sampkind & HASVQ) RuntimeError("htkfeatreader:we do not support VQ"); - // skip additional GUID in FESTREAM features - if (H.sampkind == FESTREAM) - { // ... note: untested - unsigned char guid[16]; - freadOrDie (&guid, sizeof (guid), 1, f); - kind += ";guid="; - for (int i = 0; i < sizeof (guid)/sizeof (*guid); i++) - kind += msra::strfun::strprintf ("%02x", guid[i]); - } - - // other checks - size_t bytesPerValue = isidxformat ? 1 : (compressed ? sizeof (short) : sizeof (float)); - - if (H.sampsize % bytesPerValue != 0) RuntimeError("htkfeatreader:sample size not multiple of dimension"); - size_t dim = H.sampsize / bytesPerValue; - - // read the values for decompressing - vector a, b; - if (compressed) - { - freadOrDie (a, dim, f); - freadOrDie (b, dim, f); - H.nsamples -= 4; // these are counted as 4 frames--that's the space they use - if (needbyteswapping) { msra::util::byteswap (a); msra::util::byteswap (b); } - } - - // done: swap it in - int64_t bytepos = fgetpos (f); - setkind (kind, dim, H.sampperiod, ppath); // this checks consistency - this->physicalpath.swap (physpath); - this->physicaldatastart = bytepos; - this->physicalframes = H.nsamples; - this->f.swap (f); // note: this will get the previous f auto-closed at the end of this function - this->needbyteswapping = needbyteswapping; - this->compressed = compressed; - this->a.swap (a); - this->b.swap (b); - this->vecbytesize = H.sampsize; - this->hascrcc = hascrcc; - } - void close() // force close the open file --use this in case of read failure - { - f = NULL; // assigning a new FILE* to f will close the old FILE* if any - physicalpath.clear(); - } - -public: - - htkfeatreader() {addEnergy = false; energyElements = 0;} - - // helper to create a parsed-path object - // const auto path = parse (xpath) - parsedpath parse (const wstring & xpath) { return parsedpath (xpath); } - - // read a feature file - // Returns number of frames in that file. - // This understands the more complex syntax a=b[s,e] and optimizes a little - size_t open (const parsedpath & ppath) - { - // do not reopen the file if it is the same; use fsetpos() instead - if (f == NULL || ppath.physicallocation() != physicalpath) - openphysical (ppath); - - if (ppath.isarchive) // reading a sub-range from an archive - { - if (ppath.s > ppath.e) - RuntimeError("open: start frame %d > end frame %d in '%ls'", (int)ppath.s, (int)ppath.e, ppath.logicalpath.c_str()); - if (ppath.e >= physicalframes) - RuntimeError("open: end frame exceeds archive's total number of frames %d in '%ls'", (int)physicalframes, ppath.logicalpath.c_str()); - - int64_t dataoffset = physicaldatastart + ppath.s * vecbytesize; - fsetpos (f, dataoffset); // we assume fsetpos(), which is our own, is smart to not flush the read buffer - curframe = 0; - numframes = ppath.e + 1 - ppath.s; - } - else // reading a full file - { - curframe = 0; - numframes = physicalframes; - assert (fgetpos (f) == physicaldatastart); - } - return numframes; - } - // get dimension and type information for a feature file - // This will alter the state of this object in that it opens the file. It is efficient to read it right afterwards - void getinfo (const parsedpath & ppath, string & featkind, size_t & featdim, unsigned int & featperiod) - { - open (ppath); - featkind = this->featkind; - featdim = this->featdim; - featperiod = this->featperiod; - } - - // called to add energy as we read - void AddEnergy(size_t energyElements) - { - this->energyElements = energyElements; - this->addEnergy = energyElements != 0; - } - const string & getfeattype() const { return featkind; } - operator bool() const { return curframe < numframes; } - // read a vector from the open file - void read (std::vector & v) - { - if (curframe >= numframes) RuntimeError("htkfeatreader:attempted to read beyond end"); - if (!compressed && !isidxformat) // not compressed--the easy one - { - freadOrDie (v, featdim, f); - if (needbyteswapping) msra::util::byteswap (v); - } - else if (isidxformat) - { - // read into temp vector - freadOrDie (tmpByteVector, featdim, f); - v.resize (featdim); - foreach_index (k, v) - v[k] = (float) tmpByteVector[k]; - } - else // need to decompress - { - // read into temp vector - freadOrDie (tmp, featdim, f); - if (needbyteswapping) msra::util::byteswap (tmp); - // 'decompress' it - v.resize (tmp.size()); - foreach_index (k, v) - v[k] = (tmp[k] + b[k]) / a[k]; - } - curframe++; - } - // read a sequence of vectors from the open file into a range of frames [ts,te) - template void read (MATRIX & feat, size_t ts, size_t te) - { - // read vectors from file and push to our target structure - vector v(featdim+energyElements); - for (size_t t = ts; t < te; t++) - { - read (v); - // add the energy elements (all zero) if needed - if (addEnergy) - { - // we add the energy elements at the end of each section of features, (features, delta, delta-delta) - size_t posIncrement = featdim/energyElements; - size_t pos = posIncrement; - for (size_t i=0;i < energyElements;i++,pos+=posIncrement) - { - auto iter = v.begin() + pos + i; - v.insert(iter,0.0f); - } - } - foreach_index (k, v) - feat(k,t) = v[k]; - } - } - // read an entire utterance into an already allocated matrix - // Matrix type needs to have operator(i,j) - template void read (const parsedpath & ppath, const string & kindstr, const unsigned int period, MATRIX & feat) - { - // open the file and check dimensions - size_t numframes = open (ppath); - if (feat.cols() != numframes || feat.rows() != featdim) - LogicError("read: stripe read called with wrong dimensions"); - if (kindstr != featkind || period != featperiod) - LogicError("read: attempting to mixing different feature kinds"); - - // read vectors from file and push to our target structure - try { read (feat, 0, numframes); } catch (...) { close(); throw; } - } - // read an entire utterance into a virgen, allocatable matrix - // Matrix type needs to have operator(i,j) and resize(n,m) - template void read (const parsedpath & ppath, string & kindstr, unsigned int & period, MATRIX & feat) - { - // get the file - size_t numframes = open (ppath); - feat.resize (featdim+energyElements, numframes); // result matrix--columns are features - - // read vectors from file and push to our target structure - try { read (feat, 0, numframes); } catch (...) { close(); throw; } - - // return file info - kindstr = featkind; - period = featperiod; - } -}; - -struct htkmlfentry -{ - unsigned int firstframe; // range [firstframe,firstframe+numframes) - unsigned int numframes; - msra::dbn::CLASSIDTYPE classid; // numeric state id - msra::dbn::HMMIDTYPE phonestart; // numeric phone start time - -private: - // verify and save data - void setdata (size_t ts, size_t te, size_t uid) - { - if (te < ts) RuntimeError("htkmlfentry: end time below start time??"); - // save - firstframe = (unsigned int) ts; - numframes = (unsigned int) (te - ts); - classid = (msra::dbn::CLASSIDTYPE) uid; - // check for numeric overflow - if (firstframe != ts || firstframe + numframes != te || classid != uid) - RuntimeError("htkmlfentry: not enough bits for one of the values"); - } - - // parse the time range - // There are two formats: - // - original HTK - // - Dong's hacked format: ts te senonename senoneid - // We distinguish - static void parseframerange (const vector & toks, size_t & ts, size_t & te, const double htkTimeToFrame) - { - const double maxFrameNumber = htkTimeToFrame / 2.0; // if frame number is greater than this we assume it is time instead of frame - double rts = msra::strfun::todouble (toks[0]); - double rte = msra::strfun::todouble (toks[1]); - if (rte > maxFrameNumber) // convert time to frame - { - ts = (size_t) (rts/htkTimeToFrame + 0.5); // get start frame - te = (size_t) (rte/htkTimeToFrame + 0.5); // get end frame - } - else - { - ts = (size_t)(rts); - te = (size_t)(rte); - } - } - -public: - - // parse format with original HTK state align MLF format and state list - void parsewithstatelist (const vector & toks, const unordered_map & statelisthash, const double htkTimeToFrame, - std::unordered_map & hmmnamehash) - { - size_t ts, te; - parseframerange (toks, ts, te, htkTimeToFrame); - auto iter = statelisthash.find (toks[2]); - if (iter == statelisthash.end()) - RuntimeError("htkmlfentry: state %s not found in statelist", toks[2]); - const size_t uid = iter->second; // get state index - setdata (ts, te, uid); - //phone boundary - if (hmmnamehash.size() > 0) - { - if (toks.size() > 4) - { - auto hmmiter = hmmnamehash.find(toks[4]); - if (hmmiter == hmmnamehash.end()) - RuntimeError("htkmlfentry: hmm %s not found in hmmlist", toks[4]); - phonestart = (msra::dbn::HMMIDTYPE)(hmmiter->second + 1); - - // check for numeric overflow - if ((hmmiter->second + 1) != phonestart) - RuntimeError("htkmlfentry: not enough bits for one of the values"); - } - else - phonestart = 0; - } - } - - // ... note: this will be too simplistic for parsing more complex MLF formats. Fix when needed. - // add support so that it can handle conditions where time instead of frame numer is used. - void parse (const vector & toks, const double htkTimeToFrame) - { - if (toks.size() != 4) RuntimeError("htkmlfentry: currently we only support 4-column format"); - size_t ts, te; - parseframerange (toks, ts, te, htkTimeToFrame); - size_t uid = msra::strfun::toint (toks[3]); - setdata(ts, te, uid); - } -}; - -template -class htkmlfreader : public map> // [key][i] the data -{ - wstring curpath; // for error messages - unordered_map statelistmap; // for state <=> index - map wordsequences; // [key] word sequences (if we are building word entries as well, for MMI) - std::unordered_map symmap; - - void strtok (char * s, const char * delim, vector & toks) - { - toks.resize (0); - char * context = nullptr; - for (char * p = strtok_s (s, delim, &context); p; p = strtok_s (NULL, delim, &context)) - toks.push_back (p); - } - void malformed (string what) - { - RuntimeError("htkmlfreader: %s in '%ls'", what.c_str(), curpath.c_str()); - } - - vector readlines (const wstring & path, vector & buffer) - { - // load it into RAM in one huge chunk - auto_file_ptr f(fopenOrDie (path, L"rb")); - size_t len = filesize (f); - buffer.reserve (len +1); - freadOrDie (buffer, len, f); - buffer.push_back (0); // this makes it a proper C string - - // parse into lines - vector lines; - lines.reserve (len / 20); - strtok (&buffer[0], "\r\n", lines); - return lines; - } - - template - void parseentry (const vector & lines, size_t line, const set & restricttokeys, - const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, - vector & wordseqbuffer, vector & alignseqbuffer, - const double htkTimeToFrame) - { - size_t idx = 0; - string filename = lines[idx++]; - while (filename == "#!MLF!#") // skip embedded duplicate MLF headers (so user can 'cat' MLFs) - filename = lines[idx++]; - - // some mlf file have write errors, so skip malformed entry - if (filename.length() < 3 || filename[0] != '"' || filename[filename.length()-1] != '"') - { - fprintf (stderr, "warning: filename entry (%s)\n", filename.c_str()); - fprintf(stderr, "skip current mlf entry from line (%lu) until line (%lu).\n", line + idx, line + lines.size()); - return; - } - - filename = filename.substr (1, filename.length() -2); // strip quotes - if (filename.find ("*/") == 0) filename = filename.substr (2); +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// htkfeatio.h -- helper for I/O of HTK feature files + +#pragma once + +#include "Basics.h" +#include "basetypes.h" +#include "fileutil.h" +#include "simple_checked_arrays.h" + +#include +#include +#include +#include +#include +#include +#include +#include "simplesenonehmm.h" +#include +#include "minibatchsourcehelpers.h" + +namespace msra { namespace asr { + +using namespace std; + +// =========================================================================== +// htkfeatio -- common base class for reading and writing HTK feature files +// =========================================================================== + +class htkfeatio +{ +protected: + auto_file_ptr f; + wstring physicalpath; // path of this file + bool needbyteswapping; // need to swap the bytes? + + string featkind; // HTK feature-kind string + size_t featdim; // feature dimension + unsigned int featperiod; // sampling period + + // note that by default we assume byte swapping (seems to be HTK default) + htkfeatio() : needbyteswapping (true), featdim (0), featperiod (0) {} + + // set the feature kind variables --if already set then validate that they are the same + // Path is only for error message. + void setkind (string kind, size_t dim, unsigned int period, const wstring & path) + { + if (featkind.empty()) // not set yet: just memorize them + { + assert (featdim == 0 && featperiod == 0); + featkind = kind; + featdim = dim; + featperiod = period; + } + else // set already: check if consistent + { + if (featkind != kind || featdim != dim || featperiod != period) + RuntimeError("setkind: inconsistent feature kind for file '%ls'", path.c_str()); + } + } + + static short swapshort (short v) throw() + { + const unsigned char * b = (const unsigned char *) &v; + return (short) ((b[0] << 8) + b[1]); + } + static int swapint (int v) throw() + { + const unsigned char * b = (const unsigned char *) &v; + return (int) (((((b[0] << 8) + b[1]) << 8) + b[2]) << 8) + b[3]; + } + + struct fileheader + { + int nsamples; + int sampperiod; + short sampsize; + short sampkind; + void read (FILE * f) + { + nsamples = fgetint (f); + sampperiod = fgetint (f); + sampsize = fgetshort (f); + sampkind = fgetshort (f); + } + + // read header of idx feature cach + void idxRead (FILE * f) + { + int magic = swapint(fgetint (f)); + if (magic != 2051) + RuntimeError("reading idx feature cache header: invalid magic"); + nsamples = swapint(fgetint(f)); + sampperiod = 0; + sampkind = (short)9; //user type + int nRows = swapint(fgetint(f)); + int nCols = swapint(fgetint(f)); + sampsize = (short) (nRows * nCols); // features are stored as bytes; + } + + void write (FILE * f) + { + fputint (f, nsamples); + fputint (f, sampperiod); + fputshort (f, sampsize); + fputshort (f, sampkind); + } + void byteswap() + { + nsamples = swapint (nsamples); + sampperiod = swapint (sampperiod); + sampsize = swapshort (sampsize); + sampkind = swapshort (sampkind); + } + }; + + static const int BASEMASK = 077; + static const int PLP = 11; + static const int MFCC = 6; + static const int FBANK = 7; + static const int USER = 9; + static const int FESTREAM = 12; + static const int HASENERGY = 0100; // _E log energy included + static const int HASNULLE = 0200; // _N absolute energy suppressed + static const int HASDELTA = 0400; // _D delta coef appended + static const int HASACCS = 01000; // _A acceleration coefs appended + static const int HASCOMPX = 02000; // _C is compressed + static const int HASZEROM = 04000; // _Z zero meaned + static const int HASCRCC = 010000; // _K has CRC check + static const int HASZEROC = 020000; // _0 0'th Cepstra included + static const int HASVQ = 040000; // _V has VQ index attached + static const int HASTHIRD = 0100000; // _T has Delta-Delta-Delta index attached +}; + +// =========================================================================== +// htkfeatwriter -- write HTK feature file +// This is designed to write a single file only (no archive mode support). +// =========================================================================== + +class htkfeatwriter : protected htkfeatio +{ + size_t curframe; + vector tmp; +public: + short parsekind (const string & str) + { + vector params = msra::strfun::split (str, ";"); + if (params.empty()) + RuntimeError("parsekind: invalid param kind string"); + vector parts = msra::strfun::split (params[0], "_"); + // map base kind + short sampkind; + string basekind = parts[0]; + if (basekind == "PLP") sampkind = PLP; + else if (basekind == "MFCC") sampkind = MFCC; + else if (basekind == "FBANK") sampkind = FBANK; + else if (basekind == "USER") sampkind = USER; + else RuntimeError("parsekind: unsupported param base kind"); + // map qualifiers + for (size_t i = 1; i < parts.size(); i++) + { + string opt = parts[i]; + if (opt.length() != 1) + RuntimeError("parsekind: invalid param kind string"); + switch (opt[0]) + { + case 'E': sampkind |= HASENERGY; break; + case 'D': sampkind |= HASDELTA; break; + case 'N': sampkind |= HASNULLE; break; + case 'A': sampkind |= HASACCS; break; + case 'T': sampkind |= HASTHIRD; break; + case 'Z': sampkind |= HASZEROM; break; + case '0': sampkind |= HASZEROC; break; + default: RuntimeError("parsekind: invalid qualifier in param kind string"); + } + } + return sampkind; + } +public: + // open the file for writing + htkfeatwriter (wstring path, string kind, size_t dim, unsigned int period) + { + setkind (kind, dim, period, path); + // write header + fileheader H; + H.nsamples = 0; // unknown for now, updated in close() + H.sampperiod = period; + const int bytesPerValue = sizeof (float); // we do not support compression for now + H.sampsize = (short) featdim * bytesPerValue; + H.sampkind = parsekind (kind); + if (needbyteswapping) + H.byteswap(); + f = fopenOrDie (path, L"wbS"); + H.write (f); + curframe = 0; + } + // write a frame + void write (const vector & v) + { + if (v.size() != featdim) + LogicError("htkfeatwriter: inconsistent feature dimension"); + if (needbyteswapping) + { + tmp.resize (v.size()); + foreach_index (k, v) tmp[k] = v[k]; + msra::util::byteswap (tmp); + fwriteOrDie (tmp, f); + } + else + fwriteOrDie (v, f); + curframe++; + } + // finish + // This updates the header. + // BUGBUG: need to implement safe-save semantics! Otherwise won't work reliably with -make mode. + // ... e.g. set DeleteOnClose temporarily, and clear at the end? + void close (size_t numframes) + { + if (curframe != numframes) + LogicError("htkfeatwriter: inconsistent number of frames passed to close()"); + fflushOrDie (f); + // now implant the length field; it's at offset 0 + int nSamplesFile = (int) numframes; + if (needbyteswapping) + nSamplesFile = swapint (nSamplesFile); + fseekOrDie (f, 0); + fputint (f, nSamplesFile); + fflushOrDie (f); + f = NULL; // this triggers an fclose() on auto_file_ptr + } + // read an entire utterance into a matrix + // Matrix type needs to have operator(i,j) and resize(n,m). + // We write to a tmp file first to ensure we don't leave broken files that would confuse make mode. + template static void write (const wstring & path, const string & kindstr, unsigned int period, const MATRIX & feat) + { + wstring tmppath = path + L"$$"; // tmp path for make-mode compliant + unlinkOrDie (path); // delete if old file is already there + // write it out + size_t featdim = feat.rows(); + size_t numframes = feat.cols(); + vector v (featdim); + htkfeatwriter W (tmppath, kindstr, feat.rows(), period); +#ifdef SAMPLING_EXPERIMENT + for (size_t i = 0; i < numframes; i++) + { + foreach_index (k, v) + { + float val = feat(k,i) - logf((float) SAMPLING_EXPERIMENT); + if (i % SAMPLING_EXPERIMENT == 0) + v[k] = val; + else + v[k] += (float) (log (1 + exp (val - v[k]))); // log add + } + if (i % SAMPLING_EXPERIMENT == SAMPLING_EXPERIMENT -1) + W.write (v); + } +#else + for (size_t i = 0; i < numframes; i++) + { + foreach_index (k, v) + v[k] = feat(k,i); + W.write (v); + } +#endif +#ifdef SAMPLING_EXPERIMENT + W.close (numframes / SAMPLING_EXPERIMENT); +#else + W.close (numframes); +#endif +#ifdef _WIN32 // BUGBUG: and on Linux?? + // rename to final destination + // (This would only fail in strange circumstances such as accidental multiple processes writing to the same file.) + renameOrDie (tmppath, path); +#endif + } +}; + +// =========================================================================== +// htkfeatreader -- read HTK feature file, with archive support +// +// To support archives, one instance of this can (and is supposed to) be used +// repeatedly. All feat files read on the same instance are validated to have +// the same feature kind. +// +// For archives, this caches the last used file handle, in expectation that most reads +// are sequential anyway. In conjunction with a big buffer, this makes a huge difference. +// =========================================================================== + +class htkfeatreader : protected htkfeatio +{ + // information on current file + // File handle and feature type information is stored in the underlying htkfeatio object. + size_t physicalframes; // total number of frames in physical file + //TODO make this nicer + bool isidxformat; // support reading of features in idxformat as well (it's a hack, but different format's are not supported yet) + uint64_t physicaldatastart; // byte offset of first data byte + size_t vecbytesize; // size of one vector in bytes + + bool addEnergy; // add in energy as data is read (will all have zero values) + bool compressed; // is compressed to 16-bit values + bool hascrcc; // need to skip crcc + vector a, b; // for decompression + vector tmp; // for decompression + vector tmpByteVector; // for decompression of idx files + size_t curframe; // current # samples read so far + size_t numframes; // number of samples for current logical file + size_t energyElements; // how many energy elements to add if addEnergy is true + +public: + + // parser for complex a=b[s,e] syntax + struct parsedpath + { + protected: + friend class htkfeatreader; + wstring logicalpath; // virtual path that this file should be understood to belong to + wstring archivepath; // physical path of archive file + size_t s, e; // first and last frame inside the archive file; (0, INT_MAX) if not given + bool isarchive; // true if archive (range specified) + bool isidxformat; // support reading of features in idxformat as well (it's a hack, but different format's are not supported yet) + void malformed(const wstring& path) const { RuntimeError("parsedpath: malformed path '%ls'", path.c_str()); } + + // consume and return up to 'delim'; remove from 'input' (we try to avoid C++0x here for VS 2008 compat) + wstring consume (wstring & input, const wchar_t * delim) + { + vector parts = msra::strfun::split (input, delim); // (not very efficient, but does not matter here) + if (parts.size() == 1) input.clear(); // not found: consume to end + else input = parts[1]; // found: break at delimiter + return parts[0]; + } + + public: + // constructor parses a=b[s,e] syntax and fills in the file + // Can be used implicitly e.g. by passing a string to open(). + parsedpath(const wstring& pathParam) + { + wstring xpath(pathParam); + + // parse out logical path + logicalpath = consume (xpath, L"="); + isidxformat = false; + if (xpath.empty()) // no '=' detected: pass entire file (it's not an archive) + { + archivepath = logicalpath; + s = 0; + e = INT_MAX; + isarchive = false; + // check for "-ubyte" suffix in path name => it is an idx file + wstring ubyte(L"-ubyte"); + size_t pos = archivepath.size() >= ubyte.size() ? archivepath.size() - ubyte.size() : 0; + wstring suffix = archivepath.substr(pos , ubyte.size()); + isidxformat = ubyte == suffix; + } + else // a=b[s,e] syntax detected + { + archivepath = consume (xpath, L"["); + if (xpath.empty()) // actually it's only a=b + { + s = 0; + e = INT_MAX; + isarchive = false; + } + else + { + s = msra::strfun::toint (consume (xpath, L",")); + if (xpath.empty()) malformed(pathParam); + e = msra::strfun::toint (consume (xpath, L"]")); + if (!xpath.empty()) malformed(pathParam); + isarchive = true; + } + } + } + + // get the physical path for 'make' test + const wstring & physicallocation() const { return archivepath; } + + // casting to wstring yields the logical path + operator const wstring & () const { return logicalpath; } + + // get duration in frames + size_t numframes() const + { + if (!isarchive) + RuntimeError("parsedpath: this mode requires an input script with start and end frames given"); + return e - s + 1; + } + }; + + // Make sure 'parsedpath' type has a move constructor + static_assert(std::is_move_constructible::value, "Type 'parsedpath' should be move constructible!"); + +private: + + // open the physical HTK file + // This is different from the logical (virtual) path name in the case of an archive. + void openphysical (const parsedpath & ppath) + { + wstring physpath = ppath.physicallocation(); + //auto_file_ptr f = fopenOrDie (physpath, L"rbS"); + auto_file_ptr f(fopenOrDie (physpath, L"rb")); // removed 'S' for now, as we mostly run local anyway, and this will speed up debugging + + // read the header (12 bytes for htk feature files) + fileheader H; + isidxformat = ppath.isidxformat; + if (!isidxformat) + H.read (f); + else // read header of idxfile + H.idxRead (f); + + // take a guess as to whether we need byte swapping or not + bool needbyteswapping = ((unsigned int) swapint (H.sampperiod) < (unsigned int) H.sampperiod); + if (needbyteswapping) + H.byteswap(); + + // interpret sampkind + int basekind = H.sampkind & BASEMASK; + string kind; + switch (basekind) + { + case PLP: kind = "PLP"; break; + case MFCC: kind = "MFCC"; break; + case FBANK: kind = "FBANK"; break; + case USER: kind = "USER"; break; + case FESTREAM: kind = "USER"; break; // we return this as USER type (with guid) + default: RuntimeError("htkfeatreader:unsupported feature kind"); + } + // add qualifiers + if (H.sampkind & HASENERGY) kind += "_E"; + if (H.sampkind & HASDELTA) kind += "_D"; + if (H.sampkind & HASNULLE) kind += "_N"; + if (H.sampkind & HASACCS) kind += "_A"; + if (H.sampkind & HASTHIRD) kind += "_T"; + bool compressed = (H.sampkind & HASCOMPX) != 0; + bool hascrcc = (H.sampkind & HASCRCC) != 0; + if (H.sampkind & HASZEROM) kind += "_Z"; + if (H.sampkind & HASZEROC) kind += "_0"; + if (H.sampkind & HASVQ) RuntimeError("htkfeatreader:we do not support VQ"); + // skip additional GUID in FESTREAM features + if (H.sampkind == FESTREAM) + { // ... note: untested + unsigned char guid[16]; + freadOrDie (&guid, sizeof (guid), 1, f); + kind += ";guid="; + for (int i = 0; i < sizeof (guid)/sizeof (*guid); i++) + kind += msra::strfun::strprintf ("%02x", guid[i]); + } + + // other checks + size_t bytesPerValue = isidxformat ? 1 : (compressed ? sizeof (short) : sizeof (float)); + + if (H.sampsize % bytesPerValue != 0) RuntimeError("htkfeatreader:sample size not multiple of dimension"); + size_t dim = H.sampsize / bytesPerValue; + + // read the values for decompressing + vector a, b; + if (compressed) + { + freadOrDie (a, dim, f); + freadOrDie (b, dim, f); + H.nsamples -= 4; // these are counted as 4 frames--that's the space they use + if (needbyteswapping) { msra::util::byteswap (a); msra::util::byteswap (b); } + } + + // done: swap it in + int64_t bytepos = fgetpos (f); + setkind (kind, dim, H.sampperiod, ppath); // this checks consistency + this->physicalpath.swap (physpath); + this->physicaldatastart = bytepos; + this->physicalframes = H.nsamples; + this->f.swap (f); // note: this will get the previous f auto-closed at the end of this function + this->needbyteswapping = needbyteswapping; + this->compressed = compressed; + this->a.swap (a); + this->b.swap (b); + this->vecbytesize = H.sampsize; + this->hascrcc = hascrcc; + } + void close() // force close the open file --use this in case of read failure + { + f = NULL; // assigning a new FILE* to f will close the old FILE* if any + physicalpath.clear(); + } + +public: + + htkfeatreader() {addEnergy = false; energyElements = 0;} + + // helper to create a parsed-path object + // const auto path = parse (xpath) + parsedpath parse (const wstring & xpath) { return parsedpath (xpath); } + + // read a feature file + // Returns number of frames in that file. + // This understands the more complex syntax a=b[s,e] and optimizes a little + size_t open (const parsedpath & ppath) + { + // do not reopen the file if it is the same; use fsetpos() instead + if (f == NULL || ppath.physicallocation() != physicalpath) + openphysical (ppath); + + if (ppath.isarchive) // reading a sub-range from an archive + { + if (ppath.s > ppath.e) + RuntimeError("open: start frame %d > end frame %d in '%ls'", (int)ppath.s, (int)ppath.e, ppath.logicalpath.c_str()); + if (ppath.e >= physicalframes) + RuntimeError("open: end frame exceeds archive's total number of frames %d in '%ls'", (int)physicalframes, ppath.logicalpath.c_str()); + + int64_t dataoffset = physicaldatastart + ppath.s * vecbytesize; + fsetpos (f, dataoffset); // we assume fsetpos(), which is our own, is smart to not flush the read buffer + curframe = 0; + numframes = ppath.e + 1 - ppath.s; + } + else // reading a full file + { + curframe = 0; + numframes = physicalframes; + assert (fgetpos (f) == physicaldatastart); + } + return numframes; + } + // get dimension and type information for a feature file + // This will alter the state of this object in that it opens the file. It is efficient to read it right afterwards + void getinfo (const parsedpath & ppath, string & featkind, size_t & featdim, unsigned int & featperiod) + { + open (ppath); + featkind = this->featkind; + featdim = this->featdim; + featperiod = this->featperiod; + } + + // called to add energy as we read + void AddEnergy(size_t energyElements) + { + this->energyElements = energyElements; + this->addEnergy = energyElements != 0; + } + const string & getfeattype() const { return featkind; } + operator bool() const { return curframe < numframes; } + // read a vector from the open file + void read (std::vector & v) + { + if (curframe >= numframes) RuntimeError("htkfeatreader:attempted to read beyond end"); + if (!compressed && !isidxformat) // not compressed--the easy one + { + freadOrDie (v, featdim, f); + if (needbyteswapping) msra::util::byteswap (v); + } + else if (isidxformat) + { + // read into temp vector + freadOrDie (tmpByteVector, featdim, f); + v.resize (featdim); + foreach_index (k, v) + v[k] = (float) tmpByteVector[k]; + } + else // need to decompress + { + // read into temp vector + freadOrDie (tmp, featdim, f); + if (needbyteswapping) msra::util::byteswap (tmp); + // 'decompress' it + v.resize (tmp.size()); + foreach_index (k, v) + v[k] = (tmp[k] + b[k]) / a[k]; + } + curframe++; + } + // read a sequence of vectors from the open file into a range of frames [ts,te) + template void read (MATRIX & feat, size_t ts, size_t te) + { + // read vectors from file and push to our target structure + vector v(featdim+energyElements); + for (size_t t = ts; t < te; t++) + { + read (v); + // add the energy elements (all zero) if needed + if (addEnergy) + { + // we add the energy elements at the end of each section of features, (features, delta, delta-delta) + size_t posIncrement = featdim/energyElements; + size_t pos = posIncrement; + for (size_t i=0;i < energyElements;i++,pos+=posIncrement) + { + auto iter = v.begin() + pos + i; + v.insert(iter,0.0f); + } + } + foreach_index (k, v) + feat(k,t) = v[k]; + } + } + // read an entire utterance into an already allocated matrix + // Matrix type needs to have operator(i,j) + template void read (const parsedpath & ppath, const string & kindstr, const unsigned int period, MATRIX & feat) + { + // open the file and check dimensions + size_t numframes = open (ppath); + if (feat.cols() != numframes || feat.rows() != featdim) + LogicError("read: stripe read called with wrong dimensions"); + if (kindstr != featkind || period != featperiod) + LogicError("read: attempting to mixing different feature kinds"); + + // read vectors from file and push to our target structure + try { read (feat, 0, numframes); } catch (...) { close(); throw; } + } + // read an entire utterance into a virgen, allocatable matrix + // Matrix type needs to have operator(i,j) and resize(n,m) + template void read (const parsedpath & ppath, string & kindstr, unsigned int & period, MATRIX & feat) + { + // get the file + size_t numframes = open (ppath); + feat.resize (featdim+energyElements, numframes); // result matrix--columns are features + + // read vectors from file and push to our target structure + try { read (feat, 0, numframes); } catch (...) { close(); throw; } + + // return file info + kindstr = featkind; + period = featperiod; + } +}; + +struct htkmlfentry +{ + unsigned int firstframe; // range [firstframe,firstframe+numframes) + unsigned int numframes; + msra::dbn::CLASSIDTYPE classid; // numeric state id + msra::dbn::HMMIDTYPE phonestart; // numeric phone start time + +private: + // verify and save data + void setdata (size_t ts, size_t te, size_t uid) + { + if (te < ts) RuntimeError("htkmlfentry: end time below start time??"); + // save + firstframe = (unsigned int) ts; + numframes = (unsigned int) (te - ts); + classid = (msra::dbn::CLASSIDTYPE) uid; + // check for numeric overflow + if (firstframe != ts || firstframe + numframes != te || classid != uid) + RuntimeError("htkmlfentry: not enough bits for one of the values"); + } + + // parse the time range + // There are two formats: + // - original HTK + // - Dong's hacked format: ts te senonename senoneid + // We distinguish + static void parseframerange (const vector & toks, size_t & ts, size_t & te, const double htkTimeToFrame) + { + const double maxFrameNumber = htkTimeToFrame / 2.0; // if frame number is greater than this we assume it is time instead of frame + double rts = msra::strfun::todouble (toks[0]); + double rte = msra::strfun::todouble (toks[1]); + if (rte > maxFrameNumber) // convert time to frame + { + ts = (size_t) (rts/htkTimeToFrame + 0.5); // get start frame + te = (size_t) (rte/htkTimeToFrame + 0.5); // get end frame + } + else + { + ts = (size_t)(rts); + te = (size_t)(rte); + } + } + +public: + + // parse format with original HTK state align MLF format and state list + void parsewithstatelist (const vector & toks, const unordered_map & statelisthash, const double htkTimeToFrame, + std::unordered_map & hmmnamehash) + { + size_t ts, te; + parseframerange (toks, ts, te, htkTimeToFrame); + auto iter = statelisthash.find (toks[2]); + if (iter == statelisthash.end()) + RuntimeError("htkmlfentry: state %s not found in statelist", toks[2]); + const size_t uid = iter->second; // get state index + setdata (ts, te, uid); + //phone boundary + if (hmmnamehash.size() > 0) + { + if (toks.size() > 4) + { + auto hmmiter = hmmnamehash.find(toks[4]); + if (hmmiter == hmmnamehash.end()) + RuntimeError("htkmlfentry: hmm %s not found in hmmlist", toks[4]); + phonestart = (msra::dbn::HMMIDTYPE)(hmmiter->second + 1); + + // check for numeric overflow + if ((hmmiter->second + 1) != phonestart) + RuntimeError("htkmlfentry: not enough bits for one of the values"); + } + else + phonestart = 0; + } + } + + // ... note: this will be too simplistic for parsing more complex MLF formats. Fix when needed. + // add support so that it can handle conditions where time instead of frame numer is used. + void parse (const vector & toks, const double htkTimeToFrame) + { + if (toks.size() != 4) RuntimeError("htkmlfentry: currently we only support 4-column format"); + size_t ts, te; + parseframerange (toks, ts, te, htkTimeToFrame); + size_t uid = msra::strfun::toint (toks[3]); + setdata(ts, te, uid); + } +}; + +template +class htkmlfreader : public map> // [key][i] the data +{ + wstring curpath; // for error messages + unordered_map statelistmap; // for state <=> index + map wordsequences; // [key] word sequences (if we are building word entries as well, for MMI) + std::unordered_map symmap; + + void strtok (char * s, const char * delim, vector & toks) + { + toks.resize (0); + char * context = nullptr; + for (char * p = strtok_s (s, delim, &context); p; p = strtok_s (NULL, delim, &context)) + toks.push_back (p); + } + void malformed (string what) + { + RuntimeError("htkmlfreader: %s in '%ls'", what.c_str(), curpath.c_str()); + } + + vector readlines (const wstring & path, vector & buffer) + { + // load it into RAM in one huge chunk + auto_file_ptr f(fopenOrDie (path, L"rb")); + size_t len = filesize (f); + buffer.reserve (len +1); + freadOrDie (buffer, len, f); + buffer.push_back (0); // this makes it a proper C string + + // parse into lines + vector lines; + lines.reserve (len / 20); + strtok (&buffer[0], "\r\n", lines); + return lines; + } + + template + void parseentry (const vector & lines, size_t line, const set & restricttokeys, + const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, + vector & wordseqbuffer, vector & alignseqbuffer, + const double htkTimeToFrame) + { + size_t idx = 0; + string filename = lines[idx++]; + while (filename == "#!MLF!#") // skip embedded duplicate MLF headers (so user can 'cat' MLFs) + filename = lines[idx++]; + + // some mlf file have write errors, so skip malformed entry + if (filename.length() < 3 || filename[0] != '"' || filename[filename.length()-1] != '"') + { + fprintf (stderr, "warning: filename entry (%s)\n", filename.c_str()); + fprintf(stderr, "skip current mlf entry from line (%lu) until line (%lu).\n", line + idx, line + lines.size()); + return; + } + + filename = filename.substr (1, filename.length() -2); // strip quotes + if (filename.find ("*/") == 0) filename = filename.substr (2); #ifdef _MSC_VER - wstring key = msra::strfun::utf16(regex_replace(filename, regex("\\.[^\\.\\\\/:]*$"), string())); // delete extension (or not if none) -#else - wstring key = msra::strfun::utf16(msra::dbn::removeExtension(filename)); // note that c++ 4.8 is incomplete for supporting regex -#endif - - // determine lines range - size_t s = idx; - size_t e = lines.size() - 1; - // lines range: [s,e) - - // don't parse unused entries (this is supposed to be used for very small debugging setups with huge MLFs) - if (!restricttokeys.empty() && restricttokeys.find (key) == restricttokeys.end()) - return; - - vector & entries = (*this)[key]; // this creates a new entry - if (!entries.empty()) malformed (msra::strfun::strprintf ("duplicate entry '%ls'", key.c_str())); - entries.resize (e-s); - wordseqbuffer.resize (0); - alignseqbuffer.resize (0); - vector toks; - for (size_t i = s; i < e; i++) - { - // We can mutate the original string as it is no longer needed after tokenization - strtok(const_cast(lines[i].c_str()), " \t", toks); - if (statelistmap.size() == 0) - entries[i-s].parse (toks, htkTimeToFrame); - else - entries[i-s].parsewithstatelist (toks, statelistmap, htkTimeToFrame,symmap); - // if we also read word entries, do it here - if (wordmap) - { - if (toks.size() > 6/*word entry are in this column*/) - { - const char * w = toks[6]; // the word name - int wid = (*wordmap)[w]; // map to word id --may be -1 for unseen words in the transcript (word list typically comes from a test LM) - size_t wordindex = (wid == -1) ? WORDSEQUENCE::word::unknownwordindex : (size_t) wid; - wordseqbuffer.push_back (typename WORDSEQUENCE::word (wordindex, entries[i-s].firstframe, alignseqbuffer.size())); - } - if (unitmap) - { - if (toks.size() > 4) - { - const char * u = toks[4]; // the triphone name - auto iter = unitmap->find (u); // map to unit id - if (iter == unitmap->end()) - RuntimeError("parseentry: unknown unit %s in utterance %ls", u, key.c_str()); - const size_t uid = iter->second; - alignseqbuffer.push_back (typename WORDSEQUENCE::aligninfo (uid, 0/*#frames--we accumulate*/)); - } - if (alignseqbuffer.empty()) - RuntimeError("parseentry: lonely senone entry at start without phone/word entry found, for utterance %ls", key.c_str()); - alignseqbuffer.back().frames += entries[i-s].numframes; // (we do not have an overflow check here, but should...) - } - } - } - if (wordmap) // if reading word sequences as well (for MMI), then record it (in a separate map) - { - if (!entries.empty() && wordseqbuffer.empty()) - RuntimeError("parseentry: got state alignment but no word-level info, although being requested, for utterance %ls", key.c_str()); - // post-process silence - // - first !silence -> !sent_start - // - last !silence -> !sent_end - int silence = (*wordmap)["!silence"]; - if (silence >= 0) - { - int sentstart = (*wordmap)["!sent_start"]; // these must have been created - int sentend = (*wordmap)["!sent_end"]; - // map first and last !silence to !sent_start and !sent_end, respectively - if (sentstart >= 0 && wordseqbuffer.front().wordindex == (size_t) silence) - wordseqbuffer.front().wordindex = sentstart; - if (sentend >= 0 && wordseqbuffer.back().wordindex == (size_t) silence) - wordseqbuffer.back().wordindex = sentend; - } - //if (sentstart < 0 || sentend < 0 || silence < 0) - // LogicError("parseentry: word map must contain !silence, !sent_start, and !sent_end"); - // implant - auto & wordsequence = wordsequences[key]; // this creates the map entry - wordsequence.words = wordseqbuffer; // makes a copy - wordsequence.align = alignseqbuffer; - } - } - -public: - - // return if input statename is sil state (hard code to compared first 3 chars with "sil") - bool issilstate (const string & statename) const // (later use some configuration table) - { - return (statename.size() > 3 && statename.at(0) == 's' && statename.at(1) == 'i' && statename.at(2) == 'l'); - } - - vector issilstatetable; // [state index] => true if is sil state (cached) - - // return if input stateid represent sil state (by table lookup) - bool issilstate (const size_t id) const - { - assert (id < issilstatetable.size()); - return issilstatetable[id]; - } - - struct nullmap { int operator[] (const char * s) const { LogicError("nullmap: should never be used"); } }; // to satisfy a template, never used... :( - - // constructor reads multiple MLF files - htkmlfreader (const vector & paths, const set & restricttokeys, const wstring & stateListPath = L"", const double htkTimeToFrame = 100000.0) - { - // read state list - if (stateListPath != L"") - readstatelist (stateListPath); - - // read MLF(s) --note: there can be multiple, so this is a loop - foreach_index (i, paths) - read (paths[i], restricttokeys, (nullmap* /*to satisfy C++ template resolution*/) NULL, (map*) NULL, htkTimeToFrame); - } - - // alternate constructor that optionally also reads word alignments (for MMI training); triggered by providing a 'wordmap' - // (We cannot use an optional arg in the constructor aboe because it interferes with teh template resolution.) - template - htkmlfreader (const vector & paths, const set & restricttokeys, const wstring & stateListPath, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, const double htkTimeToFrame) - { - // read state list - if (stateListPath != L"") - readstatelist (stateListPath); - - // read MLF(s) --note: there can be multiple, so this is a loop - foreach_index (i, paths) - read (paths[i], restricttokeys, wordmap, unitmap, htkTimeToFrame); - } - - //phone boundary - template - htkmlfreader(const vector & paths, const set & restricttokeys, const wstring & stateListPath, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, - const double htkTimeToFrame, const msra::asr::simplesenonehmm & hset) - { - if (stateListPath != L"") - readstatelist(stateListPath); - symmap = hset.symmap; - foreach_index(i, paths) - read(paths[i], restricttokeys, wordmap, unitmap, htkTimeToFrame); - } - - // note: this function is not designed to be pretty but to be fast - template - void read (const wstring & path, const set & restricttokeys, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, const double htkTimeToFrame) - { - if (!restricttokeys.empty() && this->size() >= restricttokeys.size()) // no need to even read the file if we are there (we support multiple files) - return; - - fprintf (stderr, "htkmlfreader: reading MLF file %ls ...", path.c_str()); - curpath = path; // for error messages only - - auto_file_ptr f(fopenOrDie(path, L"rb")); - std::string headerLine = fgetline(f); - if (headerLine != "#!MLF!#") - malformed("header missing"); - - // Read the file in blocks and parse MLF entries - std::vector wordsequencebuffer; - std::vector alignsequencebuffer; - size_t readBlockSize = 1000000; - std::vector currBlockBuf(readBlockSize + 1); - size_t currLineNum = 1; - std::vector currMLFLines; - bool reachedEOF = (feof(f) != 0); - char* nextReadPtr = currBlockBuf.data(); - size_t nextReadSize = readBlockSize; - while (!reachedEOF) - { - size_t numBytesRead = fread(nextReadPtr, sizeof(char), nextReadSize, f); - reachedEOF = (numBytesRead != nextReadSize); - if (ferror(f)) - RuntimeError("error reading from file: %s", strerror(errno)); - - // Add 0 at the end to make it a proper C string - nextReadPtr[numBytesRead] = 0; - - // Now extract lines from the currBlockBuf and parse MLF entries - char* context = nullptr; - const char* delim = "\r\n"; - - auto consumeMLFLine = [&](const char* mlfLine) - { - currLineNum++; - currMLFLines.push_back(mlfLine); - if ((mlfLine[0] == '.') && (mlfLine[1] == 0)) // utterance end delimiter: a single dot on a line - { - if (restricttokeys.empty() || (this->size() < restricttokeys.size())) - { - parseentry(currMLFLines, currLineNum - currMLFLines.size(), restricttokeys, wordmap, unitmap, wordsequencebuffer, alignsequencebuffer, htkTimeToFrame); - } - - currMLFLines.clear(); - } - }; - - char* prevLine = strtok_s(currBlockBuf.data(), delim, &context); - for (char* currLine = strtok_s(NULL, delim, &context); currLine; currLine = strtok_s(NULL, delim, &context)) - { - consumeMLFLine(prevLine); - prevLine = currLine; - } - - // The last line read from the block may be a full line or part of a line - // We can tell by whether the terminating NULL for this line is the NULL - // we inserted after reading from the file - size_t prevLineLen = strlen(prevLine); - if ((prevLine + prevLineLen) == (nextReadPtr + numBytesRead)) - { - // This is not a full line, but just a truncated part of a line. - // Lets copy this to the start of the currBlockBuf and read new data - // from there on - strcpy_s(currBlockBuf.data(), currBlockBuf.size(), prevLine); - nextReadPtr = currBlockBuf.data() + prevLineLen; - nextReadSize = readBlockSize - prevLineLen; - } - else - { - // A full line - consumeMLFLine(prevLine); - nextReadPtr = currBlockBuf.data(); - nextReadSize = readBlockSize; - } - } - - if (!currMLFLines.empty()) - malformed("unexpected end in mid-utterance"); - - curpath.clear(); - fprintf (stderr, " total %lu entries\n", this->size()); - } - - // read state list, index is from 0 - void readstatelist (const wstring & stateListPath = L"") - { - if (stateListPath != L"") - { - vector buffer; // buffer owns the characters--don't release until done - vector lines = readlines (stateListPath, buffer); - size_t index; - issilstatetable.reserve (lines.size()); - for (index = 0; index < lines.size(); index++) - { - statelistmap[lines[index]] = index; - issilstatetable.push_back (issilstate (lines[index])); - } - if (index != statelistmap.size()) - RuntimeError("readstatelist: lines (%d) not equal to statelistmap size (%d)", (int)index, (int)statelistmap.size()); - if (statelistmap.size() != issilstatetable.size()) - RuntimeError("readstatelist: size of statelookuparray (%d) not equal to statelistmap size (%d)", (int)issilstatetable.size(), (int)statelistmap.size()); - fprintf (stderr, "total %lu state names in state list %ls\n", statelistmap.size(), stateListPath.c_str()); - } - } - - // return state num: varify the fintune layer dim - size_t getstatenum () const - { - return statelistmap.size(); - } - - size_t getstateid (string statename) // added by Hang Su adaptation - { - return statelistmap[statename]; - } - - // access to word sequences - const map & allwordtranscripts() const { return wordsequences; } -}; - -};}; // namespaces + wstring key = msra::strfun::utf16(regex_replace(filename, regex("\\.[^\\.\\\\/:]*$"), string())); // delete extension (or not if none) +#else + wstring key = msra::strfun::utf16(msra::dbn::removeExtension(filename)); // note that c++ 4.8 is incomplete for supporting regex +#endif + + // determine lines range + size_t s = idx; + size_t e = lines.size() - 1; + // lines range: [s,e) + + // don't parse unused entries (this is supposed to be used for very small debugging setups with huge MLFs) + if (!restricttokeys.empty() && restricttokeys.find (key) == restricttokeys.end()) + return; + + vector & entries = (*this)[key]; // this creates a new entry + if (!entries.empty()) malformed (msra::strfun::strprintf ("duplicate entry '%ls'", key.c_str())); + entries.resize (e-s); + wordseqbuffer.resize (0); + alignseqbuffer.resize (0); + vector toks; + for (size_t i = s; i < e; i++) + { + // We can mutate the original string as it is no longer needed after tokenization + strtok(const_cast(lines[i].c_str()), " \t", toks); + if (statelistmap.size() == 0) + entries[i-s].parse (toks, htkTimeToFrame); + else + entries[i-s].parsewithstatelist (toks, statelistmap, htkTimeToFrame,symmap); + // if we also read word entries, do it here + if (wordmap) + { + if (toks.size() > 6/*word entry are in this column*/) + { + const char * w = toks[6]; // the word name + int wid = (*wordmap)[w]; // map to word id --may be -1 for unseen words in the transcript (word list typically comes from a test LM) + size_t wordindex = (wid == -1) ? WORDSEQUENCE::word::unknownwordindex : (size_t) wid; + wordseqbuffer.push_back (typename WORDSEQUENCE::word (wordindex, entries[i-s].firstframe, alignseqbuffer.size())); + } + if (unitmap) + { + if (toks.size() > 4) + { + const char * u = toks[4]; // the triphone name + auto iter = unitmap->find (u); // map to unit id + if (iter == unitmap->end()) + RuntimeError("parseentry: unknown unit %s in utterance %ls", u, key.c_str()); + const size_t uid = iter->second; + alignseqbuffer.push_back (typename WORDSEQUENCE::aligninfo (uid, 0/*#frames--we accumulate*/)); + } + if (alignseqbuffer.empty()) + RuntimeError("parseentry: lonely senone entry at start without phone/word entry found, for utterance %ls", key.c_str()); + alignseqbuffer.back().frames += entries[i-s].numframes; // (we do not have an overflow check here, but should...) + } + } + } + if (wordmap) // if reading word sequences as well (for MMI), then record it (in a separate map) + { + if (!entries.empty() && wordseqbuffer.empty()) + RuntimeError("parseentry: got state alignment but no word-level info, although being requested, for utterance %ls", key.c_str()); + // post-process silence + // - first !silence -> !sent_start + // - last !silence -> !sent_end + int silence = (*wordmap)["!silence"]; + if (silence >= 0) + { + int sentstart = (*wordmap)["!sent_start"]; // these must have been created + int sentend = (*wordmap)["!sent_end"]; + // map first and last !silence to !sent_start and !sent_end, respectively + if (sentstart >= 0 && wordseqbuffer.front().wordindex == (size_t) silence) + wordseqbuffer.front().wordindex = sentstart; + if (sentend >= 0 && wordseqbuffer.back().wordindex == (size_t) silence) + wordseqbuffer.back().wordindex = sentend; + } + //if (sentstart < 0 || sentend < 0 || silence < 0) + // LogicError("parseentry: word map must contain !silence, !sent_start, and !sent_end"); + // implant + auto & wordsequence = wordsequences[key]; // this creates the map entry + wordsequence.words = wordseqbuffer; // makes a copy + wordsequence.align = alignseqbuffer; + } + } + +public: + + // return if input statename is sil state (hard code to compared first 3 chars with "sil") + bool issilstate (const string & statename) const // (later use some configuration table) + { + return (statename.size() > 3 && statename.at(0) == 's' && statename.at(1) == 'i' && statename.at(2) == 'l'); + } + + vector issilstatetable; // [state index] => true if is sil state (cached) + + // return if input stateid represent sil state (by table lookup) + bool issilstate (const size_t id) const + { + assert (id < issilstatetable.size()); + return issilstatetable[id]; + } + + struct nullmap { int operator[] (const char * s) const { LogicError("nullmap: should never be used"); } }; // to satisfy a template, never used... :( + + // constructor reads multiple MLF files + htkmlfreader (const vector & paths, const set & restricttokeys, const wstring & stateListPath = L"", const double htkTimeToFrame = 100000.0) + { + // read state list + if (stateListPath != L"") + readstatelist (stateListPath); + + // read MLF(s) --note: there can be multiple, so this is a loop + foreach_index (i, paths) + read (paths[i], restricttokeys, (nullmap* /*to satisfy C++ template resolution*/) NULL, (map*) NULL, htkTimeToFrame); + } + + // alternate constructor that optionally also reads word alignments (for MMI training); triggered by providing a 'wordmap' + // (We cannot use an optional arg in the constructor aboe because it interferes with teh template resolution.) + template + htkmlfreader (const vector & paths, const set & restricttokeys, const wstring & stateListPath, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, const double htkTimeToFrame) + { + // read state list + if (stateListPath != L"") + readstatelist (stateListPath); + + // read MLF(s) --note: there can be multiple, so this is a loop + foreach_index (i, paths) + read (paths[i], restricttokeys, wordmap, unitmap, htkTimeToFrame); + } + + //phone boundary + template + htkmlfreader(const vector & paths, const set & restricttokeys, const wstring & stateListPath, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, + const double htkTimeToFrame, const msra::asr::simplesenonehmm & hset) + { + if (stateListPath != L"") + readstatelist(stateListPath); + symmap = hset.symmap; + foreach_index(i, paths) + read(paths[i], restricttokeys, wordmap, unitmap, htkTimeToFrame); + } + + // note: this function is not designed to be pretty but to be fast + template + void read (const wstring & path, const set & restricttokeys, const WORDSYMBOLTABLE * wordmap, const UNITSYMBOLTABLE * unitmap, const double htkTimeToFrame) + { + if (!restricttokeys.empty() && this->size() >= restricttokeys.size()) // no need to even read the file if we are there (we support multiple files) + return; + + fprintf (stderr, "htkmlfreader: reading MLF file %ls ...", path.c_str()); + curpath = path; // for error messages only + + auto_file_ptr f(fopenOrDie(path, L"rb")); + std::string headerLine = fgetline(f); + if (headerLine != "#!MLF!#") + malformed("header missing"); + + // Read the file in blocks and parse MLF entries + std::vector wordsequencebuffer; + std::vector alignsequencebuffer; + size_t readBlockSize = 1000000; + std::vector currBlockBuf(readBlockSize + 1); + size_t currLineNum = 1; + std::vector currMLFLines; + bool reachedEOF = (feof(f) != 0); + char* nextReadPtr = currBlockBuf.data(); + size_t nextReadSize = readBlockSize; + while (!reachedEOF) + { + size_t numBytesRead = fread(nextReadPtr, sizeof(char), nextReadSize, f); + reachedEOF = (numBytesRead != nextReadSize); + if (ferror(f)) + RuntimeError("error reading from file: %s", strerror(errno)); + + // Add 0 at the end to make it a proper C string + nextReadPtr[numBytesRead] = 0; + + // Now extract lines from the currBlockBuf and parse MLF entries + char* context = nullptr; + const char* delim = "\r\n"; + + auto consumeMLFLine = [&](const char* mlfLine) + { + currLineNum++; + currMLFLines.push_back(mlfLine); + if ((mlfLine[0] == '.') && (mlfLine[1] == 0)) // utterance end delimiter: a single dot on a line + { + if (restricttokeys.empty() || (this->size() < restricttokeys.size())) + { + parseentry(currMLFLines, currLineNum - currMLFLines.size(), restricttokeys, wordmap, unitmap, wordsequencebuffer, alignsequencebuffer, htkTimeToFrame); + } + + currMLFLines.clear(); + } + }; + + char* prevLine = strtok_s(currBlockBuf.data(), delim, &context); + for (char* currLine = strtok_s(NULL, delim, &context); currLine; currLine = strtok_s(NULL, delim, &context)) + { + consumeMLFLine(prevLine); + prevLine = currLine; + } + + // The last line read from the block may be a full line or part of a line + // We can tell by whether the terminating NULL for this line is the NULL + // we inserted after reading from the file + size_t prevLineLen = strlen(prevLine); + if ((prevLine + prevLineLen) == (nextReadPtr + numBytesRead)) + { + // This is not a full line, but just a truncated part of a line. + // Lets copy this to the start of the currBlockBuf and read new data + // from there on + strcpy_s(currBlockBuf.data(), currBlockBuf.size(), prevLine); + nextReadPtr = currBlockBuf.data() + prevLineLen; + nextReadSize = readBlockSize - prevLineLen; + } + else + { + // A full line + consumeMLFLine(prevLine); + nextReadPtr = currBlockBuf.data(); + nextReadSize = readBlockSize; + } + } + + if (!currMLFLines.empty()) + malformed("unexpected end in mid-utterance"); + + curpath.clear(); + fprintf (stderr, " total %lu entries\n", this->size()); + } + + // read state list, index is from 0 + void readstatelist (const wstring & stateListPath = L"") + { + if (stateListPath != L"") + { + vector buffer; // buffer owns the characters--don't release until done + vector lines = readlines (stateListPath, buffer); + size_t index; + issilstatetable.reserve (lines.size()); + for (index = 0; index < lines.size(); index++) + { + statelistmap[lines[index]] = index; + issilstatetable.push_back (issilstate (lines[index])); + } + if (index != statelistmap.size()) + RuntimeError("readstatelist: lines (%d) not equal to statelistmap size (%d)", (int)index, (int)statelistmap.size()); + if (statelistmap.size() != issilstatetable.size()) + RuntimeError("readstatelist: size of statelookuparray (%d) not equal to statelistmap size (%d)", (int)issilstatetable.size(), (int)statelistmap.size()); + fprintf (stderr, "total %lu state names in state list %ls\n", statelistmap.size(), stateListPath.c_str()); + } + } + + // return state num: varify the fintune layer dim + size_t getstatenum () const + { + return statelistmap.size(); + } + + size_t getstateid (string statename) // added by Hang Su adaptation + { + return statelistmap[statename]; + } + + // access to word sequences + const map & allwordtranscripts() const { return wordsequences; } +}; + +};}; // namespaces diff --git a/Source/Readers/HTKMLFReader/minibatchsourcehelpers.h b/Source/Readers/HTKMLFReader/minibatchsourcehelpers.h index 710770dad9c5..3d6c60d323d8 100644 --- a/Source/Readers/HTKMLFReader/minibatchsourcehelpers.h +++ b/Source/Readers/HTKMLFReader/minibatchsourcehelpers.h @@ -1,308 +1,308 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// minibatchsourcehelpers.h -- helper classes for minibatch sources -// - -#pragma once - -#include "Basics.h" -#include -#include -#include -#ifndef __unix__ -#include "ssematrix.h" // for matrix type -#endif - -#pragma warning (disable: 4505) // unreferenced local function has been removed - -namespace msra { namespace dbn { - -// --------------------------------------------------------------------------- -// augmentneighbors() -- augmenting features with their neighbor frames -// --------------------------------------------------------------------------- - -// implant a sub-vector into a vector, for use in augmentneighbors -template static void copytosubvector (const INV & inv, size_t subvecindex, OUTV & outv) -{ - size_t subdim = inv.size(); - assert (outv.size() % subdim == 0); - size_t k0 = subvecindex * subdim; - foreach_index (k, inv) - outv[k + k0] = inv[k]; -} - -// compute the augmentation extent (how many frames added on each side) -static size_t augmentationextent (size_t featdim/*augment from*/, size_t modeldim/*to*/) -{ - const size_t windowframes = modeldim / featdim; // total number of frames to generate - const size_t extent = windowframes / 2; // extend each side by this - - if (modeldim % featdim != 0) - RuntimeError("augmentationextent: model vector size not multiple of input features"); - if (windowframes % 2 == 0) - RuntimeError("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowframes); - - return extent; -} - -// augment neighbor frames for a frame (correctly not expanding across utterance boundaries) -// The boundaryflags[] array, if not empty, flags first (-1) and last (+1) frame, i.e. frames to not expand across. -// The output 'v' must have te-ts columns. -template static void augmentneighbors (const MATRIX & frames, const std::vector & boundaryflags, size_t t, - VECTOR & v) -{ - // how many frames are we adding on each side - const size_t extent = augmentationextent (frames[t].size(), v.size()); - - // copy the frame and its neighbors - // Once we hit a boundaryflag in either direction, do not move index beyond. - copytosubvector (frames[t], extent, v); // frame[t] sits right in the middle - size_t t1 = t; // index for frames on to the left - size_t t2 = t; // and right - for (size_t n = 1; n <= extent; n++) - { -#ifdef SAMPLING_EXPERIMENT - if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance - { - if (t1 >= SAMPLING_EXPERIMENT) t1 -= SAMPLING_EXPERIMENT; // index does not move beyond boundary - if (t2 + SAMPLING_EXPERIMENT < frames.size()) t2 += SAMPLING_EXPERIMENT; - } - else - { - if (boundaryflags[t1] != -1) t1 -= SAMPLING_EXPERIMENT; // index does not move beyond a set boundaryflag, - if (boundaryflags[t2] != 1) t2 += SAMPLING_EXPERIMENT; // because that's the start/end of the utterance - } -#else - if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance - { - if (t1 > 0) t1--; // index does not move beyond boundary - if (t2 + 1 < frames.size()) t2++; - } - else - { - if (boundaryflags[t1] != -1) t1--; // index does not move beyond a set boundaryflag, - if (boundaryflags[t2] != 1) t2++; // because that's the start/end of the utterance - } -#endif - copytosubvector (frames[t1], extent - n, v); - copytosubvector (frames[t2], extent + n, v); - } -} - -// augment neighbor frames for a frame (correctly not expanding across utterance boundaries) -// The boundaryflags[] array, if not empty, flags first (-1) and last (+1) frame, i.e. frames to not expand across. -// The output 'v' must have te-ts columns. -template static void augmentneighbors(const MATRIX & frames, const std::vector & boundaryflags, size_t t, const size_t leftextent, const size_t rightextent, - VECTOR & v) -{ - - // copy the frame and its neighbors - // Once we hit a boundaryflag in either direction, do not move index beyond. - copytosubvector(frames[t], leftextent, v); // frame[t] sits right in the middle - size_t t1 = t; // index for frames on to the left - size_t t2 = t; // and right - - for (size_t n = 1; n <= leftextent; n++) - { - if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance - { - if (t1 > 0) t1--; // index does not move beyond boundary - } - else - { - if (boundaryflags[t1] != -1) t1--; // index does not move beyond a set boundaryflag, - } - copytosubvector(frames[t1], leftextent - n, v); - } - for (size_t n = 1; n <= rightextent; n++) - { - if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance - { - if (t2 + 1 < frames.size()) t2++; - } - else - { - if (boundaryflags[t2] != 1) t2++; // because that's the start/end of the utterance - } - copytosubvector(frames[t2], rightextent + n, v); - } -} - -// augment neighbor frames for one frame t in frames[] according to boundaryflags[]; result returned in column j of v -template static void augmentneighbors (const INMATRIX & frames, const std::vector & boundaryflags, size_t t, - OUTMATRIX & v, size_t j) -{ - auto v_j = v.col(j); // the vector to fill in - augmentneighbors (frames, boundaryflags, t, v_j); -} - -// augment neighbor frames for one frame t in frames[] according to boundaryflags[]; result returned in column j of v -template static void augmentneighbors(const INMATRIX & frames, const std::vector & boundaryflags, size_t t, size_t leftextent, size_t rightextent, - OUTMATRIX & v, size_t j) -{ - auto v_j = v.col(j); // the vector to fill in - augmentneighbors(frames, boundaryflags, t, leftextent, rightextent, v_j); -} - -// augment neighbor frames for a sequence of frames (part of an utterance, possibly spanning across boundaries) -template static void augmentneighbors (const std::vector> & frames, const std::vector & boundaryflags, - size_t ts, size_t te, // range [ts,te) - MATRIX & v) -{ - for (size_t t = ts; t < te; t++) - { - auto v_t = v.col(t-ts); // the vector to fill in - augmentneighbors (frames, boundaryflags, t, v_t); - } -} - - -// augment neighbor frames for a sequence of frames (part of an utterance, possibly spanning across boundaries) -template static void augmentneighbors(const std::vector> & frames, const std::vector & boundaryflags, size_t leftextent, size_t rightextent, - size_t ts, size_t te, // range [ts,te) - MATRIX & v) -{ - for (size_t t = ts; t < te; t++) - { - auto v_t = v.col(t - ts); // the vector to fill in - augmentneighbors(frames, boundaryflags, t, leftextent, rightextent, v_t); - } -} - -// --------------------------------------------------------------------------- -// randomordering -- class to help manage randomization of input data -// --------------------------------------------------------------------------- - -static inline size_t rand (const size_t begin, const size_t end) -{ - const size_t randno = ::rand() * RAND_MAX + ::rand(); // BUGBUG: still only covers 32-bit range - return begin + randno % (end - begin); -} - -class randomordering // note: NOT thread-safe at all -{ - // constants for randomization - const static size_t randomizeAuto=0; - const static size_t randomizeDisable=(size_t)-1; - - typedef unsigned int INDEXTYPE; // don't use size_t, as this saves HUGE amounts of RAM - std::vector map; // [t] -> t' indices in randomized order - size_t currentseed; // seed for current sequence - size_t randomizationrange; // t - randomizationrange/2 <= t' < t + randomizationrange/2 (we support this to enable swapping) - // special values (randomizeAuto, randomizeDisable) - void invalidate() { currentseed = (size_t) -1; } -public: - randomordering() { invalidate(); } - - void resize (size_t len, size_t p_randomizationrange) { randomizationrange = p_randomizationrange>0?p_randomizationrange:len; map.resize (len); invalidate(); } - - // return the randomized feature bounds for a time range - std::pair bounds (size_t ts, size_t te) const - { - size_t tbegin = max (ts, randomizationrange/2) - randomizationrange/2; - size_t tend = min (te + randomizationrange/2, map.size()); - return std::make_pair(std::move(tbegin), std::move(tend)); - } - - // this returns the map directly (read-only) and will lazily initialize it for a given seed - const std::vector & operator() (size_t seed) //throw() - { - // if wrong seed then lazily recache the sequence - if (seed != currentseed) - { - // test for numeric overflow - if (map.size()-1 != (INDEXTYPE) (map.size()-1)) - RuntimeError("randomordering: INDEXTYPE has too few bits for this corpus"); - // 0, 1, 2... - foreach_index (t, map) map[t] = (INDEXTYPE) t; - // now randomize them - if (randomizationrange != randomizeDisable) - { - #if 1 // change to 0 to disable randomizing - if (map.size() > RAND_MAX * (size_t) RAND_MAX) - RuntimeError("randomordering: too large training set: need to change to different random generator!"); - srand ((unsigned int) seed); - size_t retries = 0; - foreach_index (t, map) - { - for (int tries = 0; tries < 5; tries++) - { - // swap current pos with a random position - // Random positions are limited to t+randomizationrange. - // This ensures some locality suitable for paging with a sliding window. - const size_t tbegin = max ((size_t) t, randomizationrange/2) - randomizationrange/2; // range of window --TODO: use bounds() function above - const size_t tend = min (t + randomizationrange/2, map.size()); - assert (tend >= tbegin); // (guard against potential numeric-wraparound bug) - const size_t trand = rand (tbegin, tend); // random number within windows - assert ((size_t) t <= trand + randomizationrange/2 && trand < (size_t) t + randomizationrange/2); - // if range condition is fulfilled then swap - if (trand <= map[t] + randomizationrange/2 && map[t] < trand + randomizationrange/2 - && (size_t) t <= map[trand] + randomizationrange/2 && map[trand] < (size_t) t + randomizationrange/2) - { - std::swap (map[t], map[trand]); - break; - } - // but don't multi-swap stuff out of its range (for swapping positions that have been swapped before) - // instead, try again with a different random number - retries++; - } - } - fprintf (stderr, "randomordering: %d retries for %d elements (%.1f%%) to ensure window condition\n", (int)retries, (int)map.size(), 100.0 * retries / map.size()); - // ensure the window condition - foreach_index (t, map) assert ((size_t) t <= map[t] + randomizationrange/2 && map[t] < (size_t) t + randomizationrange/2); - #if 1 // and a live check since I don't trust myself here yet - foreach_index (t, map) if (!((size_t) t <= map[t] + randomizationrange/2 && map[t] < (size_t) t + randomizationrange/2)) - { - fprintf (stderr, "randomordering: windowing condition violated %d -> %d\n", t, map[t]); - LogicError("randomordering: windowing condition violated"); - } - #endif - #endif - #if 1 // test whether it is indeed a unique complete sequence - auto map2 = map; - std::sort (map2.begin(), map2.end()); - foreach_index (t, map2) assert (map2[t] == (size_t) t); - #endif - fprintf (stderr, "randomordering: recached sequence for seed %d: %d, %d, ...\n", (int) seed, (int) map[0], (int) map[1]); - } - currentseed = seed; - } - return map; // caller can now access it through operator[] - } -}; - -// Using a smaller sized integer type to store the state ids saves a huge amount of space -// We need more than 2 bytes (i.e. an unsigned int or bigger) to support untied context-dependent phones -// TODO: This is currently being hardcoded to unsigned short for saving space, which means untied context-dependent phones -// will not work. This needs to be changed to dynamically choose what size to use based on the number of class ids. -typedef unsigned short CLASSIDTYPE; -typedef unsigned short HMMIDTYPE; - -#ifndef _MSC_VER -// these needed below with gcc because some regex function is not implemented properly there -struct MatchPathSeparator -{ - bool operator()(char ch) const { return ch == '\\' || ch == '/'; } -}; -static inline std::string basename(std::string const& pathname) -{ - return std::string(std::find_if(pathname.rbegin(), pathname.rend(), MatchPathSeparator()).base(), pathname.end()); -} -static inline std::string removeExtension(std::string const& filename) -{ - return filename.substr(0, filename.find_last_of(".")); -} -static inline std::wstring basename(std::wstring const& pathname) -{ - return std::wstring(std::find_if(pathname.rbegin(), pathname.rend(), MatchPathSeparator()).base(), pathname.end()); -} -static inline std::wstring removeExtension(std::wstring const& filename) -{ - return filename.substr(0, filename.find_last_of(L".")); -} -#endif - -};}; +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// minibatchsourcehelpers.h -- helper classes for minibatch sources +// + +#pragma once + +#include "Basics.h" +#include +#include +#include +#ifndef __unix__ +#include "ssematrix.h" // for matrix type +#endif + +#pragma warning (disable: 4505) // unreferenced local function has been removed + +namespace msra { namespace dbn { + +// --------------------------------------------------------------------------- +// augmentneighbors() -- augmenting features with their neighbor frames +// --------------------------------------------------------------------------- + +// implant a sub-vector into a vector, for use in augmentneighbors +template static void copytosubvector (const INV & inv, size_t subvecindex, OUTV & outv) +{ + size_t subdim = inv.size(); + assert (outv.size() % subdim == 0); + size_t k0 = subvecindex * subdim; + foreach_index (k, inv) + outv[k + k0] = inv[k]; +} + +// compute the augmentation extent (how many frames added on each side) +static size_t augmentationextent (size_t featdim/*augment from*/, size_t modeldim/*to*/) +{ + const size_t windowframes = modeldim / featdim; // total number of frames to generate + const size_t extent = windowframes / 2; // extend each side by this + + if (modeldim % featdim != 0) + RuntimeError("augmentationextent: model vector size not multiple of input features"); + if (windowframes % 2 == 0) + RuntimeError("augmentationextent: neighbor expansion of input features to %d not symmetrical", (int)windowframes); + + return extent; +} + +// augment neighbor frames for a frame (correctly not expanding across utterance boundaries) +// The boundaryflags[] array, if not empty, flags first (-1) and last (+1) frame, i.e. frames to not expand across. +// The output 'v' must have te-ts columns. +template static void augmentneighbors (const MATRIX & frames, const std::vector & boundaryflags, size_t t, + VECTOR & v) +{ + // how many frames are we adding on each side + const size_t extent = augmentationextent (frames[t].size(), v.size()); + + // copy the frame and its neighbors + // Once we hit a boundaryflag in either direction, do not move index beyond. + copytosubvector (frames[t], extent, v); // frame[t] sits right in the middle + size_t t1 = t; // index for frames on to the left + size_t t2 = t; // and right + for (size_t n = 1; n <= extent; n++) + { +#ifdef SAMPLING_EXPERIMENT + if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance + { + if (t1 >= SAMPLING_EXPERIMENT) t1 -= SAMPLING_EXPERIMENT; // index does not move beyond boundary + if (t2 + SAMPLING_EXPERIMENT < frames.size()) t2 += SAMPLING_EXPERIMENT; + } + else + { + if (boundaryflags[t1] != -1) t1 -= SAMPLING_EXPERIMENT; // index does not move beyond a set boundaryflag, + if (boundaryflags[t2] != 1) t2 += SAMPLING_EXPERIMENT; // because that's the start/end of the utterance + } +#else + if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance + { + if (t1 > 0) t1--; // index does not move beyond boundary + if (t2 + 1 < frames.size()) t2++; + } + else + { + if (boundaryflags[t1] != -1) t1--; // index does not move beyond a set boundaryflag, + if (boundaryflags[t2] != 1) t2++; // because that's the start/end of the utterance + } +#endif + copytosubvector (frames[t1], extent - n, v); + copytosubvector (frames[t2], extent + n, v); + } +} + +// augment neighbor frames for a frame (correctly not expanding across utterance boundaries) +// The boundaryflags[] array, if not empty, flags first (-1) and last (+1) frame, i.e. frames to not expand across. +// The output 'v' must have te-ts columns. +template static void augmentneighbors(const MATRIX & frames, const std::vector & boundaryflags, size_t t, const size_t leftextent, const size_t rightextent, + VECTOR & v) +{ + + // copy the frame and its neighbors + // Once we hit a boundaryflag in either direction, do not move index beyond. + copytosubvector(frames[t], leftextent, v); // frame[t] sits right in the middle + size_t t1 = t; // index for frames on to the left + size_t t2 = t; // and right + + for (size_t n = 1; n <= leftextent; n++) + { + if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance + { + if (t1 > 0) t1--; // index does not move beyond boundary + } + else + { + if (boundaryflags[t1] != -1) t1--; // index does not move beyond a set boundaryflag, + } + copytosubvector(frames[t1], leftextent - n, v); + } + for (size_t n = 1; n <= rightextent; n++) + { + if (boundaryflags.empty()) // boundary flags not given: 'frames' is full utterance + { + if (t2 + 1 < frames.size()) t2++; + } + else + { + if (boundaryflags[t2] != 1) t2++; // because that's the start/end of the utterance + } + copytosubvector(frames[t2], rightextent + n, v); + } +} + +// augment neighbor frames for one frame t in frames[] according to boundaryflags[]; result returned in column j of v +template static void augmentneighbors (const INMATRIX & frames, const std::vector & boundaryflags, size_t t, + OUTMATRIX & v, size_t j) +{ + auto v_j = v.col(j); // the vector to fill in + augmentneighbors (frames, boundaryflags, t, v_j); +} + +// augment neighbor frames for one frame t in frames[] according to boundaryflags[]; result returned in column j of v +template static void augmentneighbors(const INMATRIX & frames, const std::vector & boundaryflags, size_t t, size_t leftextent, size_t rightextent, + OUTMATRIX & v, size_t j) +{ + auto v_j = v.col(j); // the vector to fill in + augmentneighbors(frames, boundaryflags, t, leftextent, rightextent, v_j); +} + +// augment neighbor frames for a sequence of frames (part of an utterance, possibly spanning across boundaries) +template static void augmentneighbors (const std::vector> & frames, const std::vector & boundaryflags, + size_t ts, size_t te, // range [ts,te) + MATRIX & v) +{ + for (size_t t = ts; t < te; t++) + { + auto v_t = v.col(t-ts); // the vector to fill in + augmentneighbors (frames, boundaryflags, t, v_t); + } +} + + +// augment neighbor frames for a sequence of frames (part of an utterance, possibly spanning across boundaries) +template static void augmentneighbors(const std::vector> & frames, const std::vector & boundaryflags, size_t leftextent, size_t rightextent, + size_t ts, size_t te, // range [ts,te) + MATRIX & v) +{ + for (size_t t = ts; t < te; t++) + { + auto v_t = v.col(t - ts); // the vector to fill in + augmentneighbors(frames, boundaryflags, t, leftextent, rightextent, v_t); + } +} + +// --------------------------------------------------------------------------- +// randomordering -- class to help manage randomization of input data +// --------------------------------------------------------------------------- + +static inline size_t rand (const size_t begin, const size_t end) +{ + const size_t randno = ::rand() * RAND_MAX + ::rand(); // BUGBUG: still only covers 32-bit range + return begin + randno % (end - begin); +} + +class randomordering // note: NOT thread-safe at all +{ + // constants for randomization + const static size_t randomizeAuto=0; + const static size_t randomizeDisable=(size_t)-1; + + typedef unsigned int INDEXTYPE; // don't use size_t, as this saves HUGE amounts of RAM + std::vector map; // [t] -> t' indices in randomized order + size_t currentseed; // seed for current sequence + size_t randomizationrange; // t - randomizationrange/2 <= t' < t + randomizationrange/2 (we support this to enable swapping) + // special values (randomizeAuto, randomizeDisable) + void invalidate() { currentseed = (size_t) -1; } +public: + randomordering() { invalidate(); } + + void resize (size_t len, size_t p_randomizationrange) { randomizationrange = p_randomizationrange>0?p_randomizationrange:len; map.resize (len); invalidate(); } + + // return the randomized feature bounds for a time range + std::pair bounds (size_t ts, size_t te) const + { + size_t tbegin = max (ts, randomizationrange/2) - randomizationrange/2; + size_t tend = min (te + randomizationrange/2, map.size()); + return std::make_pair(std::move(tbegin), std::move(tend)); + } + + // this returns the map directly (read-only) and will lazily initialize it for a given seed + const std::vector & operator() (size_t seed) //throw() + { + // if wrong seed then lazily recache the sequence + if (seed != currentseed) + { + // test for numeric overflow + if (map.size()-1 != (INDEXTYPE) (map.size()-1)) + RuntimeError("randomordering: INDEXTYPE has too few bits for this corpus"); + // 0, 1, 2... + foreach_index (t, map) map[t] = (INDEXTYPE) t; + // now randomize them + if (randomizationrange != randomizeDisable) + { + #if 1 // change to 0 to disable randomizing + if (map.size() > RAND_MAX * (size_t) RAND_MAX) + RuntimeError("randomordering: too large training set: need to change to different random generator!"); + srand ((unsigned int) seed); + size_t retries = 0; + foreach_index (t, map) + { + for (int tries = 0; tries < 5; tries++) + { + // swap current pos with a random position + // Random positions are limited to t+randomizationrange. + // This ensures some locality suitable for paging with a sliding window. + const size_t tbegin = max ((size_t) t, randomizationrange/2) - randomizationrange/2; // range of window --TODO: use bounds() function above + const size_t tend = min (t + randomizationrange/2, map.size()); + assert (tend >= tbegin); // (guard against potential numeric-wraparound bug) + const size_t trand = rand (tbegin, tend); // random number within windows + assert ((size_t) t <= trand + randomizationrange/2 && trand < (size_t) t + randomizationrange/2); + // if range condition is fulfilled then swap + if (trand <= map[t] + randomizationrange/2 && map[t] < trand + randomizationrange/2 + && (size_t) t <= map[trand] + randomizationrange/2 && map[trand] < (size_t) t + randomizationrange/2) + { + std::swap (map[t], map[trand]); + break; + } + // but don't multi-swap stuff out of its range (for swapping positions that have been swapped before) + // instead, try again with a different random number + retries++; + } + } + fprintf (stderr, "randomordering: %d retries for %d elements (%.1f%%) to ensure window condition\n", (int)retries, (int)map.size(), 100.0 * retries / map.size()); + // ensure the window condition + foreach_index (t, map) assert ((size_t) t <= map[t] + randomizationrange/2 && map[t] < (size_t) t + randomizationrange/2); + #if 1 // and a live check since I don't trust myself here yet + foreach_index (t, map) if (!((size_t) t <= map[t] + randomizationrange/2 && map[t] < (size_t) t + randomizationrange/2)) + { + fprintf (stderr, "randomordering: windowing condition violated %d -> %d\n", t, map[t]); + LogicError("randomordering: windowing condition violated"); + } + #endif + #endif + #if 1 // test whether it is indeed a unique complete sequence + auto map2 = map; + std::sort (map2.begin(), map2.end()); + foreach_index (t, map2) assert (map2[t] == (size_t) t); + #endif + fprintf (stderr, "randomordering: recached sequence for seed %d: %d, %d, ...\n", (int) seed, (int) map[0], (int) map[1]); + } + currentseed = seed; + } + return map; // caller can now access it through operator[] + } +}; + +// Using a smaller sized integer type to store the state ids saves a huge amount of space +// We need more than 2 bytes (i.e. an unsigned int or bigger) to support untied context-dependent phones +// TODO: This is currently being hardcoded to unsigned short for saving space, which means untied context-dependent phones +// will not work. This needs to be changed to dynamically choose what size to use based on the number of class ids. +typedef unsigned short CLASSIDTYPE; +typedef unsigned short HMMIDTYPE; + +#ifndef _MSC_VER +// these needed below with gcc because some regex function is not implemented properly there +struct MatchPathSeparator +{ + bool operator()(char ch) const { return ch == '\\' || ch == '/'; } +}; +static inline std::string basename(std::string const& pathname) +{ + return std::string(std::find_if(pathname.rbegin(), pathname.rend(), MatchPathSeparator()).base(), pathname.end()); +} +static inline std::string removeExtension(std::string const& filename) +{ + return filename.substr(0, filename.find_last_of(".")); +} +static inline std::wstring basename(std::wstring const& pathname) +{ + return std::wstring(std::find_if(pathname.rbegin(), pathname.rend(), MatchPathSeparator()).base(), pathname.end()); +} +static inline std::wstring removeExtension(std::wstring const& filename) +{ + return filename.substr(0, filename.find_last_of(L".")); +} +#endif + +};}; diff --git a/Source/Readers/HTKMLFReader/rollingwindowsource.h b/Source/Readers/HTKMLFReader/rollingwindowsource.h index 3b18490cbcca..8e7f5f6bd673 100644 --- a/Source/Readers/HTKMLFReader/rollingwindowsource.h +++ b/Source/Readers/HTKMLFReader/rollingwindowsource.h @@ -1,838 +1,838 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// rollingwindowsource.h -- implementation of a rolling-window minibatch source ('minibatchframesource') with a disk page file -// - -#pragma once - -#include "Basics.h" // for attempt() -#ifdef _WIN32 -#include "numahelpers.h" // for NUMA allocation -#endif -#include "minibatchsourcehelpers.h" -#include "minibatchiterator.h" -#include "biggrowablevectors.h" -#include "ssematrix.h" - -namespace msra { namespace dbn { - - // --------------------------------------------------------------------------- - // biggrowablevectorarray -- a big array of vectors for features, growable (push_back) - // Data is striped across NUMA nodes, as to not clog them up. - // This also supports paging to disk, which is used for the old minibatchframesource. - // --------------------------------------------------------------------------- - class biggrowablevectorarray : public growablevectorbase - { - size_t m; // dim - - size_t inmembegin; // range we have in memory, rounded to enclosing blocks (not rounded at end) - size_t inmemend; - - wstring pagepath; // path for paging, empty if no paging - auto_file_ptr f; // file handle for paging - bool reading; // have we begun reading? - - // allocate a block - msra::dbn::matrix * newblock() const - { - // we stripe the data across NUMA nodes as to not fill up one node with the feature data -#ifdef _WIN32 - msra::numa::overridenode ((int) msra::numa::getmostspaciousnumanode()); -#endif - msra::dbn::matrix * res = new msra::dbn::matrix (m, elementsperblock); -#ifdef _WIN32 - msra::numa::overridenode (-1); // note: we really should reset it also in case of failure -#endif - return res; - } - - // handling of page file - bool paging() const { return !pagepath.empty(); } - void openpagefile (bool wantread) - { - if (!paging()) return; - msra::files::make_intermediate_dirs (pagepath); - - if (!wantread) - { - FILE *ftry = NULL; - wstring pathname (pagepath); - ftry = _wfopen (pathname.c_str(), L"wbS"); - if (ftry) fclose (ftry); - } - - /* - code below to cycle through a-z appended to file name is no longer necessary - since caller guarantees unique file names via HTKMLFReader - and we want the pagepath logged to the user to be the actual one used by the code - - // try to open the pagepath from a to z - if (!wantread) - { - FILE *ftry = NULL; - char trynum = 'a'; - while (!ftry && trynum <= 'z') - { - wstring pathname (pagepath); - pathname += trynum++; - ftry = _wfopen (pathname.c_str(), L"wbS"); - } - if (ftry) fclose (ftry); - pagepath += --trynum; - } - */ - f = fopenOrDie (pagepath, wantread ? L"rbS" : L"wbS"); - reading = wantread; - } - void flushlastblock() // during population phase, must be called once per block in sequence - { - if (!paging()) return; - assert (!reading); - if (blocks.empty()) return; - const size_t blockid = blocks.size() -1; - msra::dbn::matrix & block = *blocks[blockid]; - assert (fgetpos (f) == blockid * block.sizeinpagefile()); - block.topagefile (f); - blocks[blockid].reset(); // free the memory - assert (blockid * elementsperblock == inmembegin); - inmembegin = inmemend; // empty range - } - void releaseblock (size_t t0) // t0=block start time - { - assert (paging() && reading); - size_t blockid = t0 / elementsperblock; - assert (blockid * elementsperblock == t0); - assert (blocks[blockid]); - fprintf (stderr, "recoverblock: releasing feature block %d [%d..%d)\n", (int)blockid, (int)t0, (int)(t0 + elementsperblock -1)); - blocks[blockid].reset(); // free the memory - } - void recoverblock (size_t t0) // t0=block start time - { - assert (paging() && reading); - size_t blockid = t0 / elementsperblock; - assert (blockid * elementsperblock == t0); - assert (!blocks[blockid]); - fprintf (stderr, "recoverblock: recovering feature block %d [%d..%d)\n", (int)blockid, (int)t0, (int)(t0 + elementsperblock -1)); - blocks[blockid].reset (newblock()); - msra::dbn::matrix & block = *blocks[blockid]; - fsetpos (f, blockid * block.sizeinpagefile()); - block.frompagefile (f); - } - - public: - biggrowablevectorarray (const wstring & pagepath) - : growablevectorbase (65536), m (0), - inmembegin (0), inmemend (0), pagepath (pagepath), reading (false) - { - openpagefile (false); - if (paging()) - fprintf (stderr, "biggrowablevectorarray: creating disk backup store at '%ls'\n", pagepath.c_str()); - } - ~biggrowablevectorarray() { // clean up the big temp file - if (paging()) { - fclose (f); - if (_wunlink (pagepath.c_str())==0) - fprintf (stderr, "biggrowablevectorarray: deleted disk backup store at '%ls'\n", pagepath.c_str()); - else - fprintf (stderr, "biggrowablevectorarray: unable to delete disk backup store at '%ls'\n", pagepath.c_str()); - } - } - - size_t dim() const { return m; } // dimension of a frame - - // reading phase - void push_back (const std::vector & in) - { - assert (!in.empty()); - assert (m == 0 || m == in.size()); - m = in.size(); - const size_t blockid = n / elementsperblock; - assert (blockid <= blocks.size()); - if (blockid == blocks.size()) // a new block is needed - { - flushlastblock(); - blocks.push_back (std::unique_ptr (newblock())); - } - const size_t blockn = n % elementsperblock; - msra::dbn::matrix & block = *blocks[blockid].get(); - foreach_index (k, in) - block(k,blockn) = in[k]; - n++; - inmemend = n; - } - void no_more_push_back() // done pushing --switch to consumption mode - { - if (!paging()) return; - // finish off last block - flushlastblock(); - fflushOrDie (f); - fprintf (stderr, "biggrowablevectorarray: disk backup store created, %d frames, %lu bytes\n", (int) n, fgetpos (f)); - fclose (f); - foreach_index (i, blocks) assert (!blocks[i]); // ensure we flushed - assert (inmembegin == inmemend); // nothing in cache - // switch to reading mode - openpagefile (true); - } - - // access phase - // Returns 'true' if data was actually read from disk. - bool require (pair bounds) // we require this range of frames - { - bool readfromdisk = false; - - // get bounds rounded to block boundaries - const size_t ts = bounds.first / elementsperblock * elementsperblock; - const size_t te = min (n, (bounds.second + elementsperblock -1) / elementsperblock * elementsperblock); - assert (paging()); - // free all the memmory - for (size_t t = inmembegin; t < inmemend; t += elementsperblock) - { - if (t >= ts && t < te) // if in wanted range then skip to end of it - t = te - elementsperblock; - else - releaseblock (t); - } - // page in all required blocks - for (size_t t = ts; t < te; t += elementsperblock) - { - if (t >= inmembegin && t < inmemend) // if in memory already then skip to end of it - t = inmemend - elementsperblock; - else - { - recoverblock (t); - readfromdisk = true; // tell caller we did something expensive - } - } - // got it - inmembegin = ts; - inmemend = te; - return readfromdisk; - } - const msra::dbn::matrixstripe operator[] (size_t t) const // get a feature vector - { - if (t < inmembegin || t >= inmemend) - LogicError("biggrowablevectorarray: attempt to access vector without requesting to page it in first"); - const size_t blockt = getblockt (t); - /*const*/ msra::dbn::matrix & block = getblock (t); - return msra::dbn::matrixstripe (block, blockt, 1); - } - wstring pagepathname(){ return pagepath;} - void cleanuppagefile() - { - if (paging()) { - fclose (f); - if (_wunlink (pagepath.c_str())==0){ - fprintf (stderr, "biggrowablevectorarray: deleted disk backup store at '%ls'\n", pagepath.c_str()); - } - else{ - fprintf (stderr, "biggrowablevectorarray: could NOT delete disk backup store at '%ls'\n", pagepath.c_str()); - } - } - } - }; - - // --------------------------------------------------------------------------- - // minibatchframesource -- feature source to provide randomized frames in minibatches - // This is the old code that pages all frames to a huge disk file first. - // (The new minibatchutterancesource pages from input files directly and can also - // operate in utterance mode for MMI training.) - // --------------------------------------------------------------------------- - class minibatchframesource : public minibatchsource - { - size_t vdim; // feature dimension after augmenting neighhors (0: don't read features) - unsigned int sampperiod; // (for reference and to check against model) - string featkind; - size_t featdim; - // cache - biggrowablevectorarray frames; // [t][i] all features concatenated - std::vector boundaryflags; // [t] -1 for first and +1 for last frame, 0 else (for augmentneighbors()) - std::vector classids; // [t] the state that the frame belongs to - size_t numframes; // total frames (==frames.size()==boundaryflags.size()==classids.size()) unless special modes vdim == 0 and/or no labels - msra::dbn::randomordering randomordering; // [t] -> t' - double timegetbatch; - int verbosity; - public: - // constructor - // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). - minibatchframesource (const std::vector & infiles, const map> & labels, - size_t vdim, size_t udim, size_t randomizationrange, const wstring & pagepath, const bool mayhavenoframe=false, int addEnergy=0) - : vdim (vdim), sampperiod (0), featdim (0), numframes (0), frames (pagepath), timegetbatch (0), verbosity(2) - { - if (vdim == 0 && labels.empty()) - RuntimeError("minibatchframesource: when running without features, labels are needed"); - // at this stage, we simply page in the entire training set at once and work off RAM - // We will benefit from feature archives indirectly through htkfeatio. - // TODO: - // - infiles must specify time range - // - at this stage only reserve() (we know the time range; allocate second-layer structure) - // - implement block-wise paging directly from HTK feature files through htkfeatreader - featkind.clear(); - std::vector frame; - fprintf (stderr, "minibatchframesource: reading %d utterances..", (int)infiles.size()); - size_t numclasses = 0; // number of units found (actually max id +1) - size_t notfound = 0; // number of entries missing in MLF - msra::asr::htkfeatreader reader; // feature reader - reader.AddEnergy(addEnergy); - - foreach_index (i, infiles) - { - if (i % (infiles.size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } - msra::basetypes::matrix feat; - msra::asr::htkfeatreader::parsedpath ppath (infiles[i]); - - // skip files for which labels don't exist (assuming bad alignment) - wstring key; - if (!labels.empty()) // empty means unsupervised mode (don't load any) - { +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// rollingwindowsource.h -- implementation of a rolling-window minibatch source ('minibatchframesource') with a disk page file +// + +#pragma once + +#include "Basics.h" // for attempt() +#ifdef _WIN32 +#include "numahelpers.h" // for NUMA allocation +#endif +#include "minibatchsourcehelpers.h" +#include "minibatchiterator.h" +#include "biggrowablevectors.h" +#include "ssematrix.h" + +namespace msra { namespace dbn { + + // --------------------------------------------------------------------------- + // biggrowablevectorarray -- a big array of vectors for features, growable (push_back) + // Data is striped across NUMA nodes, as to not clog them up. + // This also supports paging to disk, which is used for the old minibatchframesource. + // --------------------------------------------------------------------------- + class biggrowablevectorarray : public growablevectorbase + { + size_t m; // dim + + size_t inmembegin; // range we have in memory, rounded to enclosing blocks (not rounded at end) + size_t inmemend; + + wstring pagepath; // path for paging, empty if no paging + auto_file_ptr f; // file handle for paging + bool reading; // have we begun reading? + + // allocate a block + msra::dbn::matrix * newblock() const + { + // we stripe the data across NUMA nodes as to not fill up one node with the feature data +#ifdef _WIN32 + msra::numa::overridenode ((int) msra::numa::getmostspaciousnumanode()); +#endif + msra::dbn::matrix * res = new msra::dbn::matrix (m, elementsperblock); +#ifdef _WIN32 + msra::numa::overridenode (-1); // note: we really should reset it also in case of failure +#endif + return res; + } + + // handling of page file + bool paging() const { return !pagepath.empty(); } + void openpagefile (bool wantread) + { + if (!paging()) return; + msra::files::make_intermediate_dirs (pagepath); + + if (!wantread) + { + FILE *ftry = NULL; + wstring pathname (pagepath); + ftry = _wfopen (pathname.c_str(), L"wbS"); + if (ftry) fclose (ftry); + } + + /* + code below to cycle through a-z appended to file name is no longer necessary + since caller guarantees unique file names via HTKMLFReader + and we want the pagepath logged to the user to be the actual one used by the code + + // try to open the pagepath from a to z + if (!wantread) + { + FILE *ftry = NULL; + char trynum = 'a'; + while (!ftry && trynum <= 'z') + { + wstring pathname (pagepath); + pathname += trynum++; + ftry = _wfopen (pathname.c_str(), L"wbS"); + } + if (ftry) fclose (ftry); + pagepath += --trynum; + } + */ + f = fopenOrDie (pagepath, wantread ? L"rbS" : L"wbS"); + reading = wantread; + } + void flushlastblock() // during population phase, must be called once per block in sequence + { + if (!paging()) return; + assert (!reading); + if (blocks.empty()) return; + const size_t blockid = blocks.size() -1; + msra::dbn::matrix & block = *blocks[blockid]; + assert (fgetpos (f) == blockid * block.sizeinpagefile()); + block.topagefile (f); + blocks[blockid].reset(); // free the memory + assert (blockid * elementsperblock == inmembegin); + inmembegin = inmemend; // empty range + } + void releaseblock (size_t t0) // t0=block start time + { + assert (paging() && reading); + size_t blockid = t0 / elementsperblock; + assert (blockid * elementsperblock == t0); + assert (blocks[blockid]); + fprintf (stderr, "recoverblock: releasing feature block %d [%d..%d)\n", (int)blockid, (int)t0, (int)(t0 + elementsperblock -1)); + blocks[blockid].reset(); // free the memory + } + void recoverblock (size_t t0) // t0=block start time + { + assert (paging() && reading); + size_t blockid = t0 / elementsperblock; + assert (blockid * elementsperblock == t0); + assert (!blocks[blockid]); + fprintf (stderr, "recoverblock: recovering feature block %d [%d..%d)\n", (int)blockid, (int)t0, (int)(t0 + elementsperblock -1)); + blocks[blockid].reset (newblock()); + msra::dbn::matrix & block = *blocks[blockid]; + fsetpos (f, blockid * block.sizeinpagefile()); + block.frompagefile (f); + } + + public: + biggrowablevectorarray (const wstring & pagepath) + : growablevectorbase (65536), m (0), + inmembegin (0), inmemend (0), pagepath (pagepath), reading (false) + { + openpagefile (false); + if (paging()) + fprintf (stderr, "biggrowablevectorarray: creating disk backup store at '%ls'\n", pagepath.c_str()); + } + ~biggrowablevectorarray() { // clean up the big temp file + if (paging()) { + fclose (f); + if (_wunlink (pagepath.c_str())==0) + fprintf (stderr, "biggrowablevectorarray: deleted disk backup store at '%ls'\n", pagepath.c_str()); + else + fprintf (stderr, "biggrowablevectorarray: unable to delete disk backup store at '%ls'\n", pagepath.c_str()); + } + } + + size_t dim() const { return m; } // dimension of a frame + + // reading phase + void push_back (const std::vector & in) + { + assert (!in.empty()); + assert (m == 0 || m == in.size()); + m = in.size(); + const size_t blockid = n / elementsperblock; + assert (blockid <= blocks.size()); + if (blockid == blocks.size()) // a new block is needed + { + flushlastblock(); + blocks.push_back (std::unique_ptr (newblock())); + } + const size_t blockn = n % elementsperblock; + msra::dbn::matrix & block = *blocks[blockid].get(); + foreach_index (k, in) + block(k,blockn) = in[k]; + n++; + inmemend = n; + } + void no_more_push_back() // done pushing --switch to consumption mode + { + if (!paging()) return; + // finish off last block + flushlastblock(); + fflushOrDie (f); + fprintf (stderr, "biggrowablevectorarray: disk backup store created, %d frames, %lu bytes\n", (int) n, fgetpos (f)); + fclose (f); + foreach_index (i, blocks) assert (!blocks[i]); // ensure we flushed + assert (inmembegin == inmemend); // nothing in cache + // switch to reading mode + openpagefile (true); + } + + // access phase + // Returns 'true' if data was actually read from disk. + bool require (pair bounds) // we require this range of frames + { + bool readfromdisk = false; + + // get bounds rounded to block boundaries + const size_t ts = bounds.first / elementsperblock * elementsperblock; + const size_t te = min (n, (bounds.second + elementsperblock -1) / elementsperblock * elementsperblock); + assert (paging()); + // free all the memmory + for (size_t t = inmembegin; t < inmemend; t += elementsperblock) + { + if (t >= ts && t < te) // if in wanted range then skip to end of it + t = te - elementsperblock; + else + releaseblock (t); + } + // page in all required blocks + for (size_t t = ts; t < te; t += elementsperblock) + { + if (t >= inmembegin && t < inmemend) // if in memory already then skip to end of it + t = inmemend - elementsperblock; + else + { + recoverblock (t); + readfromdisk = true; // tell caller we did something expensive + } + } + // got it + inmembegin = ts; + inmemend = te; + return readfromdisk; + } + const msra::dbn::matrixstripe operator[] (size_t t) const // get a feature vector + { + if (t < inmembegin || t >= inmemend) + LogicError("biggrowablevectorarray: attempt to access vector without requesting to page it in first"); + const size_t blockt = getblockt (t); + /*const*/ msra::dbn::matrix & block = getblock (t); + return msra::dbn::matrixstripe (block, blockt, 1); + } + wstring pagepathname(){ return pagepath;} + void cleanuppagefile() + { + if (paging()) { + fclose (f); + if (_wunlink (pagepath.c_str())==0){ + fprintf (stderr, "biggrowablevectorarray: deleted disk backup store at '%ls'\n", pagepath.c_str()); + } + else{ + fprintf (stderr, "biggrowablevectorarray: could NOT delete disk backup store at '%ls'\n", pagepath.c_str()); + } + } + } + }; + + // --------------------------------------------------------------------------- + // minibatchframesource -- feature source to provide randomized frames in minibatches + // This is the old code that pages all frames to a huge disk file first. + // (The new minibatchutterancesource pages from input files directly and can also + // operate in utterance mode for MMI training.) + // --------------------------------------------------------------------------- + class minibatchframesource : public minibatchsource + { + size_t vdim; // feature dimension after augmenting neighhors (0: don't read features) + unsigned int sampperiod; // (for reference and to check against model) + string featkind; + size_t featdim; + // cache + biggrowablevectorarray frames; // [t][i] all features concatenated + std::vector boundaryflags; // [t] -1 for first and +1 for last frame, 0 else (for augmentneighbors()) + std::vector classids; // [t] the state that the frame belongs to + size_t numframes; // total frames (==frames.size()==boundaryflags.size()==classids.size()) unless special modes vdim == 0 and/or no labels + msra::dbn::randomordering randomordering; // [t] -> t' + double timegetbatch; + int verbosity; + public: + // constructor + // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). + minibatchframesource (const std::vector & infiles, const map> & labels, + size_t vdim, size_t udim, size_t randomizationrange, const wstring & pagepath, const bool mayhavenoframe=false, int addEnergy=0) + : vdim (vdim), sampperiod (0), featdim (0), numframes (0), frames (pagepath), timegetbatch (0), verbosity(2) + { + if (vdim == 0 && labels.empty()) + RuntimeError("minibatchframesource: when running without features, labels are needed"); + // at this stage, we simply page in the entire training set at once and work off RAM + // We will benefit from feature archives indirectly through htkfeatio. + // TODO: + // - infiles must specify time range + // - at this stage only reserve() (we know the time range; allocate second-layer structure) + // - implement block-wise paging directly from HTK feature files through htkfeatreader + featkind.clear(); + std::vector frame; + fprintf (stderr, "minibatchframesource: reading %d utterances..", (int)infiles.size()); + size_t numclasses = 0; // number of units found (actually max id +1) + size_t notfound = 0; // number of entries missing in MLF + msra::asr::htkfeatreader reader; // feature reader + reader.AddEnergy(addEnergy); + + foreach_index (i, infiles) + { + if (i % (infiles.size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } + msra::basetypes::matrix feat; + msra::asr::htkfeatreader::parsedpath ppath (infiles[i]); + + // skip files for which labels don't exist (assuming bad alignment) + wstring key; + if (!labels.empty()) // empty means unsupervised mode (don't load any) + { #ifdef _MSC_VER - key = regex_replace((wstring)ppath, wregex(L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) -#else - key = removeExtension(basename(ppath)); -#endif - if (labels.find (key) == labels.end()) - { - if (notfound < 5) - fprintf (stderr, "\nminibatchframesource: %d-th file not found in MLF label set: %ls", i, key.c_str()); - notfound++; - continue; // skip this utterance at all - } - } - - // get feature frames - if (vdim != 0) // (vdim == special mode to not read features at all) - { - msra::util::attempt (5, [&]() - { - reader.read (ppath, featkind, sampperiod, feat); // whole file read as columns of feature vectors - }); - if (featdim == 0) // first time - featdim = feat.rows(); - else if (featdim != feat.rows()) - RuntimeError("minibatchframesource: inconsistent feature dimension across files"); - // HVite occasionally generates mismatching output --skip such files - if (!key.empty()) // (we have a key if supervised mode) - { - const auto & labseq = labels.find (key)->second; // (we already checked above that it exists) - size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); - if (abs ((int) labframes - (int) feat.cols()) > 0) - { - fprintf (stderr, "\nminibatchframesource: %d-th file has small duration mismatch (%d in label vs. %d in feat file), skipping: %ls", i, (int)labframes, (int)feat.cols(), key.c_str()); - notfound++; - continue; // skip this utterance at all - } - } - // append to cache - frame.resize (featdim); - if (feat.cols() < 2) // (2 frames needed for boundary markers) - RuntimeError("minibatchframesource: utterances < 2 frames not supported"); - foreach_column (t, feat) - { - foreach_index (k, frame) - frame[k] = feat(k,t); - frames.push_back (frame); - numframes++; - boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); - } - assert (numframes == frames.size()); - assert (numframes == boundaryflags.size()); - } - - // get label sequence - if (!key.empty()) // (we have a key if supervised mode) - { - const auto & labseq = labels.find (key)->second; // (we already checked above that it exists) - foreach_index (i, labseq) - { - const auto & e = labseq[i]; - if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) - RuntimeError("minibatchframesource: labels not in consecutive order MLF in label set: %ls", key.c_str()); - for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) - { - if (e.classid >= udim) - RuntimeError("minibatchframesource: class id exceeds model dimension in file %ls", key.c_str()); - if (e.classid != (CLASSIDTYPE) e.classid) - RuntimeError("CLASSIDTYPE has too few bits"); - classids.push_back ((CLASSIDTYPE) e.classid); - numclasses = max (numclasses, (size_t)(1u + e.classid)); - } - } - if (vdim == 0) - numframes = classids.size(); - if (numframes != classids.size()) // TODO: remove this once we are confident - RuntimeError("minibatchframesource: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); - assert (numframes == classids.size()); - } - else - { - assert (classids.empty()); // that's how we detect it later - } - } - assert (vdim == 0 || numframes == frames.size()); - assert (labels.empty() || numframes == classids.size()); - if ((vdim != 0 && numframes != frames.size()) || (!labels.empty() && numframes != classids.size())) - RuntimeError("minibatchframesource: numframes variable screwup"); - fprintf (stderr, " %d frames read from %d utterances; %d classes\n", (int)numframes, (int)infiles.size(), (int)numclasses); - if (notfound > 0) - { - fprintf (stderr, "minibatchframesource: %d files out of %d not found in label set\n", (int)notfound, (int)infiles.size()); - if (notfound > infiles.size() / 2) - RuntimeError("minibatchframesource: too many files not found in label set--assuming broken configuration\n"); - } - - if (numframes == 0 && !mayhavenoframe) - RuntimeError("minibatchframesource: no input features given!"); - - // notify frames source to switch from population to consumption mode - frames.no_more_push_back(); - - // initialize randomizer - if (numframes > 0) - randomordering.resize (numframes, randomizationrange); - } - virtual ~minibatchframesource() {} - size_t totalframes() const { assert (vdim == 0 || numframes == frames.size()); assert (!issupervised() || numframes == classids.size()); return numframes; } - - bool issupervised() const { return !classids.empty(); } - - void setverbosity(int newverbosity) { verbosity = newverbosity; } - - // retrieve one minibatch - // Minibatches are deterministic pseudo-random samples. The entire corpus - // is repeated infinitely, but each repetition (a 'sweep') is randomized - // differently. - // This function allows to retrieve a mini-batch starting from any frame - // within this infinitely extended repetition. To the end, mini-batches are - // specified by start frame and #frames. - // This function returns the same data independent on #frames, i.e. the concept - // of the mini-batch is not defined in here, but on the caller side. The caller - // can retrieve the frames of a mini-batch in chunks that do not match the - // caller's definition of "mini-batch," e.g. bigger or smaller chunks. - // If a requested mini-batch spans a sweep boundary, then this function will - // not return samples after the sweep boundary. Instead, the returned frame - // set is shortened to not exceed the end of the sweep. The caller must make - // a separate second call to get the rest. In trainlayer(), the one - // sweep-boundary-spanning mini-batch will simply be shortened. - // This function is NOT thread-safe (due to caching of random sequence). - bool getbatch (const size_t globalts, const size_t framesrequested, msra::dbn::matrix & feat, std::vector & uids, - std::vector> & transcripts, - std::vector> & latticepairs) - { - auto_timer timergetbatch; - - transcripts.clear(); // word-level transcripts not supported by frame source (aimed at MMI) - latticepairs.clear(); // neither are lattices - - assert (totalframes() > 0); - const size_t sweep = globalts / totalframes(); // which sweep (this determines randomization) - const size_t ts = globalts % totalframes(); // start frame within the sweep - const size_t te = min (ts + framesrequested, totalframes()); // do not go beyond sweep boundary - assert (te > ts); - if (verbosity >= 2) - fprintf (stderr, "getbatch: frames [%d..%d] in sweep %d\n", (int)ts, (int)(te-1), (int)sweep); - - // get random sequence (each time index occurs exactly once) - // If the sweep changes, this will re-cache the sequence. We optimize for rare, monotonous sweep changes. - const auto & tmap = randomordering (sweep); - - // page in the needed range of frames - const size_t extent = augmentationextent (frames.dim(), vdim); - bool readfromdisk = frames.require (randomordering.bounds (max (ts, extent) - extent, te + 1 + extent)); - - // generate features and uids - feat.resize (vdim, te - ts); // note: special mode vdim == 0 means no features to be loaded - if (issupervised()) // empty means unsupervised training -> return empty uids - uids.resize (te - ts); - else - uids.clear(); - for (size_t t = ts; t < te; t++) - { - size_t trand = tmap[t]; // the random-sequence sample point for this point in time - if (vdim != 0) - { - auto v_t = feat.col(t-ts); // the vector to fill in - augmentneighbors (frames, boundaryflags, trand, v_t); - } - if (issupervised()) - uids[t-ts] = classids[trand]; - } - timegetbatch = timergetbatch; - return readfromdisk; - } - - bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, std::vector> & uids, - std::vector> & transcripts, - std::vector> & latticepairs, std::vector> & sentendmark, - std::vector> & phoneboundaries) - { - // for single input/output set size to be 1 and run old getbatch - feat.resize(1); - uids.resize(1); - //transcripts.resize(1); - //latticepairs.resize(1); - sentendmark.resize(1); - phoneboundaries.resize(1); - return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); - } - - double gettimegetbatch () { return timegetbatch;} - - // return first valid globalts to ask getbatch() for - // In frame mode, there is no constraint, i.e. it is 'globalts' itself. - /*implement*/ size_t firstvalidglobalts (const size_t globalts) { return globalts; } - - /*implement*/ const std::vector & unitcounts() const { LogicError("unitcounts: not implemented for this feature source"); static std::vector x; return x;/*keep compiler happy*/ } - }; - - // --------------------------------------------------------------------------- - // minibatchframesourcemulti -- feature source to provide randomized frames in minibatches - // this is derived from minibatchframesource but worked with multiple inputs and/or outputs - // by making "frames" and "classids" a vector of vectors - // --------------------------------------------------------------------------- - class minibatchframesourcemulti : public minibatchsource - { - std::vector vdim; // feature dimension after augmenting neighhors (0: don't read features) - std::vector leftcontext; // number of frames to the left of the target frame in the context window - std::vector rightcontext; // number of frames to the right of the target frame in the context window - unsigned int sampperiod; // (for reference and to check against model) - string featkind; - size_t featdim; - size_t maxvdim; - // cache - //std::vector frames; - std::vector> pframes; // [t][i] all features concatenated - std::vector boundaryflags; // [t] -1 for first and +1 for last frame, 0 else (for augmentneighbors()) - std::vector> classids; // [t] the state that the frame belongs to - size_t numframes; // total frames (==frames.size()==boundaryflags.size()==classids.size()) unless special modes vdim == 0 and/or no labels - msra::dbn::randomordering randomordering; // [t] -> t' - double timegetbatch; - int verbosity; - - public: - // constructor - // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). - minibatchframesourcemulti (const std::vector> & infiles, const std::vector>> & labels, - std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, const std::vector & pagepath, const bool mayhavenoframe=false, int addEnergy=0) - : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), numframes (0), timegetbatch (0), verbosity(2), maxvdim(0) - { - - if (vdim[0] == 0 && labels.empty()) - RuntimeError("minibatchframesourcemulti: when running without features, labels are needed"); - // at this stage, we simply page in the entire training set at once and work off RAM - // We will benefit from feature archives indirectly through htkfeatio. - // TODO: - // - infiles must specify time range - // - at this stage only reserve() (we know the time range; allocate second-layer structure) - // - implement block-wise paging directly from HTK feature files through htkfeatreader - featkind.clear(); - std::vector frame; - std::vectornumclasses; // number of units found (actually max id +1) - size_t notfound = 0; // number of entries missing in MLF - - - std::vectorframesaccum; - - if (infiles.size()==0) - RuntimeError("minibatchframesourcemulti: need at least one network input specified with features"); - - if (labels.size()==0) - fprintf(stderr,"no MLF label files detected\n"); - - foreach_index (i, infiles) - { - pframes.push_back(unique_ptr(new biggrowablevectorarray(pagepath[i]))); - - if (vdim[i]>maxvdim) - maxvdim=vdim[i]; - } - - - foreach_index (i, labels) - { - classids.push_back(std::vector()); - numclasses.push_back(0); - } - - - fprintf (stderr, "minibatchframesourcemulti: reading %d feature sets and %d label sets...", (int)infiles.size(), (int)labels.size()); - - foreach_index (m, infiles) - { - - - featdim=0; - numframes=0; - featkind.clear(); - msra::asr::htkfeatreader reader; // feature reader - reader.AddEnergy(addEnergy); - - foreach_index (i, infiles[m]) // read each feature file in set m - { - if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } - msra::basetypes::matrix feat; - msra::asr::htkfeatreader::parsedpath ppath (infiles[m][i]); - - // skip files for which labels don't exist (assuming bad alignment) - wstring key; - if (!labels.empty()) - { - if (!labels[0].empty()) // empty means unsupervised mode (don't load any) - { -#ifdef _WIN32 - key = regex_replace ((wstring)ppath, wregex (L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) -#endif -#ifdef __unix__ - key = removeExtension(basename(ppath)); -#endif - if (labels[0].find (key) == labels[0].end()) - { - if (notfound < 5) - fprintf (stderr, "\nminibatchframesourcemulti: %d-th file not found in MLF label set: %ls", i, key.c_str()); - notfound++; - continue; // skip this utterance at all - } - } - } - // get feature frames - if (vdim[m] != 0) // (vdim == special mode to not read features at all) - { - msra::util::attempt (5, [&]() - { - reader.read (ppath, featkind, sampperiod, feat); // whole file read as columns of feature vectors - }); - if (featdim == 0) // first time - featdim = feat.rows(); - else if (featdim != feat.rows()) - RuntimeError("minibatchframesourcemulti: inconsistent feature dimension across files"); - // HVite occasionally generates mismatching output --skip such files - if (!key.empty()) // (we have a key if supervised mode) - { - const auto & labseq = labels[0].find (key)->second; // (we already checked above that it exists) - size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); - if (abs ((int) labframes - (int) feat.cols()) > 0) - { - fprintf (stderr, "\nminibatchframesourcemulti: %d-th file has small duration mismatch (%d in label vs. %d in feat file), skipping: %ls", i, (int)labframes, (int)feat.cols(), key.c_str()); - notfound++; - continue; // skip this utterance at all - } - } - // append to cache - frame.resize (featdim); - if (feat.cols() < 2) // (2 frames needed for boundary markers) - RuntimeError("minibatchframesourcemulti: utterances < 2 frames not supported"); - foreach_column (t, feat) - { - foreach_index (k, frame) - frame[k] = feat(k,t); - - pframes[m]->push_back (frame); - numframes++; - if (m==0) - boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); - } - if (m==0) - framesaccum.push_back(numframes); - else - assert(numframes == framesaccum[i]); - - assert (numframes == pframes[m]->size()); - } - if (m==0) - assert (numframes == boundaryflags.size()); - - - - if (m==0) // after we get the key for this file, read all labels (only done for first feature) - { - if (!key.empty()) - { - foreach_index (j, labels) - { - const auto & labseq = labels[j].find (key)->second; // (we already checked above that it exists) - foreach_index (i, labseq) - { - const auto & e = labseq[i]; - if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) - RuntimeError("minibatchframesourcemulti: labels not in consecutive order MLF in label set: %ls", key.c_str()); - for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) - { - if (e.classid >= udim[j]) - RuntimeError("minibatchframesourcemulti: class id exceeds model dimension in file %ls", key.c_str()); - if (e.classid != (CLASSIDTYPE) e.classid) - RuntimeError("CLASSIDTYPE has too few bits"); - classids[j].push_back ((CLASSIDTYPE) e.classid); - numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); - } - } - if (vdim[m] == 0) - numframes = classids[j].size(); - if (numframes != classids[j].size()) // TODO: remove this once we are confident - RuntimeError("minibatchframesourcemulti: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); - assert (numframes == classids[j].size()); - - } - } - else - { - assert(classids.empty()); - } - - } - - } - - - assert (vdim[m] == 0 || numframes == pframes[m]->size()); - - foreach_index(j, labels) - assert (labels[j].empty() || numframes == classids[j].size()); - - if (vdim[m] != 0 && numframes != pframes[m]->size()) // || (!labels.empty() && numframes != classids.size())) - RuntimeError("\nminibatchframesource: numframes variable screwup"); - if (m==0) - { - foreach_index (j, numclasses) - fprintf (stderr, "\nminibatchframesourcemulti: read label set %d: %d classes\n", j, (int)numclasses[j]); - } - fprintf (stderr, "\nminibatchframesourcemulti: feature set %d: %d frames read from %d utterances\n", m, (int)pframes[m]->size(), (int)infiles[m].size()); - if (notfound > 0) - { - fprintf (stderr, "minibatchframesourcemulti: %d files out of %d not found in label set\n", (int)notfound, (int)infiles[m].size()); - if (notfound > infiles[m].size() / 2) - RuntimeError("minibatchframesourcemulti: too many files not found in label set--assuming broken configuration\n"); - } - // notify frames source to switch from population to consumption mode - pframes[m]->no_more_push_back(); - - } - - if (numframes == 0 && !mayhavenoframe) - RuntimeError("minibatchframesource: no input features given!"); - - - // initialize randomizer - if (numframes > 0) - randomordering.resize (numframes, randomizationrange); - - } - virtual ~minibatchframesourcemulti() {} - size_t totalframes() const { - assert (maxvdim == 0 || numframes == pframes[0]->size()); assert (!issupervised() || numframes == classids[0].size()); return numframes; } - - bool issupervised() const { return !classids.empty(); } - - void setverbosity(int newverbosity) { verbosity = newverbosity; } - - // retrieve one minibatch - // Minibatches are deterministic pseudo-random samples. The entire corpus - // is repeated infinitely, but each repetition (a 'sweep') is randomized - // differently. - // This function allows to retrieve a mini-batch starting from any frame - // within this infinitely extended repetition. To the end, mini-batches are - // specified by start frame and #frames. - // This function returns the same data independent on #frames, i.e. the concept - // of the mini-batch is not defined in here, but on the caller side. The caller - // can retrieve the frames of a mini-batch in chunks that do not match the - // caller's definition of "mini-batch," e.g. bigger or smaller chunks. - // If a requested mini-batch spans a sweep boundary, then this function will - // not return samples after the sweep boundary. Instead, the returned frame - // set is shortened to not exceed the end of the sweep. The caller must make - // a separate second call to get the rest. In trainlayer(), the one - // sweep-boundary-spanning mini-batch will simply be shortened. - // This function is NOT thread-safe (due to caching of random sequence). - bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, std::vector> & uids, - std::vector> & transcripts, - std::vector> & latticepairs, std::vector> & sentendmark, - std::vector> & phoneboundaries) - { - - auto_timer timergetbatch; - bool readfromdisk; - size_t nreadfromdisk=0; - transcripts.clear(); // word-level transcripts not supported by frame source (aimed at MMI) - latticepairs.clear(); // neither are lattices - - assert (totalframes() > 0); - const size_t sweep = globalts / totalframes(); // which sweep (this determines randomization) - const size_t ts = globalts % totalframes(); // start frame within the sweep - const size_t te = min (ts + framesrequested, totalframes()); // do not go beyond sweep boundary - assert (te > ts); - if (verbosity >= 2) - fprintf (stderr, "getbatch: frames [%d..%d] in sweep %d\n", (int)ts, (int)(te-1), (int)sweep); - - // get random sequence (each time index occurs exactly once) - // If the sweep changes, this will re-cache the sequence. We optimize for rare, monotonous sweep changes. - const auto & tmap = randomordering (sweep); - - feat.resize(pframes.size()); - uids.resize(classids.size()); - sentendmark.resize(classids.size()); - phoneboundaries.resize(classids.size()); - foreach_index(i, feat) - { - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(pframes[i]->dim(), vdim[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - readfromdisk = pframes[i]->require (randomordering.bounds (max (ts, leftextent) - leftextent, te + 1 + rightextent)); - // generate features and uids - feat[i].resize (vdim[i], te - ts); // note: special mode vdim == 0 means no features to be loaded - if (issupervised()) // empty means unsupervised training -> return empty uids - foreach_index(j, uids) - uids[j].resize (te - ts); - else - uids.clear(); - - for (size_t t = ts; t < te; t++) - { - size_t trand = tmap[t]; // the random-sequence sample point for this point in time - if (vdim[i] != 0) - { - auto v_t = feat[i].col(t-ts); // the vector to fill in - augmentneighbors (*pframes[i], boundaryflags, trand, leftextent, rightextent, v_t); - } - if (i==0){ // read labels for all outputs on first pass thru features. this guarantees they will be read if only one feature set but > 1 label set - if (issupervised()) - foreach_index(j, uids) - uids[j][t-ts] = classids[j][trand]; - } - } - timegetbatch = timergetbatch; - if (readfromdisk) - nreadfromdisk++; - - } - - (nreadfromdisk==feat.size()) ? readfromdisk = true : readfromdisk = false; - - return readfromdisk; - - } - - bool getbatch (const size_t /*globalts*/, const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, std::vector & /*uids*/, - std::vector> & /*transcripts*/, - std::vector> & /*latticepairs*/) - { - // should never get here - RuntimeError("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchframesource instead\n"); - } - - double gettimegetbatch () { return timegetbatch;} - - // return first valid globalts to ask getbatch() for - // In frame mode, there is no constraint, i.e. it is 'globalts' itself. - /*implement*/ size_t firstvalidglobalts (const size_t globalts) { return globalts; } - - /*implement*/ const std::vector & unitcounts() const { LogicError("unitcounts: not implemented for this feature source"); } - - }; -};}; + key = regex_replace((wstring)ppath, wregex(L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) +#else + key = removeExtension(basename(ppath)); +#endif + if (labels.find (key) == labels.end()) + { + if (notfound < 5) + fprintf (stderr, "\nminibatchframesource: %d-th file not found in MLF label set: %ls", i, key.c_str()); + notfound++; + continue; // skip this utterance at all + } + } + + // get feature frames + if (vdim != 0) // (vdim == special mode to not read features at all) + { + msra::util::attempt (5, [&]() + { + reader.read (ppath, featkind, sampperiod, feat); // whole file read as columns of feature vectors + }); + if (featdim == 0) // first time + featdim = feat.rows(); + else if (featdim != feat.rows()) + RuntimeError("minibatchframesource: inconsistent feature dimension across files"); + // HVite occasionally generates mismatching output --skip such files + if (!key.empty()) // (we have a key if supervised mode) + { + const auto & labseq = labels.find (key)->second; // (we already checked above that it exists) + size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); + if (abs ((int) labframes - (int) feat.cols()) > 0) + { + fprintf (stderr, "\nminibatchframesource: %d-th file has small duration mismatch (%d in label vs. %d in feat file), skipping: %ls", i, (int)labframes, (int)feat.cols(), key.c_str()); + notfound++; + continue; // skip this utterance at all + } + } + // append to cache + frame.resize (featdim); + if (feat.cols() < 2) // (2 frames needed for boundary markers) + RuntimeError("minibatchframesource: utterances < 2 frames not supported"); + foreach_column (t, feat) + { + foreach_index (k, frame) + frame[k] = feat(k,t); + frames.push_back (frame); + numframes++; + boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); + } + assert (numframes == frames.size()); + assert (numframes == boundaryflags.size()); + } + + // get label sequence + if (!key.empty()) // (we have a key if supervised mode) + { + const auto & labseq = labels.find (key)->second; // (we already checked above that it exists) + foreach_index (i, labseq) + { + const auto & e = labseq[i]; + if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) + RuntimeError("minibatchframesource: labels not in consecutive order MLF in label set: %ls", key.c_str()); + for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) + { + if (e.classid >= udim) + RuntimeError("minibatchframesource: class id exceeds model dimension in file %ls", key.c_str()); + if (e.classid != (CLASSIDTYPE) e.classid) + RuntimeError("CLASSIDTYPE has too few bits"); + classids.push_back ((CLASSIDTYPE) e.classid); + numclasses = max (numclasses, (size_t)(1u + e.classid)); + } + } + if (vdim == 0) + numframes = classids.size(); + if (numframes != classids.size()) // TODO: remove this once we are confident + RuntimeError("minibatchframesource: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); + assert (numframes == classids.size()); + } + else + { + assert (classids.empty()); // that's how we detect it later + } + } + assert (vdim == 0 || numframes == frames.size()); + assert (labels.empty() || numframes == classids.size()); + if ((vdim != 0 && numframes != frames.size()) || (!labels.empty() && numframes != classids.size())) + RuntimeError("minibatchframesource: numframes variable screwup"); + fprintf (stderr, " %d frames read from %d utterances; %d classes\n", (int)numframes, (int)infiles.size(), (int)numclasses); + if (notfound > 0) + { + fprintf (stderr, "minibatchframesource: %d files out of %d not found in label set\n", (int)notfound, (int)infiles.size()); + if (notfound > infiles.size() / 2) + RuntimeError("minibatchframesource: too many files not found in label set--assuming broken configuration\n"); + } + + if (numframes == 0 && !mayhavenoframe) + RuntimeError("minibatchframesource: no input features given!"); + + // notify frames source to switch from population to consumption mode + frames.no_more_push_back(); + + // initialize randomizer + if (numframes > 0) + randomordering.resize (numframes, randomizationrange); + } + virtual ~minibatchframesource() {} + size_t totalframes() const { assert (vdim == 0 || numframes == frames.size()); assert (!issupervised() || numframes == classids.size()); return numframes; } + + bool issupervised() const { return !classids.empty(); } + + void setverbosity(int newverbosity) { verbosity = newverbosity; } + + // retrieve one minibatch + // Minibatches are deterministic pseudo-random samples. The entire corpus + // is repeated infinitely, but each repetition (a 'sweep') is randomized + // differently. + // This function allows to retrieve a mini-batch starting from any frame + // within this infinitely extended repetition. To the end, mini-batches are + // specified by start frame and #frames. + // This function returns the same data independent on #frames, i.e. the concept + // of the mini-batch is not defined in here, but on the caller side. The caller + // can retrieve the frames of a mini-batch in chunks that do not match the + // caller's definition of "mini-batch," e.g. bigger or smaller chunks. + // If a requested mini-batch spans a sweep boundary, then this function will + // not return samples after the sweep boundary. Instead, the returned frame + // set is shortened to not exceed the end of the sweep. The caller must make + // a separate second call to get the rest. In trainlayer(), the one + // sweep-boundary-spanning mini-batch will simply be shortened. + // This function is NOT thread-safe (due to caching of random sequence). + bool getbatch (const size_t globalts, const size_t framesrequested, msra::dbn::matrix & feat, std::vector & uids, + std::vector> & transcripts, + std::vector> & latticepairs) + { + auto_timer timergetbatch; + + transcripts.clear(); // word-level transcripts not supported by frame source (aimed at MMI) + latticepairs.clear(); // neither are lattices + + assert (totalframes() > 0); + const size_t sweep = globalts / totalframes(); // which sweep (this determines randomization) + const size_t ts = globalts % totalframes(); // start frame within the sweep + const size_t te = min (ts + framesrequested, totalframes()); // do not go beyond sweep boundary + assert (te > ts); + if (verbosity >= 2) + fprintf (stderr, "getbatch: frames [%d..%d] in sweep %d\n", (int)ts, (int)(te-1), (int)sweep); + + // get random sequence (each time index occurs exactly once) + // If the sweep changes, this will re-cache the sequence. We optimize for rare, monotonous sweep changes. + const auto & tmap = randomordering (sweep); + + // page in the needed range of frames + const size_t extent = augmentationextent (frames.dim(), vdim); + bool readfromdisk = frames.require (randomordering.bounds (max (ts, extent) - extent, te + 1 + extent)); + + // generate features and uids + feat.resize (vdim, te - ts); // note: special mode vdim == 0 means no features to be loaded + if (issupervised()) // empty means unsupervised training -> return empty uids + uids.resize (te - ts); + else + uids.clear(); + for (size_t t = ts; t < te; t++) + { + size_t trand = tmap[t]; // the random-sequence sample point for this point in time + if (vdim != 0) + { + auto v_t = feat.col(t-ts); // the vector to fill in + augmentneighbors (frames, boundaryflags, trand, v_t); + } + if (issupervised()) + uids[t-ts] = classids[trand]; + } + timegetbatch = timergetbatch; + return readfromdisk; + } + + bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, std::vector> & uids, + std::vector> & transcripts, + std::vector> & latticepairs, std::vector> & sentendmark, + std::vector> & phoneboundaries) + { + // for single input/output set size to be 1 and run old getbatch + feat.resize(1); + uids.resize(1); + //transcripts.resize(1); + //latticepairs.resize(1); + sentendmark.resize(1); + phoneboundaries.resize(1); + return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); + } + + double gettimegetbatch () { return timegetbatch;} + + // return first valid globalts to ask getbatch() for + // In frame mode, there is no constraint, i.e. it is 'globalts' itself. + /*implement*/ size_t firstvalidglobalts (const size_t globalts) { return globalts; } + + /*implement*/ const std::vector & unitcounts() const { LogicError("unitcounts: not implemented for this feature source"); static std::vector x; return x;/*keep compiler happy*/ } + }; + + // --------------------------------------------------------------------------- + // minibatchframesourcemulti -- feature source to provide randomized frames in minibatches + // this is derived from minibatchframesource but worked with multiple inputs and/or outputs + // by making "frames" and "classids" a vector of vectors + // --------------------------------------------------------------------------- + class minibatchframesourcemulti : public minibatchsource + { + std::vector vdim; // feature dimension after augmenting neighhors (0: don't read features) + std::vector leftcontext; // number of frames to the left of the target frame in the context window + std::vector rightcontext; // number of frames to the right of the target frame in the context window + unsigned int sampperiod; // (for reference and to check against model) + string featkind; + size_t featdim; + size_t maxvdim; + // cache + //std::vector frames; + std::vector> pframes; // [t][i] all features concatenated + std::vector boundaryflags; // [t] -1 for first and +1 for last frame, 0 else (for augmentneighbors()) + std::vector> classids; // [t] the state that the frame belongs to + size_t numframes; // total frames (==frames.size()==boundaryflags.size()==classids.size()) unless special modes vdim == 0 and/or no labels + msra::dbn::randomordering randomordering; // [t] -> t' + double timegetbatch; + int verbosity; + + public: + // constructor + // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). + minibatchframesourcemulti (const std::vector> & infiles, const std::vector>> & labels, + std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, const std::vector & pagepath, const bool mayhavenoframe=false, int addEnergy=0) + : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), numframes (0), timegetbatch (0), verbosity(2), maxvdim(0) + { + + if (vdim[0] == 0 && labels.empty()) + RuntimeError("minibatchframesourcemulti: when running without features, labels are needed"); + // at this stage, we simply page in the entire training set at once and work off RAM + // We will benefit from feature archives indirectly through htkfeatio. + // TODO: + // - infiles must specify time range + // - at this stage only reserve() (we know the time range; allocate second-layer structure) + // - implement block-wise paging directly from HTK feature files through htkfeatreader + featkind.clear(); + std::vector frame; + std::vectornumclasses; // number of units found (actually max id +1) + size_t notfound = 0; // number of entries missing in MLF + + + std::vectorframesaccum; + + if (infiles.size()==0) + RuntimeError("minibatchframesourcemulti: need at least one network input specified with features"); + + if (labels.size()==0) + fprintf(stderr,"no MLF label files detected\n"); + + foreach_index (i, infiles) + { + pframes.push_back(unique_ptr(new biggrowablevectorarray(pagepath[i]))); + + if (vdim[i]>maxvdim) + maxvdim=vdim[i]; + } + + + foreach_index (i, labels) + { + classids.push_back(std::vector()); + numclasses.push_back(0); + } + + + fprintf (stderr, "minibatchframesourcemulti: reading %d feature sets and %d label sets...", (int)infiles.size(), (int)labels.size()); + + foreach_index (m, infiles) + { + + + featdim=0; + numframes=0; + featkind.clear(); + msra::asr::htkfeatreader reader; // feature reader + reader.AddEnergy(addEnergy); + + foreach_index (i, infiles[m]) // read each feature file in set m + { + if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } + msra::basetypes::matrix feat; + msra::asr::htkfeatreader::parsedpath ppath (infiles[m][i]); + + // skip files for which labels don't exist (assuming bad alignment) + wstring key; + if (!labels.empty()) + { + if (!labels[0].empty()) // empty means unsupervised mode (don't load any) + { +#ifdef _WIN32 + key = regex_replace ((wstring)ppath, wregex (L"\\.[^\\.\\\\/:]*$"), wstring()); // delete extension (or not if none) +#endif +#ifdef __unix__ + key = removeExtension(basename(ppath)); +#endif + if (labels[0].find (key) == labels[0].end()) + { + if (notfound < 5) + fprintf (stderr, "\nminibatchframesourcemulti: %d-th file not found in MLF label set: %ls", i, key.c_str()); + notfound++; + continue; // skip this utterance at all + } + } + } + // get feature frames + if (vdim[m] != 0) // (vdim == special mode to not read features at all) + { + msra::util::attempt (5, [&]() + { + reader.read (ppath, featkind, sampperiod, feat); // whole file read as columns of feature vectors + }); + if (featdim == 0) // first time + featdim = feat.rows(); + else if (featdim != feat.rows()) + RuntimeError("minibatchframesourcemulti: inconsistent feature dimension across files"); + // HVite occasionally generates mismatching output --skip such files + if (!key.empty()) // (we have a key if supervised mode) + { + const auto & labseq = labels[0].find (key)->second; // (we already checked above that it exists) + size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); + if (abs ((int) labframes - (int) feat.cols()) > 0) + { + fprintf (stderr, "\nminibatchframesourcemulti: %d-th file has small duration mismatch (%d in label vs. %d in feat file), skipping: %ls", i, (int)labframes, (int)feat.cols(), key.c_str()); + notfound++; + continue; // skip this utterance at all + } + } + // append to cache + frame.resize (featdim); + if (feat.cols() < 2) // (2 frames needed for boundary markers) + RuntimeError("minibatchframesourcemulti: utterances < 2 frames not supported"); + foreach_column (t, feat) + { + foreach_index (k, frame) + frame[k] = feat(k,t); + + pframes[m]->push_back (frame); + numframes++; + if (m==0) + boundaryflags.push_back ((t == 0) ? -1 : (t == feat.cols() -1) ? +1 : 0); + } + if (m==0) + framesaccum.push_back(numframes); + else + assert(numframes == framesaccum[i]); + + assert (numframes == pframes[m]->size()); + } + if (m==0) + assert (numframes == boundaryflags.size()); + + + + if (m==0) // after we get the key for this file, read all labels (only done for first feature) + { + if (!key.empty()) + { + foreach_index (j, labels) + { + const auto & labseq = labels[j].find (key)->second; // (we already checked above that it exists) + foreach_index (i, labseq) + { + const auto & e = labseq[i]; + if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) + RuntimeError("minibatchframesourcemulti: labels not in consecutive order MLF in label set: %ls", key.c_str()); + for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) + { + if (e.classid >= udim[j]) + RuntimeError("minibatchframesourcemulti: class id exceeds model dimension in file %ls", key.c_str()); + if (e.classid != (CLASSIDTYPE) e.classid) + RuntimeError("CLASSIDTYPE has too few bits"); + classids[j].push_back ((CLASSIDTYPE) e.classid); + numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); + } + } + if (vdim[m] == 0) + numframes = classids[j].size(); + if (numframes != classids[j].size()) // TODO: remove this once we are confident + RuntimeError("minibatchframesourcemulti: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); + assert (numframes == classids[j].size()); + + } + } + else + { + assert(classids.empty()); + } + + } + + } + + + assert (vdim[m] == 0 || numframes == pframes[m]->size()); + + foreach_index(j, labels) + assert (labels[j].empty() || numframes == classids[j].size()); + + if (vdim[m] != 0 && numframes != pframes[m]->size()) // || (!labels.empty() && numframes != classids.size())) + RuntimeError("\nminibatchframesource: numframes variable screwup"); + if (m==0) + { + foreach_index (j, numclasses) + fprintf (stderr, "\nminibatchframesourcemulti: read label set %d: %d classes\n", j, (int)numclasses[j]); + } + fprintf (stderr, "\nminibatchframesourcemulti: feature set %d: %d frames read from %d utterances\n", m, (int)pframes[m]->size(), (int)infiles[m].size()); + if (notfound > 0) + { + fprintf (stderr, "minibatchframesourcemulti: %d files out of %d not found in label set\n", (int)notfound, (int)infiles[m].size()); + if (notfound > infiles[m].size() / 2) + RuntimeError("minibatchframesourcemulti: too many files not found in label set--assuming broken configuration\n"); + } + // notify frames source to switch from population to consumption mode + pframes[m]->no_more_push_back(); + + } + + if (numframes == 0 && !mayhavenoframe) + RuntimeError("minibatchframesource: no input features given!"); + + + // initialize randomizer + if (numframes > 0) + randomordering.resize (numframes, randomizationrange); + + } + virtual ~minibatchframesourcemulti() {} + size_t totalframes() const { + assert (maxvdim == 0 || numframes == pframes[0]->size()); assert (!issupervised() || numframes == classids[0].size()); return numframes; } + + bool issupervised() const { return !classids.empty(); } + + void setverbosity(int newverbosity) { verbosity = newverbosity; } + + // retrieve one minibatch + // Minibatches are deterministic pseudo-random samples. The entire corpus + // is repeated infinitely, but each repetition (a 'sweep') is randomized + // differently. + // This function allows to retrieve a mini-batch starting from any frame + // within this infinitely extended repetition. To the end, mini-batches are + // specified by start frame and #frames. + // This function returns the same data independent on #frames, i.e. the concept + // of the mini-batch is not defined in here, but on the caller side. The caller + // can retrieve the frames of a mini-batch in chunks that do not match the + // caller's definition of "mini-batch," e.g. bigger or smaller chunks. + // If a requested mini-batch spans a sweep boundary, then this function will + // not return samples after the sweep boundary. Instead, the returned frame + // set is shortened to not exceed the end of the sweep. The caller must make + // a separate second call to get the rest. In trainlayer(), the one + // sweep-boundary-spanning mini-batch will simply be shortened. + // This function is NOT thread-safe (due to caching of random sequence). + bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, std::vector> & uids, + std::vector> & transcripts, + std::vector> & latticepairs, std::vector> & sentendmark, + std::vector> & phoneboundaries) + { + + auto_timer timergetbatch; + bool readfromdisk; + size_t nreadfromdisk=0; + transcripts.clear(); // word-level transcripts not supported by frame source (aimed at MMI) + latticepairs.clear(); // neither are lattices + + assert (totalframes() > 0); + const size_t sweep = globalts / totalframes(); // which sweep (this determines randomization) + const size_t ts = globalts % totalframes(); // start frame within the sweep + const size_t te = min (ts + framesrequested, totalframes()); // do not go beyond sweep boundary + assert (te > ts); + if (verbosity >= 2) + fprintf (stderr, "getbatch: frames [%d..%d] in sweep %d\n", (int)ts, (int)(te-1), (int)sweep); + + // get random sequence (each time index occurs exactly once) + // If the sweep changes, this will re-cache the sequence. We optimize for rare, monotonous sweep changes. + const auto & tmap = randomordering (sweep); + + feat.resize(pframes.size()); + uids.resize(classids.size()); + sentendmark.resize(classids.size()); + phoneboundaries.resize(classids.size()); + foreach_index(i, feat) + { + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(pframes[i]->dim(), vdim[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + readfromdisk = pframes[i]->require (randomordering.bounds (max (ts, leftextent) - leftextent, te + 1 + rightextent)); + // generate features and uids + feat[i].resize (vdim[i], te - ts); // note: special mode vdim == 0 means no features to be loaded + if (issupervised()) // empty means unsupervised training -> return empty uids + foreach_index(j, uids) + uids[j].resize (te - ts); + else + uids.clear(); + + for (size_t t = ts; t < te; t++) + { + size_t trand = tmap[t]; // the random-sequence sample point for this point in time + if (vdim[i] != 0) + { + auto v_t = feat[i].col(t-ts); // the vector to fill in + augmentneighbors (*pframes[i], boundaryflags, trand, leftextent, rightextent, v_t); + } + if (i==0){ // read labels for all outputs on first pass thru features. this guarantees they will be read if only one feature set but > 1 label set + if (issupervised()) + foreach_index(j, uids) + uids[j][t-ts] = classids[j][trand]; + } + } + timegetbatch = timergetbatch; + if (readfromdisk) + nreadfromdisk++; + + } + + (nreadfromdisk==feat.size()) ? readfromdisk = true : readfromdisk = false; + + return readfromdisk; + + } + + bool getbatch (const size_t /*globalts*/, const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, std::vector & /*uids*/, + std::vector> & /*transcripts*/, + std::vector> & /*latticepairs*/) + { + // should never get here + RuntimeError("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchframesource instead\n"); + } + + double gettimegetbatch () { return timegetbatch;} + + // return first valid globalts to ask getbatch() for + // In frame mode, there is no constraint, i.e. it is 'globalts' itself. + /*implement*/ size_t firstvalidglobalts (const size_t globalts) { return globalts; } + + /*implement*/ const std::vector & unitcounts() const { LogicError("unitcounts: not implemented for this feature source"); } + + }; +};}; diff --git a/Source/Readers/HTKMLFReader/utterancesourcemulti.h b/Source/Readers/HTKMLFReader/utterancesourcemulti.h index 1ed69989206e..35761b3aef35 100644 --- a/Source/Readers/HTKMLFReader/utterancesourcemulti.h +++ b/Source/Readers/HTKMLFReader/utterancesourcemulti.h @@ -1,1446 +1,1446 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// utterancesourcemulti.h -- implementation of utterancesource.h that supports multiple feature and label sets -// - -#pragma once - -#include "Basics.h" // for attempt() -#include "htkfeatio.h" // for htkmlfreader -#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) -#include "minibatchsourcehelpers.h" -#include "minibatchiterator.h" -#include "unordered_set" - -namespace msra { namespace dbn { - -// --------------------------------------------------------------------------- -// minibatchutterancesource -- feature source to provide randomized utterances -// This also implements a frame-wise mode, which is layered on top of the utterance-wise mode -// and thus benefits from its goodies such as corpus-wide high-level randomization and chunk paging. -// --------------------------------------------------------------------------- -class minibatchutterancesourcemulti : public minibatchsource -{ - void operator=(const minibatchutterancesourcemulti & other); // non-assignable - std::vector vdim; // feature dimension after augmenting neighhors - std::vector leftcontext; // number of frames to the left of the target frame in the context window - std::vector rightcontext; // number of frames to the right of the target frame in the context window - std::vector sampperiod; // (for reference and to check against model) - std::vector featkind; - std::vector featdim; - const bool framemode; // true -> actually return frame-level randomized frames (not possible in lattice mode) - std::vector> counts; // [s] occurence count for all states (used for priors) - int verbosity; - // lattice reader - //const std::vector> &lattices; - const latticesource & lattices; - - //std::vector lattices; - // word-level transcripts (for MMI mode when adding best path to lattices) - const map & allwordtranscripts; // (used for getting word-level transcripts) - //std::vector> allwordtranscripts; - // data store (incl. paging in/out of features and lattices) - struct utterancedesc // data descriptor for one utterance - { - msra::asr::htkfeatreader::parsedpath parsedpath; // archive filename and frame range in that file - size_t classidsbegin; // index into allclassids[] array (first frame) - - utterancedesc (msra::asr::htkfeatreader::parsedpath&& ppath, size_t classidsbegin) : parsedpath (std::move(ppath)), classidsbegin (classidsbegin) {} - - const wstring & logicalpath() const { return parsedpath; /*type cast will return logical path*/ } - size_t numframes() const { return parsedpath.numframes(); } - wstring key() const // key used for looking up lattice (not stored to save space) - { -#ifdef _MSC_VER - static const wstring emptywstring; - static const wregex deleteextensionre (L"\\.[^\\.\\\\/:]*$"); - return regex_replace (logicalpath(), deleteextensionre, emptywstring); // delete extension (or not if none) -#else - return removeExtension(logicalpath()); -#endif - } - }; - - // Make sure type 'utterancedesc' has a move constructor - static_assert(std::is_move_constructible::value, "Type 'utterancedesc' should be move constructible!"); - - struct utterancechunkdata // data for a chunk of utterances - { - std::vector utteranceset; // utterances in this set - size_t numutterances() const { return utteranceset.size(); } - - std::vector firstframes; // [utteranceindex] first frame for given utterance - mutable msra::dbn::matrix frames; // stores all frames consecutively (mutable since this is a cache) - size_t totalframes; // total #frames for all utterances in this chunk - mutable std::vector> lattices; // (may be empty if none) - - // construction - utterancechunkdata() : totalframes (0) {} - void push_back (utterancedesc &&/*destructive*/ utt) - { - if (isinram()) - LogicError("utterancechunkdata: frames already paged into RAM--too late to add data"); - firstframes.push_back (totalframes); - totalframes += utt.numframes(); - utteranceset.push_back (std::move(utt)); - } - - // accessors to an utterance's data - size_t numframes (size_t i) const { return utteranceset[i].numframes(); } - size_t getclassidsbegin (size_t i) const { return utteranceset[i].classidsbegin; } - msra::dbn::matrixstripe getutteranceframes (size_t i) const // return the frame set for a given utterance - { - if (!isinram()) - LogicError("getutteranceframes: called when data have not been paged in"); - const size_t ts = firstframes[i]; - const size_t n = numframes(i); - return msra::dbn::matrixstripe (frames, ts, n); - } - shared_ptr getutterancelattice (size_t i) const // return the frame set for a given utterance - { - if (!isinram()) - LogicError("getutteranceframes: called when data have not been paged in"); - return lattices[i]; - } - - // paging - // test if data is in memory at the moment - bool isinram() const { return !frames.empty(); } - // page in data for this chunk - // We pass in the feature info variables by ref which will be filled lazily upon first read - void requiredata (string & featkind, size_t & featdim, unsigned int & sampperiod, const latticesource & latticesource, int verbosity=0) const - { - if (numutterances() == 0) - LogicError("requiredata: cannot page in virgin block"); - if (isinram()) - LogicError("requiredata: called when data is already in memory"); - try // this function supports retrying since we read from the unrealible network, i.e. do not return in a broken state - { - msra::asr::htkfeatreader reader; // feature reader (we reinstantiate it for each block, i.e. we reopen the file actually) - // if this is the first feature read ever, we explicitly open the first file to get the information such as feature dimension - if (featdim == 0) - { - reader.getinfo (utteranceset[0].parsedpath, featkind, featdim, sampperiod); - fprintf (stderr, "requiredata: determined feature kind as %d-dimensional '%s' with frame shift %.1f ms\n", (int)featdim, featkind.c_str(), sampperiod / 1e4); - } - // read all utterances; if they are in the same archive, htkfeatreader will be efficient in not closing the file - frames.resize (featdim, totalframes); - if (!latticesource.empty()) - lattices.resize (utteranceset.size()); - foreach_index (i, utteranceset) - { - //fprintf (stderr, "."); - // read features for this file - auto uttframes = getutteranceframes (i); // matrix stripe for this utterance (currently unfilled) - reader.read (utteranceset[i].parsedpath, (const string &) featkind, sampperiod, uttframes); // note: file info here used for checkuing only - // page in lattice data - if (!latticesource.empty()) - latticesource.getlattices (utteranceset[i].key(), lattices[i], uttframes.cols()); - } - //fprintf (stderr, "\n"); - if (verbosity) - fprintf (stderr, "requiredata: %d utterances read\n", (int)utteranceset.size()); - } - catch (...) - { - releasedata(); - throw; - } - } - // page out data for this chunk - void releasedata() const - { - if (numutterances() == 0) - LogicError("releasedata: cannot page out virgin block"); - if (!isinram()) - LogicError("releasedata: called when data is not memory"); - // release frames - frames.resize (0, 0); - // release lattice data - lattices.clear(); - } - }; - std::vector> allchunks; // set of utterances organized in chunks, referred to by an iterator (not an index) - std::vector>> classids; // [classidsbegin+t] concatenation of all state sequences - std::vector>> phoneboundaries; - bool issupervised() const { return !classids.empty(); } - size_t numutterances; // total number of utterances - size_t _totalframes; // total frames (same as classids.size() if we have labels) - double timegetbatch; // [v-hansu] for time measurement - // sequence in random order of actual use (randomized, where randomization is cached) - const size_t randomizationrange;// parameter remembered; this is the full window (e.g. 48 hours), not the half window - size_t currentsweep; // randomization is currently cached for this sweep; if it changes, rebuild all below - struct chunk // chunk as used in actual processing order (randomized sequence) - { - // the underlying chunk (as a non-indexed reference into the chunk set) - std::vector::const_iterator uttchunkdata; - const utterancechunkdata & getchunkdata() const { return *uttchunkdata; } - size_t numutterances() const { return uttchunkdata->numutterances(); } - size_t numframes() const { return uttchunkdata->totalframes; } - - // position in utterance-position space - size_t utteranceposbegin; - size_t utteranceposend() const { return utteranceposbegin + numutterances(); } - - // position on global time line - size_t globalts; // start frame on global timeline (after randomization) - size_t globalte() const { return globalts + numframes(); } - - // randomization range limits - size_t windowbegin; // randomizedchunk index of earliest chunk that utterances in here can be randomized with - size_t windowend; // and end index [windowbegin, windowend) - chunk (std::vector::const_iterator uttchunkdata, size_t utteranceposbegin, size_t globalts) : uttchunkdata (uttchunkdata), utteranceposbegin (utteranceposbegin), globalts (globalts) {} - }; - std::vector> randomizedchunks; // utterance chunks after being brought into random order (we randomize within a rolling window over them) - size_t chunksinram; // (for diagnostics messages) - struct utteranceref // describes the underlying random utterance associated with an utterance position - { - size_t chunkindex; // lives in this chunk (index into randomizedchunks[]) - size_t utteranceindex; // utterance index in that chunk - size_t numframes; // (cached since we cannot directly access the underlying data from here) - size_t globalts; // start frame in global space after randomization (for mapping frame index to utterance position) - size_t globalte() const { return globalts + numframes; } // end frame - utteranceref (size_t chunkindex, size_t utteranceindex) : chunkindex (chunkindex), utteranceindex (utteranceindex), globalts (SIZE_MAX), numframes (0) {} - void swap (utteranceref & other) // used in randomization - { - ::swap (chunkindex, other.chunkindex); - ::swap (utteranceindex, other.utteranceindex); - assert (globalts == SIZE_MAX && other.globalts == SIZE_MAX && numframes == 0 && other.numframes == 0); // can only swap before assigning these - } - }; - std::vector randomizedutterancerefs; // [pos] randomized utterance ids - std::unordered_map randomizedutteranceposmap; // [globalts] -> pos lookup table - struct positionchunkwindow // chunk window required in memory when at a certain position, for controlling paging - { - std::vector::iterator definingchunk; // the chunk in randomizedchunks[] that defined the utterance position of this utterance - size_t windowbegin() const { return definingchunk->windowbegin; } - size_t windowend() const { return definingchunk->windowend; } - bool isvalidforthisposition (const utteranceref & utt) const - { - return utt.chunkindex >= windowbegin() && utt.chunkindex < windowend(); // check if 'utt' lives in is in allowed range for this position - } - positionchunkwindow (std::vector::iterator definingchunk) : definingchunk (definingchunk) {} - }; - std::vector positionchunkwindows; // [utterance position] -> [windowbegin, windowend) for controlling paging - - // frame-level randomization layered on top of utterance chunking (randomized, where randomization is cached) - struct frameref - { - unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) - unsigned short utteranceindex; // utterance index in that chunk - static const size_t maxutterancesperchunk = 65535; - unsigned short frameindex; // frame index within the utterance - static const size_t maxframesperutterance = 65535; - frameref (size_t ci, size_t ui, size_t fi) : chunkindex ((unsigned short) ci), utteranceindex ((unsigned short) ui), frameindex ((unsigned short) fi) - { - if (ci == chunkindex && ui == utteranceindex && fi == frameindex) - return; - LogicError("frameref: bit fields too small"); - } - frameref() : chunkindex (0), utteranceindex (0), frameindex (0) {} - }; - biggrowablevector randomizedframerefs; // [globalt-sweepts] -> (chunk, utt, frame) lookup table for randomized frames --this can be REALLY big! - - // TODO: this may go away if we store classids directly in the utterance data - template class shiftedvector // accessing a vector with a non-0 starting index - { - void operator= (const shiftedvector &); - VECTOR & v; - size_t first; - size_t n; - void check (size_t i) const { if (i >= n) LogicError("shiftedvector: index out of bounds"); } - public: - shiftedvector (VECTOR & v, size_t first, size_t n) : v (v), first (first), n (n) { } - // TODO: the following is not templated--do it if needed; also should return a const reference then - size_t operator[] (size_t i) const { check (i); return v[first + i]; } - }; - template std::vector>> getclassids (const UTTREF & uttref) // return sub-vector of classids[] for a given utterance - { - std::vector>> allclassids; - allclassids.empty(); - - if (!issupervised()) - { - foreach_index(i,classids) - allclassids.push_back(std::move(shiftedvector> ((*classids[i]), 0, 0))); - return allclassids; // nothing to return - } - const auto & chunk = randomizedchunks[0][uttref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - const size_t classidsbegin = chunkdata.getclassidsbegin (uttref.utteranceindex); // index of first state label in global concatenated classids[] array - const size_t n = chunkdata.numframes (uttref.utteranceindex); - foreach_index(i,classids) - { - if ((*classids[i])[classidsbegin + n] != (CLASSIDTYPE) -1) - LogicError("getclassids: expected boundary marker not found, internal data structure screwed up"); - allclassids.push_back(std::move(shiftedvector> ((*classids[i]), classidsbegin, n))); - } - return allclassids; // nothing to return - } - template std::vector>> getphonebound(const UTTREF & uttref) // return sub-vector of classids[] for a given utterance - { - std::vector>> allphoneboundaries; - allphoneboundaries.empty(); - - if (!issupervised()) - { - foreach_index(i, classids) - allphoneboundaries.push_back(std::move(shiftedvector>((*phoneboundaries[i]), 0, 0))); - return allphoneboundaries; // nothing to return - } - const auto & chunk = randomizedchunks[0][uttref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - const size_t classidsbegin = chunkdata.getclassidsbegin(uttref.utteranceindex); // index of first state label in global concatenated classids[] array - const size_t n = chunkdata.numframes(uttref.utteranceindex); - foreach_index(i, phoneboundaries) - { - if ((*phoneboundaries[i])[classidsbegin + n] != (HMMIDTYPE)-1) - LogicError("getclassids: expected boundary marker not found, internal data structure screwed up"); - allphoneboundaries.push_back(std::move(shiftedvector>((*phoneboundaries[i]), classidsbegin, n))); - } - return allphoneboundaries; // nothing to return - } - -public: - // constructor - // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). - // This mode requires utterances with time stamps. - minibatchutterancesourcemulti (const std::vector> & infiles, const std::vector>> & labels, - std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, - const latticesource & lattices, const map & allwordtranscripts, const bool framemode) - : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), randomizationrange (randomizationrange), currentsweep (SIZE_MAX), - lattices (lattices), allwordtranscripts (allwordtranscripts), framemode (framemode), chunksinram (0), timegetbatch (0), verbosity(2) - // [v-hansu] change framemode (lattices.empty()) into framemode (false) to run utterance mode without lattice - // you also need to change another line, search : [v-hansu] comment out to run utterance mode without lattice - { - // process infiles to know dimensions of things (but not loading features) - size_t nomlf = 0; // number of entries missing in MLF (diagnostics) - size_t nolat = 0; // number of entries missing in lattice archive (diagnostics) - std::vector numclasses; // number of output classes as found in the label file (diagnostics) - _totalframes = 0; - wstring key; - size_t numutts=0; - - std::vectoruttisvalid; // boolean flag to check that utterance is valid. valid means number of - //frames is consistent across all feature and label streams - std::vectoruttduration; // track utterance durations to determine utterance validity - - std::vector classidsbegin; - - - allchunks = std::vector>(infiles.size(), std::vector()); - featdim = std::vector(infiles.size(), 0); - sampperiod = std::vector(infiles.size(), 0); - featkind = std::vector(infiles.size(), ""); - - numclasses = std::vector(labels.size(), 0); - counts = std::vector>(labels.size(), std::vector()); - - foreach_index (i, labels) - { - classids.push_back(unique_ptr>(new biggrowablevector())); - phoneboundaries.push_back(unique_ptr>(new biggrowablevector())); - //std::pair,std::vector> latticetocs; - //std::unordered_map modelsymmap; - //lattices.push_back(shared_ptr(new latticesource(latticetocs, modelsymmap))); - } - - - // first check consistency across feature streams - // We'll go through the SCP files for each stream to make sure the duration is consistent - // If not, we'll plan to ignore the utterance, and inform the user - // m indexes the feature stream - // i indexes the files within a stream, i.e. in the SCP file) - foreach_index(m, infiles){ - if (m == 0){ - numutts = infiles[m].size(); - uttisvalid = std::vector(numutts, true); - uttduration = std::vector(numutts, 0); - } - else if (infiles[m].size()!=numutts) - RuntimeError("minibatchutterancesourcemulti: all feature files must have same number of utterances"); - - foreach_index(i, infiles[m]){ - utterancedesc utterance(msra::asr::htkfeatreader::parsedpath(infiles[m][i]), 0); //mseltzer - is this foolproof for multiio? is classids always non-empty? - const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode - // we need at least 2 frames for boundary markers to work - if (uttframes < 2) - RuntimeError("minibatchutterancesource: utterances < 2 frames not supported"); - if (uttframes > frameref::maxframesperutterance) - { - fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %ls\n", i, (int)uttframes, (int)frameref::maxframesperutterance, key.c_str()); - uttduration[i] = 0; - uttisvalid[i] = false; - } - else - { - if (m == 0){ - uttduration[i] = uttframes; - uttisvalid[i] = true; - } - else if (uttduration[i] != uttframes){ - fprintf(stderr, "minibatchutterancesource: skipping %d-th file due to inconsistency in duration in different feature streams (%d vs %d frames)\n", i, (int)uttduration[i], (int)uttframes); - uttduration[i] = 0; - uttisvalid[i] = false; - } - } - } - } - size_t invalidutts=0; - foreach_index(i, uttisvalid){ - if (!uttisvalid[i]) - invalidutts++; - } - if (invalidutts > uttisvalid.size() / 2) - RuntimeError("minibatchutterancesource: too many files with inconsistent durations, assuming broken configuration\n"); - else if (invalidutts>0) - fprintf(stderr, "Found inconsistent durations across feature streams in %d out of %d files\n", (int)invalidutts, (int)uttisvalid.size()); - - - // now process the features and labels - size_t utterancesetsize = 0; - foreach_index (m, infiles) - { - std::vector utteranceset;// read all utterances to here first; at the end, distribute to chunks - utteranceset.reserve(infiles[m].size()); - //if (m==0) - // numutts = infiles[m].size(); - //else - // if (infiles[m].size()!=numutts) - // RuntimeError("minibatchutterancesourcemulti: all feature files must have same number of utterances\n"); - if (m==0) - classidsbegin.clear(); - - foreach_index (i, infiles[m]) - { - if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } - // build utterance descriptor - if (m == 0 && !labels.empty()) - classidsbegin.push_back(classids[0]->size()); - - if (uttisvalid[i]) - { - utterancedesc utterance (msra::asr::htkfeatreader::parsedpath (infiles[m][i]), labels.empty() ? 0 : classidsbegin[i] ); //mseltzer - is this foolproof for multiio? is classids always non-empty? - const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode - assert(uttframes == uttduration[i]); // ensure nothing funky happened - // already performed these checks above - // we need at least 2 frames for boundary markers to work - //if (uttframes < 2) - // RuntimeError("minibatchutterancesource: utterances < 2 frames not supported"); - //if (uttframes > frameref::maxframesperutterance) - //{ - // fprintf (stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %ls", i, uttframes, frameref::maxframesperutterance, key.c_str()); - // continue; - //} - - // check whether we have the ref transcript - //auto labelsiter = labels[0].end(); - bool lacksmlf = true; - if (!labels.empty()) // empty means unsupervised mode (don't load any) - { - key = utterance.key(); - // check if labels are available (if not, it normally means that no path was found in realignment) - auto labelsiter = labels[0].find (key); - //const bool lacksmlf = (labelsiter == labels[0].end()); - lacksmlf = (labelsiter == labels[0].end()); - if (lacksmlf) - if (nomlf++ < 5) - fprintf (stderr, " [no labels for %ls]", key.c_str()); - // check if lattice is available (when in lattice mode) - // TODO: also check the #frames here; requires a design change of the TOC format & a rerun - const bool lackslat = !lattices.empty() && !lattices.haslattice (key); // ('true' if we have no lattices) - if (lackslat) - if (nolat++ < 5) - fprintf (stderr, " [no lattice for %ls]", key.c_str()); - // skip if either one is missing - if (lacksmlf || lackslat){ - uttisvalid[i] = false; - continue; // skip this utterance at all - } - } - // push the label sequence into classids[], since we already looked it up - // TODO: we can store labels more efficiently now since we don't do frame-wise random access anymore. - - // OK, utterance has all we need --remember it - - if (m==0) - { - if (!labels.empty() && !lacksmlf) - //if (!labels.empty() && labelsiter != labels[0].end()) - { - // first verify that all the label files have the proper duration - foreach_index (j, labels) - { - const auto & labseq = labels[j].find(key)->second; - // check if durations match; skip if not - size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); - if (labframes != uttframes) - { - fprintf (stderr, " [duration mismatch (%d in label vs. %d in feat file), skipping %ls]", (int)labframes, (int)uttframes, key.c_str()); - nomlf++; - uttisvalid[i] = false; - //continue; // skip this utterance at all - break; - } - } - if (uttisvalid[i]) - { - utteranceset.push_back(std::move(utterance)); - _totalframes += uttframes; - // then parse each mlf if the durations are consistent - foreach_index(j, labels) - { - const auto & labseq = labels[j].find(key)->second; - // expand classid sequence into flat array - foreach_index (i, labseq) - { - const auto & e = labseq[i]; - if ((i > 0 && labseq[i - 1].firstframe + labseq[i - 1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) - { - RuntimeError("minibatchutterancesource: labels not in consecutive order MLF in label set: %ls", key.c_str()); - } - if (e.classid >= udim[j]) - { - RuntimeError("minibatchutterancesource: class id %d exceeds model output dimension %d in file %ls", (int)e.classid, (int)udim[j], key.c_str()); - } - if (e.classid != (CLASSIDTYPE) e.classid) - RuntimeError("CLASSIDTYPE has too few bits"); - for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) - { - classids[j]->push_back (e.classid); - if (e.phonestart != 0 && t == e.firstframe) - phoneboundaries[j]->push_back((HMMIDTYPE)e.phonestart); - else - phoneboundaries[j]->push_back((HMMIDTYPE)0); - } - numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); - counts[j].resize (numclasses[j], 0); - counts[j][e.classid] += e.numframes; - } - - classids[j]->push_back ((CLASSIDTYPE) -1); // append a boundary marker marker for checking - phoneboundaries[j]->push_back((HMMIDTYPE)-1); // append a boundary marker marker for checking - - if (!labels[j].empty() && classids[j]->size() != _totalframes + utteranceset.size()) - LogicError("minibatchutterancesource: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); - assert (labels[j].empty() || classids[j]->size() == _totalframes + utteranceset.size()); - } - } - } - else - { - assert(classids.empty() && labels.empty()); - utteranceset.push_back(std::move(utterance)); - _totalframes += uttframes; - } - } - else - { - utteranceset.push_back(std::move(utterance)); - } - } - } - if (m == 0) - utterancesetsize = utteranceset.size(); - else - assert(utteranceset.size() == utterancesetsize); - - fprintf (stderr, "feature set %d: %d frames in %d out of %d utterances\n", m, (int)_totalframes, (int)utteranceset.size(), (int)infiles[m].size()); - - if (!labels.empty()){ - foreach_index (j, labels){ - biggrowablevector & cid = *classids[j]; - foreach_index (i, utteranceset){ - //if ((*classids[j])[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) - //printf("index = %d\n",utteranceset[i].classidsbegin + utteranceset[i].numframes()); - //printf("cid[index] = %d\n",cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()]); - //printf("CLASSIDTYPE(-1) = %d\n",(CLASSIDTYPE) -1); - if (cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) - LogicError("minibatchutterancesource: classids[] out of sync"); - } - } - } - if (nomlf + nolat > 0) - { - fprintf (stderr, "minibatchutterancesource: out of %d files, %d files not found in label set and %d have no lattice\n", (int)infiles[0].size(), (int)nomlf, (int)nolat); - if (nomlf + nolat > infiles[m].size() / 2) - RuntimeError("minibatchutterancesource: too many files not found in label set--assuming broken configuration\n"); - } - if (m==0) {foreach_index(j, numclasses) { fprintf(stderr,"label set %d: %d classes\n", j, (int)numclasses[j]); } } - // distribute them over chunks - // We simply count off frames until we reach the chunk size. - // Note that we first randomize the chunks, i.e. when used, chunks are non-consecutive and thus cause the disk head to seek for each chunk. - const size_t framespersec = 100; // we just assume this; our efficiency calculation is based on this - const size_t chunkframes = 15 * 60 * framespersec; // number of frames to target for each chunk - // Loading an initial 24-hour range will involve 96 disk seeks, acceptable. - // When paging chunk by chunk, chunk size ~14 MB. - std::vector & thisallchunks = allchunks[m]; - - thisallchunks.resize (0); - thisallchunks.reserve (_totalframes / chunkframes); - - foreach_index (i, utteranceset) - { - // if exceeding current entry--create a new one - // I.e. our chunks are a little larger than wanted (on av. half the av. utterance length). - if (thisallchunks.empty() || thisallchunks.back().totalframes > chunkframes || thisallchunks.back().numutterances() >= frameref::maxutterancesperchunk) - thisallchunks.push_back (utterancechunkdata()); - // append utterance to last chunk - utterancechunkdata & currentchunk = thisallchunks.back(); - currentchunk.push_back (std::move (utteranceset[i])); // move it out from our temp array into the chunk - // TODO: above push_back does not actually 'move' because the internal push_back does not accept that - } - numutterances = utteranceset.size(); - fprintf (stderr, "minibatchutterancesource: %d utterances grouped into %d chunks, av. chunk size: %.1f utterances, %.1f frames\n", - (int)numutterances, (int)thisallchunks.size(), numutterances / (double) thisallchunks.size(), _totalframes / (double) thisallchunks.size()); - // Now utterances are stored exclusively in allchunks[]. They are never referred to by a sequential utterance id at this point, only by chunk/within-chunk index. - } - } - -private: - // shuffle a vector into random order by randomly swapping elements - - template static void randomshuffle (VECTOR & v, size_t randomseed) - { - if (v.size() > RAND_MAX * (size_t) RAND_MAX) - RuntimeError("randomshuffle: too large set: need to change to different random generator!"); - srand ((unsigned int) randomseed); - foreach_index (i, v) - { - // pick a random location - const size_t irand = msra::dbn::rand (0, v.size()); - - // swap element i with it - if (irand == (size_t) i) - continue; - ::swap (v[i], v[irand]); - } - } -#if 0 - template static void randomshuffle(std::vector v, size_t randomseed) - { - foreach_index(j, v) - { - if (v[j].size() > RAND_MAX * (size_t) RAND_MAX) - RuntimeError("randomshuffle: too large set: need to change to different random generator!"); - } - srand ((unsigned int) randomseed); - - foreach_index (i, v[0]) - { - // pick a random location - const size_t irand = msra::dbn::rand (0, v[0].size()); - - foreach_index(j, v){ - // swap element i with it - if (irand == (size_t) i) - continue; - ::swap (v[j][i], v[j][irand]); - } - } - } -#endif //0 - static void checkoverflow (size_t fieldval, size_t targetval, const char * fieldname) - { - if (fieldval != targetval) - RuntimeError("checkoverflow: bit field %s too small for value 0x%x (cut from 0x%x)", fieldname, (int)targetval, (int)fieldval); - } - - // helper for testing whether a swapped frame position is valid (w.r.t. beign in RAM when being at position 't') - bool isframepositionvalid (const size_t t, const biggrowablevector & ttochunk) const - { - // look up valid range for time position - const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this original chunk (relationship is monotonous, not random) - const auto & chunk = randomizedchunks[0][positionchunkindex]; - // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) - const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) - const size_t poswindowend = chunk.windowend; - // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. - - // now see if the randomized location is within that window - const size_t actualchunkindexforpos = randomizedframerefs[t].chunkindex; // where this frame pos has been mapped to - return actualchunkindexforpos >= poswindowbegin && actualchunkindexforpos < poswindowend; - // We only need to test the chunk index. Utterance and frame can be randomized within a chunk as we want, as long it is in RAM. - } - - // big long helper to update all cached randomization information - // This is a rather complex process since we randomize on two levels: - // - chunks of consecutive data in the feature archive - // - within a range of chunks that is paged into RAM - // - utterances (in utt mode), or - // - frames (in frame mode) - // The 'globalts' parameter is the start time that triggered the rerandomization; it is NOT the base time of the randomized area. - size_t lazyrandomization (const size_t globalts) - { - const size_t sweep = globalts / _totalframes; // which sweep (this determines randomization) - if (sweep == currentsweep) // already got this one--nothing to do - return sweep; - - currentsweep = sweep; - if (verbosity>0) - fprintf (stderr, "lazyrandomization: re-randomizing for sweep %d in %s mode\n", (int)currentsweep, framemode ? "frame" : "utterance"); - - const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep - - // first randomize chunks - std::vector::const_iterator>> randomizedchunkrefs; - foreach_index (i, allchunks) - randomizedchunkrefs.push_back(std::vector::const_iterator>()); - - foreach_index (i, allchunks) - randomizedchunkrefs[i].reserve (allchunks[i].size()); - - foreach_index (i, allchunks) // TODO: this cries for iterating using the iterator! - { - foreach_index(j, allchunks[i]) - randomizedchunkrefs[i].push_back (allchunks[i].begin() + j); - assert (randomizedchunkrefs[i].size() == allchunks[i].size()); - - // note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams - randomshuffle (randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep) - - } - - // place them onto the global timeline -> randomizedchunks[] - // We are processing with randomization within a rolling window over this chunk sequence. - // Paging will happen on a chunk-by-chunk basis. - // The global time stamp is needed to determine the paging window. - randomizedchunks.clear(); // data chunks after being brought into random order (we randomize within a rolling window over them) - - foreach_index(i, allchunks) - randomizedchunks.push_back(std::vector()); - - foreach_index(i, allchunks) - { - randomizedchunks[i].reserve (randomizedchunkrefs[i].size()); - foreach_index (k, randomizedchunkrefs[i]) - randomizedchunks[i].push_back (chunk (randomizedchunkrefs[i][k], randomizedchunks[i].empty() ? 0 : randomizedchunks[i].back().utteranceposend(), randomizedchunks[i].empty() ? sweepts : randomizedchunks[i].back().globalte())); - assert (randomizedchunks[i].size() == allchunks[i].size()); - assert (randomizedchunks[i].empty() || (randomizedchunks[i].back().utteranceposend() == numutterances && randomizedchunks[i].back().globalte() == sweepts + _totalframes)); - } - // for each chunk, compute the randomization range (w.r.t. the randomized chunk sequence) - foreach_index (i, randomizedchunks) - { - foreach_index (k, randomizedchunks[i]) - { - chunk & chunk = randomizedchunks[i][k]; - // start with the range of left neighbor - if (k == 0) - { - chunk.windowbegin = 0; - chunk.windowend = 1; - } - else - { - chunk.windowbegin = randomizedchunks[i][k-1].windowbegin; // might be too early - chunk.windowend = randomizedchunks[i][k-1].windowend; // might have more space - } - while (chunk.globalts - randomizedchunks[i][chunk.windowbegin].globalts > randomizationrange/2) - chunk.windowbegin++; // too early - while (chunk.windowend < randomizedchunks[i].size() && randomizedchunks[i][chunk.windowend].globalte() - chunk.globalts < randomizationrange/2) - chunk.windowend++; // got more space - } - } - if (!framemode) // utterance mode - { - // This sets up the following members: - // - positionchunkwindows - // - randomizedutterancerefs - // - randomizedutteranceposmap - - // We will now introduce the concept of utterance *position*. - // During processing, utterances will be indexed by position (which is in turn derived from a frame index in getbatch()), - // and it is assumed (required) that positions are requested consecutively. - // Each utterance position has an underlying associated utterance, which is represented as (chunkid, within-chunk index) and randomly assigned. - // Each utterance position also has an associated range of chunks that are kept in memory, - // and the associated underlying utterance is guaranteed to be found within that associated range of chunks. - // That allows to page out/in data when processing utterance positions in a consecutive manner. - - // compute chunk windows for every utterance position -> positionchunkwindows[] - // Utterance positions can only reference underlying utterance data within the chunk window. - // Utterance positions are defined by the randomized chunk sequence (i.e. their underlying 'defining' chunk differs from sweep to sweep). - positionchunkwindows.clear(); // [utterance position] -> [windowbegin, windowend) for controlling paging - positionchunkwindows.reserve (numutterances); - - // positionchunkwindows should be consistent for all inputs (distinct feature streams), so just build based on feature[0] - // contains pointer to chunk elements but only to compute index - foreach_index (k, randomizedchunks[0]) // TODO: this really cries for iterating using iterators! - { - chunk & chunk = randomizedchunks[0][k]; - for (size_t i = chunk.utteranceposbegin; i < chunk.utteranceposend(); i++) // loop over utterances in this chunk - positionchunkwindows.push_back (randomizedchunks[0].begin() + k); - // to look up the chunk range in memory for a position, look up the defining chunk and its range - } - assert (positionchunkwindows.size() == numutterances); - - // build the randomized utterances array -> randomizedutterancerefs[] - // start by assigning all utterance positions to utterances in non-random consecutive manner - randomizedutterancerefs.clear(); // [pos] randomized utterance ids - randomizedutterancerefs.reserve (numutterances); - foreach_index (k, randomizedchunks[0]) - { - chunk & chunk = randomizedchunks[0][k]; - for (size_t i = 0; i < chunk.numutterances(); i++) // loop over utterances in this chunk - randomizedutterancerefs.push_back (utteranceref (k, i)); - } - assert (randomizedutterancerefs.size() == numutterances); - foreach_index (i, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[i]; - assert (positionchunkwindows[i].isvalidforthisposition(uttref)); uttref; - } - - // check we got those setup right - - // we now randomly shuffle randomizedutterancerefs[pos], while considering the constraints of what chunk range needs to be in memory - srand ((unsigned int) sweep + 1); - for (size_t i = 0; i < randomizedutterancerefs.size(); i++) - { - // get valid randomization range, expressed in chunks - const size_t windowbegin = positionchunkwindows[i].windowbegin(); - const size_t windowend = positionchunkwindows[i].windowend(); - - // get valid randomization range, expressed in utterance positions - // Remember, utterance positions are defined by chunks. - const size_t posbegin = randomizedchunks[0][windowbegin].utteranceposbegin; - const size_t posend = randomizedchunks[0][windowend-1].utteranceposend(); - - // randomization range for this utterance position is [posbegin, posend) - for(;;) - { - // pick a random location - const size_t j = msra::dbn::rand (posbegin, posend); // a random number within the window - if (i == j) - break; // the random gods say "this one points to its original position"... nothing wrong about that, but better not try to swap - - // We want to swap utterances at i and j, but need to make sure they remain in their allowed range. - // This is guaranteed for a so-far untouched utterance, but both i and j may have been touched by a previous swap. - - // We want to use the utterance previously referenced at utterance position j at position i. Is that allowed? - if (!positionchunkwindows[i].isvalidforthisposition (randomizedutterancerefs[j])) - continue; // nope --try another - - // Likewise may we use the utterance previously referenced at utterance position i at position j? - if (!positionchunkwindows[j].isvalidforthisposition (randomizedutterancerefs[i])) - continue; // nope --try another - - // yep--swap them - randomizedutterancerefs[i].swap (randomizedutterancerefs[j]); - break; - } - } - - // place the randomized utterances on the global timeline so we can find them by globalts - size_t t = sweepts; - foreach_index (i, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[i]; - uttref.globalts = t; - uttref.numframes = randomizedchunks[0][uttref.chunkindex].getchunkdata().numframes (uttref.utteranceindex); - t = uttref.globalte(); - } - assert (t == sweepts + _totalframes); - - // verify that we got it right (I got a knot in my head!) - foreach_index (i, randomizedutterancerefs) - { - // get utterance referenced at this position - const auto & uttref = randomizedutterancerefs[i]; - // check if it is valid for this position - if (uttref.chunkindex < positionchunkwindows[i].windowbegin() || uttref.chunkindex >= positionchunkwindows[i].windowend()) - LogicError("lazyrandomization: randomization logic mangled!"); - } - - // create lookup table for (globalts values -> pos) -> randomizedutteranceposmap[] - randomizedutteranceposmap.clear(); // [globalts] -> pos lookup table - foreach_index (pos, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[pos]; - randomizedutteranceposmap[uttref.globalts] = (size_t) pos; - } - } - else // frame mode - { - // This sets up the following members: - // - randomizedframerefs - - // Lazy mem allocation for frame references - if (randomizedframerefs.size() != _totalframes) - randomizedframerefs.resize(_totalframes); - - srand ((unsigned int) sweep + 1); - // An original timeline is established by the randomized chunks, denoted by 't'. - // Returned frames are indexed by frame position j = (globalt - sweept), which have an associated underlying 't'. - // It is guaranteed that uttterance frame position j maps to an underlying frame within the corresponding chunk window. - biggrowablevector ttochunk; // randomized chunk index associated with frame position - ttochunk.resize (_totalframes); - size_t t = 0; - frameref frameref; - // enumerate chunks in their randomized order and assign frame indices in that order -> randomizedframerefs[t] - // At this point, chunks are in randomized order, but utterances and frames within utterances are not randomized. - // Later we will randomize those as well. - foreach_index (i, randomizedchunks[0]) - { - frameref.chunkindex = (unsigned short)i; - checkoverflow (frameref.chunkindex, i, "frameref::chunkindex"); - const auto & chunk = randomizedchunks[0][i]; - const auto & chunkdata = chunk.getchunkdata(); - const size_t numutt = chunkdata.numutterances(); - for (size_t k = 0; k < numutt; k++) - { - frameref.utteranceindex = (short)k; - checkoverflow (frameref.utteranceindex, k, "frameref::utteranceindex"); - const size_t n = chunkdata.numframes (k); - for (size_t m = 0; m < n; m++) - { - frameref.frameindex = (short)m; - checkoverflow (frameref.frameindex, m, "frameref::utteranceindex"); - randomizedframerefs[t] = frameref; // hopefully this is a memory copy, not a bit-wise assignment! If not, then code it explicitly - ttochunk[t] = (unsigned short) i; - checkoverflow (ttochunk[t], i, "ttochunk[]"); - t++; - } - } - } - assert (t == _totalframes); - - // now randomize them --we use the nested loop again to avoid storing a backpointer - // The condition is that a randomized frame may not be moved out of its associated chunk window. - foreach_index (t, randomizedframerefs) - { - const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this chunk (relationship is monotonous, not random) - const auto & chunk = randomizedchunks[0][positionchunkindex]; // for window - - // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) - const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) - const size_t poswindowend = chunk.windowend; - // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. - // These chunks are associated with a range of frame positions. - // It is implied that if we are at position 't', the frames covered by chunks [poswindowbegin, poswindowend) are in RAM. - const size_t postbegin = randomizedchunks[0][poswindowbegin].globalts - sweepts; - const size_t postend = randomizedchunks[0][poswindowend-1].globalte() - sweepts; - // The position that this frame gets randomized to must be guaranteed to belong to a chunk within [postbegin, postend). - - for (;;) // (randomization retry loop) - { - size_t tswap = msra::dbn::rand (postbegin, postend); // random frame position within allowed range - // We want to swap 't' to 'tswap' and 'tswap' to 't'. - // - Both may have been swapped before. - // - Both must stay within the randomization window of their respective position. - // check admissibility of where the element at 'tswap' gets swapped to 't' (range = [windowbegin,windowend)) - size_t tswapchunkindex = randomizedframerefs[tswap].chunkindex; - if (tswapchunkindex < poswindowbegin || tswapchunkindex >= poswindowend) - continue; - // check admissibility of where the element at t gets swapped to (which is frame position 'tswap') - const size_t sourcechunkindex = randomizedframerefs[t].chunkindex; - size_t targetchunkindex = ttochunk[tswap]; // chunk associated with this frame position defines value range - const auto & targetchunk = randomizedchunks[0][targetchunkindex]; - const size_t targetwindowbegin = targetchunk.windowbegin; - const size_t targetwindowend = targetchunk.windowend; - if (sourcechunkindex < targetwindowbegin || sourcechunkindex >= targetwindowend) - continue; - // admissible--swap the two - ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); -#if 0 - break; -#else // post-check --so far did not trigger, can be removed - - // do a post-check if we got it right --we seem not to - if (isframepositionvalid (t, ttochunk) && isframepositionvalid (tswap, ttochunk)) - break; - // not valid: swap them back and try again --we actually discovered a bug in the code above - ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); - fprintf (stderr, "lazyrandomization: BUGBUG --invalid swapping condition detected\n"); -#endif - } - } - - // check it --my head spins - t = 0; - foreach_index (i, randomizedchunks[0]) - { - const auto & chunk = randomizedchunks[0][i]; // for window and chunkdata - const size_t poswindowbegin = chunk.windowbegin; - const size_t poswindowend = chunk.windowend; - - const auto & chunkdata = chunk.getchunkdata(); // for numutterances/numframes - const size_t numutt = chunkdata.numutterances(); - for (size_t k = 0; k < numutt; k++) - { - const size_t n = chunkdata.numframes (k); - for (size_t m = 0; m < n; m++) - { - const size_t randomizedchunkindex = randomizedframerefs[t].chunkindex; - if (randomizedchunkindex < poswindowbegin || randomizedchunkindex >= poswindowend) - LogicError("lazyrandomization: nope, you got frame randomization wrong, dude"); - t++; - } - } - } - assert (t == _totalframes); - } - - return sweep; - } - - // helper to page out a chunk with log message - void releaserandomizedchunk (size_t k) - { - size_t numreleased=0; - foreach_index(m, randomizedchunks){ - auto & chunkdata = randomizedchunks[m][k].getchunkdata(); - if (chunkdata.isinram()) - { - if (verbosity) - fprintf (stderr, "releaserandomizedchunk: paging out randomized chunk %d (frame range [%d..%d]), %d resident in RAM\n", - (int)k, (int)randomizedchunks[m][k].globalts, (int)(randomizedchunks[m][k].globalte()-1), (int)(chunksinram-1)); - chunkdata.releasedata(); - numreleased++; - } - } - if (numreleased>0 && numreleased= windowend) - LogicError("requirerandomizedchunk: requested utterance outside in-memory chunk range"); - - foreach_index(m, randomizedchunks) - { - auto & chunk = randomizedchunks[m][chunkindex]; - auto & chunkdata = chunk.getchunkdata(); - if (chunkdata.isinram()) - numinram++; - } - if (numinram==randomizedchunks.size()) - return false; - else if (numinram==0) - { - foreach_index(m, randomizedchunks) - { - auto & chunk = randomizedchunks[m][chunkindex]; - auto & chunkdata = chunk.getchunkdata(); - if (verbosity) - fprintf (stderr, "feature set %d: requirerandomizedchunk: paging in randomized chunk %d (frame range [%d..%d]), %d resident in RAM\n", m, (int)chunkindex, (int)chunk.globalts, (int)(chunk.globalte()-1), (int)(chunksinram+1)); - msra::util::attempt (5, [&]() // (reading from network) - { - chunkdata.requiredata (featkind[m], featdim[m], sampperiod[m], this->lattices, verbosity); - }); - } - chunksinram++; - return true; - } - else{ - LogicError ("requirerandomizedchunk: inconsistency detected - some inputs need chunks paged in, some not"); - } - } - - class matrixasvectorofvectors // wrapper around a matrix that views it as a vector of column vectors - { - void operator= (const matrixasvectorofvectors &); // non-assignable - msra::dbn::matrixbase & m; - public: - matrixasvectorofvectors (msra::dbn::matrixbase & m) : m (m) {} - size_t size() const { return m.cols(); } - const_array_ref operator[] (size_t j) const { return array_ref (&m(0,j), m.rows()); } - }; - - size_t chunkforframepos (const size_t t) const // find chunk for a given frame position - { - //inspect chunk of first feature stream only - auto iter = std::lower_bound (randomizedchunks[0].begin(), randomizedchunks[0].end(), t, [&] (const chunk & chunk, size_t t) { return chunk.globalte() <= t; }); - const size_t chunkindex = iter - randomizedchunks[0].begin(); - if (t < randomizedchunks[0][chunkindex].globalts || t >= randomizedchunks[0][chunkindex].globalte()) - LogicError("chunkforframepos: dude, learn STL!"); - return chunkindex; - } - -public: - - void setverbosity(int newverbosity){ verbosity = newverbosity; } - - // get the next minibatch - // A minibatch is made up of one or more utterances. - // We will return less than 'framesrequested' unless the first utterance is too long. - // Note that this may return frames that are beyond the epoch end, but the first frame is always within the epoch. - // We specify the utterance by its global start time (in a space of a infinitely repeated training set). - // This is efficient since getbatch() is called with sequential 'globalts' except at epoch start. - // Note that the start of an epoch does not necessarily fall onto an utterance boundary. The caller must use firstvalidglobalts() to find the first valid globalts at or after a given time. - // Support for data parallelism: If mpinodes > 1 then we will - // - load only a subset of blocks from the disk - // - skip frames/utterances in not-loaded blocks in the returned data - // - 'framesadvanced' will still return the logical #frames; that is, by how much the global time index is advanced - bool getbatch(const size_t globalts, const size_t framesrequested, - const size_t subsetnum, const size_t numsubsets, size_t & framesadvanced, - std::vector & feat, std::vector> & uids, - std::vector> & transcripts, - std::vector> & latticepairs, std::vector> & sentendmark, - std::vector> & phoneboundaries) override - { - bool readfromdisk = false; // return value: shall be 'true' if we paged in anything - - auto_timer timergetbatch; - assert (_totalframes > 0); - - // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below - const size_t sweep = lazyrandomization (globalts); - - size_t mbframes = 0; - const std::vector noboundaryflags; // dummy - if (!framemode) // regular utterance mode - { - // find utterance position for globalts - // There must be a precise match; it is not possible to specify frames that are not on boundaries. - auto positer = randomizedutteranceposmap.find (globalts); - if (positer == randomizedutteranceposmap.end()) - LogicError("getbatch: invalid 'globalts' parameter; must match an existing utterance boundary"); - const size_t spos = positer->second; - - // determine how many utterances will fit into the requested minibatch size - mbframes = randomizedutterancerefs[spos].numframes; // at least one utterance, even if too long - size_t epos; - for (epos = spos + 1; epos < numutterances && ((mbframes + randomizedutterancerefs[epos].numframes) < framesrequested); epos++) // add more utterances as long as they fit within requested minibatch size - mbframes += randomizedutterancerefs[epos].numframes; - - // do some paging housekeeping - // This will also set the feature-kind information if it's the first time. - // Free all chunks left of the range. - // Page-in all chunks right of the range. - // We are a little more blunt for now: Free all outside the range, and page in only what is touched. We could save some loop iterations. - const size_t windowbegin = positionchunkwindows[spos].windowbegin(); - const size_t windowend = positionchunkwindows[epos-1].windowend(); - for (size_t k = 0; k < windowbegin; k++) - releaserandomizedchunk (k); - for (size_t k = windowend; k < randomizedchunks[0].size(); k++) - releaserandomizedchunk (k); - for (size_t pos = spos; pos < epos; pos++) - if ((randomizedutterancerefs[pos].chunkindex % numsubsets) == subsetnum) - readfromdisk |= requirerandomizedchunk(randomizedutterancerefs[pos].chunkindex, windowbegin, windowend); // (window range passed in for checking only) - - // Note that the above loop loops over all chunks incl. those that we already should have. - // This has an effect, e.g., if 'numsubsets' has changed (we will fill gaps). - - // determine the true #frames we return, for allocation--it is less than mbframes in the case of MPI/data-parallel sub-set mode - size_t tspos = 0; - for (size_t pos = spos; pos < epos; pos++) - { - const auto & uttref = randomizedutterancerefs[pos]; - if ((uttref.chunkindex % numsubsets) != subsetnum) // chunk not to be returned for this MPI node - continue; - - tspos += uttref.numframes; - } - - // resize feat and uids - feat.resize(vdim.size()); - uids.resize(classids.size()); - phoneboundaries.resize(classids.size()); - sentendmark.resize(vdim.size()); - assert(feat.size()==vdim.size()); - assert(feat.size()==randomizedchunks.size()); - foreach_index(i, feat) - { - feat[i].resize (vdim[i], tspos); - - if (i==0) - { - foreach_index(j, uids) - { - if (issupervised()) // empty means unsupervised training -> return empty uids - { - uids[j].resize(tspos); - phoneboundaries[j].resize(tspos); - } - else - { - uids[i].clear(); - phoneboundaries[i].clear(); - } - latticepairs.clear(); // will push_back() below - transcripts.clear(); - } - foreach_index(j, sentendmark) - { - sentendmark[j].clear(); - } - } - } - // return these utterances - if (verbosity > 0) - fprintf(stderr, "getbatch: getting utterances %d..%d (%d subset of %d frames out of %d requested) in sweep %d\n", (int)spos, (int)(epos - 1), (int)tspos, (int)mbframes, (int)framesrequested, (int)sweep); - tspos = 0; // relative start of utterance 'pos' within the returned minibatch - for (size_t pos = spos; pos < epos; pos++) - { - const auto & uttref = randomizedutterancerefs[pos]; - if ((uttref.chunkindex % numsubsets) != subsetnum) // chunk not to be returned for this MPI node - continue; - - size_t n = 0; - foreach_index(i, randomizedchunks) - { - const auto & chunk = randomizedchunks[i][uttref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - assert((numsubsets > 1) || (uttref.globalts == globalts + tspos)); - auto uttframes = chunkdata.getutteranceframes (uttref.utteranceindex); - matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) - n = uttframevectors.size(); - sentendmark[i].push_back(n + tspos); - assert (n == uttframes.cols() && uttref.numframes == n && chunkdata.numframes (uttref.utteranceindex) == n); - - // copy the frames and class labels - for (size_t t = 0; t < n; t++) // t = time index into source utterance - { - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], t + tspos); - //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], t + tspos); - } - - // copy the frames and class labels - if (i==0) - { - auto uttclassids = getclassids (uttref); - auto uttphoneboudaries = getphonebound(uttref); - foreach_index(j, uttclassids) - { - for (size_t t = 0; t < n; t++) // t = time index into source utterance - { - if (issupervised()) - { - uids[j][t + tspos] = uttclassids[j][t]; - phoneboundaries[j][t + tspos] = uttphoneboudaries[j][t]; - } - } - - if (!this->lattices.empty()) - { - auto latticepair = chunkdata.getutterancelattice (uttref.utteranceindex); - latticepairs.push_back (latticepair); - // look up reference - const auto & key = latticepair->getkey(); - if (!allwordtranscripts.empty()) - { - const auto & transcript = allwordtranscripts.find (key)->second; - transcripts.push_back (transcript.words); - } - } - } - } - } - tspos += n; - } - - foreach_index(i, feat) - { - assert(tspos == feat[i].cols()); - } - } - else - { - const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep - const size_t sweepte = sweepts + _totalframes; // and its end - const size_t globalte = min (globalts + framesrequested, sweepte); // we return as much as requested, but not exceeding sweep end - mbframes = globalte - globalts; // that's our mb size - - // determine window range - // We enumerate all frames--can this be done more efficiently? - const size_t firstchunk = chunkforframepos (globalts); - const size_t lastchunk = chunkforframepos (globalte-1); - const size_t windowbegin = randomizedchunks[0][firstchunk].windowbegin; - const size_t windowend = randomizedchunks[0][lastchunk].windowend; - if (verbosity > 0) - fprintf (stderr, "getbatch: getting randomized frames [%d..%d] (%d frames out of %d requested) in sweep %d; chunks [%d..%d] -> chunk window [%d..%d)\n", - (int)globalts, (int)globalte, (int)mbframes, (int)framesrequested, (int)sweep, (int)firstchunk, (int)lastchunk, (int)windowbegin, (int)windowend); - // release all data outside, and page in all data inside - for (size_t k = 0; k < windowbegin; k++) - releaserandomizedchunk (k); - for (size_t k = windowbegin; k < windowend; k++) - if ((k % numsubsets) == subsetnum) // in MPI mode, we skip chunks this way - readfromdisk |= requirerandomizedchunk(k, windowbegin, windowend); // (window range passed in for checking only, redundant here) - for (size_t k = windowend; k < randomizedchunks[0].size(); k++) - releaserandomizedchunk (k); - - // determine the true #frames we return--it is less than mbframes in the case of MPI/data-parallel sub-set mode - // First determine it for all nodes, then pick the min over all nodes, as to give all the same #frames for better load balancing. - // TODO: No, return all; and leave it to caller to redistribute them [Zhijie Yan] - std::vector subsetsizes(numsubsets, 0); - for (size_t i = 0; i < mbframes; i++) // i is input frame index; j < i in case of MPI/data-parallel sub-set mode - { - const size_t framepos = (globalts + i) % _totalframes; // (for comments, see main loop below) - const frameref & frameref = randomizedframerefs[framepos]; - subsetsizes[frameref.chunkindex % numsubsets]++; - } - size_t j = subsetsizes[subsetnum]; // return what we have --TODO: we can remove the above full computation again now - const size_t allocframes = max(j, (mbframes + numsubsets - 1) / numsubsets); // we leave space for the desired #frames, assuming caller will try to pad them later - - // resize feat and uids - feat.resize(vdim.size()); - uids.resize(classids.size()); - assert(feat.size()==vdim.size()); - assert(feat.size()==randomizedchunks.size()); - foreach_index(i, feat) - { - feat[i].resize(vdim[i], allocframes); - feat[i].shrink(vdim[i], j); - - if (i==0) - { - foreach_index(k, uids) - { - if (issupervised()) // empty means unsupervised training -> return empty uids - uids[k].resize (j); - else - uids[k].clear(); - latticepairs.clear(); // will push_back() below - transcripts.clear(); - } - } - } - - // return randomized frames for the time range of those utterances - size_t currmpinodeframecount = 0; - for (size_t j = 0; j < mbframes; j++) - { - if (currmpinodeframecount >= feat[0].cols()) // MPI/data-parallel mode: all nodes return the same #frames, which is how feat(,) is allocated - break; - - // map to time index inside arrays - const size_t framepos = (globalts + j) % _totalframes; // using mod because we may actually run beyond the sweep for the last call - const frameref & frameref = randomizedframerefs[framepos]; - - // in MPI/data-parallel mode, skip frames that are not in chunks loaded for this MPI node - if ((frameref.chunkindex % numsubsets) != subsetnum) - continue; - - // random utterance - readfromdisk |= requirerandomizedchunk (frameref.chunkindex, windowbegin, windowend); // (this is just a check; should not actually page in anything) - - foreach_index(i, randomizedchunks) - { - const auto & chunk = randomizedchunks[i][frameref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - auto uttframes = chunkdata.getutteranceframes (frameref.utteranceindex); - matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[.].size() and m[.][.] as required by augmentneighbors()) - const size_t n = uttframevectors.size(); - assert (n == uttframes.cols() && chunkdata.numframes (frameref.utteranceindex) == n); n; - - // copy frame and class labels - const size_t t = frameref.frameindex; - - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], currmpinodeframecount); - - if (issupervised() && i == 0) - { - auto frameclassids = getclassids(frameref); - foreach_index(k, uids) - uids[k][currmpinodeframecount] = frameclassids[k][t]; - } - } - - currmpinodeframecount++; - } - } - timegetbatch = timergetbatch; - - // this is the number of frames we actually moved ahead in time - framesadvanced = mbframes; - - return readfromdisk; - } - - bool supportsbatchsubsetting() const override - { - return true; - } - - bool getbatch(const size_t globalts, - const size_t framesrequested, std::vector & feat, std::vector> & uids, - std::vector> & transcripts, - std::vector> & lattices, std::vector> & sentendmark, - std::vector> & phoneboundaries) - { - size_t dummy; - return getbatch(globalts, framesrequested, 0, 1, dummy, feat, uids, transcripts, lattices,sentendmark,phoneboundaries); - } - - double gettimegetbatch() { return timegetbatch;} - - // alternate (updated) definition for multiple inputs/outputs - read as a vector of feature matrixes or a vector of label strings - bool getbatch (const size_t /*globalts*/, - const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, std::vector & /*uids*/, - std::vector> & /*transcripts*/, - std::vector> & /*latticepairs*/) - { - // should never get here - RuntimeError("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchutterancesource instead\n"); - - // for single input/output set size to be 1 and run old getbatch - //feat.resize(1); - //uids.resize(1); - //return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); - } - - size_t totalframes() const { return _totalframes; } - - // return first valid globalts to ask getbatch() for - // In utterance mode, the epoch start may fall in the middle of an utterance. - // We return the end time of that utterance (which, in pathological cases, may in turn be outside the epoch; handle that). - /*implement*/ size_t firstvalidglobalts (const size_t globalts) - { - // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below - const size_t sweep = lazyrandomization (globalts); - // frame mode: start at sweep boundary directly - if (framemode) - return globalts; - // utterance mode - assert (globalts >= sweep * _totalframes && globalts < (sweep + 1) * _totalframes); sweep; - foreach_index (pos, randomizedutterancerefs) - if (randomizedutterancerefs[pos].globalts >= globalts) - return randomizedutterancerefs[pos].globalts; // exact or inexact match - return randomizedutterancerefs.back().globalte(); // boundary case: requested time falls within the last utterance - } - - const std::vector & unitcounts() const { return counts[0]; } - const std::vector & unitcounts(size_t index) const { return counts[index]; } - -}; - -};}; - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// utterancesourcemulti.h -- implementation of utterancesource.h that supports multiple feature and label sets +// + +#pragma once + +#include "Basics.h" // for attempt() +#include "htkfeatio.h" // for htkmlfreader +#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) +#include "minibatchsourcehelpers.h" +#include "minibatchiterator.h" +#include "unordered_set" + +namespace msra { namespace dbn { + +// --------------------------------------------------------------------------- +// minibatchutterancesource -- feature source to provide randomized utterances +// This also implements a frame-wise mode, which is layered on top of the utterance-wise mode +// and thus benefits from its goodies such as corpus-wide high-level randomization and chunk paging. +// --------------------------------------------------------------------------- +class minibatchutterancesourcemulti : public minibatchsource +{ + void operator=(const minibatchutterancesourcemulti & other); // non-assignable + std::vector vdim; // feature dimension after augmenting neighhors + std::vector leftcontext; // number of frames to the left of the target frame in the context window + std::vector rightcontext; // number of frames to the right of the target frame in the context window + std::vector sampperiod; // (for reference and to check against model) + std::vector featkind; + std::vector featdim; + const bool framemode; // true -> actually return frame-level randomized frames (not possible in lattice mode) + std::vector> counts; // [s] occurence count for all states (used for priors) + int verbosity; + // lattice reader + //const std::vector> &lattices; + const latticesource & lattices; + + //std::vector lattices; + // word-level transcripts (for MMI mode when adding best path to lattices) + const map & allwordtranscripts; // (used for getting word-level transcripts) + //std::vector> allwordtranscripts; + // data store (incl. paging in/out of features and lattices) + struct utterancedesc // data descriptor for one utterance + { + msra::asr::htkfeatreader::parsedpath parsedpath; // archive filename and frame range in that file + size_t classidsbegin; // index into allclassids[] array (first frame) + + utterancedesc (msra::asr::htkfeatreader::parsedpath&& ppath, size_t classidsbegin) : parsedpath (std::move(ppath)), classidsbegin (classidsbegin) {} + + const wstring & logicalpath() const { return parsedpath; /*type cast will return logical path*/ } + size_t numframes() const { return parsedpath.numframes(); } + wstring key() const // key used for looking up lattice (not stored to save space) + { +#ifdef _MSC_VER + static const wstring emptywstring; + static const wregex deleteextensionre (L"\\.[^\\.\\\\/:]*$"); + return regex_replace (logicalpath(), deleteextensionre, emptywstring); // delete extension (or not if none) +#else + return removeExtension(logicalpath()); +#endif + } + }; + + // Make sure type 'utterancedesc' has a move constructor + static_assert(std::is_move_constructible::value, "Type 'utterancedesc' should be move constructible!"); + + struct utterancechunkdata // data for a chunk of utterances + { + std::vector utteranceset; // utterances in this set + size_t numutterances() const { return utteranceset.size(); } + + std::vector firstframes; // [utteranceindex] first frame for given utterance + mutable msra::dbn::matrix frames; // stores all frames consecutively (mutable since this is a cache) + size_t totalframes; // total #frames for all utterances in this chunk + mutable std::vector> lattices; // (may be empty if none) + + // construction + utterancechunkdata() : totalframes (0) {} + void push_back (utterancedesc &&/*destructive*/ utt) + { + if (isinram()) + LogicError("utterancechunkdata: frames already paged into RAM--too late to add data"); + firstframes.push_back (totalframes); + totalframes += utt.numframes(); + utteranceset.push_back (std::move(utt)); + } + + // accessors to an utterance's data + size_t numframes (size_t i) const { return utteranceset[i].numframes(); } + size_t getclassidsbegin (size_t i) const { return utteranceset[i].classidsbegin; } + msra::dbn::matrixstripe getutteranceframes (size_t i) const // return the frame set for a given utterance + { + if (!isinram()) + LogicError("getutteranceframes: called when data have not been paged in"); + const size_t ts = firstframes[i]; + const size_t n = numframes(i); + return msra::dbn::matrixstripe (frames, ts, n); + } + shared_ptr getutterancelattice (size_t i) const // return the frame set for a given utterance + { + if (!isinram()) + LogicError("getutteranceframes: called when data have not been paged in"); + return lattices[i]; + } + + // paging + // test if data is in memory at the moment + bool isinram() const { return !frames.empty(); } + // page in data for this chunk + // We pass in the feature info variables by ref which will be filled lazily upon first read + void requiredata (string & featkind, size_t & featdim, unsigned int & sampperiod, const latticesource & latticesource, int verbosity=0) const + { + if (numutterances() == 0) + LogicError("requiredata: cannot page in virgin block"); + if (isinram()) + LogicError("requiredata: called when data is already in memory"); + try // this function supports retrying since we read from the unrealible network, i.e. do not return in a broken state + { + msra::asr::htkfeatreader reader; // feature reader (we reinstantiate it for each block, i.e. we reopen the file actually) + // if this is the first feature read ever, we explicitly open the first file to get the information such as feature dimension + if (featdim == 0) + { + reader.getinfo (utteranceset[0].parsedpath, featkind, featdim, sampperiod); + fprintf (stderr, "requiredata: determined feature kind as %d-dimensional '%s' with frame shift %.1f ms\n", (int)featdim, featkind.c_str(), sampperiod / 1e4); + } + // read all utterances; if they are in the same archive, htkfeatreader will be efficient in not closing the file + frames.resize (featdim, totalframes); + if (!latticesource.empty()) + lattices.resize (utteranceset.size()); + foreach_index (i, utteranceset) + { + //fprintf (stderr, "."); + // read features for this file + auto uttframes = getutteranceframes (i); // matrix stripe for this utterance (currently unfilled) + reader.read (utteranceset[i].parsedpath, (const string &) featkind, sampperiod, uttframes); // note: file info here used for checkuing only + // page in lattice data + if (!latticesource.empty()) + latticesource.getlattices (utteranceset[i].key(), lattices[i], uttframes.cols()); + } + //fprintf (stderr, "\n"); + if (verbosity) + fprintf (stderr, "requiredata: %d utterances read\n", (int)utteranceset.size()); + } + catch (...) + { + releasedata(); + throw; + } + } + // page out data for this chunk + void releasedata() const + { + if (numutterances() == 0) + LogicError("releasedata: cannot page out virgin block"); + if (!isinram()) + LogicError("releasedata: called when data is not memory"); + // release frames + frames.resize (0, 0); + // release lattice data + lattices.clear(); + } + }; + std::vector> allchunks; // set of utterances organized in chunks, referred to by an iterator (not an index) + std::vector>> classids; // [classidsbegin+t] concatenation of all state sequences + std::vector>> phoneboundaries; + bool issupervised() const { return !classids.empty(); } + size_t numutterances; // total number of utterances + size_t _totalframes; // total frames (same as classids.size() if we have labels) + double timegetbatch; // [v-hansu] for time measurement + // sequence in random order of actual use (randomized, where randomization is cached) + const size_t randomizationrange;// parameter remembered; this is the full window (e.g. 48 hours), not the half window + size_t currentsweep; // randomization is currently cached for this sweep; if it changes, rebuild all below + struct chunk // chunk as used in actual processing order (randomized sequence) + { + // the underlying chunk (as a non-indexed reference into the chunk set) + std::vector::const_iterator uttchunkdata; + const utterancechunkdata & getchunkdata() const { return *uttchunkdata; } + size_t numutterances() const { return uttchunkdata->numutterances(); } + size_t numframes() const { return uttchunkdata->totalframes; } + + // position in utterance-position space + size_t utteranceposbegin; + size_t utteranceposend() const { return utteranceposbegin + numutterances(); } + + // position on global time line + size_t globalts; // start frame on global timeline (after randomization) + size_t globalte() const { return globalts + numframes(); } + + // randomization range limits + size_t windowbegin; // randomizedchunk index of earliest chunk that utterances in here can be randomized with + size_t windowend; // and end index [windowbegin, windowend) + chunk (std::vector::const_iterator uttchunkdata, size_t utteranceposbegin, size_t globalts) : uttchunkdata (uttchunkdata), utteranceposbegin (utteranceposbegin), globalts (globalts) {} + }; + std::vector> randomizedchunks; // utterance chunks after being brought into random order (we randomize within a rolling window over them) + size_t chunksinram; // (for diagnostics messages) + struct utteranceref // describes the underlying random utterance associated with an utterance position + { + size_t chunkindex; // lives in this chunk (index into randomizedchunks[]) + size_t utteranceindex; // utterance index in that chunk + size_t numframes; // (cached since we cannot directly access the underlying data from here) + size_t globalts; // start frame in global space after randomization (for mapping frame index to utterance position) + size_t globalte() const { return globalts + numframes; } // end frame + utteranceref (size_t chunkindex, size_t utteranceindex) : chunkindex (chunkindex), utteranceindex (utteranceindex), globalts (SIZE_MAX), numframes (0) {} + void swap (utteranceref & other) // used in randomization + { + ::swap (chunkindex, other.chunkindex); + ::swap (utteranceindex, other.utteranceindex); + assert (globalts == SIZE_MAX && other.globalts == SIZE_MAX && numframes == 0 && other.numframes == 0); // can only swap before assigning these + } + }; + std::vector randomizedutterancerefs; // [pos] randomized utterance ids + std::unordered_map randomizedutteranceposmap; // [globalts] -> pos lookup table + struct positionchunkwindow // chunk window required in memory when at a certain position, for controlling paging + { + std::vector::iterator definingchunk; // the chunk in randomizedchunks[] that defined the utterance position of this utterance + size_t windowbegin() const { return definingchunk->windowbegin; } + size_t windowend() const { return definingchunk->windowend; } + bool isvalidforthisposition (const utteranceref & utt) const + { + return utt.chunkindex >= windowbegin() && utt.chunkindex < windowend(); // check if 'utt' lives in is in allowed range for this position + } + positionchunkwindow (std::vector::iterator definingchunk) : definingchunk (definingchunk) {} + }; + std::vector positionchunkwindows; // [utterance position] -> [windowbegin, windowend) for controlling paging + + // frame-level randomization layered on top of utterance chunking (randomized, where randomization is cached) + struct frameref + { + unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) + unsigned short utteranceindex; // utterance index in that chunk + static const size_t maxutterancesperchunk = 65535; + unsigned short frameindex; // frame index within the utterance + static const size_t maxframesperutterance = 65535; + frameref (size_t ci, size_t ui, size_t fi) : chunkindex ((unsigned short) ci), utteranceindex ((unsigned short) ui), frameindex ((unsigned short) fi) + { + if (ci == chunkindex && ui == utteranceindex && fi == frameindex) + return; + LogicError("frameref: bit fields too small"); + } + frameref() : chunkindex (0), utteranceindex (0), frameindex (0) {} + }; + biggrowablevector randomizedframerefs; // [globalt-sweepts] -> (chunk, utt, frame) lookup table for randomized frames --this can be REALLY big! + + // TODO: this may go away if we store classids directly in the utterance data + template class shiftedvector // accessing a vector with a non-0 starting index + { + void operator= (const shiftedvector &); + VECTOR & v; + size_t first; + size_t n; + void check (size_t i) const { if (i >= n) LogicError("shiftedvector: index out of bounds"); } + public: + shiftedvector (VECTOR & v, size_t first, size_t n) : v (v), first (first), n (n) { } + // TODO: the following is not templated--do it if needed; also should return a const reference then + size_t operator[] (size_t i) const { check (i); return v[first + i]; } + }; + template std::vector>> getclassids (const UTTREF & uttref) // return sub-vector of classids[] for a given utterance + { + std::vector>> allclassids; + allclassids.empty(); + + if (!issupervised()) + { + foreach_index(i,classids) + allclassids.push_back(std::move(shiftedvector> ((*classids[i]), 0, 0))); + return allclassids; // nothing to return + } + const auto & chunk = randomizedchunks[0][uttref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + const size_t classidsbegin = chunkdata.getclassidsbegin (uttref.utteranceindex); // index of first state label in global concatenated classids[] array + const size_t n = chunkdata.numframes (uttref.utteranceindex); + foreach_index(i,classids) + { + if ((*classids[i])[classidsbegin + n] != (CLASSIDTYPE) -1) + LogicError("getclassids: expected boundary marker not found, internal data structure screwed up"); + allclassids.push_back(std::move(shiftedvector> ((*classids[i]), classidsbegin, n))); + } + return allclassids; // nothing to return + } + template std::vector>> getphonebound(const UTTREF & uttref) // return sub-vector of classids[] for a given utterance + { + std::vector>> allphoneboundaries; + allphoneboundaries.empty(); + + if (!issupervised()) + { + foreach_index(i, classids) + allphoneboundaries.push_back(std::move(shiftedvector>((*phoneboundaries[i]), 0, 0))); + return allphoneboundaries; // nothing to return + } + const auto & chunk = randomizedchunks[0][uttref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + const size_t classidsbegin = chunkdata.getclassidsbegin(uttref.utteranceindex); // index of first state label in global concatenated classids[] array + const size_t n = chunkdata.numframes(uttref.utteranceindex); + foreach_index(i, phoneboundaries) + { + if ((*phoneboundaries[i])[classidsbegin + n] != (HMMIDTYPE)-1) + LogicError("getclassids: expected boundary marker not found, internal data structure screwed up"); + allphoneboundaries.push_back(std::move(shiftedvector>((*phoneboundaries[i]), classidsbegin, n))); + } + return allphoneboundaries; // nothing to return + } + +public: + // constructor + // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). + // This mode requires utterances with time stamps. + minibatchutterancesourcemulti (const std::vector> & infiles, const std::vector>> & labels, + std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, + const latticesource & lattices, const map & allwordtranscripts, const bool framemode) + : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), randomizationrange (randomizationrange), currentsweep (SIZE_MAX), + lattices (lattices), allwordtranscripts (allwordtranscripts), framemode (framemode), chunksinram (0), timegetbatch (0), verbosity(2) + // [v-hansu] change framemode (lattices.empty()) into framemode (false) to run utterance mode without lattice + // you also need to change another line, search : [v-hansu] comment out to run utterance mode without lattice + { + // process infiles to know dimensions of things (but not loading features) + size_t nomlf = 0; // number of entries missing in MLF (diagnostics) + size_t nolat = 0; // number of entries missing in lattice archive (diagnostics) + std::vector numclasses; // number of output classes as found in the label file (diagnostics) + _totalframes = 0; + wstring key; + size_t numutts=0; + + std::vectoruttisvalid; // boolean flag to check that utterance is valid. valid means number of + //frames is consistent across all feature and label streams + std::vectoruttduration; // track utterance durations to determine utterance validity + + std::vector classidsbegin; + + + allchunks = std::vector>(infiles.size(), std::vector()); + featdim = std::vector(infiles.size(), 0); + sampperiod = std::vector(infiles.size(), 0); + featkind = std::vector(infiles.size(), ""); + + numclasses = std::vector(labels.size(), 0); + counts = std::vector>(labels.size(), std::vector()); + + foreach_index (i, labels) + { + classids.push_back(unique_ptr>(new biggrowablevector())); + phoneboundaries.push_back(unique_ptr>(new biggrowablevector())); + //std::pair,std::vector> latticetocs; + //std::unordered_map modelsymmap; + //lattices.push_back(shared_ptr(new latticesource(latticetocs, modelsymmap))); + } + + + // first check consistency across feature streams + // We'll go through the SCP files for each stream to make sure the duration is consistent + // If not, we'll plan to ignore the utterance, and inform the user + // m indexes the feature stream + // i indexes the files within a stream, i.e. in the SCP file) + foreach_index(m, infiles){ + if (m == 0){ + numutts = infiles[m].size(); + uttisvalid = std::vector(numutts, true); + uttduration = std::vector(numutts, 0); + } + else if (infiles[m].size()!=numutts) + RuntimeError("minibatchutterancesourcemulti: all feature files must have same number of utterances"); + + foreach_index(i, infiles[m]){ + utterancedesc utterance(msra::asr::htkfeatreader::parsedpath(infiles[m][i]), 0); //mseltzer - is this foolproof for multiio? is classids always non-empty? + const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode + // we need at least 2 frames for boundary markers to work + if (uttframes < 2) + RuntimeError("minibatchutterancesource: utterances < 2 frames not supported"); + if (uttframes > frameref::maxframesperutterance) + { + fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %ls\n", i, (int)uttframes, (int)frameref::maxframesperutterance, key.c_str()); + uttduration[i] = 0; + uttisvalid[i] = false; + } + else + { + if (m == 0){ + uttduration[i] = uttframes; + uttisvalid[i] = true; + } + else if (uttduration[i] != uttframes){ + fprintf(stderr, "minibatchutterancesource: skipping %d-th file due to inconsistency in duration in different feature streams (%d vs %d frames)\n", i, (int)uttduration[i], (int)uttframes); + uttduration[i] = 0; + uttisvalid[i] = false; + } + } + } + } + size_t invalidutts=0; + foreach_index(i, uttisvalid){ + if (!uttisvalid[i]) + invalidutts++; + } + if (invalidutts > uttisvalid.size() / 2) + RuntimeError("minibatchutterancesource: too many files with inconsistent durations, assuming broken configuration\n"); + else if (invalidutts>0) + fprintf(stderr, "Found inconsistent durations across feature streams in %d out of %d files\n", (int)invalidutts, (int)uttisvalid.size()); + + + // now process the features and labels + size_t utterancesetsize = 0; + foreach_index (m, infiles) + { + std::vector utteranceset;// read all utterances to here first; at the end, distribute to chunks + utteranceset.reserve(infiles[m].size()); + //if (m==0) + // numutts = infiles[m].size(); + //else + // if (infiles[m].size()!=numutts) + // RuntimeError("minibatchutterancesourcemulti: all feature files must have same number of utterances\n"); + if (m==0) + classidsbegin.clear(); + + foreach_index (i, infiles[m]) + { + if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } + // build utterance descriptor + if (m == 0 && !labels.empty()) + classidsbegin.push_back(classids[0]->size()); + + if (uttisvalid[i]) + { + utterancedesc utterance (msra::asr::htkfeatreader::parsedpath (infiles[m][i]), labels.empty() ? 0 : classidsbegin[i] ); //mseltzer - is this foolproof for multiio? is classids always non-empty? + const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode + assert(uttframes == uttduration[i]); // ensure nothing funky happened + // already performed these checks above + // we need at least 2 frames for boundary markers to work + //if (uttframes < 2) + // RuntimeError("minibatchutterancesource: utterances < 2 frames not supported"); + //if (uttframes > frameref::maxframesperutterance) + //{ + // fprintf (stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %ls", i, uttframes, frameref::maxframesperutterance, key.c_str()); + // continue; + //} + + // check whether we have the ref transcript + //auto labelsiter = labels[0].end(); + bool lacksmlf = true; + if (!labels.empty()) // empty means unsupervised mode (don't load any) + { + key = utterance.key(); + // check if labels are available (if not, it normally means that no path was found in realignment) + auto labelsiter = labels[0].find (key); + //const bool lacksmlf = (labelsiter == labels[0].end()); + lacksmlf = (labelsiter == labels[0].end()); + if (lacksmlf) + if (nomlf++ < 5) + fprintf (stderr, " [no labels for %ls]", key.c_str()); + // check if lattice is available (when in lattice mode) + // TODO: also check the #frames here; requires a design change of the TOC format & a rerun + const bool lackslat = !lattices.empty() && !lattices.haslattice (key); // ('true' if we have no lattices) + if (lackslat) + if (nolat++ < 5) + fprintf (stderr, " [no lattice for %ls]", key.c_str()); + // skip if either one is missing + if (lacksmlf || lackslat){ + uttisvalid[i] = false; + continue; // skip this utterance at all + } + } + // push the label sequence into classids[], since we already looked it up + // TODO: we can store labels more efficiently now since we don't do frame-wise random access anymore. + + // OK, utterance has all we need --remember it + + if (m==0) + { + if (!labels.empty() && !lacksmlf) + //if (!labels.empty() && labelsiter != labels[0].end()) + { + // first verify that all the label files have the proper duration + foreach_index (j, labels) + { + const auto & labseq = labels[j].find(key)->second; + // check if durations match; skip if not + size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); + if (labframes != uttframes) + { + fprintf (stderr, " [duration mismatch (%d in label vs. %d in feat file), skipping %ls]", (int)labframes, (int)uttframes, key.c_str()); + nomlf++; + uttisvalid[i] = false; + //continue; // skip this utterance at all + break; + } + } + if (uttisvalid[i]) + { + utteranceset.push_back(std::move(utterance)); + _totalframes += uttframes; + // then parse each mlf if the durations are consistent + foreach_index(j, labels) + { + const auto & labseq = labels[j].find(key)->second; + // expand classid sequence into flat array + foreach_index (i, labseq) + { + const auto & e = labseq[i]; + if ((i > 0 && labseq[i - 1].firstframe + labseq[i - 1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) + { + RuntimeError("minibatchutterancesource: labels not in consecutive order MLF in label set: %ls", key.c_str()); + } + if (e.classid >= udim[j]) + { + RuntimeError("minibatchutterancesource: class id %d exceeds model output dimension %d in file %ls", (int)e.classid, (int)udim[j], key.c_str()); + } + if (e.classid != (CLASSIDTYPE) e.classid) + RuntimeError("CLASSIDTYPE has too few bits"); + for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) + { + classids[j]->push_back (e.classid); + if (e.phonestart != 0 && t == e.firstframe) + phoneboundaries[j]->push_back((HMMIDTYPE)e.phonestart); + else + phoneboundaries[j]->push_back((HMMIDTYPE)0); + } + numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); + counts[j].resize (numclasses[j], 0); + counts[j][e.classid] += e.numframes; + } + + classids[j]->push_back ((CLASSIDTYPE) -1); // append a boundary marker marker for checking + phoneboundaries[j]->push_back((HMMIDTYPE)-1); // append a boundary marker marker for checking + + if (!labels[j].empty() && classids[j]->size() != _totalframes + utteranceset.size()) + LogicError("minibatchutterancesource: label duration inconsistent with feature file in MLF label set: %ls", key.c_str()); + assert (labels[j].empty() || classids[j]->size() == _totalframes + utteranceset.size()); + } + } + } + else + { + assert(classids.empty() && labels.empty()); + utteranceset.push_back(std::move(utterance)); + _totalframes += uttframes; + } + } + else + { + utteranceset.push_back(std::move(utterance)); + } + } + } + if (m == 0) + utterancesetsize = utteranceset.size(); + else + assert(utteranceset.size() == utterancesetsize); + + fprintf (stderr, "feature set %d: %d frames in %d out of %d utterances\n", m, (int)_totalframes, (int)utteranceset.size(), (int)infiles[m].size()); + + if (!labels.empty()){ + foreach_index (j, labels){ + biggrowablevector & cid = *classids[j]; + foreach_index (i, utteranceset){ + //if ((*classids[j])[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) + //printf("index = %d\n",utteranceset[i].classidsbegin + utteranceset[i].numframes()); + //printf("cid[index] = %d\n",cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()]); + //printf("CLASSIDTYPE(-1) = %d\n",(CLASSIDTYPE) -1); + if (cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) + LogicError("minibatchutterancesource: classids[] out of sync"); + } + } + } + if (nomlf + nolat > 0) + { + fprintf (stderr, "minibatchutterancesource: out of %d files, %d files not found in label set and %d have no lattice\n", (int)infiles[0].size(), (int)nomlf, (int)nolat); + if (nomlf + nolat > infiles[m].size() / 2) + RuntimeError("minibatchutterancesource: too many files not found in label set--assuming broken configuration\n"); + } + if (m==0) {foreach_index(j, numclasses) { fprintf(stderr,"label set %d: %d classes\n", j, (int)numclasses[j]); } } + // distribute them over chunks + // We simply count off frames until we reach the chunk size. + // Note that we first randomize the chunks, i.e. when used, chunks are non-consecutive and thus cause the disk head to seek for each chunk. + const size_t framespersec = 100; // we just assume this; our efficiency calculation is based on this + const size_t chunkframes = 15 * 60 * framespersec; // number of frames to target for each chunk + // Loading an initial 24-hour range will involve 96 disk seeks, acceptable. + // When paging chunk by chunk, chunk size ~14 MB. + std::vector & thisallchunks = allchunks[m]; + + thisallchunks.resize (0); + thisallchunks.reserve (_totalframes / chunkframes); + + foreach_index (i, utteranceset) + { + // if exceeding current entry--create a new one + // I.e. our chunks are a little larger than wanted (on av. half the av. utterance length). + if (thisallchunks.empty() || thisallchunks.back().totalframes > chunkframes || thisallchunks.back().numutterances() >= frameref::maxutterancesperchunk) + thisallchunks.push_back (utterancechunkdata()); + // append utterance to last chunk + utterancechunkdata & currentchunk = thisallchunks.back(); + currentchunk.push_back (std::move (utteranceset[i])); // move it out from our temp array into the chunk + // TODO: above push_back does not actually 'move' because the internal push_back does not accept that + } + numutterances = utteranceset.size(); + fprintf (stderr, "minibatchutterancesource: %d utterances grouped into %d chunks, av. chunk size: %.1f utterances, %.1f frames\n", + (int)numutterances, (int)thisallchunks.size(), numutterances / (double) thisallchunks.size(), _totalframes / (double) thisallchunks.size()); + // Now utterances are stored exclusively in allchunks[]. They are never referred to by a sequential utterance id at this point, only by chunk/within-chunk index. + } + } + +private: + // shuffle a vector into random order by randomly swapping elements + + template static void randomshuffle (VECTOR & v, size_t randomseed) + { + if (v.size() > RAND_MAX * (size_t) RAND_MAX) + RuntimeError("randomshuffle: too large set: need to change to different random generator!"); + srand ((unsigned int) randomseed); + foreach_index (i, v) + { + // pick a random location + const size_t irand = msra::dbn::rand (0, v.size()); + + // swap element i with it + if (irand == (size_t) i) + continue; + ::swap (v[i], v[irand]); + } + } +#if 0 + template static void randomshuffle(std::vector v, size_t randomseed) + { + foreach_index(j, v) + { + if (v[j].size() > RAND_MAX * (size_t) RAND_MAX) + RuntimeError("randomshuffle: too large set: need to change to different random generator!"); + } + srand ((unsigned int) randomseed); + + foreach_index (i, v[0]) + { + // pick a random location + const size_t irand = msra::dbn::rand (0, v[0].size()); + + foreach_index(j, v){ + // swap element i with it + if (irand == (size_t) i) + continue; + ::swap (v[j][i], v[j][irand]); + } + } + } +#endif //0 + static void checkoverflow (size_t fieldval, size_t targetval, const char * fieldname) + { + if (fieldval != targetval) + RuntimeError("checkoverflow: bit field %s too small for value 0x%x (cut from 0x%x)", fieldname, (int)targetval, (int)fieldval); + } + + // helper for testing whether a swapped frame position is valid (w.r.t. beign in RAM when being at position 't') + bool isframepositionvalid (const size_t t, const biggrowablevector & ttochunk) const + { + // look up valid range for time position + const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this original chunk (relationship is monotonous, not random) + const auto & chunk = randomizedchunks[0][positionchunkindex]; + // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) + const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) + const size_t poswindowend = chunk.windowend; + // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. + + // now see if the randomized location is within that window + const size_t actualchunkindexforpos = randomizedframerefs[t].chunkindex; // where this frame pos has been mapped to + return actualchunkindexforpos >= poswindowbegin && actualchunkindexforpos < poswindowend; + // We only need to test the chunk index. Utterance and frame can be randomized within a chunk as we want, as long it is in RAM. + } + + // big long helper to update all cached randomization information + // This is a rather complex process since we randomize on two levels: + // - chunks of consecutive data in the feature archive + // - within a range of chunks that is paged into RAM + // - utterances (in utt mode), or + // - frames (in frame mode) + // The 'globalts' parameter is the start time that triggered the rerandomization; it is NOT the base time of the randomized area. + size_t lazyrandomization (const size_t globalts) + { + const size_t sweep = globalts / _totalframes; // which sweep (this determines randomization) + if (sweep == currentsweep) // already got this one--nothing to do + return sweep; + + currentsweep = sweep; + if (verbosity>0) + fprintf (stderr, "lazyrandomization: re-randomizing for sweep %d in %s mode\n", (int)currentsweep, framemode ? "frame" : "utterance"); + + const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep + + // first randomize chunks + std::vector::const_iterator>> randomizedchunkrefs; + foreach_index (i, allchunks) + randomizedchunkrefs.push_back(std::vector::const_iterator>()); + + foreach_index (i, allchunks) + randomizedchunkrefs[i].reserve (allchunks[i].size()); + + foreach_index (i, allchunks) // TODO: this cries for iterating using the iterator! + { + foreach_index(j, allchunks[i]) + randomizedchunkrefs[i].push_back (allchunks[i].begin() + j); + assert (randomizedchunkrefs[i].size() == allchunks[i].size()); + + // note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams + randomshuffle (randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep) + + } + + // place them onto the global timeline -> randomizedchunks[] + // We are processing with randomization within a rolling window over this chunk sequence. + // Paging will happen on a chunk-by-chunk basis. + // The global time stamp is needed to determine the paging window. + randomizedchunks.clear(); // data chunks after being brought into random order (we randomize within a rolling window over them) + + foreach_index(i, allchunks) + randomizedchunks.push_back(std::vector()); + + foreach_index(i, allchunks) + { + randomizedchunks[i].reserve (randomizedchunkrefs[i].size()); + foreach_index (k, randomizedchunkrefs[i]) + randomizedchunks[i].push_back (chunk (randomizedchunkrefs[i][k], randomizedchunks[i].empty() ? 0 : randomizedchunks[i].back().utteranceposend(), randomizedchunks[i].empty() ? sweepts : randomizedchunks[i].back().globalte())); + assert (randomizedchunks[i].size() == allchunks[i].size()); + assert (randomizedchunks[i].empty() || (randomizedchunks[i].back().utteranceposend() == numutterances && randomizedchunks[i].back().globalte() == sweepts + _totalframes)); + } + // for each chunk, compute the randomization range (w.r.t. the randomized chunk sequence) + foreach_index (i, randomizedchunks) + { + foreach_index (k, randomizedchunks[i]) + { + chunk & chunk = randomizedchunks[i][k]; + // start with the range of left neighbor + if (k == 0) + { + chunk.windowbegin = 0; + chunk.windowend = 1; + } + else + { + chunk.windowbegin = randomizedchunks[i][k-1].windowbegin; // might be too early + chunk.windowend = randomizedchunks[i][k-1].windowend; // might have more space + } + while (chunk.globalts - randomizedchunks[i][chunk.windowbegin].globalts > randomizationrange/2) + chunk.windowbegin++; // too early + while (chunk.windowend < randomizedchunks[i].size() && randomizedchunks[i][chunk.windowend].globalte() - chunk.globalts < randomizationrange/2) + chunk.windowend++; // got more space + } + } + if (!framemode) // utterance mode + { + // This sets up the following members: + // - positionchunkwindows + // - randomizedutterancerefs + // - randomizedutteranceposmap + + // We will now introduce the concept of utterance *position*. + // During processing, utterances will be indexed by position (which is in turn derived from a frame index in getbatch()), + // and it is assumed (required) that positions are requested consecutively. + // Each utterance position has an underlying associated utterance, which is represented as (chunkid, within-chunk index) and randomly assigned. + // Each utterance position also has an associated range of chunks that are kept in memory, + // and the associated underlying utterance is guaranteed to be found within that associated range of chunks. + // That allows to page out/in data when processing utterance positions in a consecutive manner. + + // compute chunk windows for every utterance position -> positionchunkwindows[] + // Utterance positions can only reference underlying utterance data within the chunk window. + // Utterance positions are defined by the randomized chunk sequence (i.e. their underlying 'defining' chunk differs from sweep to sweep). + positionchunkwindows.clear(); // [utterance position] -> [windowbegin, windowend) for controlling paging + positionchunkwindows.reserve (numutterances); + + // positionchunkwindows should be consistent for all inputs (distinct feature streams), so just build based on feature[0] + // contains pointer to chunk elements but only to compute index + foreach_index (k, randomizedchunks[0]) // TODO: this really cries for iterating using iterators! + { + chunk & chunk = randomizedchunks[0][k]; + for (size_t i = chunk.utteranceposbegin; i < chunk.utteranceposend(); i++) // loop over utterances in this chunk + positionchunkwindows.push_back (randomizedchunks[0].begin() + k); + // to look up the chunk range in memory for a position, look up the defining chunk and its range + } + assert (positionchunkwindows.size() == numutterances); + + // build the randomized utterances array -> randomizedutterancerefs[] + // start by assigning all utterance positions to utterances in non-random consecutive manner + randomizedutterancerefs.clear(); // [pos] randomized utterance ids + randomizedutterancerefs.reserve (numutterances); + foreach_index (k, randomizedchunks[0]) + { + chunk & chunk = randomizedchunks[0][k]; + for (size_t i = 0; i < chunk.numutterances(); i++) // loop over utterances in this chunk + randomizedutterancerefs.push_back (utteranceref (k, i)); + } + assert (randomizedutterancerefs.size() == numutterances); + foreach_index (i, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[i]; + assert (positionchunkwindows[i].isvalidforthisposition(uttref)); uttref; + } + + // check we got those setup right + + // we now randomly shuffle randomizedutterancerefs[pos], while considering the constraints of what chunk range needs to be in memory + srand ((unsigned int) sweep + 1); + for (size_t i = 0; i < randomizedutterancerefs.size(); i++) + { + // get valid randomization range, expressed in chunks + const size_t windowbegin = positionchunkwindows[i].windowbegin(); + const size_t windowend = positionchunkwindows[i].windowend(); + + // get valid randomization range, expressed in utterance positions + // Remember, utterance positions are defined by chunks. + const size_t posbegin = randomizedchunks[0][windowbegin].utteranceposbegin; + const size_t posend = randomizedchunks[0][windowend-1].utteranceposend(); + + // randomization range for this utterance position is [posbegin, posend) + for(;;) + { + // pick a random location + const size_t j = msra::dbn::rand (posbegin, posend); // a random number within the window + if (i == j) + break; // the random gods say "this one points to its original position"... nothing wrong about that, but better not try to swap + + // We want to swap utterances at i and j, but need to make sure they remain in their allowed range. + // This is guaranteed for a so-far untouched utterance, but both i and j may have been touched by a previous swap. + + // We want to use the utterance previously referenced at utterance position j at position i. Is that allowed? + if (!positionchunkwindows[i].isvalidforthisposition (randomizedutterancerefs[j])) + continue; // nope --try another + + // Likewise may we use the utterance previously referenced at utterance position i at position j? + if (!positionchunkwindows[j].isvalidforthisposition (randomizedutterancerefs[i])) + continue; // nope --try another + + // yep--swap them + randomizedutterancerefs[i].swap (randomizedutterancerefs[j]); + break; + } + } + + // place the randomized utterances on the global timeline so we can find them by globalts + size_t t = sweepts; + foreach_index (i, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[i]; + uttref.globalts = t; + uttref.numframes = randomizedchunks[0][uttref.chunkindex].getchunkdata().numframes (uttref.utteranceindex); + t = uttref.globalte(); + } + assert (t == sweepts + _totalframes); + + // verify that we got it right (I got a knot in my head!) + foreach_index (i, randomizedutterancerefs) + { + // get utterance referenced at this position + const auto & uttref = randomizedutterancerefs[i]; + // check if it is valid for this position + if (uttref.chunkindex < positionchunkwindows[i].windowbegin() || uttref.chunkindex >= positionchunkwindows[i].windowend()) + LogicError("lazyrandomization: randomization logic mangled!"); + } + + // create lookup table for (globalts values -> pos) -> randomizedutteranceposmap[] + randomizedutteranceposmap.clear(); // [globalts] -> pos lookup table + foreach_index (pos, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[pos]; + randomizedutteranceposmap[uttref.globalts] = (size_t) pos; + } + } + else // frame mode + { + // This sets up the following members: + // - randomizedframerefs + + // Lazy mem allocation for frame references + if (randomizedframerefs.size() != _totalframes) + randomizedframerefs.resize(_totalframes); + + srand ((unsigned int) sweep + 1); + // An original timeline is established by the randomized chunks, denoted by 't'. + // Returned frames are indexed by frame position j = (globalt - sweept), which have an associated underlying 't'. + // It is guaranteed that uttterance frame position j maps to an underlying frame within the corresponding chunk window. + biggrowablevector ttochunk; // randomized chunk index associated with frame position + ttochunk.resize (_totalframes); + size_t t = 0; + frameref frameref; + // enumerate chunks in their randomized order and assign frame indices in that order -> randomizedframerefs[t] + // At this point, chunks are in randomized order, but utterances and frames within utterances are not randomized. + // Later we will randomize those as well. + foreach_index (i, randomizedchunks[0]) + { + frameref.chunkindex = (unsigned short)i; + checkoverflow (frameref.chunkindex, i, "frameref::chunkindex"); + const auto & chunk = randomizedchunks[0][i]; + const auto & chunkdata = chunk.getchunkdata(); + const size_t numutt = chunkdata.numutterances(); + for (size_t k = 0; k < numutt; k++) + { + frameref.utteranceindex = (short)k; + checkoverflow (frameref.utteranceindex, k, "frameref::utteranceindex"); + const size_t n = chunkdata.numframes (k); + for (size_t m = 0; m < n; m++) + { + frameref.frameindex = (short)m; + checkoverflow (frameref.frameindex, m, "frameref::utteranceindex"); + randomizedframerefs[t] = frameref; // hopefully this is a memory copy, not a bit-wise assignment! If not, then code it explicitly + ttochunk[t] = (unsigned short) i; + checkoverflow (ttochunk[t], i, "ttochunk[]"); + t++; + } + } + } + assert (t == _totalframes); + + // now randomize them --we use the nested loop again to avoid storing a backpointer + // The condition is that a randomized frame may not be moved out of its associated chunk window. + foreach_index (t, randomizedframerefs) + { + const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this chunk (relationship is monotonous, not random) + const auto & chunk = randomizedchunks[0][positionchunkindex]; // for window + + // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) + const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) + const size_t poswindowend = chunk.windowend; + // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. + // These chunks are associated with a range of frame positions. + // It is implied that if we are at position 't', the frames covered by chunks [poswindowbegin, poswindowend) are in RAM. + const size_t postbegin = randomizedchunks[0][poswindowbegin].globalts - sweepts; + const size_t postend = randomizedchunks[0][poswindowend-1].globalte() - sweepts; + // The position that this frame gets randomized to must be guaranteed to belong to a chunk within [postbegin, postend). + + for (;;) // (randomization retry loop) + { + size_t tswap = msra::dbn::rand (postbegin, postend); // random frame position within allowed range + // We want to swap 't' to 'tswap' and 'tswap' to 't'. + // - Both may have been swapped before. + // - Both must stay within the randomization window of their respective position. + // check admissibility of where the element at 'tswap' gets swapped to 't' (range = [windowbegin,windowend)) + size_t tswapchunkindex = randomizedframerefs[tswap].chunkindex; + if (tswapchunkindex < poswindowbegin || tswapchunkindex >= poswindowend) + continue; + // check admissibility of where the element at t gets swapped to (which is frame position 'tswap') + const size_t sourcechunkindex = randomizedframerefs[t].chunkindex; + size_t targetchunkindex = ttochunk[tswap]; // chunk associated with this frame position defines value range + const auto & targetchunk = randomizedchunks[0][targetchunkindex]; + const size_t targetwindowbegin = targetchunk.windowbegin; + const size_t targetwindowend = targetchunk.windowend; + if (sourcechunkindex < targetwindowbegin || sourcechunkindex >= targetwindowend) + continue; + // admissible--swap the two + ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); +#if 0 + break; +#else // post-check --so far did not trigger, can be removed + + // do a post-check if we got it right --we seem not to + if (isframepositionvalid (t, ttochunk) && isframepositionvalid (tswap, ttochunk)) + break; + // not valid: swap them back and try again --we actually discovered a bug in the code above + ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); + fprintf (stderr, "lazyrandomization: BUGBUG --invalid swapping condition detected\n"); +#endif + } + } + + // check it --my head spins + t = 0; + foreach_index (i, randomizedchunks[0]) + { + const auto & chunk = randomizedchunks[0][i]; // for window and chunkdata + const size_t poswindowbegin = chunk.windowbegin; + const size_t poswindowend = chunk.windowend; + + const auto & chunkdata = chunk.getchunkdata(); // for numutterances/numframes + const size_t numutt = chunkdata.numutterances(); + for (size_t k = 0; k < numutt; k++) + { + const size_t n = chunkdata.numframes (k); + for (size_t m = 0; m < n; m++) + { + const size_t randomizedchunkindex = randomizedframerefs[t].chunkindex; + if (randomizedchunkindex < poswindowbegin || randomizedchunkindex >= poswindowend) + LogicError("lazyrandomization: nope, you got frame randomization wrong, dude"); + t++; + } + } + } + assert (t == _totalframes); + } + + return sweep; + } + + // helper to page out a chunk with log message + void releaserandomizedchunk (size_t k) + { + size_t numreleased=0; + foreach_index(m, randomizedchunks){ + auto & chunkdata = randomizedchunks[m][k].getchunkdata(); + if (chunkdata.isinram()) + { + if (verbosity) + fprintf (stderr, "releaserandomizedchunk: paging out randomized chunk %d (frame range [%d..%d]), %d resident in RAM\n", + (int)k, (int)randomizedchunks[m][k].globalts, (int)(randomizedchunks[m][k].globalte()-1), (int)(chunksinram-1)); + chunkdata.releasedata(); + numreleased++; + } + } + if (numreleased>0 && numreleased= windowend) + LogicError("requirerandomizedchunk: requested utterance outside in-memory chunk range"); + + foreach_index(m, randomizedchunks) + { + auto & chunk = randomizedchunks[m][chunkindex]; + auto & chunkdata = chunk.getchunkdata(); + if (chunkdata.isinram()) + numinram++; + } + if (numinram==randomizedchunks.size()) + return false; + else if (numinram==0) + { + foreach_index(m, randomizedchunks) + { + auto & chunk = randomizedchunks[m][chunkindex]; + auto & chunkdata = chunk.getchunkdata(); + if (verbosity) + fprintf (stderr, "feature set %d: requirerandomizedchunk: paging in randomized chunk %d (frame range [%d..%d]), %d resident in RAM\n", m, (int)chunkindex, (int)chunk.globalts, (int)(chunk.globalte()-1), (int)(chunksinram+1)); + msra::util::attempt (5, [&]() // (reading from network) + { + chunkdata.requiredata (featkind[m], featdim[m], sampperiod[m], this->lattices, verbosity); + }); + } + chunksinram++; + return true; + } + else{ + LogicError ("requirerandomizedchunk: inconsistency detected - some inputs need chunks paged in, some not"); + } + } + + class matrixasvectorofvectors // wrapper around a matrix that views it as a vector of column vectors + { + void operator= (const matrixasvectorofvectors &); // non-assignable + msra::dbn::matrixbase & m; + public: + matrixasvectorofvectors (msra::dbn::matrixbase & m) : m (m) {} + size_t size() const { return m.cols(); } + const_array_ref operator[] (size_t j) const { return array_ref (&m(0,j), m.rows()); } + }; + + size_t chunkforframepos (const size_t t) const // find chunk for a given frame position + { + //inspect chunk of first feature stream only + auto iter = std::lower_bound (randomizedchunks[0].begin(), randomizedchunks[0].end(), t, [&] (const chunk & chunk, size_t t) { return chunk.globalte() <= t; }); + const size_t chunkindex = iter - randomizedchunks[0].begin(); + if (t < randomizedchunks[0][chunkindex].globalts || t >= randomizedchunks[0][chunkindex].globalte()) + LogicError("chunkforframepos: dude, learn STL!"); + return chunkindex; + } + +public: + + void setverbosity(int newverbosity){ verbosity = newverbosity; } + + // get the next minibatch + // A minibatch is made up of one or more utterances. + // We will return less than 'framesrequested' unless the first utterance is too long. + // Note that this may return frames that are beyond the epoch end, but the first frame is always within the epoch. + // We specify the utterance by its global start time (in a space of a infinitely repeated training set). + // This is efficient since getbatch() is called with sequential 'globalts' except at epoch start. + // Note that the start of an epoch does not necessarily fall onto an utterance boundary. The caller must use firstvalidglobalts() to find the first valid globalts at or after a given time. + // Support for data parallelism: If mpinodes > 1 then we will + // - load only a subset of blocks from the disk + // - skip frames/utterances in not-loaded blocks in the returned data + // - 'framesadvanced' will still return the logical #frames; that is, by how much the global time index is advanced + bool getbatch(const size_t globalts, const size_t framesrequested, + const size_t subsetnum, const size_t numsubsets, size_t & framesadvanced, + std::vector & feat, std::vector> & uids, + std::vector> & transcripts, + std::vector> & latticepairs, std::vector> & sentendmark, + std::vector> & phoneboundaries) override + { + bool readfromdisk = false; // return value: shall be 'true' if we paged in anything + + auto_timer timergetbatch; + assert (_totalframes > 0); + + // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below + const size_t sweep = lazyrandomization (globalts); + + size_t mbframes = 0; + const std::vector noboundaryflags; // dummy + if (!framemode) // regular utterance mode + { + // find utterance position for globalts + // There must be a precise match; it is not possible to specify frames that are not on boundaries. + auto positer = randomizedutteranceposmap.find (globalts); + if (positer == randomizedutteranceposmap.end()) + LogicError("getbatch: invalid 'globalts' parameter; must match an existing utterance boundary"); + const size_t spos = positer->second; + + // determine how many utterances will fit into the requested minibatch size + mbframes = randomizedutterancerefs[spos].numframes; // at least one utterance, even if too long + size_t epos; + for (epos = spos + 1; epos < numutterances && ((mbframes + randomizedutterancerefs[epos].numframes) < framesrequested); epos++) // add more utterances as long as they fit within requested minibatch size + mbframes += randomizedutterancerefs[epos].numframes; + + // do some paging housekeeping + // This will also set the feature-kind information if it's the first time. + // Free all chunks left of the range. + // Page-in all chunks right of the range. + // We are a little more blunt for now: Free all outside the range, and page in only what is touched. We could save some loop iterations. + const size_t windowbegin = positionchunkwindows[spos].windowbegin(); + const size_t windowend = positionchunkwindows[epos-1].windowend(); + for (size_t k = 0; k < windowbegin; k++) + releaserandomizedchunk (k); + for (size_t k = windowend; k < randomizedchunks[0].size(); k++) + releaserandomizedchunk (k); + for (size_t pos = spos; pos < epos; pos++) + if ((randomizedutterancerefs[pos].chunkindex % numsubsets) == subsetnum) + readfromdisk |= requirerandomizedchunk(randomizedutterancerefs[pos].chunkindex, windowbegin, windowend); // (window range passed in for checking only) + + // Note that the above loop loops over all chunks incl. those that we already should have. + // This has an effect, e.g., if 'numsubsets' has changed (we will fill gaps). + + // determine the true #frames we return, for allocation--it is less than mbframes in the case of MPI/data-parallel sub-set mode + size_t tspos = 0; + for (size_t pos = spos; pos < epos; pos++) + { + const auto & uttref = randomizedutterancerefs[pos]; + if ((uttref.chunkindex % numsubsets) != subsetnum) // chunk not to be returned for this MPI node + continue; + + tspos += uttref.numframes; + } + + // resize feat and uids + feat.resize(vdim.size()); + uids.resize(classids.size()); + phoneboundaries.resize(classids.size()); + sentendmark.resize(vdim.size()); + assert(feat.size()==vdim.size()); + assert(feat.size()==randomizedchunks.size()); + foreach_index(i, feat) + { + feat[i].resize (vdim[i], tspos); + + if (i==0) + { + foreach_index(j, uids) + { + if (issupervised()) // empty means unsupervised training -> return empty uids + { + uids[j].resize(tspos); + phoneboundaries[j].resize(tspos); + } + else + { + uids[i].clear(); + phoneboundaries[i].clear(); + } + latticepairs.clear(); // will push_back() below + transcripts.clear(); + } + foreach_index(j, sentendmark) + { + sentendmark[j].clear(); + } + } + } + // return these utterances + if (verbosity > 0) + fprintf(stderr, "getbatch: getting utterances %d..%d (%d subset of %d frames out of %d requested) in sweep %d\n", (int)spos, (int)(epos - 1), (int)tspos, (int)mbframes, (int)framesrequested, (int)sweep); + tspos = 0; // relative start of utterance 'pos' within the returned minibatch + for (size_t pos = spos; pos < epos; pos++) + { + const auto & uttref = randomizedutterancerefs[pos]; + if ((uttref.chunkindex % numsubsets) != subsetnum) // chunk not to be returned for this MPI node + continue; + + size_t n = 0; + foreach_index(i, randomizedchunks) + { + const auto & chunk = randomizedchunks[i][uttref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + assert((numsubsets > 1) || (uttref.globalts == globalts + tspos)); + auto uttframes = chunkdata.getutteranceframes (uttref.utteranceindex); + matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) + n = uttframevectors.size(); + sentendmark[i].push_back(n + tspos); + assert (n == uttframes.cols() && uttref.numframes == n && chunkdata.numframes (uttref.utteranceindex) == n); + + // copy the frames and class labels + for (size_t t = 0; t < n; t++) // t = time index into source utterance + { + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], t + tspos); + //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], t + tspos); + } + + // copy the frames and class labels + if (i==0) + { + auto uttclassids = getclassids (uttref); + auto uttphoneboudaries = getphonebound(uttref); + foreach_index(j, uttclassids) + { + for (size_t t = 0; t < n; t++) // t = time index into source utterance + { + if (issupervised()) + { + uids[j][t + tspos] = uttclassids[j][t]; + phoneboundaries[j][t + tspos] = uttphoneboudaries[j][t]; + } + } + + if (!this->lattices.empty()) + { + auto latticepair = chunkdata.getutterancelattice (uttref.utteranceindex); + latticepairs.push_back (latticepair); + // look up reference + const auto & key = latticepair->getkey(); + if (!allwordtranscripts.empty()) + { + const auto & transcript = allwordtranscripts.find (key)->second; + transcripts.push_back (transcript.words); + } + } + } + } + } + tspos += n; + } + + foreach_index(i, feat) + { + assert(tspos == feat[i].cols()); + } + } + else + { + const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep + const size_t sweepte = sweepts + _totalframes; // and its end + const size_t globalte = min (globalts + framesrequested, sweepte); // we return as much as requested, but not exceeding sweep end + mbframes = globalte - globalts; // that's our mb size + + // determine window range + // We enumerate all frames--can this be done more efficiently? + const size_t firstchunk = chunkforframepos (globalts); + const size_t lastchunk = chunkforframepos (globalte-1); + const size_t windowbegin = randomizedchunks[0][firstchunk].windowbegin; + const size_t windowend = randomizedchunks[0][lastchunk].windowend; + if (verbosity > 0) + fprintf (stderr, "getbatch: getting randomized frames [%d..%d] (%d frames out of %d requested) in sweep %d; chunks [%d..%d] -> chunk window [%d..%d)\n", + (int)globalts, (int)globalte, (int)mbframes, (int)framesrequested, (int)sweep, (int)firstchunk, (int)lastchunk, (int)windowbegin, (int)windowend); + // release all data outside, and page in all data inside + for (size_t k = 0; k < windowbegin; k++) + releaserandomizedchunk (k); + for (size_t k = windowbegin; k < windowend; k++) + if ((k % numsubsets) == subsetnum) // in MPI mode, we skip chunks this way + readfromdisk |= requirerandomizedchunk(k, windowbegin, windowend); // (window range passed in for checking only, redundant here) + for (size_t k = windowend; k < randomizedchunks[0].size(); k++) + releaserandomizedchunk (k); + + // determine the true #frames we return--it is less than mbframes in the case of MPI/data-parallel sub-set mode + // First determine it for all nodes, then pick the min over all nodes, as to give all the same #frames for better load balancing. + // TODO: No, return all; and leave it to caller to redistribute them [Zhijie Yan] + std::vector subsetsizes(numsubsets, 0); + for (size_t i = 0; i < mbframes; i++) // i is input frame index; j < i in case of MPI/data-parallel sub-set mode + { + const size_t framepos = (globalts + i) % _totalframes; // (for comments, see main loop below) + const frameref & frameref = randomizedframerefs[framepos]; + subsetsizes[frameref.chunkindex % numsubsets]++; + } + size_t j = subsetsizes[subsetnum]; // return what we have --TODO: we can remove the above full computation again now + const size_t allocframes = max(j, (mbframes + numsubsets - 1) / numsubsets); // we leave space for the desired #frames, assuming caller will try to pad them later + + // resize feat and uids + feat.resize(vdim.size()); + uids.resize(classids.size()); + assert(feat.size()==vdim.size()); + assert(feat.size()==randomizedchunks.size()); + foreach_index(i, feat) + { + feat[i].resize(vdim[i], allocframes); + feat[i].shrink(vdim[i], j); + + if (i==0) + { + foreach_index(k, uids) + { + if (issupervised()) // empty means unsupervised training -> return empty uids + uids[k].resize (j); + else + uids[k].clear(); + latticepairs.clear(); // will push_back() below + transcripts.clear(); + } + } + } + + // return randomized frames for the time range of those utterances + size_t currmpinodeframecount = 0; + for (size_t j = 0; j < mbframes; j++) + { + if (currmpinodeframecount >= feat[0].cols()) // MPI/data-parallel mode: all nodes return the same #frames, which is how feat(,) is allocated + break; + + // map to time index inside arrays + const size_t framepos = (globalts + j) % _totalframes; // using mod because we may actually run beyond the sweep for the last call + const frameref & frameref = randomizedframerefs[framepos]; + + // in MPI/data-parallel mode, skip frames that are not in chunks loaded for this MPI node + if ((frameref.chunkindex % numsubsets) != subsetnum) + continue; + + // random utterance + readfromdisk |= requirerandomizedchunk (frameref.chunkindex, windowbegin, windowend); // (this is just a check; should not actually page in anything) + + foreach_index(i, randomizedchunks) + { + const auto & chunk = randomizedchunks[i][frameref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + auto uttframes = chunkdata.getutteranceframes (frameref.utteranceindex); + matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[.].size() and m[.][.] as required by augmentneighbors()) + const size_t n = uttframevectors.size(); + assert (n == uttframes.cols() && chunkdata.numframes (frameref.utteranceindex) == n); n; + + // copy frame and class labels + const size_t t = frameref.frameindex; + + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], currmpinodeframecount); + + if (issupervised() && i == 0) + { + auto frameclassids = getclassids(frameref); + foreach_index(k, uids) + uids[k][currmpinodeframecount] = frameclassids[k][t]; + } + } + + currmpinodeframecount++; + } + } + timegetbatch = timergetbatch; + + // this is the number of frames we actually moved ahead in time + framesadvanced = mbframes; + + return readfromdisk; + } + + bool supportsbatchsubsetting() const override + { + return true; + } + + bool getbatch(const size_t globalts, + const size_t framesrequested, std::vector & feat, std::vector> & uids, + std::vector> & transcripts, + std::vector> & lattices, std::vector> & sentendmark, + std::vector> & phoneboundaries) + { + size_t dummy; + return getbatch(globalts, framesrequested, 0, 1, dummy, feat, uids, transcripts, lattices,sentendmark,phoneboundaries); + } + + double gettimegetbatch() { return timegetbatch;} + + // alternate (updated) definition for multiple inputs/outputs - read as a vector of feature matrixes or a vector of label strings + bool getbatch (const size_t /*globalts*/, + const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, std::vector & /*uids*/, + std::vector> & /*transcripts*/, + std::vector> & /*latticepairs*/) + { + // should never get here + RuntimeError("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchutterancesource instead\n"); + + // for single input/output set size to be 1 and run old getbatch + //feat.resize(1); + //uids.resize(1); + //return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); + } + + size_t totalframes() const { return _totalframes; } + + // return first valid globalts to ask getbatch() for + // In utterance mode, the epoch start may fall in the middle of an utterance. + // We return the end time of that utterance (which, in pathological cases, may in turn be outside the epoch; handle that). + /*implement*/ size_t firstvalidglobalts (const size_t globalts) + { + // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below + const size_t sweep = lazyrandomization (globalts); + // frame mode: start at sweep boundary directly + if (framemode) + return globalts; + // utterance mode + assert (globalts >= sweep * _totalframes && globalts < (sweep + 1) * _totalframes); sweep; + foreach_index (pos, randomizedutterancerefs) + if (randomizedutterancerefs[pos].globalts >= globalts) + return randomizedutterancerefs[pos].globalts; // exact or inexact match + return randomizedutterancerefs.back().globalte(); // boundary case: requested time falls within the last utterance + } + + const std::vector & unitcounts() const { return counts[0]; } + const std::vector & unitcounts(size_t index) const { return counts[index]; } + +}; + +};}; + diff --git a/Source/Readers/ImageReader/ImageReader.vcxproj b/Source/Readers/ImageReader/ImageReader.vcxproj index 7d3a3b01c636..7c680605149e 100644 --- a/Source/Readers/ImageReader/ImageReader.vcxproj +++ b/Source/Readers/ImageReader/ImageReader.vcxproj @@ -1,146 +1,146 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {9BD0A746-0BBD-45B6-B81C-053F03C26CFB} - - - - - - - - - Win32Proj - ImageReader - - - - DynamicLibrary - v120 - Unicode - - - - - true - - - - - false - - - - - true - - - false - true - - - - - - - - ..\..\common\include;..\..\Math;$(OPENCV_PATH)\include;$(IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(OPENCV_PATH)\x64\vc12\lib;$(LibraryPath); - $(Platform)\$(Configuration)\$(ProjectName)\ - opencv_world300.lib - - - true - - - false - - - - Use - Level4 - WIN32;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - true - true - - - Console - true - Math.lib;$(OpenCVLib);%(AdditionalDependencies) - - - xcopy /Y $(OPENCV_PATH)\x64\vc12\bin\opencv_world300.dll $(TargetDir) - - - - - Disabled - _DEBUG;%(PreprocessorDefinitions) - - - - - MaxSpeed - true - true - NDEBUG;%(PreprocessorDefinitions) - /d2Zi+ %(AdditionalOptions) - - - true - true - true - - - - - - - - - - - - - - NotUsing - - - NotUsing - - - NotUsing - - - NotUsing - NotUsing - - - - true - - - true - - - Create - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {9BD0A746-0BBD-45B6-B81C-053F03C26CFB} + + + + + + + + + Win32Proj + ImageReader + + + + DynamicLibrary + v120 + Unicode + + + + + true + + + + + false + + + + + true + + + false + true + + + + + + + + ..\..\common\include;..\..\Math;$(OPENCV_PATH)\include;$(IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(OPENCV_PATH)\x64\vc12\lib;$(LibraryPath); + $(Platform)\$(Configuration)\$(ProjectName)\ + opencv_world300.lib + + + true + + + false + + + + Use + Level4 + WIN32;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + true + true + + + Console + true + Math.lib;$(OpenCVLib);%(AdditionalDependencies) + + + xcopy /Y $(OPENCV_PATH)\x64\vc12\bin\opencv_world300.dll $(TargetDir) + + + + + Disabled + _DEBUG;%(PreprocessorDefinitions) + + + + + MaxSpeed + true + true + NDEBUG;%(PreprocessorDefinitions) + /d2Zi+ %(AdditionalOptions) + + + true + true + true + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + NotUsing + + + NotUsing + NotUsing + + + + true + + + true + + + Create + + + + + + + + \ No newline at end of file diff --git a/Source/Readers/ImageReader/ImageReader.vcxproj.filters b/Source/Readers/ImageReader/ImageReader.vcxproj.filters index ea3eb1eaa14a..db03c1800a4e 100644 --- a/Source/Readers/ImageReader/ImageReader.vcxproj.filters +++ b/Source/Readers/ImageReader/ImageReader.vcxproj.filters @@ -1,44 +1,44 @@ - - - - - - - Common - - - Common - - - Common - - - - - Common - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - - - - {0D0EFA10-72A8-4078-840A-B7F76AFEC0A4} - - - {C6F55578-121A-4D7C-8F57-4172BC5C463B} - - + + + + + + + Common + + + Common + + + Common + + + + + Common + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + + + + {0D0EFA10-72A8-4078-840A-B7F76AFEC0A4} + + + {C6F55578-121A-4D7C-8F57-4172BC5C463B} + + \ No newline at end of file diff --git a/Source/Readers/Kaldi2Reader/utterancesourcemulti.h b/Source/Readers/Kaldi2Reader/utterancesourcemulti.h index 3ab72df48551..640a706b5586 100644 --- a/Source/Readers/Kaldi2Reader/utterancesourcemulti.h +++ b/Source/Readers/Kaldi2Reader/utterancesourcemulti.h @@ -1,1367 +1,1367 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// utterancesourcemulti.h -- implementation of utterancesource.h that supports multiple feature and label sets -// - -#pragma once - -#include "basetypes.h" // for attempt() -#include "htkfeatio.h" // for htkmlfreader -#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) -#include "minibatchsourcehelpers.h" -#include "minibatchiterator.h" - -namespace msra { namespace dbn { - -// --------------------------------------------------------------------------- -// minibatchutterancesource -- feature source to provide randomized utterances -// This also implements a frame-wise mode, which is layered on top of the utterance-wise mode -// and thus benefits from its goodies such as corpus-wide high-level randomization and chunk paging. -// --------------------------------------------------------------------------- -class minibatchutterancesourcemulti : public minibatchsource -{ - void operator=(const minibatchutterancesourcemulti & other); // non-assignable - std::vector vdim; // feature dimension after augmenting neighhors - std::vector leftcontext; // number of frames to the left of the target frame in the context window - std::vector rightcontext; // number of frames to the right of the target frame in the context window - std::vector sampperiod; // (for reference and to check against model) - std::vector featkind; - std::vector featdim; - const bool framemode; // true -> actually return frame-level randomized frames (not possible in lattice mode) - std::vector> counts; // [s] occurence count for all states (used for priors) - int verbosity; - // lattice reader - //const std::vector> &lattices; - const latticesource & lattices; - - //std::vector lattices; - // word-level transcripts (for MMI mode when adding best path to lattices) - const map & allwordtranscripts; // (used for getting word-level transcripts) - //std::vector> allwordtranscripts; - // data store (incl. paging in/out of features and lattices) - struct utterancedesc // data descriptor for one utterance - { - msra::asr::htkfeatreader::parsedpath parsedpath; // archive filename and frame range in that file - size_t classidsbegin; // index into allclassids[] array (first frame) - - utterancedesc (msra::asr::htkfeatreader::parsedpath && ppath, size_t classidsbegin) : parsedpath (ppath), classidsbegin (classidsbegin) {} - - const wstring & logicalpath() const { return parsedpath; /*type cast will return logical path*/ } - size_t numframes() const { return parsedpath.numframes(); } - const wstring key() const // key used for looking up lattice (not stored to save space) - { - // the logical path is the uttid, not a filename. don't need to remove extension - return logicalpath(); - } - }; - struct utterancechunkdata // data for a chunk of utterances - { - std::vector utteranceset; // utterances in this set - size_t numutterances() const { return utteranceset.size(); } - - std::vector firstframes; // [utteranceindex] first frame for given utterance - mutable msra::dbn::matrix frames; // stores all frames consecutively (mutable since this is a cache) - size_t totalframes; // total #frames for all utterances in this chunk - mutable std::vector> lattices; // (may be empty if none) - - // construction - utterancechunkdata() : totalframes (0) {} - //utterancechunkdata (const utterancechunkdata& other) : utteranceset(other.utteranceset), firstframes(other.firstframes), frames (other.frames), totalframes (other.totalframes), lattices (other.lattices){}; - void push_back (utterancedesc &&/*destructive*/ utt) - { - //printf ("start push %d %d\n",frames.rows(), frames.cols()); - - if (isinram()) - { - - throw std::logic_error ("utterancechunkdata: frames already paged into RAM--too late to add data"); - } - firstframes.push_back (totalframes); - totalframes += utt.numframes(); - utteranceset.push_back (utt); - - - } - - // accessors to an utterance's data - size_t numframes (size_t i) const { return utteranceset[i].numframes(); } - size_t getclassidsbegin (size_t i) const { return utteranceset[i].classidsbegin; } - msra::dbn::matrixstripe getutteranceframes (size_t i) const // return the frame set for a given utterance - { - if (!isinram()) - throw std::logic_error ("getutteranceframes: called when data have not been paged in"); - const size_t ts = firstframes[i]; - const size_t n = numframes(i); - return msra::dbn::matrixstripe (frames, ts, n); - } - shared_ptr getutterancelattice (size_t i) const // return the frame set for a given utterance - { - if (!isinram()) - throw std::logic_error ("getutteranceframes: called when data have not been paged in"); - return lattices[i]; - } - - // paging - // test if data is in memory at the moment - bool isinram() const { - return !frames.empty(); - } - // page in data for this chunk - // We pass in the feature info variables by ref which will be filled lazily upon first read - void requiredata (string & featkind, size_t & featdim, unsigned int & sampperiod, const latticesource & latticesource, int verbosity=0) const - { - - if (numutterances() == 0) - throw std::logic_error ("requiredata: cannot page in virgin block"); - if (isinram()) - throw std::logic_error ("requiredata: called when data is already in memory"); - try // this function supports retrying since we read from the unrealible network, i.e. do not return in a broken state - { - msra::asr::htkfeatreader reader; // feature reader (we reinstantiate it for each block, i.e. we reopen the file actually) - // if this is the first feature read ever, we explicitly open the first file to get the information such as feature dimension - if (featdim == 0) - { - reader.getinfo (utteranceset[0].parsedpath, featdim); - fprintf (stderr, "requiredata: determined feature kind as %zu-dimensional\n", featdim); - } - // read all utterances; if they are in the same archive, htkfeatreader will be efficient in not closing the file - frames.resize (featdim, totalframes); - if (!latticesource.empty()) - lattices.resize (utteranceset.size()); - foreach_index (i, utteranceset) - { - //fprintf (stderr, "."); - // read features for this file - auto uttframes = getutteranceframes (i); // matrix stripe for this utterance (currently unfilled) - reader.readNoAlloc (utteranceset[i].parsedpath, (const string &) featkind, sampperiod, uttframes); // note: file info here used for checkuing only - // page in lattice data - if (!latticesource.empty()) - latticesource.getlattices (utteranceset[i].key(), lattices[i], uttframes.cols()); - } - //fprintf (stderr, "\n"); - if (verbosity) - { - fprintf (stderr, "requiredata: %zu utterances read\n", utteranceset.size()); - } - } - catch (...) - { - releasedata(); - throw; - } - } - // page out data for this chunk - void releasedata() const - { - if (numutterances() == 0) - throw std::logic_error ("releasedata: cannot page out virgin block"); - if (!isinram()) - throw std::logic_error ("releasedata: called when data is not memory"); - // release frames - frames.resize (0, 0); - // release lattice data - lattices.clear(); - } - }; - std::vector> allchunks; // set of utterances organized in chunks, referred to by an iterator (not an index) - std::vector>> classids; // [classidsbegin+t] concatenation of all state sequences - bool issupervised() const { return !classids.empty(); } - size_t numutterances; // total number of utterances - size_t _totalframes; // total frames (same as classids.size() if we have labels) - double timegetbatch; // [v-hansu] for time measurement - // sequence in random order of actual use (randomized, where randomization is cached) - const size_t randomizationrange;// parameter remembered; this is the full window (e.g. 48 hours), not the half window - size_t currentsweep; // randomization is currently cached for this sweep; if it changes, rebuild all below - struct chunk // chunk as used in actual processing order (randomized sequence) - { - // the underlying chunk (as a non-indexed reference into the chunk set) - std::vector::const_iterator uttchunkdata; - const utterancechunkdata & getchunkdata() const { return *uttchunkdata; } - size_t numutterances() const { return uttchunkdata->numutterances(); } - size_t numframes() const { return uttchunkdata->totalframes; } - - // position in utterance-position space - size_t utteranceposbegin; - size_t utteranceposend() const { return utteranceposbegin + numutterances(); } - - // position on global time line - size_t globalts; // start frame on global timeline (after randomization) - size_t globalte() const { return globalts + numframes(); } - - // randomization range limits - size_t windowbegin; // randomizedchunk index of earliest chunk that utterances in here can be randomized with - size_t windowend; // and end index [windowbegin, windowend) - chunk (std::vector::const_iterator uttchunkdata, size_t utteranceposbegin, size_t globalts) : uttchunkdata (uttchunkdata), utteranceposbegin (utteranceposbegin), globalts (globalts) {} - }; - std::vector> randomizedchunks; // utterance chunks after being brought into random order (we randomize within a rolling window over them) - size_t chunksinram; // (for diagnostics messages) - struct utteranceref // describes the underlying random utterance associated with an utterance position - { - size_t chunkindex; // lives in this chunk (index into randomizedchunks[]) - size_t utteranceindex; // utterance index in that chunk - size_t numframes; // (cached since we cannot directly access the underlying data from here) - size_t globalts; // start frame in global space after randomization (for mapping frame index to utterance position) - size_t globalte() const { return globalts + numframes; } // end frame - utteranceref (size_t chunkindex, size_t utteranceindex) : chunkindex (chunkindex), utteranceindex (utteranceindex), globalts (SIZE_MAX), numframes (0) {} - void swap (utteranceref & other) // used in randomization - { - ::swap (chunkindex, other.chunkindex); - ::swap (utteranceindex, other.utteranceindex); - assert (globalts == SIZE_MAX && other.globalts == SIZE_MAX && numframes == 0 && other.numframes == 0); // can only swap before assigning these - } - }; - std::vector randomizedutterancerefs; // [pos] randomized utterance ids - std::unordered_map randomizedutteranceposmap; // [globalts] -> pos lookup table - struct positionchunkwindow // chunk window required in memory when at a certain position, for controlling paging - { - std::vector::const_iterator definingchunk; // the chunk in randomizedchunks[] that defined the utterance position of this utterance - size_t windowbegin() const { return definingchunk->windowbegin; } - size_t windowend() const { return definingchunk->windowend; } - bool isvalidforthisposition (const utteranceref & utt) const - { - return utt.chunkindex >= windowbegin() && utt.chunkindex < windowend(); // check if 'utt' lives in is in allowed range for this position - } - positionchunkwindow (std::vector::iterator definingchunk) : definingchunk (definingchunk) {} - }; - std::vector positionchunkwindows; // [utterance position] -> [windowbegin, windowend) for controlling paging - - // frame-level randomization layered on top of utterance chunking (randomized, where randomization is cached) - struct frameref - { -#ifdef _WIN64 // (sadly, the compiler makes this 8 bytes, not 6) - unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) - unsigned short utteranceindex; // utterance index in that chunk - static const size_t maxutterancesperchunk = 65535; - unsigned short frameindex; // frame index within the utterance - static const size_t maxframesperutterance = 65535; -#elif __unix__ // (sadly, the compiler makes this 8 bytes, not 6) - unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) - unsigned short utteranceindex; // utterance index in that chunk - static const size_t maxutterancesperchunk = 65535; - unsigned short frameindex; // frame index within the utterance - static const size_t maxframesperutterance = 65535; -#else // For Win32, we care to keep it inside 32 bits. We have already encountered setups where that's not enough. - unsigned int chunkindex : 13; // lives in this chunk (index into randomizedchunks[]) - unsigned int utteranceindex : 8; // utterance index in that chunk - static const size_t maxutterancesperchunk = 255; - unsigned int frameindex : 11; // frame index within the utterance - static const size_t maxframesperutterance = 2047; -#endif - frameref (size_t ci, size_t ui, size_t fi) : chunkindex ((unsigned short) ci), utteranceindex ((unsigned short) ui), frameindex ((unsigned short) fi) - { -#ifdef _WIN32 - static_assert (sizeof (frameref) == 4, "frameref: bit fields too large to fit into 32-bit integer"); -#endif - if (ci == chunkindex && ui == utteranceindex && fi == frameindex) - return; - throw std::logic_error ("frameref: bit fields too small"); - } - frameref() : chunkindex (0), utteranceindex (0), frameindex (0) {} - }; - biggrowablevector randomizedframerefs; // [globalt-sweepts] -> (chunk, utt, frame) lookup table for randomized frames --this can be REALLY big! - - // TODO: this may go away if we store classids directly in the utterance data - template class shiftedvector // accessing a vector with a non-0 starting index - { - void operator= (const shiftedvector &); - VECTOR & v; - size_t first; - size_t n; - void check (size_t i) const { if (i >= n) throw std::logic_error ("shiftedvector: index out of bounds"); } - public: - shiftedvector (VECTOR & v, size_t first, size_t n) : v (v), first (first), n (n) { } - // TODO: the following is not templated--do it if needed; also should return a const reference then - size_t operator[] (size_t i) const { check (i); return v[first + i]; } - }; - template std::vector>> getclassids (const UTTREF & uttref) // return sub-vector of classids[] for a given utterance - { - std::vector>> allclassids; - allclassids.empty(); - - if (!issupervised()) - { - foreach_index(i,classids) - allclassids.push_back(std::move(shiftedvector> ((*classids[i]), 0, 0))); - return allclassids; // nothing to return - } - const auto & chunk = randomizedchunks[0][uttref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - const size_t classidsbegin = chunkdata.getclassidsbegin (uttref.utteranceindex); // index of first state label in global concatenated classids[] array - const size_t n = chunkdata.numframes (uttref.utteranceindex); - foreach_index(i,classids) - { - if ((*classids[i])[classidsbegin + n] != (CLASSIDTYPE) -1) - throw std::logic_error ("getclassids: expected boundary marker not found, internal data structure screwed up"); - allclassids.push_back(std::move(shiftedvector> ((*classids[i]), classidsbegin, n))); - } - return allclassids; // nothing to return - } -public: - // constructor - // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). - // This mode requires utterances with time stamps. - minibatchutterancesourcemulti (std::vector & featuresections, const std::vector> & infiles, const std::vector>> & labels, - std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, - const latticesource & lattices, const map & allwordtranscripts, const bool framemode) - : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), randomizationrange (randomizationrange), currentsweep (SIZE_MAX), - lattices (lattices), allwordtranscripts (allwordtranscripts), framemode (framemode), chunksinram (0), timegetbatch (0), verbosity(2) - // [v-hansu] change framemode (lattices.empty()) into framemode (false) to run utterance mode without lattice - // you also need to change another line, search : [v-hansu] comment out to run utterance mode without lattice - { - // process infiles to know dimensions of things (but not loading features) - std::vector utteranceset;// read all utterances to here first; at the end, distribute to chunks - utteranceset.reserve (infiles.size()); - size_t nomlf = 0; // number of entries missing in MLF (diagnostics) - size_t nolat = 0; // number of entries missing in lattice archive (diagnostics) - std::vector numclasses; // number of output classes as found in the label file (diagnostics) - _totalframes = 0; - wstring key; - size_t numutts=0; - - std::vectoruttisvalid; // boolean flag to check that utterance is valid. valid means number of - //frames is consistent across all feature and label streams - std::vectoruttduration; // track utterance durations to determine utterance validity - - std::vector classidsbegin; - if (!lattices.empty()) - { - LogicError("lattices not supported in utterancereadermulti"); - } - - allchunks = std::vector>(infiles.size(), std::vector()); - featdim = std::vector(infiles.size(), 0); - sampperiod = std::vector(infiles.size(), 0); - featkind = std::vector(infiles.size(), ""); - - numclasses = std::vector(labels.size(), 0); - counts = std::vector>(labels.size(), std::vector()); - foreach_index (i, labels) - { - classids.push_back(unique_ptr>(new biggrowablevector())); - //std::pair,std::vector> latticetocs; - //std::unordered_map modelsymmap; - //lattices.push_back(shared_ptr(new latticesource(latticetocs, modelsymmap))); - - } - - - // first check consistency across feature streams - // We'll go through the SCP files for each stream to make sure the duration is consistent - // If not, we'll plan to ignore the utterance, and inform the user - // m indexes the feature stream - // i indexes the files within a stream, i.e. in the SCP file) - foreach_index(m, infiles){ - if (m == 0){ - numutts = infiles[m].size(); - uttisvalid = std::vector(numutts, true); - uttduration = std::vector(numutts, 0); - } - else if (infiles[m].size()!=numutts) - throw std::runtime_error("minibatchutterancesourcemulti: all feature files must have same number of utterances"); - - foreach_index(i, infiles[m]){ - utterancedesc utterance(msra::asr::htkfeatreader::parsedpath(infiles[m][i],featuresections[m]), 0); //mseltzer - is this foolproof for multiio? is classids always non-empty? - const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode - // we need at least 2 frames for boundary markers to work - if (uttframes < 2) - { - //throw std::runtime_error("minibatchutterancesource: utterances < 2 frames not supported"); - fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%zd frames) because it less than %d frames for frameref bit field: %S\n", - i, uttframes, 2, key.c_str()); - uttduration[i] = 0; - uttisvalid[i] = false; - } else if (uttframes > frameref::maxframesperutterance || uttframes <2) - { - fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%zd frames) because it exceeds max. frames (%zd) for frameref bit field: %S\n", - i, uttframes, frameref::maxframesperutterance, key.c_str()); - uttduration[i] = 0; - uttisvalid[i] = false; - } - else{ - if (m == 0){ - uttduration[i] = uttframes; - uttisvalid[i] = true; - } - else if (uttduration[i] != uttframes){ - fprintf(stderr, "minibatchutterancesource: skipping %d-th file due to inconsistency in duration in different feature streams (%zd vs %zd frames)\n", - i, uttduration[i], uttframes); - uttduration[i] = 0; - uttisvalid[i] = false; - } - } - } - } - size_t invalidutts=0; - foreach_index(i, uttisvalid){ - if (!uttisvalid[i]) - invalidutts++; - } - if (invalidutts > uttisvalid.size() / 2) - throw std::runtime_error("minibatchutterancesource: too many files not found in with inconsistent durations, assuming broken configuration\n"); - else if (invalidutts>0) - fprintf(stderr, "Found inconsistent durations across feature streams in %zd out of %zd files.\n", invalidutts, uttisvalid.size()); - - - // now process the features and labels - size_t utterancesetsize = 0; - foreach_index (m, infiles) - { - utteranceset.clear(); - //if (m==0) - // numutts = infiles[m].size(); - //else - // if (infiles[m].size()!=numutts) - // throw std::runtime_error("minibatchutterancesourcemulti: all feature files must have same number of utterances\n"); - if (m==0) - classidsbegin.clear(); - - foreach_index (i, infiles[m]) - { - if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } - // build utterance descriptor - if (m == 0 && !labels.empty()) - classidsbegin.push_back(classids[0]->size()); - - if (uttisvalid[i]){ - utterancedesc utterance (msra::asr::htkfeatreader::parsedpath (infiles[m][i], featuresections[m]), labels.empty() ? 0 : classidsbegin[i] ); //mseltzer - is this foolproof for multiio? is classids always non-empty? - const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode - assert(uttframes == uttduration[i]); // ensure nothing funky happened - - // already performed these checks above - // we need at least 2 frames for boundary markers to work - //if (uttframes < 2) - // throw std::runtime_error ("minibatchutterancesource: utterances < 2 frames not supported"); - //if (uttframes > frameref::maxframesperutterance) - //{ - // fprintf (stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %S", i, uttframes, frameref::maxframesperutterance, key.c_str()); - // continue; - //} - - // check whether we have the ref transcript - //auto labelsiter = labels[0].end(); - bool lacksmlf = true; - if (!labels.empty()) // empty means unsupervised mode (don't load any) - { - key = utterance.key(); - // check if labels are available (if not, it normally means that no path was found in realignment) - auto labelsiter = labels[0].find (key); - //const bool lacksmlf = (labelsiter == labels[0].end()); - lacksmlf = (labelsiter == labels[0].end()); - if (lacksmlf) - if (nomlf++ < 5) - fprintf (stderr, " [no labels for %S]", key.c_str()); - // check if lattice is available (when in lattice mode) - // TODO: also check the #frames here; requires a design change of the TOC format & a rerun - const bool lackslat = !lattices.empty() && !lattices.haslattice (key); // ('true' if we have no lattices) - if (lackslat) - if (nolat++ < 5) - fprintf (stderr, " [no lattice for %S]", key.c_str()); - // skip if either one is missing - if (lacksmlf || lackslat){ - uttisvalid[i] = false; - continue; // skip this utterance at all - } - } - // push the label sequence into classids[], since we already looked it up - // TODO: we can store labels more efficiently now since we don't do frame-wise random access anymore. - - // OK, utterance has all we need --remember it - - if (m==0) - { - if (!labels.empty() && !lacksmlf) - //if (!labels.empty() && labelsiter != labels[0].end()) - { - // first verify that all the label files have the proper duration - foreach_index (j, labels) - { - const auto & labseq = labels[j].find(key)->second; - // check if durations match; skip if not - size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); - if (labframes != uttframes) - { - fprintf (stderr, " [duration mismatch (%zu in label vs. %zu in feat file), skipping %S]", labframes, uttframes, key.c_str()); - nomlf++; - uttisvalid[i] = false; - break; // continue; // skip this utterance at all - } - } - if (uttisvalid[i]) - { - utteranceset.push_back(std::move(utterance)); - _totalframes += uttframes; - // then parse each mlf if the durations are consistent - foreach_index(j, labels) - { - const auto & labseq = labels[j].find(key)->second; - - // expand classid sequence into flat array - foreach_index (i, labseq) - { - const auto & e = labseq[i]; - if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) - throw std::runtime_error (msra::strfun::strprintf ("minibatchutterancesource: labels not in consecutive order MLF in label set: %S", key.c_str())); - if (e.classid >= udim[j]) - { - throw std::runtime_error (msra::strfun::strprintf ("minibatchutterancesource: class id exceeds model output dimension")); - } - if (e.classid != (CLASSIDTYPE) e.classid) - throw std::runtime_error ("CLASSIDTYPE has too few bits"); - for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) - classids[j]->push_back ((CLASSIDTYPE) e.classid); - numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); - counts[j].resize (numclasses[j], 0); - counts[j][e.classid] += e.numframes; - } - classids[j]->push_back ((CLASSIDTYPE) -1); // append a boundary marker marker for checking - - if (!labels[j].empty() && classids[j]->size() != _totalframes + utteranceset.size()) - throw std::logic_error (msra::strfun::strprintf ("minibatchutterancesource: label duration inconsistent with feature file in MLF label set: %S", key.c_str())); - assert (labels[j].empty() || classids[j]->size() == _totalframes + utteranceset.size()); - } - } - } - else{ - assert(classids.empty() && labels.empty()); - utteranceset.push_back(std::move(utterance)); - _totalframes += uttframes; - } - } - else - { - utteranceset.push_back(std::move(utterance)); - } - } - } - if (m == 0) - utterancesetsize = utteranceset.size(); - else - assert(utteranceset.size() == utterancesetsize); - fprintf (stderr, "feature set %d: %zu frames in %zu out of %zu utterances\n", m, _totalframes, utteranceset.size(),infiles[m].size()); - - if (!labels.empty()){ - foreach_index (j, labels){ - biggrowablevector & cid = *classids[j]; - foreach_index (i, utteranceset){ - //if ((*classids[j])[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) - //printf("index = %d\n",utteranceset[i].classidsbegin + utteranceset[i].numframes()); - //printf("cid[index] = %d\n",cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()]); - //printf("CLASSIDTYPE(-1) = %d\n",(CLASSIDTYPE) -1); - if (cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) - throw std::logic_error ("minibatchutterancesource: classids[] out of sync"); - } - } - } - if (nomlf + nolat > 0) - { - fprintf (stderr, "minibatchutterancesource: out of %zu files, %zu files not found in label set and %zu have no lattice\n", infiles.size(), nomlf, nolat); - if (nomlf + nolat > infiles[m].size() / 2) - throw std::runtime_error ("minibatchutterancesource: too many files not found in label set--assuming broken configuration\n"); - } - - if (m==0) {foreach_index(j, numclasses) { fprintf(stderr,"label set %d: %zu classes\n",j, numclasses[j]); } } - // distribute them over chunks - // We simply count off frames until we reach the chunk size. - // Note that we first randomize the chunks, i.e. when used, chunks are non-consecutive and thus cause the disk head to seek for each chunk. - const size_t framespersec = 100; // we just assume this; our efficiency calculation is based on this - const size_t chunkframes = 15 * 60 * framespersec; // number of frames to target for each chunk - // Loading an initial 24-hour range will involve 96 disk seeks, acceptable. - // When paging chunk by chunk, chunk size ~14 MB. - std::vector & thisallchunks = allchunks[m]; - //std::vector thisallchunks; - - thisallchunks.resize (0); - thisallchunks.reserve (_totalframes / chunkframes); - foreach_index (i, utteranceset) - { - // if exceeding current entry--create a new one - // I.e. our chunks are a little larger than wanted (on av. half the av. utterance length). - if (thisallchunks.empty() || thisallchunks.back().totalframes > chunkframes || thisallchunks.back().numutterances() >= frameref::maxutterancesperchunk) - { - thisallchunks.push_back (utterancechunkdata()); - - - } - // append utterance to last chunk - utterancechunkdata & currentchunk = thisallchunks.back(); - - currentchunk.push_back (std::move (utteranceset[i])); // move it out from our temp array into the chunk - // TODO: above push_back does not actually 'move' because the internal push_back does not accept that - } - - numutterances = utteranceset.size(); - fprintf (stderr, "minibatchutterancesource: %zu utterances grouped into %zu chunks, av. chunk size: %.1f utterances, %.1f frames\n", - numutterances, thisallchunks.size(), numutterances / (double) thisallchunks.size(), _totalframes / (double) thisallchunks.size()); - // Now utterances are stored exclusively in allchunks[]. They are never referred to by a sequential utterance id at this point, only by chunk/within-chunk index. - } - // preliminary mem allocation for frame references (if in frame mode) - if (framemode) - randomizedframerefs.resize (_totalframes); - } - -private: - // shuffle a vector into random order by randomly swapping elements - - template static void randomshuffle (VECTOR & v, size_t randomseed) - { - if (v.size() > RAND_MAX * (size_t) RAND_MAX) - throw std::runtime_error ("randomshuffle: too large set: need to change to different random generator!"); - srand ((unsigned int) randomseed); - foreach_index (i, v) - { - // pick a random location - const size_t irand = msra::dbn::rand (0, v.size()); - - // swap element i with it - if (irand == (size_t) i) - continue; - ::swap (v[i], v[irand]); - } - } -#if 0 - template static void randomshuffle(std::vector v, size_t randomseed) - { - foreach_index(j, v) - { - if (v[j].size() > RAND_MAX * (size_t) RAND_MAX) - throw std::runtime_error ("randomshuffle: too large set: need to change to different random generator!"); - } - srand ((unsigned int) randomseed); - - foreach_index (i, v[0]) - { - // pick a random location - const size_t irand = msra::dbn::rand (0, v[0].size()); - - foreach_index(j, v){ - // swap element i with it - if (irand == (size_t) i) - continue; - ::swap (v[j][i], v[j][irand]); - } - } - } -#endif //0 - static void checkoverflow (size_t fieldval, size_t targetval, const char * fieldname) - { - if (fieldval != targetval) - throw std::runtime_error (msra::strfun::strprintf ("checkoverflow: bit field %s too small for value 0x%x (cut from 0x%x)", fieldname, targetval, fieldval)); - } - - // helper for testing whether a swapped frame position is valid (w.r.t. beign in RAM when being at position 't') - bool isframepositionvalid (const size_t t, const biggrowablevector & ttochunk) const - { - // look up valid range for time position - const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this original chunk (relationship is monotonous, not random) - const auto & chunk = randomizedchunks[0][positionchunkindex]; - // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) - const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) - const size_t poswindowend = chunk.windowend; - // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. - - // now see if the randomized location is within that window - const size_t actualchunkindexforpos = randomizedframerefs[t].chunkindex; // where this frame pos has been mapped to - return actualchunkindexforpos >= poswindowbegin && actualchunkindexforpos < poswindowend; - // We only need to test the chunk index. Utterance and frame can be randomized within a chunk as we want, as long it is in RAM. - } - - // big long helper to update all cached randomization information - // This is a rather complex process since we randomize on two levels: - // - chunks of consecutive data in the feature archive - // - within a range of chunks that is paged into RAM - // - utterances (in utt mode), or - // - frames (in frame mode) - // The 'globalts' parameter is the start time that triggered the rerandomization; it is NOT the base time of the randomized area. - size_t lazyrandomization (const size_t globalts) - { - const size_t sweep = globalts / _totalframes; // which sweep (this determines randomization) - if (sweep == currentsweep) // already got this one--nothing to do - return sweep; - - currentsweep = sweep; - if (verbosity>0) - fprintf (stderr, "lazyrandomization: re-randomizing for sweep %zu in %s mode\n", currentsweep, framemode ? "frame" : "utterance"); - - const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep - - // first randomize chunks - std::vector::const_iterator>> randomizedchunkrefs; - foreach_index (i, allchunks) - randomizedchunkrefs.push_back(std::vector::const_iterator>()); - - foreach_index (i, allchunks) - randomizedchunkrefs[i].reserve (allchunks[i].size()); - - foreach_index (i, allchunks) // TODO: this cries for iterating using the iterator! - { - foreach_index(j, allchunks[i]) - randomizedchunkrefs[i].push_back (allchunks[i].begin() + j); - assert (randomizedchunkrefs[i].size() == allchunks[i].size()); - - // note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams - randomshuffle (randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep) - - } - - // place them onto the global timeline -> randomizedchunks[] - // We are processing with randomization within a rolling window over this chunk sequence. - // Paging will happen on a chunk-by-chunk basis. - // The global time stamp is needed to determine the paging window. - randomizedchunks.clear(); // data chunks after being brought into random order (we randomize within a rolling window over them) - - foreach_index(i, allchunks) - randomizedchunks.push_back(std::vector()); - - foreach_index(i, allchunks) - { - randomizedchunks[i].reserve (randomizedchunkrefs[i].size()); - foreach_index (k, randomizedchunkrefs[i]) - randomizedchunks[i].push_back (chunk (randomizedchunkrefs[i][k], randomizedchunks[i].empty() ? 0 : randomizedchunks[i].back().utteranceposend(), randomizedchunks[i].empty() ? sweepts : randomizedchunks[i].back().globalte())); - assert (randomizedchunks[i].size() == allchunks[i].size()); - - assert (randomizedchunks[i].empty() || (randomizedchunks[i].back().utteranceposend() == numutterances && randomizedchunks[i].back().globalte() == sweepts + _totalframes)); - } - // for each chunk, compute the randomization range (w.r.t. the randomized chunk sequence) - foreach_index (i, randomizedchunks) - { - foreach_index (k, randomizedchunks[i]) - { - chunk & chunk = randomizedchunks[i][k]; - // start with the range of left neighbor - if (k == 0) - { - chunk.windowbegin = 0; - chunk.windowend = 1; - } - else - { - chunk.windowbegin = randomizedchunks[i][k-1].windowbegin; // might be too early - chunk.windowend = randomizedchunks[i][k-1].windowend; // might have more space - } - while (chunk.globalts - randomizedchunks[i][chunk.windowbegin].globalts > randomizationrange/2) - chunk.windowbegin++; // too early - while (chunk.windowend < randomizedchunks[i].size() && randomizedchunks[i][chunk.windowend].globalte() - chunk.globalts < randomizationrange/2) - chunk.windowend++; // got more space - } - } - if (!framemode) // utterance mode - { - // This sets up the following members: - // - positionchunkwindows - // - randomizedutterancerefs - // - randomizedutteranceposmap - - // We will now introduce the concept of utterance *position*. - // During processing, utterances will be indexed by position (which is in turn derived from a frame index in getbatch()), - // and it is assumed (required) that positions are requested consecutively. - // Each utterance position has an underlying associated utterance, which is represented as (chunkid, within-chunk index) and randomly assigned. - // Each utterance position also has an associated range of chunks that are kept in memory, - // and the associated underlying utterance is guaranteed to be found within that associated range of chunks. - // That allows to page out/in data when processing utterance positions in a consecutive manner. - - // compute chunk windows for every utterance position -> positionchunkwindows[] - // Utterance positions can only reference underlying utterance data within the chunk window. - // Utterance positions are defined by the randomized chunk sequence (i.e. their underlying 'defining' chunk differs from sweep to sweep). - positionchunkwindows.clear(); // [utterance position] -> [windowbegin, windowend) for controlling paging - positionchunkwindows.reserve (numutterances); - - // positionchunkwindows should be consistent for all inputs (distinct feature streams), so just build based on feature[0] - // contains pointer to chunk elements but only to compute index - foreach_index (k, randomizedchunks[0]) // TODO: this really cries for iterating using iterators! - { - chunk & chunk = randomizedchunks[0][k]; - for (size_t i = chunk.utteranceposbegin; i < chunk.utteranceposend(); i++) // loop over utterances in this chunk - { - positionchunkwindows.push_back (randomizedchunks[0].begin() + k); - } - // to look up the chunk range in memory for a position, look up the defining chunk and its range - } - assert (positionchunkwindows.size() == numutterances); - - // build the randomized utterances array -> randomizedutterancerefs[] - // start by assigning all utterance positions to utterances in non-random consecutive manner - randomizedutterancerefs.clear(); // [pos] randomized utterance ids - randomizedutterancerefs.reserve (numutterances); - foreach_index (k, randomizedchunks[0]) - { - chunk & chunk = randomizedchunks[0][k]; - for (size_t i = 0; i < chunk.numutterances(); i++) // loop over utterances in this chunk - randomizedutterancerefs.push_back (utteranceref (k, i)); - } - assert (randomizedutterancerefs.size() == numutterances); - foreach_index (i, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[i]; - assert (positionchunkwindows[i].isvalidforthisposition(uttref)); uttref; - } - - // check we got those setup right - - // we now randomly shuffle randomizedutterancerefs[pos], while considering the constraints of what chunk range needs to be in memory - srand ((unsigned int) sweep + 1); - for (size_t i = 0; i < randomizedutterancerefs.size(); i++) - { - // get valid randomization range, expressed in chunks - const size_t windowbegin = positionchunkwindows[i].windowbegin(); - const size_t windowend = positionchunkwindows[i].windowend(); - - // get valid randomization range, expressed in utterance positions - // Remember, utterance positions are defined by chunks. - const size_t posbegin = randomizedchunks[0][windowbegin].utteranceposbegin; - const size_t posend = randomizedchunks[0][windowend-1].utteranceposend(); - - // randomization range for this utterance position is [posbegin, posend) - for(;;) - { - // pick a random location - const size_t j = msra::dbn::rand (posbegin, posend); // a random number within the window - if (i == j) - break; // the random gods say "this one points to its original position"... nothing wrong about that, but better not try to swap - - // We want to swap utterances at i and j, but need to make sure they remain in their allowed range. - // This is guaranteed for a so-far untouched utterance, but both i and j may have been touched by a previous swap. - - // We want to use the utterance previously referenced at utterance position j at position i. Is that allowed? - if (!positionchunkwindows[i].isvalidforthisposition (randomizedutterancerefs[j])) - continue; // nope --try another - - // Likewise may we use the utterance previously referenced at utterance position i at position j? - if (!positionchunkwindows[j].isvalidforthisposition (randomizedutterancerefs[i])) - continue; // nope --try another - - // yep--swap them - randomizedutterancerefs[i].swap (randomizedutterancerefs[j]); - break; - } - } - - // place the randomized utterances on the global timeline so we can find them by globalts - size_t t = sweepts; - foreach_index (i, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[i]; - uttref.globalts = t; - uttref.numframes = randomizedchunks[0][uttref.chunkindex].getchunkdata().numframes (uttref.utteranceindex); - t = uttref.globalte(); - } - assert (t == sweepts + _totalframes); - - // verify that we got it right (I got a knot in my head!) - foreach_index (i, randomizedutterancerefs) - { - // get utterance referenced at this position - const auto & uttref = randomizedutterancerefs[i]; - // check if it is valid for this position - if (uttref.chunkindex < positionchunkwindows[i].windowbegin() || uttref.chunkindex >= positionchunkwindows[i].windowend()) - throw std::logic_error ("lazyrandomization: randomization logic mangled!"); - } - - // create lookup table for (globalts values -> pos) -> randomizedutteranceposmap[] - randomizedutteranceposmap.clear(); // [globalts] -> pos lookup table - foreach_index (pos, randomizedutterancerefs) - { - auto & uttref = randomizedutterancerefs[pos]; - randomizedutteranceposmap[uttref.globalts] = (size_t) pos; - } - } - else // frame mode - { - // This sets up the following members: - // - randomizedframerefs - - srand ((unsigned int) sweep + 1); - // An original timeline is established by the randomized chunks, denoted by 't'. - // Returned frames are indexed by frame position j = (globalt - sweept), which have an associated underlying 't'. - // It is guaranteed that uttterance frame position j maps to an underlying frame within the corresponding chunk window. - biggrowablevector ttochunk; // randomized chunk index associated with frame position - ttochunk.resize (_totalframes); - size_t t = 0; - frameref frameref; - // enumerate chunks in their randomized order and assign frame indices in that order -> randomizedframerefs[t] - // At this point, chunks are in randomized order, but utterances and frames within utterances are not randomized. - // Later we will randomize those as well. - foreach_index (i, randomizedchunks[0]) - { - frameref.chunkindex = (unsigned short)i; - checkoverflow (frameref.chunkindex, i, "frameref::chunkindex"); - const auto & chunk = randomizedchunks[0][i]; - const auto & chunkdata = chunk.getchunkdata(); - const size_t numutt = chunkdata.numutterances(); - for (size_t k = 0; k < numutt; k++) - { - frameref.utteranceindex = (short)k; - checkoverflow (frameref.utteranceindex, k, "frameref::utteranceindex"); - const size_t n = chunkdata.numframes (k); - for (size_t m = 0; m < n; m++) - { - frameref.frameindex = (short)m; - checkoverflow (frameref.frameindex, m, "frameref::utteranceindex"); - randomizedframerefs[t] = frameref; // hopefully this is a memory copy, not a bit-wise assignment! If not, then code it explicitly - ttochunk[t] = (unsigned short) i; - checkoverflow (ttochunk[t], i, "ttochunk[]"); - t++; - } - } - } - assert (t == _totalframes); - - // now randomize them --we use the nested loop again to avoid storing a backpointer - // The condition is that a randomized frame may not be moved out of its associated chunk window. - foreach_index (t, randomizedframerefs) - { - const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this chunk (relationship is monotonous, not random) - const auto & chunk = randomizedchunks[0][positionchunkindex]; // for window - - // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) - const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) - const size_t poswindowend = chunk.windowend; - // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. - // These chunks are associated with a range of frame positions. - // It is implied that if we are at position 't', the frames covered by chunks [poswindowbegin, poswindowend) are in RAM. - const size_t postbegin = randomizedchunks[0][poswindowbegin].globalts - sweepts; - const size_t postend = randomizedchunks[0][poswindowend-1].globalte() - sweepts; - // The position that this frame gets randomized to must be guaranteed to belong to a chunk within [postbegin, postend). - - for (;;) // (randomization retry loop) - { - size_t tswap = msra::dbn::rand (postbegin, postend); // random frame position within allowed range - // We want to swap 't' to 'tswap' and 'tswap' to 't'. - // - Both may have been swapped before. - // - Both must stay within the randomization window of their respective position. - // check admissibility of where the element at 'tswap' gets swapped to 't' (range = [windowbegin,windowend)) - size_t tswapchunkindex = randomizedframerefs[tswap].chunkindex; - if (tswapchunkindex < poswindowbegin || tswapchunkindex >= poswindowend) - continue; - // check admissibility of where the element at t gets swapped to (which is frame position 'tswap') - const size_t sourcechunkindex = randomizedframerefs[t].chunkindex; - size_t targetchunkindex = ttochunk[tswap]; // chunk associated with this frame position defines value range - const auto & targetchunk = randomizedchunks[0][targetchunkindex]; - const size_t targetwindowbegin = targetchunk.windowbegin; - const size_t targetwindowend = targetchunk.windowend; - if (sourcechunkindex < targetwindowbegin || sourcechunkindex >= targetwindowend) - continue; - // admissible--swap the two - ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); -#if 0 - break; -#else // post-check --so far did not trigger, can be removed - - // do a post-check if we got it right --we seem not to - if (isframepositionvalid (t, ttochunk) && isframepositionvalid (tswap, ttochunk)) - break; - // not valid: swap them back and try again --we actually discovered a bug in the code above - ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); - fprintf (stderr, "lazyrandomization: BUGBUG --invalid swapping condition detected\n"); -#endif - } - } - - // check it --my head spins - t = 0; - foreach_index (i, randomizedchunks[0]) - { - const auto & chunk = randomizedchunks[0][i]; // for window and chunkdata - const size_t poswindowbegin = chunk.windowbegin; - const size_t poswindowend = chunk.windowend; - - const auto & chunkdata = chunk.getchunkdata(); // for numutterances/numframes - const size_t numutt = chunkdata.numutterances(); - for (size_t k = 0; k < numutt; k++) - { - const size_t n = chunkdata.numframes (k); - for (size_t m = 0; m < n; m++) - { - const size_t randomizedchunkindex = randomizedframerefs[t].chunkindex; - if (randomizedchunkindex < poswindowbegin || randomizedchunkindex >= poswindowend) - throw std::logic_error ("lazyrandomization: nope, you got frame randomization wrong, dude"); - t++; - } - } - } - assert (t == _totalframes); - } - - return sweep; - } - - // helper to page out a chunk with log message - void releaserandomizedchunk (size_t k) - { - size_t numreleased=0; - foreach_index(m, randomizedchunks){ - auto & chunkdata = randomizedchunks[m][k].getchunkdata(); - if (chunkdata.isinram()) - { - if (verbosity) - fprintf (stderr, "releaserandomizedchunk: paging out randomized chunk %zu (frame range [%zu..%zu]), %zu resident in RAM\n", - k, randomizedchunks[m][k].globalts, randomizedchunks[m][k].globalte()-1, chunksinram-1); - chunkdata.releasedata(); - numreleased++; - } - } - if (numreleased>0 && numreleased= windowend) - throw std::logic_error ("requirerandomizedchunk: requested utterance outside in-memory chunk range"); - - foreach_index(m, randomizedchunks) - { - auto & chunk = randomizedchunks[m][chunkindex]; - auto & chunkdata = chunk.getchunkdata(); - if (chunkdata.isinram()) - numinram++; - } - if (numinram==randomizedchunks.size()) - { - - return false; - } - else if (numinram==0) - { - foreach_index(m, randomizedchunks) - { - auto & chunk = randomizedchunks[m][chunkindex]; - auto & chunkdata = chunk.getchunkdata(); - if (verbosity) - fprintf (stderr, "feature set %d: requirerandomizedchunk: paging in randomized chunk %zu (frame range [%zu..%zu]), %zu resident in RAM\n", m, chunkindex, chunk.globalts, chunk.globalte()-1, chunksinram+1); - msra::util::attempt (5, [&]() // (reading from network) - { - chunkdata.requiredata (featkind[m], featdim[m], sampperiod[m], this->lattices, verbosity); - }); - } - chunksinram++; - return true; - } - else{ - LogicError ("requirerandomizedchunk: inconsistency detected - some inputs need chunks paged in, some not"); - } - } - - class matrixasvectorofvectors // wrapper around a matrix that views it as a vector of column vectors - { - void operator= (const matrixasvectorofvectors &); // non-assignable - msra::dbn::matrixbase & m; - public: - matrixasvectorofvectors (msra::dbn::matrixbase & m) : m (m) {} - size_t size() const { return m.cols(); } - const_array_ref operator[] (size_t j) const { return array_ref (&m(0,j), m.rows()); } - }; - - size_t chunkforframepos (const size_t t) const // find chunk for a given frame position - { - //inspect chunk of first feature stream only - auto iter = std::lower_bound (randomizedchunks[0].begin(), randomizedchunks[0].end(), t, [&] (const chunk & chunk, size_t t) { return chunk.globalte() <= t; }); - const size_t chunkindex = iter - randomizedchunks[0].begin(); - if (t < randomizedchunks[0][chunkindex].globalts || t >= randomizedchunks[0][chunkindex].globalte()) - throw std::logic_error ("chunkforframepos: dude, learn STL!"); - return chunkindex; - } - -public: - - void setverbosity(int newverbosity){ verbosity = newverbosity; } - - // get the next minibatch - // A minibatch is made up of one or more utterances. - // We will return less than 'framesrequested' unless the first utterance is too long. - // Note that this may return frames that are beyond the epoch end, but the first frame is always within the epoch. - // We specify the utterance by its global start time (in a space of a infinitely repeated training set). - // This is efficient since getbatch() is called with sequential 'globalts' except at epoch start. - // Note that the start of an epoch does not necessarily fall onto an utterance boundary. The caller must use firstvalidglobalts() to find the first valid globalts at or after a given time. - // - // - /*implement*/ bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, - std::vector> & uids, std::vector> & utteranceinfo, - std::vector> & transcripts, - std::vector> & latticepairs) - { - bool readfromdisk = false; // return value: shall be 'true' if we paged in anything - - auto_timer timergetbatch; - assert (_totalframes > 0); - - // Clears vector. - utteranceinfo.clear(); - - // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below - const size_t sweep = lazyrandomization (globalts); - - const std::vector noboundaryflags; // dummy - if (!framemode) // regular utterance mode - { - - // find utterance position for globalts - // There must be a precise match; it is not possible to specify frames that are not on boundaries. - auto positer = randomizedutteranceposmap.find (globalts); - if (positer == randomizedutteranceposmap.end()) - throw std::logic_error ("getbatch: invalid 'globalts' parameter; must match an existing utterance boundary"); - const size_t spos = positer->second; - - // determine how many utterances will fit into the requested minibatch size - size_t mbframes = randomizedutterancerefs[spos].numframes; // at least one utterance, even if too long - size_t epos; - for (epos = spos + 1; epos < numutterances && mbframes + randomizedutterancerefs[epos].numframes < framesrequested; epos++) // add more utterances as long as they fit within requested minibatch size - mbframes += randomizedutterancerefs[epos].numframes; - - // do some paging housekeeping - // This will also set the feature-kind information if it's the first time. - // Free all chunks left of the range. - // Page-in all chunks right of the range. - // We are a little more blunt for now: Free all outside the range, and page in only what is touched. We could save some loop iterations. - const size_t windowbegin = positionchunkwindows[spos].windowbegin(); - const size_t windowend = positionchunkwindows[epos-1].windowend(); - for (size_t k = 0; k < windowbegin; k++) - releaserandomizedchunk (k); - for (size_t k = windowend; k < randomizedchunks[0].size(); k++) - releaserandomizedchunk (k); - - for (size_t pos = spos; pos < epos; pos++) - readfromdisk |= requirerandomizedchunk (randomizedutterancerefs[pos].chunkindex, windowbegin, windowend); // (window range passed in for checking only) - - // resize feat and uids - feat.resize(vdim.size()); - uids.resize(classids.size()); - assert(feat.size()==vdim.size()); - assert(feat.size()==randomizedchunks.size()); - foreach_index(i, feat) - { - feat[i].resize(vdim[i], mbframes); - - if (i==0) - { - foreach_index(j, uids) - { - if (issupervised()) // empty means unsupervised training -> return empty uids - uids[j].resize (mbframes); - else - uids[i].clear(); - latticepairs.clear(); // will push_back() below - transcripts.clear(); - } - } - } - // return these utterances - //fprintf (stderr, "getbatch: getting utterances %d..%d (%d frames out of %d requested) in sweep %d\n", spos, epos -1, mbframes, framesrequested, sweep); - size_t tspos = 0; // relative start of utterance 'pos' within the returned minibatch - for (size_t pos = spos; pos < epos; pos++) - { - const auto & uttref = randomizedutterancerefs[pos]; - size_t n=0; - foreach_index(i, randomizedchunks) - { - const auto & chunk = randomizedchunks[i][uttref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - assert (uttref.globalts == globalts + tspos); - auto uttframes = chunkdata.getutteranceframes (uttref.utteranceindex); - matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) - n = uttframevectors.size(); - assert (n == uttframes.cols() && uttref.numframes == n && chunkdata.numframes (uttref.utteranceindex) == n); - - // copy the frames and class labels - for (size_t t = 0; t < n; t++) // t = time index into source utterance - { - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], t + tspos); - //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], t + tspos); - } - - // copy the frames and class labels - if (i==0) - { - // Sets utterance ID information. - utteranceinfo.push_back(std::make_pair(chunkdata.utteranceset[uttref.utteranceindex].key(), uttref.numframes)); - - auto uttclassids = getclassids (uttref); - foreach_index(j, uttclassids) - { - for (size_t t = 0; t < n; t++) // t = time index into source utterance - { - if (issupervised()) - uids[j][t + tspos] = uttclassids[j][t]; - } - - if (!this->lattices.empty()) - { - auto latticepair = chunkdata.getutterancelattice (uttref.utteranceindex); - latticepairs.push_back (latticepair); - // look up reference - const auto & key = latticepair->getkey(); - if (!allwordtranscripts.empty()) - { - const auto & transcript = allwordtranscripts.find (key)->second; - transcripts.push_back (transcript.words); - } - } - } - } - } - tspos += n; - } - assert (tspos == mbframes); - } - else // // debug mode returning randomized frames again, to see whether convergence is better (we don't ensure non-repetition at this point) - { - const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep - const size_t sweepte = sweepts + _totalframes; // and its end - const size_t globalte = min (globalts + framesrequested, sweepte); // we return as much as requested, but not exceeding sweep end - const size_t mbframes = globalte - globalts; // that's our mb size - - // determine window range - // We enumerate all frames--can this be done more efficiently? - const size_t firstchunk = chunkforframepos (globalts); - const size_t lastchunk = chunkforframepos (globalte-1); - const size_t windowbegin = randomizedchunks[0][firstchunk].windowbegin; - const size_t windowend = randomizedchunks[0][lastchunk].windowend; - if (verbosity) - fprintf (stderr, "getbatch: getting randomized frames [%zu..%zu] (%zu frames out of %zu requested) in sweep %zu; chunks [%zu..%zu] -> chunk window [%zu..%zu)\n", - globalts, globalte, mbframes, framesrequested, sweep, firstchunk, lastchunk, windowbegin, windowend); - // release all data outside, and page in all data inside - for (size_t k = 0; k < windowbegin; k++) - releaserandomizedchunk (k); - for (size_t k = windowbegin; k < windowend; k++) - readfromdisk |= requirerandomizedchunk (k, windowbegin, windowend); // (window range passed in for checking only, redundant here) - for (size_t k = windowend; k < randomizedchunks[0].size(); k++) - releaserandomizedchunk (k); - - // resize feat and uids - feat.resize(vdim.size()); - uids.resize(classids.size()); - assert(feat.size()==vdim.size()); - assert(feat.size()==randomizedchunks.size()); - foreach_index(i, feat) - { - feat[i].resize(vdim[i], mbframes); - - if (i==0) - { - foreach_index(j, uids) - { - if (issupervised()) // empty means unsupervised training -> return empty uids - uids[j].resize (mbframes); - else - uids[i].clear(); - latticepairs.clear(); // will push_back() below - transcripts.clear(); - } - } - } - - // return randomized frames for the time range of those utterances - for (size_t j = 0; j < mbframes; j++) - { - // map to time index inside arrays - const size_t framepos = (globalts + j) % _totalframes; // using mod because we may actually run beyond the sweep for the last call - const frameref & frameref = randomizedframerefs[framepos]; - - // random utterance - readfromdisk |= requirerandomizedchunk (frameref.chunkindex, windowbegin, windowend); // (this is just a check; should not actually page in anything) - - foreach_index(i, randomizedchunks) - { - const auto & chunk = randomizedchunks[i][frameref.chunkindex]; - const auto & chunkdata = chunk.getchunkdata(); - auto uttframes = chunkdata.getutteranceframes (frameref.utteranceindex); - matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) - const size_t n = uttframevectors.size(); - assert (n == uttframes.cols() && chunkdata.numframes (frameref.utteranceindex) == n); n; - - // copy frame and class labels - const size_t t = frameref.frameindex; - - size_t leftextent, rightextent; - // page in the needed range of frames - if (leftcontext[i] == 0 && rightcontext[i] == 0) - { - leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); - } - else - { - leftextent = leftcontext[i]; - rightextent = rightcontext[i]; - } - augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], j); - - //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], j); - if (issupervised() && i == 0) - { - auto frameclassids = getclassids(frameref); - foreach_index(k, uids) - uids[k][j] = frameclassids[k][t]; - } - } - } - } - timegetbatch = timergetbatch; - return readfromdisk; - } - double gettimegetbatch() { return timegetbatch;} - - // alternate (updated) definition for multiple inputs/outputs - read as a vector of feature matrixes or a vector of label strings - /*implement*/ bool getbatch (const size_t /*globalts*/, const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, - std::vector & /*uids*/, std::vector> & /*utterances*/, - std::vector> & /*transcripts*/, - std::vector> & /*latticepairs*/) - { - // should never get here - throw runtime_error("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchutterancesource instead\n"); - - // for single input/output set size to be 1 and run old getbatch - //feat.resize(1); - //uids.resize(1); - //return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); - } - size_t totalframes() const { return _totalframes; } - - // return first valid globalts to ask getbatch() for - // In utterance mode, the epoch start may fall in the middle of an utterance. - // We return the end time of that utterance (which, in pathological cases, may in turn be outside the epoch; handle that). - /*implement*/ size_t firstvalidglobalts (const size_t globalts) - { - // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below - const size_t sweep = lazyrandomization (globalts); - // frame mode: start at sweep boundary directly - if (framemode) - return globalts; - // utterance mode - assert (globalts >= sweep * _totalframes && globalts < (sweep + 1) * _totalframes); sweep; - foreach_index (pos, randomizedutterancerefs) - if (randomizedutterancerefs[pos].globalts >= globalts) - return randomizedutterancerefs[pos].globalts; // exact or inexact match - return randomizedutterancerefs.back().globalte(); // boundary case: requested time falls within the last utterance - } - - const std::vector & unitcounts() const { return counts[0]; } - const std::vector & unitcounts(size_t index) const { return counts[index]; } - -}; - -};}; +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// utterancesourcemulti.h -- implementation of utterancesource.h that supports multiple feature and label sets +// + +#pragma once + +#include "basetypes.h" // for attempt() +#include "htkfeatio.h" // for htkmlfreader +#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training) +#include "minibatchsourcehelpers.h" +#include "minibatchiterator.h" + +namespace msra { namespace dbn { + +// --------------------------------------------------------------------------- +// minibatchutterancesource -- feature source to provide randomized utterances +// This also implements a frame-wise mode, which is layered on top of the utterance-wise mode +// and thus benefits from its goodies such as corpus-wide high-level randomization and chunk paging. +// --------------------------------------------------------------------------- +class minibatchutterancesourcemulti : public minibatchsource +{ + void operator=(const minibatchutterancesourcemulti & other); // non-assignable + std::vector vdim; // feature dimension after augmenting neighhors + std::vector leftcontext; // number of frames to the left of the target frame in the context window + std::vector rightcontext; // number of frames to the right of the target frame in the context window + std::vector sampperiod; // (for reference and to check against model) + std::vector featkind; + std::vector featdim; + const bool framemode; // true -> actually return frame-level randomized frames (not possible in lattice mode) + std::vector> counts; // [s] occurence count for all states (used for priors) + int verbosity; + // lattice reader + //const std::vector> &lattices; + const latticesource & lattices; + + //std::vector lattices; + // word-level transcripts (for MMI mode when adding best path to lattices) + const map & allwordtranscripts; // (used for getting word-level transcripts) + //std::vector> allwordtranscripts; + // data store (incl. paging in/out of features and lattices) + struct utterancedesc // data descriptor for one utterance + { + msra::asr::htkfeatreader::parsedpath parsedpath; // archive filename and frame range in that file + size_t classidsbegin; // index into allclassids[] array (first frame) + + utterancedesc (msra::asr::htkfeatreader::parsedpath && ppath, size_t classidsbegin) : parsedpath (ppath), classidsbegin (classidsbegin) {} + + const wstring & logicalpath() const { return parsedpath; /*type cast will return logical path*/ } + size_t numframes() const { return parsedpath.numframes(); } + const wstring key() const // key used for looking up lattice (not stored to save space) + { + // the logical path is the uttid, not a filename. don't need to remove extension + return logicalpath(); + } + }; + struct utterancechunkdata // data for a chunk of utterances + { + std::vector utteranceset; // utterances in this set + size_t numutterances() const { return utteranceset.size(); } + + std::vector firstframes; // [utteranceindex] first frame for given utterance + mutable msra::dbn::matrix frames; // stores all frames consecutively (mutable since this is a cache) + size_t totalframes; // total #frames for all utterances in this chunk + mutable std::vector> lattices; // (may be empty if none) + + // construction + utterancechunkdata() : totalframes (0) {} + //utterancechunkdata (const utterancechunkdata& other) : utteranceset(other.utteranceset), firstframes(other.firstframes), frames (other.frames), totalframes (other.totalframes), lattices (other.lattices){}; + void push_back (utterancedesc &&/*destructive*/ utt) + { + //printf ("start push %d %d\n",frames.rows(), frames.cols()); + + if (isinram()) + { + + throw std::logic_error ("utterancechunkdata: frames already paged into RAM--too late to add data"); + } + firstframes.push_back (totalframes); + totalframes += utt.numframes(); + utteranceset.push_back (utt); + + + } + + // accessors to an utterance's data + size_t numframes (size_t i) const { return utteranceset[i].numframes(); } + size_t getclassidsbegin (size_t i) const { return utteranceset[i].classidsbegin; } + msra::dbn::matrixstripe getutteranceframes (size_t i) const // return the frame set for a given utterance + { + if (!isinram()) + throw std::logic_error ("getutteranceframes: called when data have not been paged in"); + const size_t ts = firstframes[i]; + const size_t n = numframes(i); + return msra::dbn::matrixstripe (frames, ts, n); + } + shared_ptr getutterancelattice (size_t i) const // return the frame set for a given utterance + { + if (!isinram()) + throw std::logic_error ("getutteranceframes: called when data have not been paged in"); + return lattices[i]; + } + + // paging + // test if data is in memory at the moment + bool isinram() const { + return !frames.empty(); + } + // page in data for this chunk + // We pass in the feature info variables by ref which will be filled lazily upon first read + void requiredata (string & featkind, size_t & featdim, unsigned int & sampperiod, const latticesource & latticesource, int verbosity=0) const + { + + if (numutterances() == 0) + throw std::logic_error ("requiredata: cannot page in virgin block"); + if (isinram()) + throw std::logic_error ("requiredata: called when data is already in memory"); + try // this function supports retrying since we read from the unrealible network, i.e. do not return in a broken state + { + msra::asr::htkfeatreader reader; // feature reader (we reinstantiate it for each block, i.e. we reopen the file actually) + // if this is the first feature read ever, we explicitly open the first file to get the information such as feature dimension + if (featdim == 0) + { + reader.getinfo (utteranceset[0].parsedpath, featdim); + fprintf (stderr, "requiredata: determined feature kind as %zu-dimensional\n", featdim); + } + // read all utterances; if they are in the same archive, htkfeatreader will be efficient in not closing the file + frames.resize (featdim, totalframes); + if (!latticesource.empty()) + lattices.resize (utteranceset.size()); + foreach_index (i, utteranceset) + { + //fprintf (stderr, "."); + // read features for this file + auto uttframes = getutteranceframes (i); // matrix stripe for this utterance (currently unfilled) + reader.readNoAlloc (utteranceset[i].parsedpath, (const string &) featkind, sampperiod, uttframes); // note: file info here used for checkuing only + // page in lattice data + if (!latticesource.empty()) + latticesource.getlattices (utteranceset[i].key(), lattices[i], uttframes.cols()); + } + //fprintf (stderr, "\n"); + if (verbosity) + { + fprintf (stderr, "requiredata: %zu utterances read\n", utteranceset.size()); + } + } + catch (...) + { + releasedata(); + throw; + } + } + // page out data for this chunk + void releasedata() const + { + if (numutterances() == 0) + throw std::logic_error ("releasedata: cannot page out virgin block"); + if (!isinram()) + throw std::logic_error ("releasedata: called when data is not memory"); + // release frames + frames.resize (0, 0); + // release lattice data + lattices.clear(); + } + }; + std::vector> allchunks; // set of utterances organized in chunks, referred to by an iterator (not an index) + std::vector>> classids; // [classidsbegin+t] concatenation of all state sequences + bool issupervised() const { return !classids.empty(); } + size_t numutterances; // total number of utterances + size_t _totalframes; // total frames (same as classids.size() if we have labels) + double timegetbatch; // [v-hansu] for time measurement + // sequence in random order of actual use (randomized, where randomization is cached) + const size_t randomizationrange;// parameter remembered; this is the full window (e.g. 48 hours), not the half window + size_t currentsweep; // randomization is currently cached for this sweep; if it changes, rebuild all below + struct chunk // chunk as used in actual processing order (randomized sequence) + { + // the underlying chunk (as a non-indexed reference into the chunk set) + std::vector::const_iterator uttchunkdata; + const utterancechunkdata & getchunkdata() const { return *uttchunkdata; } + size_t numutterances() const { return uttchunkdata->numutterances(); } + size_t numframes() const { return uttchunkdata->totalframes; } + + // position in utterance-position space + size_t utteranceposbegin; + size_t utteranceposend() const { return utteranceposbegin + numutterances(); } + + // position on global time line + size_t globalts; // start frame on global timeline (after randomization) + size_t globalte() const { return globalts + numframes(); } + + // randomization range limits + size_t windowbegin; // randomizedchunk index of earliest chunk that utterances in here can be randomized with + size_t windowend; // and end index [windowbegin, windowend) + chunk (std::vector::const_iterator uttchunkdata, size_t utteranceposbegin, size_t globalts) : uttchunkdata (uttchunkdata), utteranceposbegin (utteranceposbegin), globalts (globalts) {} + }; + std::vector> randomizedchunks; // utterance chunks after being brought into random order (we randomize within a rolling window over them) + size_t chunksinram; // (for diagnostics messages) + struct utteranceref // describes the underlying random utterance associated with an utterance position + { + size_t chunkindex; // lives in this chunk (index into randomizedchunks[]) + size_t utteranceindex; // utterance index in that chunk + size_t numframes; // (cached since we cannot directly access the underlying data from here) + size_t globalts; // start frame in global space after randomization (for mapping frame index to utterance position) + size_t globalte() const { return globalts + numframes; } // end frame + utteranceref (size_t chunkindex, size_t utteranceindex) : chunkindex (chunkindex), utteranceindex (utteranceindex), globalts (SIZE_MAX), numframes (0) {} + void swap (utteranceref & other) // used in randomization + { + ::swap (chunkindex, other.chunkindex); + ::swap (utteranceindex, other.utteranceindex); + assert (globalts == SIZE_MAX && other.globalts == SIZE_MAX && numframes == 0 && other.numframes == 0); // can only swap before assigning these + } + }; + std::vector randomizedutterancerefs; // [pos] randomized utterance ids + std::unordered_map randomizedutteranceposmap; // [globalts] -> pos lookup table + struct positionchunkwindow // chunk window required in memory when at a certain position, for controlling paging + { + std::vector::const_iterator definingchunk; // the chunk in randomizedchunks[] that defined the utterance position of this utterance + size_t windowbegin() const { return definingchunk->windowbegin; } + size_t windowend() const { return definingchunk->windowend; } + bool isvalidforthisposition (const utteranceref & utt) const + { + return utt.chunkindex >= windowbegin() && utt.chunkindex < windowend(); // check if 'utt' lives in is in allowed range for this position + } + positionchunkwindow (std::vector::iterator definingchunk) : definingchunk (definingchunk) {} + }; + std::vector positionchunkwindows; // [utterance position] -> [windowbegin, windowend) for controlling paging + + // frame-level randomization layered on top of utterance chunking (randomized, where randomization is cached) + struct frameref + { +#ifdef _WIN64 // (sadly, the compiler makes this 8 bytes, not 6) + unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) + unsigned short utteranceindex; // utterance index in that chunk + static const size_t maxutterancesperchunk = 65535; + unsigned short frameindex; // frame index within the utterance + static const size_t maxframesperutterance = 65535; +#elif __unix__ // (sadly, the compiler makes this 8 bytes, not 6) + unsigned short chunkindex; // lives in this chunk (index into randomizedchunks[]) + unsigned short utteranceindex; // utterance index in that chunk + static const size_t maxutterancesperchunk = 65535; + unsigned short frameindex; // frame index within the utterance + static const size_t maxframesperutterance = 65535; +#else // For Win32, we care to keep it inside 32 bits. We have already encountered setups where that's not enough. + unsigned int chunkindex : 13; // lives in this chunk (index into randomizedchunks[]) + unsigned int utteranceindex : 8; // utterance index in that chunk + static const size_t maxutterancesperchunk = 255; + unsigned int frameindex : 11; // frame index within the utterance + static const size_t maxframesperutterance = 2047; +#endif + frameref (size_t ci, size_t ui, size_t fi) : chunkindex ((unsigned short) ci), utteranceindex ((unsigned short) ui), frameindex ((unsigned short) fi) + { +#ifdef _WIN32 + static_assert (sizeof (frameref) == 4, "frameref: bit fields too large to fit into 32-bit integer"); +#endif + if (ci == chunkindex && ui == utteranceindex && fi == frameindex) + return; + throw std::logic_error ("frameref: bit fields too small"); + } + frameref() : chunkindex (0), utteranceindex (0), frameindex (0) {} + }; + biggrowablevector randomizedframerefs; // [globalt-sweepts] -> (chunk, utt, frame) lookup table for randomized frames --this can be REALLY big! + + // TODO: this may go away if we store classids directly in the utterance data + template class shiftedvector // accessing a vector with a non-0 starting index + { + void operator= (const shiftedvector &); + VECTOR & v; + size_t first; + size_t n; + void check (size_t i) const { if (i >= n) throw std::logic_error ("shiftedvector: index out of bounds"); } + public: + shiftedvector (VECTOR & v, size_t first, size_t n) : v (v), first (first), n (n) { } + // TODO: the following is not templated--do it if needed; also should return a const reference then + size_t operator[] (size_t i) const { check (i); return v[first + i]; } + }; + template std::vector>> getclassids (const UTTREF & uttref) // return sub-vector of classids[] for a given utterance + { + std::vector>> allclassids; + allclassids.empty(); + + if (!issupervised()) + { + foreach_index(i,classids) + allclassids.push_back(std::move(shiftedvector> ((*classids[i]), 0, 0))); + return allclassids; // nothing to return + } + const auto & chunk = randomizedchunks[0][uttref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + const size_t classidsbegin = chunkdata.getclassidsbegin (uttref.utteranceindex); // index of first state label in global concatenated classids[] array + const size_t n = chunkdata.numframes (uttref.utteranceindex); + foreach_index(i,classids) + { + if ((*classids[i])[classidsbegin + n] != (CLASSIDTYPE) -1) + throw std::logic_error ("getclassids: expected boundary marker not found, internal data structure screwed up"); + allclassids.push_back(std::move(shiftedvector> ((*classids[i]), classidsbegin, n))); + } + return allclassids; // nothing to return + } +public: + // constructor + // Pass empty labels to denote unsupervised training (so getbatch() will not return uids). + // This mode requires utterances with time stamps. + minibatchutterancesourcemulti (std::vector & featuresections, const std::vector> & infiles, const std::vector>> & labels, + std::vector vdim, std::vector udim, std::vector leftcontext, std::vector rightcontext, size_t randomizationrange, + const latticesource & lattices, const map & allwordtranscripts, const bool framemode) + : vdim (vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod (0), featdim (0), randomizationrange (randomizationrange), currentsweep (SIZE_MAX), + lattices (lattices), allwordtranscripts (allwordtranscripts), framemode (framemode), chunksinram (0), timegetbatch (0), verbosity(2) + // [v-hansu] change framemode (lattices.empty()) into framemode (false) to run utterance mode without lattice + // you also need to change another line, search : [v-hansu] comment out to run utterance mode without lattice + { + // process infiles to know dimensions of things (but not loading features) + std::vector utteranceset;// read all utterances to here first; at the end, distribute to chunks + utteranceset.reserve (infiles.size()); + size_t nomlf = 0; // number of entries missing in MLF (diagnostics) + size_t nolat = 0; // number of entries missing in lattice archive (diagnostics) + std::vector numclasses; // number of output classes as found in the label file (diagnostics) + _totalframes = 0; + wstring key; + size_t numutts=0; + + std::vectoruttisvalid; // boolean flag to check that utterance is valid. valid means number of + //frames is consistent across all feature and label streams + std::vectoruttduration; // track utterance durations to determine utterance validity + + std::vector classidsbegin; + if (!lattices.empty()) + { + LogicError("lattices not supported in utterancereadermulti"); + } + + allchunks = std::vector>(infiles.size(), std::vector()); + featdim = std::vector(infiles.size(), 0); + sampperiod = std::vector(infiles.size(), 0); + featkind = std::vector(infiles.size(), ""); + + numclasses = std::vector(labels.size(), 0); + counts = std::vector>(labels.size(), std::vector()); + foreach_index (i, labels) + { + classids.push_back(unique_ptr>(new biggrowablevector())); + //std::pair,std::vector> latticetocs; + //std::unordered_map modelsymmap; + //lattices.push_back(shared_ptr(new latticesource(latticetocs, modelsymmap))); + + } + + + // first check consistency across feature streams + // We'll go through the SCP files for each stream to make sure the duration is consistent + // If not, we'll plan to ignore the utterance, and inform the user + // m indexes the feature stream + // i indexes the files within a stream, i.e. in the SCP file) + foreach_index(m, infiles){ + if (m == 0){ + numutts = infiles[m].size(); + uttisvalid = std::vector(numutts, true); + uttduration = std::vector(numutts, 0); + } + else if (infiles[m].size()!=numutts) + throw std::runtime_error("minibatchutterancesourcemulti: all feature files must have same number of utterances"); + + foreach_index(i, infiles[m]){ + utterancedesc utterance(msra::asr::htkfeatreader::parsedpath(infiles[m][i],featuresections[m]), 0); //mseltzer - is this foolproof for multiio? is classids always non-empty? + const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode + // we need at least 2 frames for boundary markers to work + if (uttframes < 2) + { + //throw std::runtime_error("minibatchutterancesource: utterances < 2 frames not supported"); + fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%zd frames) because it less than %d frames for frameref bit field: %S\n", + i, uttframes, 2, key.c_str()); + uttduration[i] = 0; + uttisvalid[i] = false; + } else if (uttframes > frameref::maxframesperutterance || uttframes <2) + { + fprintf(stderr, "minibatchutterancesource: skipping %d-th file (%zd frames) because it exceeds max. frames (%zd) for frameref bit field: %S\n", + i, uttframes, frameref::maxframesperutterance, key.c_str()); + uttduration[i] = 0; + uttisvalid[i] = false; + } + else{ + if (m == 0){ + uttduration[i] = uttframes; + uttisvalid[i] = true; + } + else if (uttduration[i] != uttframes){ + fprintf(stderr, "minibatchutterancesource: skipping %d-th file due to inconsistency in duration in different feature streams (%zd vs %zd frames)\n", + i, uttduration[i], uttframes); + uttduration[i] = 0; + uttisvalid[i] = false; + } + } + } + } + size_t invalidutts=0; + foreach_index(i, uttisvalid){ + if (!uttisvalid[i]) + invalidutts++; + } + if (invalidutts > uttisvalid.size() / 2) + throw std::runtime_error("minibatchutterancesource: too many files not found in with inconsistent durations, assuming broken configuration\n"); + else if (invalidutts>0) + fprintf(stderr, "Found inconsistent durations across feature streams in %zd out of %zd files.\n", invalidutts, uttisvalid.size()); + + + // now process the features and labels + size_t utterancesetsize = 0; + foreach_index (m, infiles) + { + utteranceset.clear(); + //if (m==0) + // numutts = infiles[m].size(); + //else + // if (infiles[m].size()!=numutts) + // throw std::runtime_error("minibatchutterancesourcemulti: all feature files must have same number of utterances\n"); + if (m==0) + classidsbegin.clear(); + + foreach_index (i, infiles[m]) + { + if (i % (infiles[m].size() / 100 + 1) == 0) { fprintf (stderr, "."); fflush (stderr); } + // build utterance descriptor + if (m == 0 && !labels.empty()) + classidsbegin.push_back(classids[0]->size()); + + if (uttisvalid[i]){ + utterancedesc utterance (msra::asr::htkfeatreader::parsedpath (infiles[m][i], featuresections[m]), labels.empty() ? 0 : classidsbegin[i] ); //mseltzer - is this foolproof for multiio? is classids always non-empty? + const size_t uttframes = utterance.numframes(); // will throw if frame bounds not given --required to be given in this mode + assert(uttframes == uttduration[i]); // ensure nothing funky happened + + // already performed these checks above + // we need at least 2 frames for boundary markers to work + //if (uttframes < 2) + // throw std::runtime_error ("minibatchutterancesource: utterances < 2 frames not supported"); + //if (uttframes > frameref::maxframesperutterance) + //{ + // fprintf (stderr, "minibatchutterancesource: skipping %d-th file (%d frames) because it exceeds max. frames (%d) for frameref bit field: %S", i, uttframes, frameref::maxframesperutterance, key.c_str()); + // continue; + //} + + // check whether we have the ref transcript + //auto labelsiter = labels[0].end(); + bool lacksmlf = true; + if (!labels.empty()) // empty means unsupervised mode (don't load any) + { + key = utterance.key(); + // check if labels are available (if not, it normally means that no path was found in realignment) + auto labelsiter = labels[0].find (key); + //const bool lacksmlf = (labelsiter == labels[0].end()); + lacksmlf = (labelsiter == labels[0].end()); + if (lacksmlf) + if (nomlf++ < 5) + fprintf (stderr, " [no labels for %S]", key.c_str()); + // check if lattice is available (when in lattice mode) + // TODO: also check the #frames here; requires a design change of the TOC format & a rerun + const bool lackslat = !lattices.empty() && !lattices.haslattice (key); // ('true' if we have no lattices) + if (lackslat) + if (nolat++ < 5) + fprintf (stderr, " [no lattice for %S]", key.c_str()); + // skip if either one is missing + if (lacksmlf || lackslat){ + uttisvalid[i] = false; + continue; // skip this utterance at all + } + } + // push the label sequence into classids[], since we already looked it up + // TODO: we can store labels more efficiently now since we don't do frame-wise random access anymore. + + // OK, utterance has all we need --remember it + + if (m==0) + { + if (!labels.empty() && !lacksmlf) + //if (!labels.empty() && labelsiter != labels[0].end()) + { + // first verify that all the label files have the proper duration + foreach_index (j, labels) + { + const auto & labseq = labels[j].find(key)->second; + // check if durations match; skip if not + size_t labframes = labseq.empty() ? 0 : (labseq[labseq.size()-1].firstframe + labseq[labseq.size()-1].numframes); + if (labframes != uttframes) + { + fprintf (stderr, " [duration mismatch (%zu in label vs. %zu in feat file), skipping %S]", labframes, uttframes, key.c_str()); + nomlf++; + uttisvalid[i] = false; + break; // continue; // skip this utterance at all + } + } + if (uttisvalid[i]) + { + utteranceset.push_back(std::move(utterance)); + _totalframes += uttframes; + // then parse each mlf if the durations are consistent + foreach_index(j, labels) + { + const auto & labseq = labels[j].find(key)->second; + + // expand classid sequence into flat array + foreach_index (i, labseq) + { + const auto & e = labseq[i]; + if ((i > 0 && labseq[i-1].firstframe + labseq[i-1].numframes != e.firstframe) || (i == 0 && e.firstframe != 0)) + throw std::runtime_error (msra::strfun::strprintf ("minibatchutterancesource: labels not in consecutive order MLF in label set: %S", key.c_str())); + if (e.classid >= udim[j]) + { + throw std::runtime_error (msra::strfun::strprintf ("minibatchutterancesource: class id exceeds model output dimension")); + } + if (e.classid != (CLASSIDTYPE) e.classid) + throw std::runtime_error ("CLASSIDTYPE has too few bits"); + for (size_t t = e.firstframe; t < e.firstframe + e.numframes; t++) + classids[j]->push_back ((CLASSIDTYPE) e.classid); + numclasses[j] = max (numclasses[j], (size_t)(1u + e.classid)); + counts[j].resize (numclasses[j], 0); + counts[j][e.classid] += e.numframes; + } + classids[j]->push_back ((CLASSIDTYPE) -1); // append a boundary marker marker for checking + + if (!labels[j].empty() && classids[j]->size() != _totalframes + utteranceset.size()) + throw std::logic_error (msra::strfun::strprintf ("minibatchutterancesource: label duration inconsistent with feature file in MLF label set: %S", key.c_str())); + assert (labels[j].empty() || classids[j]->size() == _totalframes + utteranceset.size()); + } + } + } + else{ + assert(classids.empty() && labels.empty()); + utteranceset.push_back(std::move(utterance)); + _totalframes += uttframes; + } + } + else + { + utteranceset.push_back(std::move(utterance)); + } + } + } + if (m == 0) + utterancesetsize = utteranceset.size(); + else + assert(utteranceset.size() == utterancesetsize); + fprintf (stderr, "feature set %d: %zu frames in %zu out of %zu utterances\n", m, _totalframes, utteranceset.size(),infiles[m].size()); + + if (!labels.empty()){ + foreach_index (j, labels){ + biggrowablevector & cid = *classids[j]; + foreach_index (i, utteranceset){ + //if ((*classids[j])[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) + //printf("index = %d\n",utteranceset[i].classidsbegin + utteranceset[i].numframes()); + //printf("cid[index] = %d\n",cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()]); + //printf("CLASSIDTYPE(-1) = %d\n",(CLASSIDTYPE) -1); + if (cid[utteranceset[i].classidsbegin + utteranceset[i].numframes()] != (CLASSIDTYPE) -1) + throw std::logic_error ("minibatchutterancesource: classids[] out of sync"); + } + } + } + if (nomlf + nolat > 0) + { + fprintf (stderr, "minibatchutterancesource: out of %zu files, %zu files not found in label set and %zu have no lattice\n", infiles.size(), nomlf, nolat); + if (nomlf + nolat > infiles[m].size() / 2) + throw std::runtime_error ("minibatchutterancesource: too many files not found in label set--assuming broken configuration\n"); + } + + if (m==0) {foreach_index(j, numclasses) { fprintf(stderr,"label set %d: %zu classes\n",j, numclasses[j]); } } + // distribute them over chunks + // We simply count off frames until we reach the chunk size. + // Note that we first randomize the chunks, i.e. when used, chunks are non-consecutive and thus cause the disk head to seek for each chunk. + const size_t framespersec = 100; // we just assume this; our efficiency calculation is based on this + const size_t chunkframes = 15 * 60 * framespersec; // number of frames to target for each chunk + // Loading an initial 24-hour range will involve 96 disk seeks, acceptable. + // When paging chunk by chunk, chunk size ~14 MB. + std::vector & thisallchunks = allchunks[m]; + //std::vector thisallchunks; + + thisallchunks.resize (0); + thisallchunks.reserve (_totalframes / chunkframes); + foreach_index (i, utteranceset) + { + // if exceeding current entry--create a new one + // I.e. our chunks are a little larger than wanted (on av. half the av. utterance length). + if (thisallchunks.empty() || thisallchunks.back().totalframes > chunkframes || thisallchunks.back().numutterances() >= frameref::maxutterancesperchunk) + { + thisallchunks.push_back (utterancechunkdata()); + + + } + // append utterance to last chunk + utterancechunkdata & currentchunk = thisallchunks.back(); + + currentchunk.push_back (std::move (utteranceset[i])); // move it out from our temp array into the chunk + // TODO: above push_back does not actually 'move' because the internal push_back does not accept that + } + + numutterances = utteranceset.size(); + fprintf (stderr, "minibatchutterancesource: %zu utterances grouped into %zu chunks, av. chunk size: %.1f utterances, %.1f frames\n", + numutterances, thisallchunks.size(), numutterances / (double) thisallchunks.size(), _totalframes / (double) thisallchunks.size()); + // Now utterances are stored exclusively in allchunks[]. They are never referred to by a sequential utterance id at this point, only by chunk/within-chunk index. + } + // preliminary mem allocation for frame references (if in frame mode) + if (framemode) + randomizedframerefs.resize (_totalframes); + } + +private: + // shuffle a vector into random order by randomly swapping elements + + template static void randomshuffle (VECTOR & v, size_t randomseed) + { + if (v.size() > RAND_MAX * (size_t) RAND_MAX) + throw std::runtime_error ("randomshuffle: too large set: need to change to different random generator!"); + srand ((unsigned int) randomseed); + foreach_index (i, v) + { + // pick a random location + const size_t irand = msra::dbn::rand (0, v.size()); + + // swap element i with it + if (irand == (size_t) i) + continue; + ::swap (v[i], v[irand]); + } + } +#if 0 + template static void randomshuffle(std::vector v, size_t randomseed) + { + foreach_index(j, v) + { + if (v[j].size() > RAND_MAX * (size_t) RAND_MAX) + throw std::runtime_error ("randomshuffle: too large set: need to change to different random generator!"); + } + srand ((unsigned int) randomseed); + + foreach_index (i, v[0]) + { + // pick a random location + const size_t irand = msra::dbn::rand (0, v[0].size()); + + foreach_index(j, v){ + // swap element i with it + if (irand == (size_t) i) + continue; + ::swap (v[j][i], v[j][irand]); + } + } + } +#endif //0 + static void checkoverflow (size_t fieldval, size_t targetval, const char * fieldname) + { + if (fieldval != targetval) + throw std::runtime_error (msra::strfun::strprintf ("checkoverflow: bit field %s too small for value 0x%x (cut from 0x%x)", fieldname, targetval, fieldval)); + } + + // helper for testing whether a swapped frame position is valid (w.r.t. beign in RAM when being at position 't') + bool isframepositionvalid (const size_t t, const biggrowablevector & ttochunk) const + { + // look up valid range for time position + const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this original chunk (relationship is monotonous, not random) + const auto & chunk = randomizedchunks[0][positionchunkindex]; + // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) + const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) + const size_t poswindowend = chunk.windowend; + // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. + + // now see if the randomized location is within that window + const size_t actualchunkindexforpos = randomizedframerefs[t].chunkindex; // where this frame pos has been mapped to + return actualchunkindexforpos >= poswindowbegin && actualchunkindexforpos < poswindowend; + // We only need to test the chunk index. Utterance and frame can be randomized within a chunk as we want, as long it is in RAM. + } + + // big long helper to update all cached randomization information + // This is a rather complex process since we randomize on two levels: + // - chunks of consecutive data in the feature archive + // - within a range of chunks that is paged into RAM + // - utterances (in utt mode), or + // - frames (in frame mode) + // The 'globalts' parameter is the start time that triggered the rerandomization; it is NOT the base time of the randomized area. + size_t lazyrandomization (const size_t globalts) + { + const size_t sweep = globalts / _totalframes; // which sweep (this determines randomization) + if (sweep == currentsweep) // already got this one--nothing to do + return sweep; + + currentsweep = sweep; + if (verbosity>0) + fprintf (stderr, "lazyrandomization: re-randomizing for sweep %zu in %s mode\n", currentsweep, framemode ? "frame" : "utterance"); + + const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep + + // first randomize chunks + std::vector::const_iterator>> randomizedchunkrefs; + foreach_index (i, allchunks) + randomizedchunkrefs.push_back(std::vector::const_iterator>()); + + foreach_index (i, allchunks) + randomizedchunkrefs[i].reserve (allchunks[i].size()); + + foreach_index (i, allchunks) // TODO: this cries for iterating using the iterator! + { + foreach_index(j, allchunks[i]) + randomizedchunkrefs[i].push_back (allchunks[i].begin() + j); + assert (randomizedchunkrefs[i].size() == allchunks[i].size()); + + // note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams + randomshuffle (randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep) + + } + + // place them onto the global timeline -> randomizedchunks[] + // We are processing with randomization within a rolling window over this chunk sequence. + // Paging will happen on a chunk-by-chunk basis. + // The global time stamp is needed to determine the paging window. + randomizedchunks.clear(); // data chunks after being brought into random order (we randomize within a rolling window over them) + + foreach_index(i, allchunks) + randomizedchunks.push_back(std::vector()); + + foreach_index(i, allchunks) + { + randomizedchunks[i].reserve (randomizedchunkrefs[i].size()); + foreach_index (k, randomizedchunkrefs[i]) + randomizedchunks[i].push_back (chunk (randomizedchunkrefs[i][k], randomizedchunks[i].empty() ? 0 : randomizedchunks[i].back().utteranceposend(), randomizedchunks[i].empty() ? sweepts : randomizedchunks[i].back().globalte())); + assert (randomizedchunks[i].size() == allchunks[i].size()); + + assert (randomizedchunks[i].empty() || (randomizedchunks[i].back().utteranceposend() == numutterances && randomizedchunks[i].back().globalte() == sweepts + _totalframes)); + } + // for each chunk, compute the randomization range (w.r.t. the randomized chunk sequence) + foreach_index (i, randomizedchunks) + { + foreach_index (k, randomizedchunks[i]) + { + chunk & chunk = randomizedchunks[i][k]; + // start with the range of left neighbor + if (k == 0) + { + chunk.windowbegin = 0; + chunk.windowend = 1; + } + else + { + chunk.windowbegin = randomizedchunks[i][k-1].windowbegin; // might be too early + chunk.windowend = randomizedchunks[i][k-1].windowend; // might have more space + } + while (chunk.globalts - randomizedchunks[i][chunk.windowbegin].globalts > randomizationrange/2) + chunk.windowbegin++; // too early + while (chunk.windowend < randomizedchunks[i].size() && randomizedchunks[i][chunk.windowend].globalte() - chunk.globalts < randomizationrange/2) + chunk.windowend++; // got more space + } + } + if (!framemode) // utterance mode + { + // This sets up the following members: + // - positionchunkwindows + // - randomizedutterancerefs + // - randomizedutteranceposmap + + // We will now introduce the concept of utterance *position*. + // During processing, utterances will be indexed by position (which is in turn derived from a frame index in getbatch()), + // and it is assumed (required) that positions are requested consecutively. + // Each utterance position has an underlying associated utterance, which is represented as (chunkid, within-chunk index) and randomly assigned. + // Each utterance position also has an associated range of chunks that are kept in memory, + // and the associated underlying utterance is guaranteed to be found within that associated range of chunks. + // That allows to page out/in data when processing utterance positions in a consecutive manner. + + // compute chunk windows for every utterance position -> positionchunkwindows[] + // Utterance positions can only reference underlying utterance data within the chunk window. + // Utterance positions are defined by the randomized chunk sequence (i.e. their underlying 'defining' chunk differs from sweep to sweep). + positionchunkwindows.clear(); // [utterance position] -> [windowbegin, windowend) for controlling paging + positionchunkwindows.reserve (numutterances); + + // positionchunkwindows should be consistent for all inputs (distinct feature streams), so just build based on feature[0] + // contains pointer to chunk elements but only to compute index + foreach_index (k, randomizedchunks[0]) // TODO: this really cries for iterating using iterators! + { + chunk & chunk = randomizedchunks[0][k]; + for (size_t i = chunk.utteranceposbegin; i < chunk.utteranceposend(); i++) // loop over utterances in this chunk + { + positionchunkwindows.push_back (randomizedchunks[0].begin() + k); + } + // to look up the chunk range in memory for a position, look up the defining chunk and its range + } + assert (positionchunkwindows.size() == numutterances); + + // build the randomized utterances array -> randomizedutterancerefs[] + // start by assigning all utterance positions to utterances in non-random consecutive manner + randomizedutterancerefs.clear(); // [pos] randomized utterance ids + randomizedutterancerefs.reserve (numutterances); + foreach_index (k, randomizedchunks[0]) + { + chunk & chunk = randomizedchunks[0][k]; + for (size_t i = 0; i < chunk.numutterances(); i++) // loop over utterances in this chunk + randomizedutterancerefs.push_back (utteranceref (k, i)); + } + assert (randomizedutterancerefs.size() == numutterances); + foreach_index (i, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[i]; + assert (positionchunkwindows[i].isvalidforthisposition(uttref)); uttref; + } + + // check we got those setup right + + // we now randomly shuffle randomizedutterancerefs[pos], while considering the constraints of what chunk range needs to be in memory + srand ((unsigned int) sweep + 1); + for (size_t i = 0; i < randomizedutterancerefs.size(); i++) + { + // get valid randomization range, expressed in chunks + const size_t windowbegin = positionchunkwindows[i].windowbegin(); + const size_t windowend = positionchunkwindows[i].windowend(); + + // get valid randomization range, expressed in utterance positions + // Remember, utterance positions are defined by chunks. + const size_t posbegin = randomizedchunks[0][windowbegin].utteranceposbegin; + const size_t posend = randomizedchunks[0][windowend-1].utteranceposend(); + + // randomization range for this utterance position is [posbegin, posend) + for(;;) + { + // pick a random location + const size_t j = msra::dbn::rand (posbegin, posend); // a random number within the window + if (i == j) + break; // the random gods say "this one points to its original position"... nothing wrong about that, but better not try to swap + + // We want to swap utterances at i and j, but need to make sure they remain in their allowed range. + // This is guaranteed for a so-far untouched utterance, but both i and j may have been touched by a previous swap. + + // We want to use the utterance previously referenced at utterance position j at position i. Is that allowed? + if (!positionchunkwindows[i].isvalidforthisposition (randomizedutterancerefs[j])) + continue; // nope --try another + + // Likewise may we use the utterance previously referenced at utterance position i at position j? + if (!positionchunkwindows[j].isvalidforthisposition (randomizedutterancerefs[i])) + continue; // nope --try another + + // yep--swap them + randomizedutterancerefs[i].swap (randomizedutterancerefs[j]); + break; + } + } + + // place the randomized utterances on the global timeline so we can find them by globalts + size_t t = sweepts; + foreach_index (i, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[i]; + uttref.globalts = t; + uttref.numframes = randomizedchunks[0][uttref.chunkindex].getchunkdata().numframes (uttref.utteranceindex); + t = uttref.globalte(); + } + assert (t == sweepts + _totalframes); + + // verify that we got it right (I got a knot in my head!) + foreach_index (i, randomizedutterancerefs) + { + // get utterance referenced at this position + const auto & uttref = randomizedutterancerefs[i]; + // check if it is valid for this position + if (uttref.chunkindex < positionchunkwindows[i].windowbegin() || uttref.chunkindex >= positionchunkwindows[i].windowend()) + throw std::logic_error ("lazyrandomization: randomization logic mangled!"); + } + + // create lookup table for (globalts values -> pos) -> randomizedutteranceposmap[] + randomizedutteranceposmap.clear(); // [globalts] -> pos lookup table + foreach_index (pos, randomizedutterancerefs) + { + auto & uttref = randomizedutterancerefs[pos]; + randomizedutteranceposmap[uttref.globalts] = (size_t) pos; + } + } + else // frame mode + { + // This sets up the following members: + // - randomizedframerefs + + srand ((unsigned int) sweep + 1); + // An original timeline is established by the randomized chunks, denoted by 't'. + // Returned frames are indexed by frame position j = (globalt - sweept), which have an associated underlying 't'. + // It is guaranteed that uttterance frame position j maps to an underlying frame within the corresponding chunk window. + biggrowablevector ttochunk; // randomized chunk index associated with frame position + ttochunk.resize (_totalframes); + size_t t = 0; + frameref frameref; + // enumerate chunks in their randomized order and assign frame indices in that order -> randomizedframerefs[t] + // At this point, chunks are in randomized order, but utterances and frames within utterances are not randomized. + // Later we will randomize those as well. + foreach_index (i, randomizedchunks[0]) + { + frameref.chunkindex = (unsigned short)i; + checkoverflow (frameref.chunkindex, i, "frameref::chunkindex"); + const auto & chunk = randomizedchunks[0][i]; + const auto & chunkdata = chunk.getchunkdata(); + const size_t numutt = chunkdata.numutterances(); + for (size_t k = 0; k < numutt; k++) + { + frameref.utteranceindex = (short)k; + checkoverflow (frameref.utteranceindex, k, "frameref::utteranceindex"); + const size_t n = chunkdata.numframes (k); + for (size_t m = 0; m < n; m++) + { + frameref.frameindex = (short)m; + checkoverflow (frameref.frameindex, m, "frameref::utteranceindex"); + randomizedframerefs[t] = frameref; // hopefully this is a memory copy, not a bit-wise assignment! If not, then code it explicitly + ttochunk[t] = (unsigned short) i; + checkoverflow (ttochunk[t], i, "ttochunk[]"); + t++; + } + } + } + assert (t == _totalframes); + + // now randomize them --we use the nested loop again to avoid storing a backpointer + // The condition is that a randomized frame may not be moved out of its associated chunk window. + foreach_index (t, randomizedframerefs) + { + const size_t positionchunkindex = ttochunk[t]; // position 't' lies within this chunk (relationship is monotonous, not random) + const auto & chunk = randomizedchunks[0][positionchunkindex]; // for window + + // get in-RAM chunk range for this frame position (shared across all frame positions within the same chunk) + const size_t poswindowbegin = chunk.windowbegin; // rolling window over chunks (which under the hood have been randomized) + const size_t poswindowend = chunk.windowend; + // Chunk implies that if we are at position 't', we are guaranteed to have chunks [poswindowbegin, poswindowend) in RAM. + // These chunks are associated with a range of frame positions. + // It is implied that if we are at position 't', the frames covered by chunks [poswindowbegin, poswindowend) are in RAM. + const size_t postbegin = randomizedchunks[0][poswindowbegin].globalts - sweepts; + const size_t postend = randomizedchunks[0][poswindowend-1].globalte() - sweepts; + // The position that this frame gets randomized to must be guaranteed to belong to a chunk within [postbegin, postend). + + for (;;) // (randomization retry loop) + { + size_t tswap = msra::dbn::rand (postbegin, postend); // random frame position within allowed range + // We want to swap 't' to 'tswap' and 'tswap' to 't'. + // - Both may have been swapped before. + // - Both must stay within the randomization window of their respective position. + // check admissibility of where the element at 'tswap' gets swapped to 't' (range = [windowbegin,windowend)) + size_t tswapchunkindex = randomizedframerefs[tswap].chunkindex; + if (tswapchunkindex < poswindowbegin || tswapchunkindex >= poswindowend) + continue; + // check admissibility of where the element at t gets swapped to (which is frame position 'tswap') + const size_t sourcechunkindex = randomizedframerefs[t].chunkindex; + size_t targetchunkindex = ttochunk[tswap]; // chunk associated with this frame position defines value range + const auto & targetchunk = randomizedchunks[0][targetchunkindex]; + const size_t targetwindowbegin = targetchunk.windowbegin; + const size_t targetwindowend = targetchunk.windowend; + if (sourcechunkindex < targetwindowbegin || sourcechunkindex >= targetwindowend) + continue; + // admissible--swap the two + ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); +#if 0 + break; +#else // post-check --so far did not trigger, can be removed + + // do a post-check if we got it right --we seem not to + if (isframepositionvalid (t, ttochunk) && isframepositionvalid (tswap, ttochunk)) + break; + // not valid: swap them back and try again --we actually discovered a bug in the code above + ::swap (randomizedframerefs[t], randomizedframerefs[tswap]); + fprintf (stderr, "lazyrandomization: BUGBUG --invalid swapping condition detected\n"); +#endif + } + } + + // check it --my head spins + t = 0; + foreach_index (i, randomizedchunks[0]) + { + const auto & chunk = randomizedchunks[0][i]; // for window and chunkdata + const size_t poswindowbegin = chunk.windowbegin; + const size_t poswindowend = chunk.windowend; + + const auto & chunkdata = chunk.getchunkdata(); // for numutterances/numframes + const size_t numutt = chunkdata.numutterances(); + for (size_t k = 0; k < numutt; k++) + { + const size_t n = chunkdata.numframes (k); + for (size_t m = 0; m < n; m++) + { + const size_t randomizedchunkindex = randomizedframerefs[t].chunkindex; + if (randomizedchunkindex < poswindowbegin || randomizedchunkindex >= poswindowend) + throw std::logic_error ("lazyrandomization: nope, you got frame randomization wrong, dude"); + t++; + } + } + } + assert (t == _totalframes); + } + + return sweep; + } + + // helper to page out a chunk with log message + void releaserandomizedchunk (size_t k) + { + size_t numreleased=0; + foreach_index(m, randomizedchunks){ + auto & chunkdata = randomizedchunks[m][k].getchunkdata(); + if (chunkdata.isinram()) + { + if (verbosity) + fprintf (stderr, "releaserandomizedchunk: paging out randomized chunk %zu (frame range [%zu..%zu]), %zu resident in RAM\n", + k, randomizedchunks[m][k].globalts, randomizedchunks[m][k].globalte()-1, chunksinram-1); + chunkdata.releasedata(); + numreleased++; + } + } + if (numreleased>0 && numreleased= windowend) + throw std::logic_error ("requirerandomizedchunk: requested utterance outside in-memory chunk range"); + + foreach_index(m, randomizedchunks) + { + auto & chunk = randomizedchunks[m][chunkindex]; + auto & chunkdata = chunk.getchunkdata(); + if (chunkdata.isinram()) + numinram++; + } + if (numinram==randomizedchunks.size()) + { + + return false; + } + else if (numinram==0) + { + foreach_index(m, randomizedchunks) + { + auto & chunk = randomizedchunks[m][chunkindex]; + auto & chunkdata = chunk.getchunkdata(); + if (verbosity) + fprintf (stderr, "feature set %d: requirerandomizedchunk: paging in randomized chunk %zu (frame range [%zu..%zu]), %zu resident in RAM\n", m, chunkindex, chunk.globalts, chunk.globalte()-1, chunksinram+1); + msra::util::attempt (5, [&]() // (reading from network) + { + chunkdata.requiredata (featkind[m], featdim[m], sampperiod[m], this->lattices, verbosity); + }); + } + chunksinram++; + return true; + } + else{ + LogicError ("requirerandomizedchunk: inconsistency detected - some inputs need chunks paged in, some not"); + } + } + + class matrixasvectorofvectors // wrapper around a matrix that views it as a vector of column vectors + { + void operator= (const matrixasvectorofvectors &); // non-assignable + msra::dbn::matrixbase & m; + public: + matrixasvectorofvectors (msra::dbn::matrixbase & m) : m (m) {} + size_t size() const { return m.cols(); } + const_array_ref operator[] (size_t j) const { return array_ref (&m(0,j), m.rows()); } + }; + + size_t chunkforframepos (const size_t t) const // find chunk for a given frame position + { + //inspect chunk of first feature stream only + auto iter = std::lower_bound (randomizedchunks[0].begin(), randomizedchunks[0].end(), t, [&] (const chunk & chunk, size_t t) { return chunk.globalte() <= t; }); + const size_t chunkindex = iter - randomizedchunks[0].begin(); + if (t < randomizedchunks[0][chunkindex].globalts || t >= randomizedchunks[0][chunkindex].globalte()) + throw std::logic_error ("chunkforframepos: dude, learn STL!"); + return chunkindex; + } + +public: + + void setverbosity(int newverbosity){ verbosity = newverbosity; } + + // get the next minibatch + // A minibatch is made up of one or more utterances. + // We will return less than 'framesrequested' unless the first utterance is too long. + // Note that this may return frames that are beyond the epoch end, but the first frame is always within the epoch. + // We specify the utterance by its global start time (in a space of a infinitely repeated training set). + // This is efficient since getbatch() is called with sequential 'globalts' except at epoch start. + // Note that the start of an epoch does not necessarily fall onto an utterance boundary. The caller must use firstvalidglobalts() to find the first valid globalts at or after a given time. + // + // + /*implement*/ bool getbatch (const size_t globalts, const size_t framesrequested, std::vector & feat, + std::vector> & uids, std::vector> & utteranceinfo, + std::vector> & transcripts, + std::vector> & latticepairs) + { + bool readfromdisk = false; // return value: shall be 'true' if we paged in anything + + auto_timer timergetbatch; + assert (_totalframes > 0); + + // Clears vector. + utteranceinfo.clear(); + + // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below + const size_t sweep = lazyrandomization (globalts); + + const std::vector noboundaryflags; // dummy + if (!framemode) // regular utterance mode + { + + // find utterance position for globalts + // There must be a precise match; it is not possible to specify frames that are not on boundaries. + auto positer = randomizedutteranceposmap.find (globalts); + if (positer == randomizedutteranceposmap.end()) + throw std::logic_error ("getbatch: invalid 'globalts' parameter; must match an existing utterance boundary"); + const size_t spos = positer->second; + + // determine how many utterances will fit into the requested minibatch size + size_t mbframes = randomizedutterancerefs[spos].numframes; // at least one utterance, even if too long + size_t epos; + for (epos = spos + 1; epos < numutterances && mbframes + randomizedutterancerefs[epos].numframes < framesrequested; epos++) // add more utterances as long as they fit within requested minibatch size + mbframes += randomizedutterancerefs[epos].numframes; + + // do some paging housekeeping + // This will also set the feature-kind information if it's the first time. + // Free all chunks left of the range. + // Page-in all chunks right of the range. + // We are a little more blunt for now: Free all outside the range, and page in only what is touched. We could save some loop iterations. + const size_t windowbegin = positionchunkwindows[spos].windowbegin(); + const size_t windowend = positionchunkwindows[epos-1].windowend(); + for (size_t k = 0; k < windowbegin; k++) + releaserandomizedchunk (k); + for (size_t k = windowend; k < randomizedchunks[0].size(); k++) + releaserandomizedchunk (k); + + for (size_t pos = spos; pos < epos; pos++) + readfromdisk |= requirerandomizedchunk (randomizedutterancerefs[pos].chunkindex, windowbegin, windowend); // (window range passed in for checking only) + + // resize feat and uids + feat.resize(vdim.size()); + uids.resize(classids.size()); + assert(feat.size()==vdim.size()); + assert(feat.size()==randomizedchunks.size()); + foreach_index(i, feat) + { + feat[i].resize(vdim[i], mbframes); + + if (i==0) + { + foreach_index(j, uids) + { + if (issupervised()) // empty means unsupervised training -> return empty uids + uids[j].resize (mbframes); + else + uids[i].clear(); + latticepairs.clear(); // will push_back() below + transcripts.clear(); + } + } + } + // return these utterances + //fprintf (stderr, "getbatch: getting utterances %d..%d (%d frames out of %d requested) in sweep %d\n", spos, epos -1, mbframes, framesrequested, sweep); + size_t tspos = 0; // relative start of utterance 'pos' within the returned minibatch + for (size_t pos = spos; pos < epos; pos++) + { + const auto & uttref = randomizedutterancerefs[pos]; + size_t n=0; + foreach_index(i, randomizedchunks) + { + const auto & chunk = randomizedchunks[i][uttref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + assert (uttref.globalts == globalts + tspos); + auto uttframes = chunkdata.getutteranceframes (uttref.utteranceindex); + matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) + n = uttframevectors.size(); + assert (n == uttframes.cols() && uttref.numframes == n && chunkdata.numframes (uttref.utteranceindex) == n); + + // copy the frames and class labels + for (size_t t = 0; t < n; t++) // t = time index into source utterance + { + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], t + tspos); + //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], t + tspos); + } + + // copy the frames and class labels + if (i==0) + { + // Sets utterance ID information. + utteranceinfo.push_back(std::make_pair(chunkdata.utteranceset[uttref.utteranceindex].key(), uttref.numframes)); + + auto uttclassids = getclassids (uttref); + foreach_index(j, uttclassids) + { + for (size_t t = 0; t < n; t++) // t = time index into source utterance + { + if (issupervised()) + uids[j][t + tspos] = uttclassids[j][t]; + } + + if (!this->lattices.empty()) + { + auto latticepair = chunkdata.getutterancelattice (uttref.utteranceindex); + latticepairs.push_back (latticepair); + // look up reference + const auto & key = latticepair->getkey(); + if (!allwordtranscripts.empty()) + { + const auto & transcript = allwordtranscripts.find (key)->second; + transcripts.push_back (transcript.words); + } + } + } + } + } + tspos += n; + } + assert (tspos == mbframes); + } + else // // debug mode returning randomized frames again, to see whether convergence is better (we don't ensure non-repetition at this point) + { + const size_t sweepts = sweep * _totalframes; // first global frame index for this sweep + const size_t sweepte = sweepts + _totalframes; // and its end + const size_t globalte = min (globalts + framesrequested, sweepte); // we return as much as requested, but not exceeding sweep end + const size_t mbframes = globalte - globalts; // that's our mb size + + // determine window range + // We enumerate all frames--can this be done more efficiently? + const size_t firstchunk = chunkforframepos (globalts); + const size_t lastchunk = chunkforframepos (globalte-1); + const size_t windowbegin = randomizedchunks[0][firstchunk].windowbegin; + const size_t windowend = randomizedchunks[0][lastchunk].windowend; + if (verbosity) + fprintf (stderr, "getbatch: getting randomized frames [%zu..%zu] (%zu frames out of %zu requested) in sweep %zu; chunks [%zu..%zu] -> chunk window [%zu..%zu)\n", + globalts, globalte, mbframes, framesrequested, sweep, firstchunk, lastchunk, windowbegin, windowend); + // release all data outside, and page in all data inside + for (size_t k = 0; k < windowbegin; k++) + releaserandomizedchunk (k); + for (size_t k = windowbegin; k < windowend; k++) + readfromdisk |= requirerandomizedchunk (k, windowbegin, windowend); // (window range passed in for checking only, redundant here) + for (size_t k = windowend; k < randomizedchunks[0].size(); k++) + releaserandomizedchunk (k); + + // resize feat and uids + feat.resize(vdim.size()); + uids.resize(classids.size()); + assert(feat.size()==vdim.size()); + assert(feat.size()==randomizedchunks.size()); + foreach_index(i, feat) + { + feat[i].resize(vdim[i], mbframes); + + if (i==0) + { + foreach_index(j, uids) + { + if (issupervised()) // empty means unsupervised training -> return empty uids + uids[j].resize (mbframes); + else + uids[i].clear(); + latticepairs.clear(); // will push_back() below + transcripts.clear(); + } + } + } + + // return randomized frames for the time range of those utterances + for (size_t j = 0; j < mbframes; j++) + { + // map to time index inside arrays + const size_t framepos = (globalts + j) % _totalframes; // using mod because we may actually run beyond the sweep for the last call + const frameref & frameref = randomizedframerefs[framepos]; + + // random utterance + readfromdisk |= requirerandomizedchunk (frameref.chunkindex, windowbegin, windowend); // (this is just a check; should not actually page in anything) + + foreach_index(i, randomizedchunks) + { + const auto & chunk = randomizedchunks[i][frameref.chunkindex]; + const auto & chunkdata = chunk.getchunkdata(); + auto uttframes = chunkdata.getutteranceframes (frameref.utteranceindex); + matrixasvectorofvectors uttframevectors (uttframes); // (wrapper that allows m[j].size() and m[j][i] as required by augmentneighbors()) + const size_t n = uttframevectors.size(); + assert (n == uttframes.cols() && chunkdata.numframes (frameref.utteranceindex) == n); n; + + // copy frame and class labels + const size_t t = frameref.frameindex; + + size_t leftextent, rightextent; + // page in the needed range of frames + if (leftcontext[i] == 0 && rightcontext[i] == 0) + { + leftextent = rightextent = augmentationextent(uttframevectors[t].size(), vdim[i]); + } + else + { + leftextent = leftcontext[i]; + rightextent = rightcontext[i]; + } + augmentneighbors(uttframevectors, noboundaryflags, t, leftextent, rightextent, feat[i], j); + + //augmentneighbors(uttframevectors, noboundaryflags, t, feat[i], j); + if (issupervised() && i == 0) + { + auto frameclassids = getclassids(frameref); + foreach_index(k, uids) + uids[k][j] = frameclassids[k][t]; + } + } + } + } + timegetbatch = timergetbatch; + return readfromdisk; + } + double gettimegetbatch() { return timegetbatch;} + + // alternate (updated) definition for multiple inputs/outputs - read as a vector of feature matrixes or a vector of label strings + /*implement*/ bool getbatch (const size_t /*globalts*/, const size_t /*framesrequested*/, msra::dbn::matrix & /*feat*/, + std::vector & /*uids*/, std::vector> & /*utterances*/, + std::vector> & /*transcripts*/, + std::vector> & /*latticepairs*/) + { + // should never get here + throw runtime_error("minibatchframesourcemulti: getbatch() being called for single input feature and single output feature, should use minibatchutterancesource instead\n"); + + // for single input/output set size to be 1 and run old getbatch + //feat.resize(1); + //uids.resize(1); + //return getbatch(globalts, framesrequested, feat[0], uids[0], transcripts, latticepairs); + } + size_t totalframes() const { return _totalframes; } + + // return first valid globalts to ask getbatch() for + // In utterance mode, the epoch start may fall in the middle of an utterance. + // We return the end time of that utterance (which, in pathological cases, may in turn be outside the epoch; handle that). + /*implement*/ size_t firstvalidglobalts (const size_t globalts) + { + // update randomization if a new sweep is entered --this is a complex operation that updates many of the data members used below + const size_t sweep = lazyrandomization (globalts); + // frame mode: start at sweep boundary directly + if (framemode) + return globalts; + // utterance mode + assert (globalts >= sweep * _totalframes && globalts < (sweep + 1) * _totalframes); sweep; + foreach_index (pos, randomizedutterancerefs) + if (randomizedutterancerefs[pos].globalts >= globalts) + return randomizedutterancerefs[pos].globalts; // exact or inexact match + return randomizedutterancerefs.back().globalte(); // boundary case: requested time falls within the last utterance + } + + const std::vector & unitcounts() const { return counts[0]; } + const std::vector & unitcounts(size_t index) const { return counts[index]; } + +}; + +};}; diff --git a/Source/Readers/LMSequenceReader/LMSequenceReader.vcxproj b/Source/Readers/LMSequenceReader/LMSequenceReader.vcxproj index 93b527173fed..82f5df0ebdcc 100644 --- a/Source/Readers/LMSequenceReader/LMSequenceReader.vcxproj +++ b/Source/Readers/LMSequenceReader/LMSequenceReader.vcxproj @@ -1,160 +1,160 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {9A2F2441-5972-4EA8-9215-4119FCE0FB68} - - - - - - - - - Win32Proj - UCIReader - LMSequenceReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - false - - - false - - - - - - Create - Create - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {9A2F2441-5972-4EA8-9215-4119FCE0FB68} + + + + + + + + + Win32Proj + UCIReader + LMSequenceReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + false + + + false + + + + + + Create + Create + + + + + + + + + + + \ No newline at end of file diff --git a/Source/Readers/LMSequenceReader/SequenceReader.cpp b/Source/Readers/LMSequenceReader/SequenceReader.cpp index 9286e0176aac..4869af532d71 100644 --- a/Source/Readers/LMSequenceReader/SequenceReader.cpp +++ b/Source/Readers/LMSequenceReader/SequenceReader.cpp @@ -409,8 +409,8 @@ void SequenceReader::Destroy() // endSequence="O" // ] //] -template -template +template +template void SequenceReader::InitFromConfig(const ConfigRecordType & readerConfig) { // See if the user wants caching @@ -1353,8 +1353,8 @@ bool SequenceReader::GetData(const std::wstring& sectionName, size_t n template class SequenceReader; template class SequenceReader; -template -template +template +template void BatchSequenceReader::InitFromConfig(const ConfigRecordType & readerConfig) { // See if the user wants caching @@ -1840,9 +1840,9 @@ bool BatchSequenceReader::GetMinibatch(std::mapInit(mToProcess.size(), 0); - return false; + { + m_pMBLayout->Init(mToProcess.size(), 0); + return false; } // actual size is the size of the next seqence size_t actualmbsize = 0; diff --git a/Source/Readers/LMSequenceReader/SequenceReader.h b/Source/Readers/LMSequenceReader/SequenceReader.h index 4ea8c905126c..6d622b6822c9 100644 --- a/Source/Readers/LMSequenceReader/SequenceReader.h +++ b/Source/Readers/LMSequenceReader/SequenceReader.h @@ -217,8 +217,8 @@ class SequenceReader : public IDataReader bool SentenceEnd(); public: - template void InitFromConfig(const ConfigRecordType &); - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + template void InitFromConfig(const ConfigRecordType &); + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } static void ReadClassInfo(const wstring & vocfile, int& class_size, map& word4idx, @@ -366,8 +366,8 @@ class BatchSequenceReader : public SequenceReader mSentenceEnd = false; } - template void InitFromConfig(const ConfigRecordType &); - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + template void InitFromConfig(const ConfigRecordType &); + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } void Reset(); @@ -390,7 +390,7 @@ class BatchSequenceReader : public SequenceReader void SetSentenceSegBatch(std::vector &sentenceEnd); void CopyMBLayoutTo(MBLayoutPtr); - bool RequireSentenceSeg() const override { return true; } + bool RequireSentenceSeg() const override { return true; } int GetSentenceEndIdFromOutputLabel(); }; diff --git a/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj b/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj index a73d0af74088..29c2e4bac776 100644 --- a/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj +++ b/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj @@ -1,158 +1,158 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {62836DC1-DF77-4B98-BF2D-45C943B7DDC6} - - - - - - - - - Win32Proj - UCIReader - LUSequenceReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - - false - - - false - - - - - - Create - Create - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {62836DC1-DF77-4B98-BF2D-45C943B7DDC6} + + + + + + + + + Win32Proj + UCIReader + LUSequenceReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + + false + + + false + + + + + + Create + Create + + + + + + + + + + \ No newline at end of file diff --git a/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj.filters b/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj.filters index d75326d9cf4c..6b3c78e5f8f6 100644 --- a/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj.filters +++ b/Source/Readers/LUSequenceReader/LUSequenceReader.vcxproj.filters @@ -1,59 +1,59 @@ - - - - - - - - - - - Common - - - Common - - - Common - - - Common - - - - - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - - - - - {75463cd7-3094-4900-8dae-5b6835c23fc4} - - - {85d2fa50-2b95-4ec7-8f2c-c5c0b1cb493e} - - - {10f819fb-8861-4607-9389-60ca80f968c2} - - + + + + + + + + + + + Common + + + Common + + + Common + + + Common + + + + + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + + + + + {75463cd7-3094-4900-8dae-5b6835c23fc4} + + + {85d2fa50-2b95-4ec7-8f2c-c5c0b1cb493e} + + + {10f819fb-8861-4607-9389-60ca80f968c2} + + \ No newline at end of file diff --git a/Source/Readers/LibSVMBinaryReader/Exports.cpp b/Source/Readers/LibSVMBinaryReader/Exports.cpp index eed376c89b80..db6765f4a555 100644 --- a/Source/Readers/LibSVMBinaryReader/Exports.cpp +++ b/Source/Readers/LibSVMBinaryReader/Exports.cpp @@ -1,31 +1,31 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// Exports.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#define DATAREADER_EXPORTS -#include "DataReader.h" -#include "LibSVMBinaryReader.h" - -namespace Microsoft { namespace MSR { namespace CNTK { - -template -void DATAREADER_API GetReader(IDataReader** preader) -{ - *preader = new LibSVMBinaryReader(); -} - -extern "C" DATAREADER_API void GetReaderF(IDataReader** preader) -{ - GetReader(preader); -} -extern "C" DATAREADER_API void GetReaderD(IDataReader** preader) -{ - GetReader(preader); -} - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// Exports.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#define DATAREADER_EXPORTS +#include "DataReader.h" +#include "LibSVMBinaryReader.h" + +namespace Microsoft { namespace MSR { namespace CNTK { + +template +void DATAREADER_API GetReader(IDataReader** preader) +{ + *preader = new LibSVMBinaryReader(); +} + +extern "C" DATAREADER_API void GetReaderF(IDataReader** preader) +{ + GetReader(preader); +} +extern "C" DATAREADER_API void GetReaderD(IDataReader** preader) +{ + GetReader(preader); +} + +}}} diff --git a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.cpp b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.cpp index 3b6b5516df07..8bcdba7fe783 100644 --- a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.cpp +++ b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.cpp @@ -1,844 +1,844 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// LibSVMBinaryReader.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#define DATAREADER_EXPORTS // creating the exports here -#include "DataReader.h" -#include "LibSVMBinaryReader.h" -#include "fileutil.h" // for fexists() -#include -#include -#include -#include -#include "CUDAPageLockedMemAllocator.h" -#include -#include -#ifndef _WIN32 -#include -#include -#include -#include -#include -#include -#endif - - -namespace Microsoft { - namespace MSR { - namespace CNTK { - - DWORD HIDWORD(size_t size) { return size >> 32; } - DWORD LODWORD(size_t size) { return size & 0xFFFFFFFF; } - - template - DenseBinaryMatrix::DenseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols) : BinaryMatrix( name, deviceID, numRows, numCols ) { - //this->m_values = (ElemType*)malloc(sizeof(ElemType)*numRows*numCols); - this->m_values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*numRows*numCols, deviceID); - } - - template - void DenseBinaryMatrix::Clear() { - this->m_numRows = 0; - } - - template - void DenseBinaryMatrix::Dispose() { - if (this->m_values != nullptr) { - //free(this->m_values); - CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); - } - this->m_values = nullptr; - } - - - template - void DenseBinaryMatrix::Fill(Matrix* matrix) { - matrix->SetValue(this->m_maxNumCols, this->m_numRows, matrix->GetDeviceId(), this->m_values, matrixFlagNormal); -#if DEBUG - matrix->Print("testname"); -#endif - } - - template - void DenseBinaryMatrix::SetMaxRows(size_t maxRows) { - if (maxRows > this->m_maxNumRows) { - //ElemType* values = (ElemType*)malloc(sizeof(ElemType)*this->m_maxNumCols*maxRows); - ElemType* values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*this->m_maxNumCols*maxRows, this->m_deviceID); - if (this->m_values != nullptr) { - if (this->m_numRows > 0) { - memcpy(values, this->m_values, sizeof(ElemType)*this->m_numRows*this->m_maxNumCols); - } - //free(this->m_values); - CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); - } - this->m_values = values; - this->m_maxNumRows = maxRows; - } - } - - template - void DenseBinaryMatrix::AddValues(void* values, size_t numRows) { - memcpy(this->m_values + this->m_numRows*this->m_maxNumCols, values, sizeof(ElemType)*numRows*this->m_maxNumCols); - this->m_numRows += numRows; - } - - template - SparseBinaryMatrix::SparseBinaryMatrix(wstring name, int deviceId, size_t numRows, size_t numCols) : BinaryMatrix( name, deviceId, numRows, numCols ), m_rowIndices(nullptr), - m_colIndices(nullptr), m_nnz(0), m_maxNNz(0) { - //m_colIndices = (int32_t*)malloc(sizeof(int32_t)*(numRows + 1)); - m_colIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*(numRows + 1), deviceId); - m_colIndices[0] = 0; - } - - template - void SparseBinaryMatrix::Dispose() { - if (this->m_values != nullptr) { - //free(this->m_values); - CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); - } - this->m_values = nullptr; - if (m_colIndices != nullptr) { - //free(m_colIndices); - CUDAPageLockedMemAllocator::Free(this->m_colIndices, this->m_deviceID); - } - m_colIndices = nullptr; - if (m_rowIndices != nullptr) { - //free(m_rowIndices); - CUDAPageLockedMemAllocator::Free(this->m_rowIndices, this->m_deviceID); - } - m_rowIndices = nullptr; - } - - template - void SparseBinaryMatrix::SetMaxRows(size_t maxRows) { - if (maxRows > this->m_maxNumRows) { - //int32_t* colIndices = (int32_t*)malloc(sizeof(int32_t)*(maxRows+1)); - int32_t* colIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*(maxRows+ 1), this->m_deviceID); - if (this->m_colIndices != nullptr) { - if (this->m_numRows > 0) { - memcpy(colIndices, m_colIndices, sizeof(int32_t)*(this->m_numRows + 1)); - } - //free(this->m_colIndices); - CUDAPageLockedMemAllocator::Free(this->m_colIndices, this->m_deviceID); - } - this->m_colIndices = colIndices; - this->m_maxNumRows = maxRows; - } - } - - template - void SparseBinaryMatrix::ResizeArrays(size_t newNNz) { - - if (newNNz + m_nnz < this->m_maxNNz) { - return; - } - size_t newMaxNNz = (size_t)((newNNz + m_nnz) * 1.3); - //int32_t* rowIndices = (int32_t*)malloc(sizeof(int32_t)*newMaxNNz); - //ElemType* values = (ElemType*)malloc(sizeof(ElemType)*newMaxNNz); - int32_t* rowIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*newMaxNNz, this->m_deviceID); - ElemType* values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*newMaxNNz, this->m_deviceID); - - if (m_nnz > 0) { - memcpy(rowIndices, m_rowIndices, sizeof(int32_t)*this->m_maxNNz); - memcpy(values, this->m_values, sizeof(ElemType)*this->m_maxNNz); - } - - if (m_rowIndices != nullptr) { - //free(m_rowIndices); - CUDAPageLockedMemAllocator::Free(this->m_rowIndices, this->m_deviceID); - } - if (this->m_values != nullptr) { - //free(this->m_values); - CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); - } - - m_rowIndices = rowIndices; - this->m_values = values; - this->m_maxNNz = newMaxNNz; - } - - template - void SparseBinaryMatrix::Clear() { - this->m_numRows = 0; - this->m_nnz = 0; - } - - template - void SparseBinaryMatrix::AddValues(void* values, size_t nnz ) { - memcpy(this->m_values + this->m_nnz, values, sizeof(ElemType)*nnz); - } - - template - void SparseBinaryMatrix::AddColIndices(void* colIndices, size_t numCols) { - int32_t* temp = (int32_t*)colIndices; - for (int c = 1; c < numCols; c++) { - m_colIndices[this->m_numRows + c] = temp[c] + (int32_t)this->m_nnz; - } - this->m_numRows += numCols - 1; - } - template - void SparseBinaryMatrix::AddRowIndices(void* rowIndices, size_t nnz) { - memcpy(m_rowIndices + this->m_nnz, rowIndices, sizeof(int32_t)*nnz); - } - - - template - void SparseBinaryMatrix::Fill( Matrix* matrix) { - matrix->SetMatrixFromCSCFormat(m_colIndices, m_rowIndices, this->m_values, this->m_nnz, this->m_maxNumCols, this->m_numRows ); -#if DEBUG - matrix->Print("testname"); -#endif - } - - - template - SparseBinaryInput::SparseBinaryInput(std::wstring fileName) : m_fileName(fileName), m_readOrder(nullptr), m_readOrderLength(0), m_randomize(false), m_tempValues(nullptr), m_tempValuesSize(0), m_offsets(nullptr), m_offsetsStart(0), m_startMB(0), m_endMB(0) { - std::string name = msra::strfun::utf8(m_fileName); - m_inFile.open(name, ifstream::binary | ifstream::in); - } - - template - SparseBinaryInput::~SparseBinaryInput() { - - } - - template - void SparseBinaryInput::Init(std::map rename ) { - - size_t base_offset = 0; - m_inFile.seekg(0, ifstream::beg); - m_inFile.read((char*)&m_numRows, sizeof(int64_t)); - base_offset += sizeof(int64_t); - - m_inFile.read((char*)&m_numBatches, sizeof(int64_t)); - base_offset += sizeof(int64_t); - - int32_t numFeatures; - int32_t numLabels; - m_inFile.read((char*)&numFeatures, sizeof(int32_t)); - base_offset += sizeof(int32_t); - - m_inFile.read((char*)&numLabels, sizeof(int32_t)); - base_offset += sizeof(int32_t); - - int32_t len; - int32_t numCols; - int32_t maxLen = 100; - char* tempName = (char*)malloc(maxLen); - for (int32_t c = 0; c < numFeatures; c++) - { - m_inFile.read((char*)&len, sizeof(int32_t)); - if (len + 1 > maxLen) { - maxLen = len + 1; - free(tempName); - tempName = (char*)malloc(maxLen); - } - base_offset += sizeof(int32_t); - - m_inFile.read((char*)tempName, len); - tempName[len] = '\0'; - //std::string name((char*)header_buffer + base_offset, len); - std::wstring wname = msra::strfun::utf16(tempName); - if (rename.find(wname) == rename.end()) - { - m_features.emplace_back(wname); - } - else - { - m_features.emplace_back(rename[wname]); - } - base_offset += sizeof(int8_t)*len; - - m_inFile.read((char*)&numCols, sizeof(numCols)); - //numCols = *(int32_t*)((char*)header_buffer + base_offset); - base_offset += sizeof(int32_t); - - m_mappedNumCols[m_features.back()] = numCols; - - } - for (int32_t c = 0; c < numLabels; c++) - { - m_inFile.read((char*)&len, sizeof(int32_t)); - if (len + 1 > maxLen) { - maxLen = len + 1; - free(tempName); - tempName = (char*)malloc(maxLen); - } - base_offset += sizeof(int32_t); - - //std::string name((char*)header_buffer + base_offset, len); - m_inFile.read((char*)tempName, len); - tempName[len] = '\0'; - std::wstring wname = msra::strfun::utf16(tempName); - if (rename.find(wname) == rename.end()) - { - m_labels.emplace_back(wname); - } - else - { - //m_features.emplace_back(rename[wname]); - m_labels.emplace_back(rename[wname]); - } - base_offset += sizeof(int8_t)*len; - - //numCols = *(int32_t*)((char*)header_buffer + base_offset); - m_inFile.read((char*)&numCols, sizeof(numCols)); - base_offset += sizeof(int32_t); - m_mappedNumCols[m_labels.back()] = numCols; - - } - free(tempName); - - m_offsetsStart = base_offset; - m_dataStart = m_offsetsStart + m_numBatches * sizeof(int64_t); - - /*Read in the microbatch size here*/ - m_inFile.seekg(m_dataStart, ios::beg); - m_inFile.read((char*)&m_microBatchSize, sizeof(int32_t)); - m_mbSize = (size_t)m_microBatchSize; - - m_inFile.seekg(0, ios::end); - m_fileSize = (size_t)m_inFile.tellg(); - } - - template - bool SparseBinaryInput::Randomize() - { - return false; - /* - if (m_randomize > 0) - { - return true; - } - return false; - */ - } - - - template - void SparseBinaryInput::FillReadOrder(size_t windowSize) - { - if (m_readOrder!= nullptr) - { - free(m_readOrder); - } - m_readOrder = (size_t*)malloc(sizeof(size_t)*windowSize); - for (size_t c = 0; c < windowSize; c++) - { - m_readOrder[c] = c; - } - } - - template - void SparseBinaryInput::ReadOffsets( size_t startMB, size_t numMBs) - { - if (startMB == m_startMB && m_endMB == startMB + numMBs) { - return; - } - if (m_offsets != nullptr) { - free(m_offsets); - } - m_offsets = (int64_t*)malloc(sizeof(int64_t)*(numMBs+1)); - m_inFile.seekg(m_offsetsStart + startMB * sizeof(int64_t), ios::beg); - m_inFile.read((char*)&(m_offsets[0]), sizeof(int64_t)*numMBs); - if (startMB + numMBs < m_numBatches) { - m_inFile.read((char*)&(m_offsets[numMBs]), sizeof(int64_t)); - } - else { - m_offsets[numMBs] = m_fileSize; - } - m_startMB = startMB; - m_endMB = startMB + numMBs; - } - - template - void SparseBinaryInput::Shuffle() - { - if (Randomize()) - { - shuffle(&(m_readOrder[0]), &(m_readOrder[m_readOrderLength-1]), m_randomEngine); - } - } - - template - void SparseBinaryInput::StartDistributedMinibatchLoop(size_t mbSize, size_t subsetNum, size_t numSubsets) { - - m_nextMB = 0; - - m_mbSize = mbSize / numSubsets; - - m_subsetNum = subsetNum; - m_numSubsets = numSubsets; - - m_epochSize = m_numBatches / numSubsets; - - size_t startMB = m_epochSize * subsetNum; - size_t endMB = m_epochSize * (subsetNum + 1); - - size_t remainder = m_numBatches % numSubsets; - - size_t lb = min(remainder, subsetNum); - size_t ub = min(remainder, subsetNum + 1); - - m_epochSize += (subsetNum < remainder) ? 1 : 0; - - startMB += lb; - endMB += ub; - m_windowSize = endMB - startMB; - - if (m_windowSize != m_readOrderLength ) { - FillReadOrder(m_windowSize); - m_readOrderLength = m_windowSize; - } - - - Shuffle(); - - ReadOffsets(startMB, m_windowSize); - - m_maxMBSize = 0; - for (size_t c = 0; c < m_windowSize; c++) { - m_maxMBSize = max(m_maxMBSize, (size_t)(m_offsets[c + 1] - m_offsets[c])); - //fprintf(stderr, "m_offsets[%lu] = %lu\n", c, m_offsets[c]); - } - //fprintf(stderr, "max mb size: %ld\n", m_maxMBSize); - size_t maxMem = 1024 * 1024 * 1024; // 1GB - size_t maxPointers = maxMem / m_maxMBSize; - for (size_t c = 0; c < maxPointers; c++) { - void* dataBuffer = malloc(m_maxMBSize); - m_dataToProduce.push(dataBuffer); - } - - std::thread readData([this] { this->ReadMinibatches(m_readOrder, m_readOrderLength); }); - readData.detach(); - - } - template - void* SparseBinaryInput::GetTempDataPointer(size_t numBytes) { - if (m_tempValuesSize < numBytes) { - if (m_tempValues != nullptr) { - free(m_tempValues); - } - m_tempValuesSize = (int32_t)(numBytes * 1.3); - m_tempValues = malloc(m_tempValuesSize); - } - return m_tempValues; - } - - template - shared_ptr> SparseBinaryInput::CreateMatrix(std::wstring matName, int deviceId ) { - shared_ptr> retVal;// = nullptr; - - //if (m_features.find(matName) != m_features.end()) { - if (std::find(m_features.begin(), m_features.end(), matName) != m_features.end()) { - retVal = make_shared>(matName, deviceId, m_mbSize, m_mappedNumCols[matName]); - } - //else if (m_labels.find(matName) != m_labels.end()) { - else if (std::find(m_labels.begin(), m_labels.end(), matName) != m_labels.end()) { - retVal = make_shared>(matName, deviceId, m_mbSize, m_mappedNumCols[matName]); - } - - return retVal; - } - - template - void SparseBinaryInput::ReadMinibatches(size_t* read_order, size_t numToRead) { -#if DEBUG - marker_series series(L"Read Minibatches"); - //diagnostic::span span(series, L"Reading Data"); - span* read_span; -#endif - for (size_t c = 0; c < numToRead; c++) { -#if DEBUG - read_span = new span(series, 1, L"Getting Buffer %ld\n", c); -#endif - //fprintf(stderr, "start reading data %ld\n", c); - size_t readSize = m_offsets[read_order[ c ] + 1] - m_offsets[read_order[ c ]]; - //void* data_buffer = GetTempDataPointer(readSize); -#if DEBUG - series.write_flag(_T("Getting buffer.")); -#endif - void* data_buffer = m_dataToProduce.pop(); -#if DEBUG - delete read_span; - series.write_flag(_T("Got buffer.")); - read_span = new span(series, 2, L"Reading Data %ld\n", c); -#endif - - m_inFile.clear(); -#if DEBUG - series.write_flag(_T("seeking.")); -#endif - m_inFile.seekg(m_dataStart + m_offsets[c], ios::beg); -#if DEBUG - series.write_flag(_T("reading.")); -#endif - m_inFile.read((char*)data_buffer, readSize); - m_dataToConsume.push(data_buffer); - //fprintf(stderr, "done reading data %ld\n", c); -#if DEBUG - series.write_flag(_T("Done read, pushed buffer.")); - delete read_span; -#endif - } - //m_dataToConsume.push(nullptr); -#if DEBUG - series.write_flag(_T("Done reading.")); -#endif - - } - - template - size_t SparseBinaryInput::ReadMinibatch(void* data_buffer, std::map>>& matrices) { - - //fprintf(stderr, "start read minibatch.\n"); - /* - size_t readSize = m_offsets[cur_batch + 1] - m_offsets[cur_batch]; - void* data_buffer = GetTempDataPointer(readSize); - - fprintf(stderr, "start reading data.\n"); - m_inFile.clear(); - m_inFile.seekg(m_dataStart + m_offsets[cur_batch], ios::beg); - m_inFile.read((char*)data_buffer, readSize); - fprintf(stderr, "done reading data.\n"); - */ - - int32_t nnz; - int32_t curMBSize; - - int64_t buffer_offset = 0; - - curMBSize = *(int32_t*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(int32_t); - - for (int32_t c = 0; c < m_features.size(); c++) - { - //fprintf(stderr, "read features %d.\n", c); - nnz = *(int32_t*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(int32_t); - - ElemType* vals = (ElemType*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(ElemType)*nnz; - - int32_t* rowIndices = (int32_t*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(int32_t)*nnz; - - int32_t* colIndices = (int32_t*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(int32_t)*(curMBSize + 1); - - auto findMat = matrices.find(m_features[c]); - if (findMat != matrices.end()) - { - auto mat = findMat->second; - mat->ResizeArrays(nnz); - mat->AddValues(vals, nnz); - mat->AddRowIndices(rowIndices, nnz); - mat->AddColIndices(colIndices, curMBSize + 1); - mat->UpdateNNz(nnz); -#ifdef DEBUG - mat->Print("features"); -#endif - } - } - - for (int32_t c = 0; c < m_labels.size(); c++) - { - //fprintf(stderr, "read labels %d.\n", c); - int32_t numCols = m_mappedNumCols[m_labels[c]]; - - ElemType* vals = (ElemType*)((char*)data_buffer + buffer_offset); - buffer_offset += sizeof(ElemType)*curMBSize* numCols; - - auto findMat = matrices.find(m_labels[c]); - if (findMat != matrices.end()) - { - auto mat = findMat->second; - mat->AddValues(vals, curMBSize); -#ifdef DEBUG - mat->Print("labels"); -#endif - } - } - return (size_t)curMBSize; - } - - template - size_t SparseBinaryInput::FillMatrices(std::map>>& matrices) { - - //fprintf(stderr, "start fill matrices\n"); - size_t curSize = 0; - for (auto mat : matrices) { - mat.second->SetMaxRows(m_mbSize); - mat.second->Clear(); - } - void* data_buffer; - //fprintf(stderr, "start while\n"); - //clock_t start_w = clock(); - //while (curSize + m_microBatchSize <= m_mbSize && (data_buffer = m_dataToConsume.pop()) != nullptr) { - while (curSize + m_microBatchSize <= m_mbSize && m_nextMB < m_epochSize) { - data_buffer = m_dataToConsume.pop(); - //clock_t in_w = clock(); - //start_w = in_w - start_w; - //fprintf(stderr, "start read mb\tIt took me %d clicks (%f seconds).\n", start_w, ((float)start_w) / CLOCKS_PER_SEC); - //start_w = in_w; - //fprintf(stderr, "start read mb\n"); - curSize += ReadMinibatch(data_buffer, matrices); - //fprintf(stderr, "end read mb\n"); - m_nextMB++; - m_dataToProduce.push(data_buffer); - } - //fprintf(stderr, "end fill matrices\n"); - return curSize; - } - - template - template - void LibSVMBinaryReader::InitFromConfig(const ConfigRecordType & readerConfig) { - - std::map rename; - RenamedMatrices(readerConfig, rename); - - if (readerConfig.Exists(L"randomize")) - { - string randomizeString = readerConfig(L"randomize"); - if (randomizeString == "None") - { - m_randomize = 0L; - } - else if (randomizeString == "Auto") - { - time_t rawtime; - struct tm* timeinfo; - time(&rawtime); - timeinfo = localtime(&rawtime); - m_randomize = (unsigned long)(timeinfo->tm_sec + timeinfo->tm_min * 60 + timeinfo->tm_hour * 60 * 60); - } - else - { - m_randomize = readerConfig(L"randomize", 0); - } - } - else - { - m_randomize = 0L; - } - - m_partialMinibatch = true; - std::string minibatchMode(readerConfig(L"minibatchMode", "Partial")); - m_partialMinibatch = !_stricmp(minibatchMode.c_str(), "Partial"); - - std::wstring file = readerConfig(L"file", L""); - - m_dataInput = make_shared>(file); - m_dataInput->Init( rename ); - - m_mbSize = (size_t)readerConfig(L"minibatch", 0); - if (m_mbSize > 0) - { - if (m_dataInput->GetMBSize() != m_mbSize) - { - RuntimeError("Data file and config file have mismatched minibatch sizes.\n"); - return; - } - } - else - { - m_mbSize = m_dataInput->GetMBSize(); - } - - m_prefetchEnabled = true; - } - - template - void LibSVMBinaryReader::Destroy() { - - } - - template - LibSVMBinaryReader::~LibSVMBinaryReader() - { - Destroy(); - } - - template - void LibSVMBinaryReader::StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples ) { - return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); - } - - template - void LibSVMBinaryReader::StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t /*requestedEpochSamples*/ ) { - m_epoch = epoch; - m_mbSize = mbSize; -#if DEBUG - if (reader_series != NULL) { - delete reader_series; - } - reader_series = new marker_series(L"Base Reader"); - cur_read = 0; -#endif - m_dataInput->StartDistributedMinibatchLoop(mbSize, subsetNum, numSubsets); - - } - - template - void LibSVMBinaryReader::CheckDataMatrices(std::map*>& matrices) { - if (m_dataMatrices.empty()) - { - for (auto inmat : matrices) { - shared_ptr> mat = m_dataInput->CreateMatrix(inmat.first,inmat.second->GetDeviceId()); - if (mat != nullptr) { - m_dataMatrices[inmat.first] = mat; - } - } - } - } - template - void LibSVMBinaryReader::DoDSSMMatrix(Matrix& mat, size_t actualMBSize) { - size_t numRows = mat.GetNumRows(); - if (DSSMCols < actualMBSize) { - if (DSSMLabels != nullptr) { - //free(DSSMLabels); - CUDAPageLockedMemAllocator::Free(DSSMLabels, mat.GetDeviceId()); - } - DSSMCols = actualMBSize; - //DSSMLabels = (ElemType*)malloc(sizeof(ElemType)*numRows*actualMBSize); - DSSMLabels = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*numRows*actualMBSize, mat.GetDeviceId()); - memset(DSSMLabels, 0, sizeof(ElemType)*numRows*actualMBSize); - for (size_t c = 0; c < numRows*actualMBSize; c += numRows) { - DSSMLabels[c] = 1; - } - } - if (mat.GetNumCols() != actualMBSize) { - mat.SetValue(numRows, actualMBSize, mat.GetDeviceId(), DSSMLabels, matrixFlagNormal); - } - } - - - template - bool LibSVMBinaryReader::GetMinibatch(std::map*>& matrices) { - //timer = clock(); -#if DEBUG - span minibatch_span(*reader_series, 1, L"Get Minibatch: %ld", cur_read); -#endif - size_t actualMBSize = 0; - if (m_prefetchEnabled) - { - if (!m_pendingAsyncGetMinibatch.valid()) { - //fprintf(stderr, "not valid\n"); - CheckDataMatrices(matrices); - m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this]() - { - return m_dataInput->FillMatrices(m_dataMatrices); - }); - } - //fprintf(stderr, "before get.\n"); - //timer = clock(); -#if DEBUG - reader_series->write_flag(_T("before get.")); -#endif - actualMBSize = m_pendingAsyncGetMinibatch.get(); -#if DEBUG - reader_series->write_flag(_T("after get.")); -#endif - //timer = clock() - timer; - //fprintf(stderr, "done get\tIt took me %d clicks (%f seconds).\n", timer, ((float)timer) / CLOCKS_PER_SEC); - - if (actualMBSize == 0) { - return false; - } - - m_pMBLayout->InitAsFrameMode(actualMBSize); -#if DEBUG - reader_series->write_flag(_T("starting fill.")); -#endif - for (auto matrix : m_dataMatrices) { - auto findMat = matrices.find(matrix.first); - if (findMat != matrices.end()) { - matrix.second->Fill(findMat->second); - } - } -#if DEBUG - reader_series->write_flag(_T("done fill.")); -#endif - auto findMat = matrices.find(L"DSSMLabel"); - if (findMat != matrices.end()) - { - DoDSSMMatrix(*(findMat->second), actualMBSize); - - } - m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this]() - { - //CheckDataMatrices(matrices); - return m_dataInput->FillMatrices(m_dataMatrices); - }); - } -#if DEBUG - cur_read++; -#endif - /* - - timer = clock() - timer; - fprintf(stderr, "It took me %d clicks (%f seconds).\n", timer, ((float)timer) / CLOCKS_PER_SEC); - */ - //fprintf(stderr, "done\n"); - return true; - } - - - template - template - void LibSVMBinaryReader::RenamedMatrices(const ConfigRecordType& config, std::map& rename) { - for (const auto & id : config.GetMemberIds()) - { - if (!config.CanBeConfigRecord(id)) - continue; - const ConfigRecordType & temp = config(id); - // see if we have a config parameters that contains a "dim" element, it's a sub key, use it - if (temp.ExistsCurrent(L"rename")) - { - - std::wstring ren = temp(L"rename"); - rename.emplace(msra::strfun::utf16(id), msra::strfun::utf16(ren)); - } - } - } - - template - bool LibSVMBinaryReader::DataEnd(EndDataType endDataType) - { - return m_dataInput->DataEnd(endDataType); - } - - template - bool SparseBinaryInput::DataEnd(EndDataType endDataType) - { - bool ret = false; - switch (endDataType) - { - case endDataNull: - assert(false); - break; - case endDataEpoch: - ret = (m_nextMB >= m_epochSize); - break; - case endDataSet: - ret = (m_nextMB >= m_epochSize); - break; - case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true - ret = true; - break; - } - return ret; - } - - // instantiate all the combinations we expect to be used - template class LibSVMBinaryReader; - template class LibSVMBinaryReader; - } - } -} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// LibSVMBinaryReader.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#define DATAREADER_EXPORTS // creating the exports here +#include "DataReader.h" +#include "LibSVMBinaryReader.h" +#include "fileutil.h" // for fexists() +#include +#include +#include +#include +#include "CUDAPageLockedMemAllocator.h" +#include +#include +#ifndef _WIN32 +#include +#include +#include +#include +#include +#include +#endif + + +namespace Microsoft { + namespace MSR { + namespace CNTK { + + DWORD HIDWORD(size_t size) { return size >> 32; } + DWORD LODWORD(size_t size) { return size & 0xFFFFFFFF; } + + template + DenseBinaryMatrix::DenseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols) : BinaryMatrix( name, deviceID, numRows, numCols ) { + //this->m_values = (ElemType*)malloc(sizeof(ElemType)*numRows*numCols); + this->m_values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*numRows*numCols, deviceID); + } + + template + void DenseBinaryMatrix::Clear() { + this->m_numRows = 0; + } + + template + void DenseBinaryMatrix::Dispose() { + if (this->m_values != nullptr) { + //free(this->m_values); + CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); + } + this->m_values = nullptr; + } + + + template + void DenseBinaryMatrix::Fill(Matrix* matrix) { + matrix->SetValue(this->m_maxNumCols, this->m_numRows, matrix->GetDeviceId(), this->m_values, matrixFlagNormal); +#if DEBUG + matrix->Print("testname"); +#endif + } + + template + void DenseBinaryMatrix::SetMaxRows(size_t maxRows) { + if (maxRows > this->m_maxNumRows) { + //ElemType* values = (ElemType*)malloc(sizeof(ElemType)*this->m_maxNumCols*maxRows); + ElemType* values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*this->m_maxNumCols*maxRows, this->m_deviceID); + if (this->m_values != nullptr) { + if (this->m_numRows > 0) { + memcpy(values, this->m_values, sizeof(ElemType)*this->m_numRows*this->m_maxNumCols); + } + //free(this->m_values); + CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); + } + this->m_values = values; + this->m_maxNumRows = maxRows; + } + } + + template + void DenseBinaryMatrix::AddValues(void* values, size_t numRows) { + memcpy(this->m_values + this->m_numRows*this->m_maxNumCols, values, sizeof(ElemType)*numRows*this->m_maxNumCols); + this->m_numRows += numRows; + } + + template + SparseBinaryMatrix::SparseBinaryMatrix(wstring name, int deviceId, size_t numRows, size_t numCols) : BinaryMatrix( name, deviceId, numRows, numCols ), m_rowIndices(nullptr), + m_colIndices(nullptr), m_nnz(0), m_maxNNz(0) { + //m_colIndices = (int32_t*)malloc(sizeof(int32_t)*(numRows + 1)); + m_colIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*(numRows + 1), deviceId); + m_colIndices[0] = 0; + } + + template + void SparseBinaryMatrix::Dispose() { + if (this->m_values != nullptr) { + //free(this->m_values); + CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); + } + this->m_values = nullptr; + if (m_colIndices != nullptr) { + //free(m_colIndices); + CUDAPageLockedMemAllocator::Free(this->m_colIndices, this->m_deviceID); + } + m_colIndices = nullptr; + if (m_rowIndices != nullptr) { + //free(m_rowIndices); + CUDAPageLockedMemAllocator::Free(this->m_rowIndices, this->m_deviceID); + } + m_rowIndices = nullptr; + } + + template + void SparseBinaryMatrix::SetMaxRows(size_t maxRows) { + if (maxRows > this->m_maxNumRows) { + //int32_t* colIndices = (int32_t*)malloc(sizeof(int32_t)*(maxRows+1)); + int32_t* colIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*(maxRows+ 1), this->m_deviceID); + if (this->m_colIndices != nullptr) { + if (this->m_numRows > 0) { + memcpy(colIndices, m_colIndices, sizeof(int32_t)*(this->m_numRows + 1)); + } + //free(this->m_colIndices); + CUDAPageLockedMemAllocator::Free(this->m_colIndices, this->m_deviceID); + } + this->m_colIndices = colIndices; + this->m_maxNumRows = maxRows; + } + } + + template + void SparseBinaryMatrix::ResizeArrays(size_t newNNz) { + + if (newNNz + m_nnz < this->m_maxNNz) { + return; + } + size_t newMaxNNz = (size_t)((newNNz + m_nnz) * 1.3); + //int32_t* rowIndices = (int32_t*)malloc(sizeof(int32_t)*newMaxNNz); + //ElemType* values = (ElemType*)malloc(sizeof(ElemType)*newMaxNNz); + int32_t* rowIndices = (int32_t*)CUDAPageLockedMemAllocator::Malloc(sizeof(int32_t)*newMaxNNz, this->m_deviceID); + ElemType* values = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*newMaxNNz, this->m_deviceID); + + if (m_nnz > 0) { + memcpy(rowIndices, m_rowIndices, sizeof(int32_t)*this->m_maxNNz); + memcpy(values, this->m_values, sizeof(ElemType)*this->m_maxNNz); + } + + if (m_rowIndices != nullptr) { + //free(m_rowIndices); + CUDAPageLockedMemAllocator::Free(this->m_rowIndices, this->m_deviceID); + } + if (this->m_values != nullptr) { + //free(this->m_values); + CUDAPageLockedMemAllocator::Free(this->m_values, this->m_deviceID); + } + + m_rowIndices = rowIndices; + this->m_values = values; + this->m_maxNNz = newMaxNNz; + } + + template + void SparseBinaryMatrix::Clear() { + this->m_numRows = 0; + this->m_nnz = 0; + } + + template + void SparseBinaryMatrix::AddValues(void* values, size_t nnz ) { + memcpy(this->m_values + this->m_nnz, values, sizeof(ElemType)*nnz); + } + + template + void SparseBinaryMatrix::AddColIndices(void* colIndices, size_t numCols) { + int32_t* temp = (int32_t*)colIndices; + for (int c = 1; c < numCols; c++) { + m_colIndices[this->m_numRows + c] = temp[c] + (int32_t)this->m_nnz; + } + this->m_numRows += numCols - 1; + } + template + void SparseBinaryMatrix::AddRowIndices(void* rowIndices, size_t nnz) { + memcpy(m_rowIndices + this->m_nnz, rowIndices, sizeof(int32_t)*nnz); + } + + + template + void SparseBinaryMatrix::Fill( Matrix* matrix) { + matrix->SetMatrixFromCSCFormat(m_colIndices, m_rowIndices, this->m_values, this->m_nnz, this->m_maxNumCols, this->m_numRows ); +#if DEBUG + matrix->Print("testname"); +#endif + } + + + template + SparseBinaryInput::SparseBinaryInput(std::wstring fileName) : m_fileName(fileName), m_readOrder(nullptr), m_readOrderLength(0), m_randomize(false), m_tempValues(nullptr), m_tempValuesSize(0), m_offsets(nullptr), m_offsetsStart(0), m_startMB(0), m_endMB(0) { + std::string name = msra::strfun::utf8(m_fileName); + m_inFile.open(name, ifstream::binary | ifstream::in); + } + + template + SparseBinaryInput::~SparseBinaryInput() { + + } + + template + void SparseBinaryInput::Init(std::map rename ) { + + size_t base_offset = 0; + m_inFile.seekg(0, ifstream::beg); + m_inFile.read((char*)&m_numRows, sizeof(int64_t)); + base_offset += sizeof(int64_t); + + m_inFile.read((char*)&m_numBatches, sizeof(int64_t)); + base_offset += sizeof(int64_t); + + int32_t numFeatures; + int32_t numLabels; + m_inFile.read((char*)&numFeatures, sizeof(int32_t)); + base_offset += sizeof(int32_t); + + m_inFile.read((char*)&numLabels, sizeof(int32_t)); + base_offset += sizeof(int32_t); + + int32_t len; + int32_t numCols; + int32_t maxLen = 100; + char* tempName = (char*)malloc(maxLen); + for (int32_t c = 0; c < numFeatures; c++) + { + m_inFile.read((char*)&len, sizeof(int32_t)); + if (len + 1 > maxLen) { + maxLen = len + 1; + free(tempName); + tempName = (char*)malloc(maxLen); + } + base_offset += sizeof(int32_t); + + m_inFile.read((char*)tempName, len); + tempName[len] = '\0'; + //std::string name((char*)header_buffer + base_offset, len); + std::wstring wname = msra::strfun::utf16(tempName); + if (rename.find(wname) == rename.end()) + { + m_features.emplace_back(wname); + } + else + { + m_features.emplace_back(rename[wname]); + } + base_offset += sizeof(int8_t)*len; + + m_inFile.read((char*)&numCols, sizeof(numCols)); + //numCols = *(int32_t*)((char*)header_buffer + base_offset); + base_offset += sizeof(int32_t); + + m_mappedNumCols[m_features.back()] = numCols; + + } + for (int32_t c = 0; c < numLabels; c++) + { + m_inFile.read((char*)&len, sizeof(int32_t)); + if (len + 1 > maxLen) { + maxLen = len + 1; + free(tempName); + tempName = (char*)malloc(maxLen); + } + base_offset += sizeof(int32_t); + + //std::string name((char*)header_buffer + base_offset, len); + m_inFile.read((char*)tempName, len); + tempName[len] = '\0'; + std::wstring wname = msra::strfun::utf16(tempName); + if (rename.find(wname) == rename.end()) + { + m_labels.emplace_back(wname); + } + else + { + //m_features.emplace_back(rename[wname]); + m_labels.emplace_back(rename[wname]); + } + base_offset += sizeof(int8_t)*len; + + //numCols = *(int32_t*)((char*)header_buffer + base_offset); + m_inFile.read((char*)&numCols, sizeof(numCols)); + base_offset += sizeof(int32_t); + m_mappedNumCols[m_labels.back()] = numCols; + + } + free(tempName); + + m_offsetsStart = base_offset; + m_dataStart = m_offsetsStart + m_numBatches * sizeof(int64_t); + + /*Read in the microbatch size here*/ + m_inFile.seekg(m_dataStart, ios::beg); + m_inFile.read((char*)&m_microBatchSize, sizeof(int32_t)); + m_mbSize = (size_t)m_microBatchSize; + + m_inFile.seekg(0, ios::end); + m_fileSize = (size_t)m_inFile.tellg(); + } + + template + bool SparseBinaryInput::Randomize() + { + return false; + /* + if (m_randomize > 0) + { + return true; + } + return false; + */ + } + + + template + void SparseBinaryInput::FillReadOrder(size_t windowSize) + { + if (m_readOrder!= nullptr) + { + free(m_readOrder); + } + m_readOrder = (size_t*)malloc(sizeof(size_t)*windowSize); + for (size_t c = 0; c < windowSize; c++) + { + m_readOrder[c] = c; + } + } + + template + void SparseBinaryInput::ReadOffsets( size_t startMB, size_t numMBs) + { + if (startMB == m_startMB && m_endMB == startMB + numMBs) { + return; + } + if (m_offsets != nullptr) { + free(m_offsets); + } + m_offsets = (int64_t*)malloc(sizeof(int64_t)*(numMBs+1)); + m_inFile.seekg(m_offsetsStart + startMB * sizeof(int64_t), ios::beg); + m_inFile.read((char*)&(m_offsets[0]), sizeof(int64_t)*numMBs); + if (startMB + numMBs < m_numBatches) { + m_inFile.read((char*)&(m_offsets[numMBs]), sizeof(int64_t)); + } + else { + m_offsets[numMBs] = m_fileSize; + } + m_startMB = startMB; + m_endMB = startMB + numMBs; + } + + template + void SparseBinaryInput::Shuffle() + { + if (Randomize()) + { + shuffle(&(m_readOrder[0]), &(m_readOrder[m_readOrderLength-1]), m_randomEngine); + } + } + + template + void SparseBinaryInput::StartDistributedMinibatchLoop(size_t mbSize, size_t subsetNum, size_t numSubsets) { + + m_nextMB = 0; + + m_mbSize = mbSize / numSubsets; + + m_subsetNum = subsetNum; + m_numSubsets = numSubsets; + + m_epochSize = m_numBatches / numSubsets; + + size_t startMB = m_epochSize * subsetNum; + size_t endMB = m_epochSize * (subsetNum + 1); + + size_t remainder = m_numBatches % numSubsets; + + size_t lb = min(remainder, subsetNum); + size_t ub = min(remainder, subsetNum + 1); + + m_epochSize += (subsetNum < remainder) ? 1 : 0; + + startMB += lb; + endMB += ub; + m_windowSize = endMB - startMB; + + if (m_windowSize != m_readOrderLength ) { + FillReadOrder(m_windowSize); + m_readOrderLength = m_windowSize; + } + + + Shuffle(); + + ReadOffsets(startMB, m_windowSize); + + m_maxMBSize = 0; + for (size_t c = 0; c < m_windowSize; c++) { + m_maxMBSize = max(m_maxMBSize, (size_t)(m_offsets[c + 1] - m_offsets[c])); + //fprintf(stderr, "m_offsets[%lu] = %lu\n", c, m_offsets[c]); + } + //fprintf(stderr, "max mb size: %ld\n", m_maxMBSize); + size_t maxMem = 1024 * 1024 * 1024; // 1GB + size_t maxPointers = maxMem / m_maxMBSize; + for (size_t c = 0; c < maxPointers; c++) { + void* dataBuffer = malloc(m_maxMBSize); + m_dataToProduce.push(dataBuffer); + } + + std::thread readData([this] { this->ReadMinibatches(m_readOrder, m_readOrderLength); }); + readData.detach(); + + } + template + void* SparseBinaryInput::GetTempDataPointer(size_t numBytes) { + if (m_tempValuesSize < numBytes) { + if (m_tempValues != nullptr) { + free(m_tempValues); + } + m_tempValuesSize = (int32_t)(numBytes * 1.3); + m_tempValues = malloc(m_tempValuesSize); + } + return m_tempValues; + } + + template + shared_ptr> SparseBinaryInput::CreateMatrix(std::wstring matName, int deviceId ) { + shared_ptr> retVal;// = nullptr; + + //if (m_features.find(matName) != m_features.end()) { + if (std::find(m_features.begin(), m_features.end(), matName) != m_features.end()) { + retVal = make_shared>(matName, deviceId, m_mbSize, m_mappedNumCols[matName]); + } + //else if (m_labels.find(matName) != m_labels.end()) { + else if (std::find(m_labels.begin(), m_labels.end(), matName) != m_labels.end()) { + retVal = make_shared>(matName, deviceId, m_mbSize, m_mappedNumCols[matName]); + } + + return retVal; + } + + template + void SparseBinaryInput::ReadMinibatches(size_t* read_order, size_t numToRead) { +#if DEBUG + marker_series series(L"Read Minibatches"); + //diagnostic::span span(series, L"Reading Data"); + span* read_span; +#endif + for (size_t c = 0; c < numToRead; c++) { +#if DEBUG + read_span = new span(series, 1, L"Getting Buffer %ld\n", c); +#endif + //fprintf(stderr, "start reading data %ld\n", c); + size_t readSize = m_offsets[read_order[ c ] + 1] - m_offsets[read_order[ c ]]; + //void* data_buffer = GetTempDataPointer(readSize); +#if DEBUG + series.write_flag(_T("Getting buffer.")); +#endif + void* data_buffer = m_dataToProduce.pop(); +#if DEBUG + delete read_span; + series.write_flag(_T("Got buffer.")); + read_span = new span(series, 2, L"Reading Data %ld\n", c); +#endif + + m_inFile.clear(); +#if DEBUG + series.write_flag(_T("seeking.")); +#endif + m_inFile.seekg(m_dataStart + m_offsets[c], ios::beg); +#if DEBUG + series.write_flag(_T("reading.")); +#endif + m_inFile.read((char*)data_buffer, readSize); + m_dataToConsume.push(data_buffer); + //fprintf(stderr, "done reading data %ld\n", c); +#if DEBUG + series.write_flag(_T("Done read, pushed buffer.")); + delete read_span; +#endif + } + //m_dataToConsume.push(nullptr); +#if DEBUG + series.write_flag(_T("Done reading.")); +#endif + + } + + template + size_t SparseBinaryInput::ReadMinibatch(void* data_buffer, std::map>>& matrices) { + + //fprintf(stderr, "start read minibatch.\n"); + /* + size_t readSize = m_offsets[cur_batch + 1] - m_offsets[cur_batch]; + void* data_buffer = GetTempDataPointer(readSize); + + fprintf(stderr, "start reading data.\n"); + m_inFile.clear(); + m_inFile.seekg(m_dataStart + m_offsets[cur_batch], ios::beg); + m_inFile.read((char*)data_buffer, readSize); + fprintf(stderr, "done reading data.\n"); + */ + + int32_t nnz; + int32_t curMBSize; + + int64_t buffer_offset = 0; + + curMBSize = *(int32_t*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(int32_t); + + for (int32_t c = 0; c < m_features.size(); c++) + { + //fprintf(stderr, "read features %d.\n", c); + nnz = *(int32_t*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(int32_t); + + ElemType* vals = (ElemType*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(ElemType)*nnz; + + int32_t* rowIndices = (int32_t*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(int32_t)*nnz; + + int32_t* colIndices = (int32_t*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(int32_t)*(curMBSize + 1); + + auto findMat = matrices.find(m_features[c]); + if (findMat != matrices.end()) + { + auto mat = findMat->second; + mat->ResizeArrays(nnz); + mat->AddValues(vals, nnz); + mat->AddRowIndices(rowIndices, nnz); + mat->AddColIndices(colIndices, curMBSize + 1); + mat->UpdateNNz(nnz); +#ifdef DEBUG + mat->Print("features"); +#endif + } + } + + for (int32_t c = 0; c < m_labels.size(); c++) + { + //fprintf(stderr, "read labels %d.\n", c); + int32_t numCols = m_mappedNumCols[m_labels[c]]; + + ElemType* vals = (ElemType*)((char*)data_buffer + buffer_offset); + buffer_offset += sizeof(ElemType)*curMBSize* numCols; + + auto findMat = matrices.find(m_labels[c]); + if (findMat != matrices.end()) + { + auto mat = findMat->second; + mat->AddValues(vals, curMBSize); +#ifdef DEBUG + mat->Print("labels"); +#endif + } + } + return (size_t)curMBSize; + } + + template + size_t SparseBinaryInput::FillMatrices(std::map>>& matrices) { + + //fprintf(stderr, "start fill matrices\n"); + size_t curSize = 0; + for (auto mat : matrices) { + mat.second->SetMaxRows(m_mbSize); + mat.second->Clear(); + } + void* data_buffer; + //fprintf(stderr, "start while\n"); + //clock_t start_w = clock(); + //while (curSize + m_microBatchSize <= m_mbSize && (data_buffer = m_dataToConsume.pop()) != nullptr) { + while (curSize + m_microBatchSize <= m_mbSize && m_nextMB < m_epochSize) { + data_buffer = m_dataToConsume.pop(); + //clock_t in_w = clock(); + //start_w = in_w - start_w; + //fprintf(stderr, "start read mb\tIt took me %d clicks (%f seconds).\n", start_w, ((float)start_w) / CLOCKS_PER_SEC); + //start_w = in_w; + //fprintf(stderr, "start read mb\n"); + curSize += ReadMinibatch(data_buffer, matrices); + //fprintf(stderr, "end read mb\n"); + m_nextMB++; + m_dataToProduce.push(data_buffer); + } + //fprintf(stderr, "end fill matrices\n"); + return curSize; + } + + template + template + void LibSVMBinaryReader::InitFromConfig(const ConfigRecordType & readerConfig) { + + std::map rename; + RenamedMatrices(readerConfig, rename); + + if (readerConfig.Exists(L"randomize")) + { + string randomizeString = readerConfig(L"randomize"); + if (randomizeString == "None") + { + m_randomize = 0L; + } + else if (randomizeString == "Auto") + { + time_t rawtime; + struct tm* timeinfo; + time(&rawtime); + timeinfo = localtime(&rawtime); + m_randomize = (unsigned long)(timeinfo->tm_sec + timeinfo->tm_min * 60 + timeinfo->tm_hour * 60 * 60); + } + else + { + m_randomize = readerConfig(L"randomize", 0); + } + } + else + { + m_randomize = 0L; + } + + m_partialMinibatch = true; + std::string minibatchMode(readerConfig(L"minibatchMode", "Partial")); + m_partialMinibatch = !_stricmp(minibatchMode.c_str(), "Partial"); + + std::wstring file = readerConfig(L"file", L""); + + m_dataInput = make_shared>(file); + m_dataInput->Init( rename ); + + m_mbSize = (size_t)readerConfig(L"minibatch", 0); + if (m_mbSize > 0) + { + if (m_dataInput->GetMBSize() != m_mbSize) + { + RuntimeError("Data file and config file have mismatched minibatch sizes.\n"); + return; + } + } + else + { + m_mbSize = m_dataInput->GetMBSize(); + } + + m_prefetchEnabled = true; + } + + template + void LibSVMBinaryReader::Destroy() { + + } + + template + LibSVMBinaryReader::~LibSVMBinaryReader() + { + Destroy(); + } + + template + void LibSVMBinaryReader::StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples ) { + return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); + } + + template + void LibSVMBinaryReader::StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t /*requestedEpochSamples*/ ) { + m_epoch = epoch; + m_mbSize = mbSize; +#if DEBUG + if (reader_series != NULL) { + delete reader_series; + } + reader_series = new marker_series(L"Base Reader"); + cur_read = 0; +#endif + m_dataInput->StartDistributedMinibatchLoop(mbSize, subsetNum, numSubsets); + + } + + template + void LibSVMBinaryReader::CheckDataMatrices(std::map*>& matrices) { + if (m_dataMatrices.empty()) + { + for (auto inmat : matrices) { + shared_ptr> mat = m_dataInput->CreateMatrix(inmat.first,inmat.second->GetDeviceId()); + if (mat != nullptr) { + m_dataMatrices[inmat.first] = mat; + } + } + } + } + template + void LibSVMBinaryReader::DoDSSMMatrix(Matrix& mat, size_t actualMBSize) { + size_t numRows = mat.GetNumRows(); + if (DSSMCols < actualMBSize) { + if (DSSMLabels != nullptr) { + //free(DSSMLabels); + CUDAPageLockedMemAllocator::Free(DSSMLabels, mat.GetDeviceId()); + } + DSSMCols = actualMBSize; + //DSSMLabels = (ElemType*)malloc(sizeof(ElemType)*numRows*actualMBSize); + DSSMLabels = (ElemType*)CUDAPageLockedMemAllocator::Malloc(sizeof(ElemType)*numRows*actualMBSize, mat.GetDeviceId()); + memset(DSSMLabels, 0, sizeof(ElemType)*numRows*actualMBSize); + for (size_t c = 0; c < numRows*actualMBSize; c += numRows) { + DSSMLabels[c] = 1; + } + } + if (mat.GetNumCols() != actualMBSize) { + mat.SetValue(numRows, actualMBSize, mat.GetDeviceId(), DSSMLabels, matrixFlagNormal); + } + } + + + template + bool LibSVMBinaryReader::GetMinibatch(std::map*>& matrices) { + //timer = clock(); +#if DEBUG + span minibatch_span(*reader_series, 1, L"Get Minibatch: %ld", cur_read); +#endif + size_t actualMBSize = 0; + if (m_prefetchEnabled) + { + if (!m_pendingAsyncGetMinibatch.valid()) { + //fprintf(stderr, "not valid\n"); + CheckDataMatrices(matrices); + m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this]() + { + return m_dataInput->FillMatrices(m_dataMatrices); + }); + } + //fprintf(stderr, "before get.\n"); + //timer = clock(); +#if DEBUG + reader_series->write_flag(_T("before get.")); +#endif + actualMBSize = m_pendingAsyncGetMinibatch.get(); +#if DEBUG + reader_series->write_flag(_T("after get.")); +#endif + //timer = clock() - timer; + //fprintf(stderr, "done get\tIt took me %d clicks (%f seconds).\n", timer, ((float)timer) / CLOCKS_PER_SEC); + + if (actualMBSize == 0) { + return false; + } + + m_pMBLayout->InitAsFrameMode(actualMBSize); +#if DEBUG + reader_series->write_flag(_T("starting fill.")); +#endif + for (auto matrix : m_dataMatrices) { + auto findMat = matrices.find(matrix.first); + if (findMat != matrices.end()) { + matrix.second->Fill(findMat->second); + } + } +#if DEBUG + reader_series->write_flag(_T("done fill.")); +#endif + auto findMat = matrices.find(L"DSSMLabel"); + if (findMat != matrices.end()) + { + DoDSSMMatrix(*(findMat->second), actualMBSize); + + } + m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this]() + { + //CheckDataMatrices(matrices); + return m_dataInput->FillMatrices(m_dataMatrices); + }); + } +#if DEBUG + cur_read++; +#endif + /* + + timer = clock() - timer; + fprintf(stderr, "It took me %d clicks (%f seconds).\n", timer, ((float)timer) / CLOCKS_PER_SEC); + */ + //fprintf(stderr, "done\n"); + return true; + } + + + template + template + void LibSVMBinaryReader::RenamedMatrices(const ConfigRecordType& config, std::map& rename) { + for (const auto & id : config.GetMemberIds()) + { + if (!config.CanBeConfigRecord(id)) + continue; + const ConfigRecordType & temp = config(id); + // see if we have a config parameters that contains a "dim" element, it's a sub key, use it + if (temp.ExistsCurrent(L"rename")) + { + + std::wstring ren = temp(L"rename"); + rename.emplace(msra::strfun::utf16(id), msra::strfun::utf16(ren)); + } + } + } + + template + bool LibSVMBinaryReader::DataEnd(EndDataType endDataType) + { + return m_dataInput->DataEnd(endDataType); + } + + template + bool SparseBinaryInput::DataEnd(EndDataType endDataType) + { + bool ret = false; + switch (endDataType) + { + case endDataNull: + assert(false); + break; + case endDataEpoch: + ret = (m_nextMB >= m_epochSize); + break; + case endDataSet: + ret = (m_nextMB >= m_epochSize); + break; + case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true + ret = true; + break; + } + return ret; + } + + // instantiate all the combinations we expect to be used + template class LibSVMBinaryReader; + template class LibSVMBinaryReader; + } + } +} diff --git a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.h b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.h index 3a614a7117c9..9596f1c14005 100644 --- a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.h +++ b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.h @@ -1,269 +1,269 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// LibSVMBinaryReader.h - Include file for the MTK and MLF format of features and samples -#pragma once -#include "stdafx.h" -#include "DataReader.h" -#include "DataWriter.h" -#include "RandomOrdering.h" -#include -#include -#include -#include -#include -#include -#include -#include -#include -#if DEBUG -#include -using namespace Concurrency::diagnostic; -#endif - -namespace Microsoft { - namespace MSR { - namespace CNTK { - - template - class BlockingQueue - { - private: - std::mutex d_mutex; - std::condition_variable d_condition; - std::deque d_queue; - public: - void push(T const& value) { - { - std::unique_lock lock(this->d_mutex); - d_queue.push_front(value); - } - this->d_condition.notify_one(); - } - T pop() { - std::unique_lock lock(this->d_mutex); - this->d_condition.wait(lock, [=]{ return !this->d_queue.empty(); }); - T rc(std::move(this->d_queue.back())); - this->d_queue.pop_back(); - return rc; - } - }; - - - template - class BinaryMatrix { - public: - BinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols) : m_matrixName(name), m_deviceID(deviceID), m_maxNumRows(numRows), m_numRows(0), m_maxNumCols(numCols), m_values(nullptr) {}; - //BinaryMatrix(wstring name, size_t numRows, size_t numCols) : m_matrixName(name), m_maxNumRows(numRows), m_numRows(0), m_maxNumCols(numCols), m_values(nullptr) {}; - virtual void Clear() = 0; - virtual void Dispose() = 0; - virtual void Fill(Matrix* ) = 0; - virtual void AddValues(void* , size_t ) = 0; - virtual void AddColIndices(void* , size_t ) = 0; - virtual void AddRowIndices(void* , size_t ) = 0; - virtual void UpdateNNz(size_t ) = 0; - virtual void UpdateCurMB(size_t mb) { m_numRows += mb; } - virtual void ResizeArrays(size_t ) = 0; - virtual void SetMaxRows(size_t maxRows) = 0; - protected: - wstring m_matrixName; - int m_deviceID; - ElemType* m_values; - size_t m_maxNumRows; - size_t m_maxNumCols; - - size_t m_numRows; - - }; - - template - class DenseBinaryMatrix : public BinaryMatrix { - public: - DenseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols); - //DenseBinaryMatrix(wstring name, size_t numRows, size_t numCols); - virtual void Clear(); - virtual void Dispose(); - virtual void Fill(Matrix* matrix) override; - virtual void AddValues(void* values, size_t numRows) override; - virtual void AddColIndices(void* /*colIndices*/, size_t /*numCols*/) override { NOT_IMPLEMENTED } - virtual void AddRowIndices(void* /*rowIndices*/, size_t /*nnz*/) override { NOT_IMPLEMENTED } - virtual void UpdateNNz(size_t /*nnz*/) override { NOT_IMPLEMENTED } - virtual void ResizeArrays(size_t ) { NOT_IMPLEMENTED } - virtual void SetMaxRows(size_t maxRows) override; - protected: - }; - - template - class SparseBinaryMatrix : public BinaryMatrix { - public: - SparseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols); - //SparseBinaryMatrix(wstring name, size_t numRows, size_t numCols); - virtual void Clear(); - virtual void Dispose(); - virtual void Fill( Matrix* matrix ) override; - virtual void AddValues(void* values, size_t nnz) override; - virtual void AddColIndices(void* colIndices, size_t numCols) override; - virtual void AddRowIndices(void* rowIndices, size_t nnz) override; - virtual void UpdateNNz(size_t nnz) override { m_nnz += nnz; } - virtual void ResizeArrays(size_t newMaxNNz) override; - virtual void SetMaxRows(size_t maxRows) override; - protected: - int32_t* m_rowIndices; - int32_t* m_colIndices; - size_t m_nnz; - size_t m_maxNNz; - }; - - template - class SparseBinaryInput { - public: - SparseBinaryInput(std::wstring fileName); - ~SparseBinaryInput(); - void Init(std::map rename); - void StartDistributedMinibatchLoop(size_t mbSize, size_t subsetNum, size_t numSubsets); - void ReadMinibatches(size_t* read_order, size_t numToRead); - size_t ReadMinibatch(void* data_buffer, std::map>>& matrices); - //void GetMinibatch(std::map*>& matrices); - size_t FillMatrices(std::map>>& matrices); - size_t GetMBSize() { return m_mbSize; } - size_t GetNumMB() { return m_numBatches / ( m_mbSize / m_microBatchSize ); } - void Shuffle(); - shared_ptr> CreateMatrix(std::wstring matName, int deviceId); - //shared_ptr> CreateMatrix(std::wstring matName); - virtual bool DataEnd(EndDataType endDataType); - - - private: - void ReadOffsets(size_t startMB, size_t numMBs); - void FillReadOrder(size_t windowSize); - void* GetTempDataPointer(size_t numVals); - bool Randomize(); - - ifstream m_inFile; - std::wstring m_fileName; - size_t m_fileSize; - - size_t m_offsetsStart; - int64_t* m_offsets; - size_t m_dataStart; - - size_t m_nextMB; // starting sample # of the next minibatch - size_t m_epochSize; // size of an epoch - - - size_t m_numRows; // size of minibatch requested - size_t m_numBatches; // size of minibatch requested - - int32_t m_microBatchSize; - size_t m_mbSize; - - size_t m_startMB; - size_t m_endMB; - size_t m_curLower; - - size_t m_subsetNum; - size_t m_numSubsets; - - size_t m_windowSize; - size_t m_curWindowSize; - - bool m_randomize; - size_t* m_readOrder; // array to shuffle to reorder the dataset - size_t m_readOrderLength; - size_t m_maxMBSize; - - std::vector m_features; - std::vector m_labels; - std::map m_mappedNumCols; - - int32_t m_tempValuesSize; - void* m_tempValues; - - RandomOrdering m_randomordering; // randomizing class - std::mt19937_64 m_randomEngine; -#ifdef _WIN32 - DWORD sysGran; -#else - int32_t sysGran; -#endif - BlockingQueue m_dataToProduce; - BlockingQueue m_dataToConsume; - }; - - template - class LibSVMBinaryReader : public IDataReader - { - public: - using LabelType = typename IDataReader::LabelType; - using LabelIdType = typename IDataReader::LabelIdType; - - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } - virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } - - template void InitFromConfig(const ConfigRecordType &); - - virtual void Destroy(); - - LibSVMBinaryReader() : DSSMLabels(nullptr), DSSMCols(0) { m_pMBLayout = make_shared(); }; - - virtual ~LibSVMBinaryReader(); - - virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize); - virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples) override; - virtual bool GetMinibatch(std::map*>& matrices); - - virtual bool SupportsDistributedMBRead() const override { return true; } - - template void RenamedMatrices(const ConfigRecordType& readerConfig, std::map& rename); - virtual void SetLabelMapping(const std::wstring& /*sectionName*/, const std::map& /*labelMapping*/) { NOT_IMPLEMENTED }; - virtual bool GetData(const std::wstring& /*sectionName*/, size_t /*numRecords*/, void* /*data*/, size_t& /*dataBufferSize*/, size_t /*recordStart = 0*/) { NOT_IMPLEMENTED }; - virtual bool DataEnd(EndDataType endDataType); - - size_t GetNumParallelSequences() { return m_pMBLayout->GetNumParallelSequences(); } - void CopyMBLayoutTo(MBLayoutPtr pMBLayout) { pMBLayout->CopyFrom(m_pMBLayout); }; - - //virtual bool DataEnd(EndDataType endDataType); - - size_t NumberSlicesInEachRecurrentIter() { return 1; } - void SetNbrSlicesEachRecurrentIter(const size_t) { }; - void SetSentenceEndInBatch(std::vector &/*sentenceEnd*/){}; - - private: -#if DEBUG - marker_series* reader_series; - size_t cur_read; -#endif - clock_t timer; - void DoDSSMMatrix(Matrix& mat, size_t actualMBSize); - - void CheckDataMatrices(std::map*>& matrices); - MBLayoutPtr m_pMBLayout; - ConfigParameters m_readerConfig; - - std::shared_ptr> m_dataInput; - - std::map>> m_dataMatrices; - - unsigned long m_randomize; // randomization range - - ElemType* DSSMLabels; - size_t DSSMCols; - - - size_t m_mbSize; // size of minibatch requested - - size_t m_requestedEpochSize; // size of an epoch - - size_t m_epoch; // which epoch are we on - - bool m_partialMinibatch; // a partial minibatch is allowed - +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// LibSVMBinaryReader.h - Include file for the MTK and MLF format of features and samples +#pragma once +#include "stdafx.h" +#include "DataReader.h" +#include "DataWriter.h" +#include "RandomOrdering.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if DEBUG +#include +using namespace Concurrency::diagnostic; +#endif + +namespace Microsoft { + namespace MSR { + namespace CNTK { + + template + class BlockingQueue + { + private: + std::mutex d_mutex; + std::condition_variable d_condition; + std::deque d_queue; + public: + void push(T const& value) { + { + std::unique_lock lock(this->d_mutex); + d_queue.push_front(value); + } + this->d_condition.notify_one(); + } + T pop() { + std::unique_lock lock(this->d_mutex); + this->d_condition.wait(lock, [=]{ return !this->d_queue.empty(); }); + T rc(std::move(this->d_queue.back())); + this->d_queue.pop_back(); + return rc; + } + }; + + + template + class BinaryMatrix { + public: + BinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols) : m_matrixName(name), m_deviceID(deviceID), m_maxNumRows(numRows), m_numRows(0), m_maxNumCols(numCols), m_values(nullptr) {}; + //BinaryMatrix(wstring name, size_t numRows, size_t numCols) : m_matrixName(name), m_maxNumRows(numRows), m_numRows(0), m_maxNumCols(numCols), m_values(nullptr) {}; + virtual void Clear() = 0; + virtual void Dispose() = 0; + virtual void Fill(Matrix* ) = 0; + virtual void AddValues(void* , size_t ) = 0; + virtual void AddColIndices(void* , size_t ) = 0; + virtual void AddRowIndices(void* , size_t ) = 0; + virtual void UpdateNNz(size_t ) = 0; + virtual void UpdateCurMB(size_t mb) { m_numRows += mb; } + virtual void ResizeArrays(size_t ) = 0; + virtual void SetMaxRows(size_t maxRows) = 0; + protected: + wstring m_matrixName; + int m_deviceID; + ElemType* m_values; + size_t m_maxNumRows; + size_t m_maxNumCols; + + size_t m_numRows; + + }; + + template + class DenseBinaryMatrix : public BinaryMatrix { + public: + DenseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols); + //DenseBinaryMatrix(wstring name, size_t numRows, size_t numCols); + virtual void Clear(); + virtual void Dispose(); + virtual void Fill(Matrix* matrix) override; + virtual void AddValues(void* values, size_t numRows) override; + virtual void AddColIndices(void* /*colIndices*/, size_t /*numCols*/) override { NOT_IMPLEMENTED } + virtual void AddRowIndices(void* /*rowIndices*/, size_t /*nnz*/) override { NOT_IMPLEMENTED } + virtual void UpdateNNz(size_t /*nnz*/) override { NOT_IMPLEMENTED } + virtual void ResizeArrays(size_t ) { NOT_IMPLEMENTED } + virtual void SetMaxRows(size_t maxRows) override; + protected: + }; + + template + class SparseBinaryMatrix : public BinaryMatrix { + public: + SparseBinaryMatrix(wstring name, int deviceID, size_t numRows, size_t numCols); + //SparseBinaryMatrix(wstring name, size_t numRows, size_t numCols); + virtual void Clear(); + virtual void Dispose(); + virtual void Fill( Matrix* matrix ) override; + virtual void AddValues(void* values, size_t nnz) override; + virtual void AddColIndices(void* colIndices, size_t numCols) override; + virtual void AddRowIndices(void* rowIndices, size_t nnz) override; + virtual void UpdateNNz(size_t nnz) override { m_nnz += nnz; } + virtual void ResizeArrays(size_t newMaxNNz) override; + virtual void SetMaxRows(size_t maxRows) override; + protected: + int32_t* m_rowIndices; + int32_t* m_colIndices; + size_t m_nnz; + size_t m_maxNNz; + }; + + template + class SparseBinaryInput { + public: + SparseBinaryInput(std::wstring fileName); + ~SparseBinaryInput(); + void Init(std::map rename); + void StartDistributedMinibatchLoop(size_t mbSize, size_t subsetNum, size_t numSubsets); + void ReadMinibatches(size_t* read_order, size_t numToRead); + size_t ReadMinibatch(void* data_buffer, std::map>>& matrices); + //void GetMinibatch(std::map*>& matrices); + size_t FillMatrices(std::map>>& matrices); + size_t GetMBSize() { return m_mbSize; } + size_t GetNumMB() { return m_numBatches / ( m_mbSize / m_microBatchSize ); } + void Shuffle(); + shared_ptr> CreateMatrix(std::wstring matName, int deviceId); + //shared_ptr> CreateMatrix(std::wstring matName); + virtual bool DataEnd(EndDataType endDataType); + + + private: + void ReadOffsets(size_t startMB, size_t numMBs); + void FillReadOrder(size_t windowSize); + void* GetTempDataPointer(size_t numVals); + bool Randomize(); + + ifstream m_inFile; + std::wstring m_fileName; + size_t m_fileSize; + + size_t m_offsetsStart; + int64_t* m_offsets; + size_t m_dataStart; + + size_t m_nextMB; // starting sample # of the next minibatch + size_t m_epochSize; // size of an epoch + + + size_t m_numRows; // size of minibatch requested + size_t m_numBatches; // size of minibatch requested + + int32_t m_microBatchSize; + size_t m_mbSize; + + size_t m_startMB; + size_t m_endMB; + size_t m_curLower; + + size_t m_subsetNum; + size_t m_numSubsets; + + size_t m_windowSize; + size_t m_curWindowSize; + + bool m_randomize; + size_t* m_readOrder; // array to shuffle to reorder the dataset + size_t m_readOrderLength; + size_t m_maxMBSize; + + std::vector m_features; + std::vector m_labels; + std::map m_mappedNumCols; + + int32_t m_tempValuesSize; + void* m_tempValues; + + RandomOrdering m_randomordering; // randomizing class + std::mt19937_64 m_randomEngine; +#ifdef _WIN32 + DWORD sysGran; +#else + int32_t sysGran; +#endif + BlockingQueue m_dataToProduce; + BlockingQueue m_dataToConsume; + }; + + template + class LibSVMBinaryReader : public IDataReader + { + public: + using LabelType = typename IDataReader::LabelType; + using LabelIdType = typename IDataReader::LabelIdType; + + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } + + template void InitFromConfig(const ConfigRecordType &); + + virtual void Destroy(); + + LibSVMBinaryReader() : DSSMLabels(nullptr), DSSMCols(0) { m_pMBLayout = make_shared(); }; + + virtual ~LibSVMBinaryReader(); + + virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize); + virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples) override; + virtual bool GetMinibatch(std::map*>& matrices); + + virtual bool SupportsDistributedMBRead() const override { return true; } + + template void RenamedMatrices(const ConfigRecordType& readerConfig, std::map& rename); + virtual void SetLabelMapping(const std::wstring& /*sectionName*/, const std::map& /*labelMapping*/) { NOT_IMPLEMENTED }; + virtual bool GetData(const std::wstring& /*sectionName*/, size_t /*numRecords*/, void* /*data*/, size_t& /*dataBufferSize*/, size_t /*recordStart = 0*/) { NOT_IMPLEMENTED }; + virtual bool DataEnd(EndDataType endDataType); + + size_t GetNumParallelSequences() { return m_pMBLayout->GetNumParallelSequences(); } + void CopyMBLayoutTo(MBLayoutPtr pMBLayout) { pMBLayout->CopyFrom(m_pMBLayout); }; + + //virtual bool DataEnd(EndDataType endDataType); + + size_t NumberSlicesInEachRecurrentIter() { return 1; } + void SetNbrSlicesEachRecurrentIter(const size_t) { }; + void SetSentenceEndInBatch(std::vector &/*sentenceEnd*/){}; + + private: +#if DEBUG + marker_series* reader_series; + size_t cur_read; +#endif + clock_t timer; + void DoDSSMMatrix(Matrix& mat, size_t actualMBSize); + + void CheckDataMatrices(std::map*>& matrices); + MBLayoutPtr m_pMBLayout; + ConfigParameters m_readerConfig; + + std::shared_ptr> m_dataInput; + + std::map>> m_dataMatrices; + + unsigned long m_randomize; // randomization range + + ElemType* DSSMLabels; + size_t DSSMCols; + + + size_t m_mbSize; // size of minibatch requested + + size_t m_requestedEpochSize; // size of an epoch + + size_t m_epoch; // which epoch are we on + + bool m_partialMinibatch; // a partial minibatch is allowed + bool m_prefetchEnabled; std::future m_pendingAsyncGetMinibatch; - - }; - } - } -} + + }; + } + } +} diff --git a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj index e5d8ac1fb2b9..c48487f6e200 100644 --- a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj +++ b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj @@ -1,156 +1,156 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {D667AF32-028A-4A5D-BE19-F46776F0F6B2} - - - - - - - - - Win32Proj - LibSVMBinaryReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /bigobj %(AdditionalOptions) - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;LIBSVMBINARYREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - - - - - - - - - - - NotUsing - - - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - - - NotUsing - - - NotUsing - - - NotUsing - Create - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {D667AF32-028A-4A5D-BE19-F46776F0F6B2} + + + + + + + + + Win32Proj + LibSVMBinaryReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;DSSMREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /bigobj %(AdditionalOptions) + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;LIBSVMBINARYREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + + + + + + + + + + + NotUsing + + + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + + + NotUsing + + + NotUsing + + + NotUsing + Create + + + + + \ No newline at end of file diff --git a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj.filters b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj.filters index d0ebf1fa5242..27b99b14ce1e 100644 --- a/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj.filters +++ b/Source/Readers/LibSVMBinaryReader/LibSVMBinaryReader.vcxproj.filters @@ -1,60 +1,60 @@ - - - - - {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} - rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms - - - {b34d649b-468d-454e-a5e5-b39ad6be3fe0} - - - {062e2b8f-c0b4-4328-98a4-32f6c99a53ac} - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - - Common\Include - - + + + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + {b34d649b-468d-454e-a5e5-b39ad6be3fe0} + + + {062e2b8f-c0b4-4328-98a4-32f6c99a53ac} + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + + Common\Include + + \ No newline at end of file diff --git a/Source/Readers/LibSVMBinaryReader/dllmain.cpp b/Source/Readers/LibSVMBinaryReader/dllmain.cpp index 6b7cdabe4cdd..aeaef9e3ca22 100644 --- a/Source/Readers/LibSVMBinaryReader/dllmain.cpp +++ b/Source/Readers/LibSVMBinaryReader/dllmain.cpp @@ -1,19 +1,19 @@ -// dllmain.cpp : Defines the entry point for the DLL application. -#include "stdafx.h" - -BOOL APIENTRY DllMain( HMODULE /*hModule*/, - DWORD ul_reason_for_call, - LPVOID /*lpReserved*/ - ) -{ - switch (ul_reason_for_call) - { - case DLL_PROCESS_ATTACH: - case DLL_THREAD_ATTACH: - case DLL_THREAD_DETACH: - case DLL_PROCESS_DETACH: - break; - } - return TRUE; -} - +// dllmain.cpp : Defines the entry point for the DLL application. +#include "stdafx.h" + +BOOL APIENTRY DllMain( HMODULE /*hModule*/, + DWORD ul_reason_for_call, + LPVOID /*lpReserved*/ + ) +{ + switch (ul_reason_for_call) + { + case DLL_PROCESS_ATTACH: + case DLL_THREAD_ATTACH: + case DLL_THREAD_DETACH: + case DLL_PROCESS_DETACH: + break; + } + return TRUE; +} + diff --git a/Source/Readers/LibSVMBinaryReader/stdafx.cpp b/Source/Readers/LibSVMBinaryReader/stdafx.cpp index bb1431c178a3..1eb86834a664 100644 --- a/Source/Readers/LibSVMBinaryReader/stdafx.cpp +++ b/Source/Readers/LibSVMBinaryReader/stdafx.cpp @@ -1,8 +1,8 @@ -// stdafx.cpp : source file that includes just the standard includes -// DSSMReader.pch will be the pre-compiled header -// stdafx.obj will contain the pre-compiled type information - -#include "stdafx.h" - -// TODO: reference any additional headers you need in STDAFX.H -// and not in this file +// stdafx.cpp : source file that includes just the standard includes +// DSSMReader.pch will be the pre-compiled header +// stdafx.obj will contain the pre-compiled type information + +#include "stdafx.h" + +// TODO: reference any additional headers you need in STDAFX.H +// and not in this file diff --git a/Source/Readers/LibSVMBinaryReader/targetver.h b/Source/Readers/LibSVMBinaryReader/targetver.h index fdf049a51ca9..7b137418ba0c 100644 --- a/Source/Readers/LibSVMBinaryReader/targetver.h +++ b/Source/Readers/LibSVMBinaryReader/targetver.h @@ -1,10 +1,10 @@ -#pragma once - -// Including SDKDDKVer.h defines the highest available Windows platform. - -// If you wish to build your application for a previous Windows platform, include WinSDKVer.h and -// set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h. - -#ifdef __WINDOWS__ -#include -#endif +#pragma once + +// Including SDKDDKVer.h defines the highest available Windows platform. + +// If you wish to build your application for a previous Windows platform, include WinSDKVer.h and +// set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h. + +#ifdef __WINDOWS__ +#include +#endif diff --git a/Source/Readers/SparsePCReader/SparsePCReader.vcxproj b/Source/Readers/SparsePCReader/SparsePCReader.vcxproj index db66c6d311d5..5d03f73484d7 100644 --- a/Source/Readers/SparsePCReader/SparsePCReader.vcxproj +++ b/Source/Readers/SparsePCReader/SparsePCReader.vcxproj @@ -1,156 +1,156 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {CE429AA2-3778-4619-8FD1-49BA3B81197B} - - - - - - - - - Win32Proj - SparsePCReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - NotUsing - Level4 - Disabled - _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;SparsePCREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - /bigobj %(AdditionalOptions) - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;SparsePCREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - false - - - false - - - false - - - false - - - - - - - - - - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - - - - Use - - - - Create - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {CE429AA2-3778-4619-8FD1-49BA3B81197B} + + + + + + + + + Win32Proj + SparsePCReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + c:\Program Files\Microsoft MPI\Inc;..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + c:\Program Files\Microsoft MPI\Lib\amd64;$(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + NotUsing + Level4 + Disabled + _CRT_SECURE_NO_WARNINGS;WIN32;_DEBUG;_WINDOWS;_USRDLL;SparsePCREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + /bigobj %(AdditionalOptions) + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + _CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_WINDOWS;_USRDLL;SparsePCREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + false + + + false + + + false + + + false + + + + + + + + + + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + + + + Use + + + + Create + + + + + \ No newline at end of file diff --git a/Source/Readers/SparsePCReader/SparsePCReader.vcxproj.filters b/Source/Readers/SparsePCReader/SparsePCReader.vcxproj.filters index 2271cda7b645..452f0874cfbf 100644 --- a/Source/Readers/SparsePCReader/SparsePCReader.vcxproj.filters +++ b/Source/Readers/SparsePCReader/SparsePCReader.vcxproj.filters @@ -1,84 +1,84 @@ - - - - - {4FC737F1-C7A5-4376-A066-2A32D752A2FF} - cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx - - - {93995380-89BD-4b04-88EB-625FBE52EBFB} - h;hh;hpp;hxx;hm;inl;inc;xsd - - - {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} - rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms - - - {b032f325-ad9c-49b8-8607-4ad5b4130f4f} - - - {28ed042e-a64c-4248-ad9c-2b9e82bef354} - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Header Files - - - Header Files - - - Header Files - - - Common\Include - - + + + + + {4FC737F1-C7A5-4376-A066-2A32D752A2FF} + cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx + + + {93995380-89BD-4b04-88EB-625FBE52EBFB} + h;hh;hpp;hxx;hm;inl;inc;xsd + + + {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} + rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms + + + {b032f325-ad9c-49b8-8607-4ad5b4130f4f} + + + {28ed042e-a64c-4248-ad9c-2b9e82bef354} + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + Source Files + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Header Files + + + Header Files + + + Header Files + + + Common\Include + + \ No newline at end of file diff --git a/Source/Readers/UCIFastReader/UCIFastReader.cpp b/Source/Readers/UCIFastReader/UCIFastReader.cpp index 2d9addff0382..86042c5a55c5 100644 --- a/Source/Readers/UCIFastReader/UCIFastReader.cpp +++ b/Source/Readers/UCIFastReader/UCIFastReader.cpp @@ -1,1132 +1,1132 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// UCIFastReader.cpp : Defines the exported functions for the DLL application. -// - -#include "stdafx.h" -#define DATAREADER_EXPORTS // creating the exports here -#include "DataReader.h" -#include "UCIFastReader.h" -#ifdef LEAKDETECT -#include // leak detection -#endif -#include "fileutil.h" // for fexists() - -namespace Microsoft { namespace MSR { namespace CNTK { - -template -size_t UCIFastReader::RandomizeSweep(size_t mbStartSample) -{ - //size_t randomRangePerEpoch = (m_epochSize+m_randomizeRange-1)/m_randomizeRange; - //return m_epoch*randomRangePerEpoch + epochSample/m_randomizeRange; - return mbStartSample/m_randomizeRange; -} - -// ReadLine - Read a line -// readSample - sample to read in global sample space -// returns - true if we successfully read a record, otherwise false -template -bool UCIFastReader::ReadRecord(size_t /*readSample*/) -{ - return false; // not used -} - -// RecordsToRead - Determine number of records to read to populate record buffers -// mbStartSample - the starting sample from which to read -// tail - we are checking for possible remainer records to read (default false) -// returns - true if we have more to read, false if we hit the end of the dataset -template -size_t UCIFastReader::RecordsToRead(size_t mbStartSample, bool tail) -{ - assert(mbStartSample >= m_epochStartSample); - // determine how far ahead we need to read - bool randomize = Randomize(); - // need to read to the end of the next minibatch - size_t epochSample = mbStartSample; - epochSample %= m_epochSize; - - // determine number left to read for this epoch - size_t numberToEpoch = m_epochSize - epochSample; - // we will take either a minibatch or the number left in the epoch - size_t numberToRead = min(numberToEpoch, m_mbSize); - if (numberToRead == 0 && !tail) - numberToRead = m_mbSize; - - if (randomize) - { - size_t randomizeSweep = RandomizeSweep(mbStartSample); - // if first read or read takes us to another randomization range - // we need to read at least randomization range records - if (randomizeSweep != m_randomordering.CurrentSeed()) // the range has changed since last time - { - numberToRead = RoundUp(epochSample, m_randomizeRange) - epochSample; - if (numberToRead == 0 && !tail) - numberToRead = m_randomizeRange; - } - } - return numberToRead; -} - -// EnsureDataAvailable - Read enough lines so we can request a minibatch starting as requested -// mbStartSample - the starting sample we are ensureing are good -// endOfDataCheck - check if we are at the end of the dataset (no wraparound) -// returns - true if we have more to read, false if we hit the end of the dataset -template -bool UCIFastReader::EnsureDataAvailable(size_t mbStartSample, bool endOfDataCheck) -{ - assert(mbStartSample >= m_epochStartSample); - // determine how far ahead we need to read - Randomize(); - // need to read to the end of the next minibatch - size_t epochSample = mbStartSample; - epochSample %= m_epochSize; - bool moreToRead = true; - - size_t numberToRead = RecordsToRead(mbStartSample); - - // check to see if we have the proper records read already - if (m_readNextSample >= mbStartSample+numberToRead && mbStartSample >= m_epochStartSample) - return true; - - // truncate the present arrays to the location we are reading from, parser appends on these arrays - if (m_featureData.size() > epochSample*m_featureCount) // should be this size, if not, truncate - m_featureData.resize(epochSample*m_featureCount); - if (m_labelType != labelNone && m_labelData.size() > epochSample) - { - // make sure the labelId array is also the correct size - if (m_labelType == labelCategory) - m_labelIdData.resize(epochSample); - m_labelData.resize(epochSample); - } - - int recordsRead = 0; - do - { - int numRead = m_parser.Parse(numberToRead-recordsRead, &m_featureData, &m_labelData); - - recordsRead += numRead; - if (!m_endReached) - m_totalSamples += numRead; // total number of records in the dataset - - // we should only get less records than requested at when we hit the end of the dataset - if (recordsRead < numberToRead) - { - // update dataset variables - size_t additionalToRead = UpdateDataVariables(mbStartSample+recordsRead); - - m_parser.SetFilePosition(0); // make another pass of the dataset - - // if doing and end of data check, and we are at the end - // or a partial minibatch was found exit now - if ((endOfDataCheck && recordsRead == 0) || - (m_partialMinibatch && recordsRead > 0)) - { - moreToRead = false; - break; - } - - // get the additional number to read - numberToRead = recordsRead + additionalToRead; - } - } - while (recordsRead < numberToRead); - m_readNextSample += recordsRead; - - // for category labels, we need to build up a list of IDs and a mapping table - if (m_labelType == labelCategory) - { - // loop through all the newly read records - for (int numberRead=0; numberRead < recordsRead; numberRead++) - { - LabelType& label = m_labelData[epochSample+numberRead]; - // check to see if we have seen this label before - auto value = m_mapLabelToId.find(label); - LabelIdType labelId; - if (value == m_mapLabelToId.end()) - { - if (m_labelFileToWrite.empty()) - RuntimeError("label found in data not specified in label mapping file: %s", label.c_str()); - // new label so add it to the mapping tables - m_mapLabelToId[label] = m_labelIdMax; - m_mapIdToLabel[m_labelIdMax] = label; - labelId = m_labelIdMax++; - - // if our label dimension is lower than the current labelId then increase it - if (m_labelDim < m_labelIdMax) - m_labelDim = m_labelIdMax; - } - else - { - labelId = value->second; - } - - // now add the label id to the label data array - m_labelIdData.push_back(labelId); - } - } - // if there more to read (always is, unless we want partial minibatches - return moreToRead; -} - -// UpdateDataVariables - Update variables that depend on the dataset being completely read -template -size_t UCIFastReader::UpdateDataVariables(size_t mbStartSample) -{ - // if we haven't been all the way through the file yet - if (!m_endReached) - { - // get the size of the dataset - assert(m_totalSamples*m_featureCount >= m_featureData.size()); - - // if they want us to determine epoch size based on dataset size, do that - if (m_epochSize == requestDataSize) - { - // set the epoch size to be a multiple of mbSize or randomization range - if (m_partialMinibatch) - m_epochSize = m_totalSamples; - else - { - size_t roundUpTo = m_mbSize; - if (m_randomizeRange != randomizeAuto && m_randomizeRange != randomizeNone) - roundUpTo = m_randomizeRange; - m_epochSize = RoundUp(m_totalSamples, roundUpTo); - } - } - - // make sure randomization range is within the sample bounds - if (m_randomizeRange > m_epochSize) - { - m_randomizeRange = m_epochSize; - m_randomordering.Resize(m_randomizeRange,m_randomizeRange); - } - - // write the label file if we hit the end of the file - WriteLabelFile(); - - // we got to the end of the dataset - m_endReached = true; - } - - // update the label dimension if it is not big enough, need it here because m_labelIdMax get's updated in the processing loop (after a read) - if (m_labelType == labelCategory && m_labelIdMax > m_labelDim) - m_labelDim = m_labelIdMax; // update the label dimensions if different - - bool recordsToRead = mbStartSample < m_epochStartSample + m_epochSize; // still some to read after potential epochSize change? - return recordsToRead?RecordsToRead(mbStartSample):0; -} - -template -void UCIFastReader::WriteLabelFile() -{ - // write out the label file if they don't have one - if (!m_labelFileToWrite.empty()) - { - if (m_mapIdToLabel.size() > 0) - { - File labelFile(m_labelFileToWrite, fileOptionsWrite | fileOptionsText); - for (int i=0; i < m_mapIdToLabel.size(); ++i) - { - labelFile << m_mapIdToLabel[i] << '\n'; - } - fprintf(stderr, "label file %ls written to disk\n", m_labelFileToWrite.c_str()); - m_labelFileToWrite.clear(); - } - else if (!m_cachingWriter) - { - fprintf(stderr, "WARNING: file %ls NOT written to disk yet, will be written the first time the end of the entire dataset is found.\n", m_labelFileToWrite.c_str()); - } - } -} - -// Destroy - cleanup and remove this class -// NOTE: this destroys the object, and it can't be used past this point -template -void UCIFastReader::Destroy() -{ - delete this; -} - -// Init - Reader Initialize for multiple data sets -// config - [in] configuration parameters for the datareader -// Sample format below: -//# Parameter values for the reader -//reader=[ -// # reader to use -// readerType=UCIFastReader -// miniBatchMode=Partial -// randomize=None -// features=[ -// dim=784 -// start=1 -// file=c:\speech\mnist\mnist_test.txt -// ] -// labels=[ -// dim=1 -// start=0 -// file=c:\speech\mnist\mnist_test.txt -// labelMappingFile=c:\speech\mnist\labels.txt -// labelDim=10 -// labelType=Category -// ] -//] -template -template -void UCIFastReader::InitFromConfig(const ConfigRecordType & readerConfig) -{ - // See if the user wants caching - m_cachingReader = NULL; - m_cachingWriter = NULL; - - // initialize the cache - InitCache(readerConfig); - - // if we have a cache, no need to parse the text files... - if (m_cachingReader) - return; - - std::vector features; - std::vector labels; - GetFileConfigNames(readerConfig, features, labels); - if (features.size() > 0) - { - m_featuresName = features[0]; - } - if (labels.size() > 0) - { - m_labelsName = labels[0]; - } - if (!readerConfig.Exists(m_featuresName)) - RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); - bool hasLabels = readerConfig.Exists(m_labelsName); - if (!hasLabels) - fprintf(stderr, "Warning: labels are not specified."); - const ConfigRecordType & configFeatures = readerConfig(m_featuresName.c_str(), ConfigRecordType::Record()); - const ConfigRecordType & configLabels = readerConfig(m_labelsName.c_str(), ConfigRecordType::Record()); - if (configFeatures(L"file", L"") != configLabels(L"file", L"")) - RuntimeError("features and label files must be the same file, use separate readers to define single use files"); - - size_t vdim = configFeatures(L"dim"); - //string name = configFeatures.Name(); // TODO: Aaargh!!! - size_t udim = configLabels(L"labelDim", (size_t)0); - - // initialize all the variables - m_mbStartSample = m_epoch = m_totalSamples = m_epochStartSample = 0; - m_labelIdMax = m_labelDim = 0; - m_partialMinibatch = m_endReached = false; - m_labelType = labelCategory; - m_featureCount = vdim; - m_readNextSample = 0; - m_traceLevel = readerConfig(L"traceLevel", 0); - m_parser.SetTraceLevel(m_traceLevel); - - m_prefetchEnabled = readerConfig(L"prefetch", false); - // set the feature count to at least one (we better have one feature...) - assert (m_featureCount != 0); - - if (readerConfig.Exists(L"randomize")) - { - string randomizeString = readerConfig(L"randomize"); - if (!_stricmp(randomizeString.c_str(), "none")) - { - m_randomizeRange = randomizeNone; - } - else if (!_stricmp(randomizeString.c_str(), "auto")) - { - m_randomizeRange = randomizeAuto; - } - else - { - m_randomizeRange = readerConfig(L"randomize"); - } - } - else - { - m_randomizeRange = randomizeAuto; - } - - // determine if we partial minibatches are desired - std::string minibatchMode(readerConfig(L"minibatchMode","partial")); - m_partialMinibatch = !_stricmp(minibatchMode.c_str(),"partial"); - - // get start and dimensions for labels and features - size_t startLabels = configLabels(L"start", (size_t)0); - size_t dimLabels = configLabels(L"dim", (size_t)0); - - size_t startFeatures = configFeatures(L"start", (size_t)0); - size_t dimFeatures = configFeatures(L"dim", (size_t)0); - - // determine label type desired - std::wstring labelType; - if (!hasLabels) - labelType = L"none"; - else - labelType = (wstring)configLabels(L"labelType", L"category"); - - //convert to lower case for case insensitive comparison - if (!_wcsicmp(labelType.c_str(), L"category")) - { - m_labelType = labelCategory; - } - else if (!_wcsicmp(labelType.c_str(), L"regression")) - { - m_labelType = labelRegression; - } - else if (!_wcsicmp(labelType.c_str(), L"none")) - { - m_labelType = labelNone; - dimLabels = 0; // override for no labels - } - - std::wstring file = configFeatures(L"file"); - if (m_traceLevel > 0) - fprintf(stderr, "Reading UCI file %ls\n", file.c_str()); - - // Simple heuristic to ensure buffer size and avoid breaking existing experiments. - size_t bufSize = max(dimFeatures * 16, (size_t)256 * 1024); - m_parser.ParseInit(file.c_str(), startFeatures, dimFeatures, startLabels, dimLabels, bufSize); - - // if we have labels, we need a label Mapping file, it will be a file with one label per line - if (m_labelType != labelNone) - { - std::wstring labelPath = configLabels(L"labelMappingFile"); - if (fexists(labelPath)) - { - // TODO: We use the old CNTK config reader for this. With BrainScript, we would have to parse the file locally here, which should be easy. - ConfigArray arrayLabels; - arrayLabels.LoadConfigFile(labelPath); - for (int i=0; i < arrayLabels.size(); ++i) - { - LabelType label = arrayLabels[i]; - m_mapIdToLabel[i] = label; - m_mapLabelToId[label] = i; - } - m_labelIdMax = (LabelIdType)arrayLabels.size(); - } - else - { - // only do label creation if we have the allow flag, should only be done as a separate command - // to ensure that the label file will exist for verification step in training - bool allowLabelCreation = readerConfig(L"allowMapCreation", false); - if (allowLabelCreation) - m_labelFileToWrite = labelPath; - else - RuntimeError("label mapping file %ls not found, can be created with a 'createLabelMap' command/action\n", labelPath.c_str()); - } - } - - // if we know the size of the randomization now, resize, otherwise wait until we know the epochSize in StartMinibatchLoop() - if (Randomize() && m_randomizeRange != randomizeAuto) - m_randomordering.Resize(m_randomizeRange, m_randomizeRange); - - // if the value they passed in as udim is not big enough, add something on - if (udim < m_labelIdMax) - udim = m_labelIdMax; - m_labelDim = (LabelIdType)udim; - - mOneLinePerFile = readerConfig(L"oneLinePerFile", false); -} - -// InitCache - Initialize the caching reader if cache files exist, otherwise the writer -// readerConfig - reader configuration -template -void UCIFastReader::InitCache(const ScriptableObjects::IConfigRecord & readerConfig) -{ - // check for a writer tag first (lets us know we are caching) - if (readerConfig.Exists(L"writerType")) - InvalidArgument("UCIFastReader: Caching ('writerType') is currently not supported for BrainScript."); - // BrainScript cannot support this because of the manipulations of ConfigRecords, which the ScriptableObjects interface does not support (IConigRecords are immutable). - // TODO: We could implement an overlay IConfigRecord implementation that fakes the two values that are being added to the interface. - // BrainScript also cannot support this because a copy of the readerConfig is kept. IConfigRecords are not copyable. - // TODO: We could copy the IConfigRecordPtr. That is allowed. Not trivial to do with template magic. -} -template -void UCIFastReader::InitCache(const ConfigParameters& readerConfig) -{ - // first time we are called we cache our parameters - // Note: This is a little ugly. It is an artifact of integrating with BrainScript. Since BrainScript does not allow to copy configs, I moved that copy code here into the ConfigParameters implementation. - if (&readerConfig != &m_readerConfig) - readerConfig.CopyTo(m_readerConfig); - - // check for a writer tag first (lets us know we are caching) - if (!readerConfig.Exists(L"writerType")) - return; - - // first try to open the binary cache - bool found = false; - try - { - // TODO: need to go down to all levels, maybe search for sectionType - ConfigArray filesList(','); - vector names; - if (readerConfig.Exists(L"wfile")) - { - filesList.push_back(readerConfig(L"wfile")); - if (fexists(readerConfig(L"wfile"))) - found = true; - } - FindConfigNames(readerConfig, "wfile", names); - for (const auto & name : names) - { - ConfigParameters config = readerConfig(name); - filesList.push_back(config("wfile")); - if (fexists(config("wfile"))) - found = true; - } - - // if we have a file already, we are going to read the cached files - if (found) - { - ConfigParameters config; - readerConfig.CopyTo(config); - // mmodify the config so the reader types look correct - config["readerType"] = config("writerType"); - config["file"] = filesList; - m_cachingReader = new DataReader(config); - } - else - { - m_cachingWriter = new DataWriter(readerConfig); - - // now get the section names for map and category types - std::map sections; - m_cachingWriter->GetSections(sections); - for (const auto & pair : sections) - { - if (pair.second == sectionTypeCategoryLabel) - { - m_labelsCategoryName = pair.first; - } - else if (pair.second == sectionTypeLabelMapping) - { - m_labelsMapName = pair.first; - } - } - } - } - catch (runtime_error err) - { - // In case caching reader/writer cannot be created, we gracefully fail over and disable caching. - fprintf(stderr,"Error attemping to create Binary%s\n%s\n",found?"Reader":"Writer",err.what()); - delete m_cachingReader; - m_cachingReader = NULL; - delete m_cachingWriter; - m_cachingWriter = NULL; - } - catch (...) - { - // if there is any error, just get rid of the object - fprintf(stderr,"Error attemping to create Binary%s\n",found?"Reader":"Writer"); - delete m_cachingReader; - m_cachingReader = NULL; - delete m_cachingWriter; - m_cachingWriter = NULL; - } -} - -// destructor - virtual so it gets called properly -template -UCIFastReader::~UCIFastReader() -{ - ReleaseMemory(); - delete m_cachingReader; - delete m_cachingWriter; -} - -// ReleaseMemory - release the memory footprint of UCIFastReader -// used when the caching reader is taking over -template -void UCIFastReader::ReleaseMemory() -{ - m_featuresBuffer=NULL; - m_labelsBuffer=NULL; - m_labelsIdBuffer=NULL; - m_featureData.clear(); - m_labelIdData.clear(); - m_labelData.clear(); -} - -//SetupEpoch - Setup the proper position in the file, and other variable settings to start a particular epoch -template -void UCIFastReader::SetupEpoch() -{ - // if we are starting fresh (epoch zero and no data read), init everything - // however if we are using cachingWriter, we need to know record count, so do that first - if (m_epoch == 0 && m_totalSamples == 0 && m_cachingWriter != NULL) - { - m_readNextSample = m_epochStartSample = m_mbStartSample = 0; - m_parser.SetFilePosition(0); - } - else // otherwise, position the read to start at the right location - { - // don't know the total number of samples yet, so count them - if (m_totalSamples == 0) - { - if (m_traceLevel > 0) - fprintf(stderr, "starting at epoch %lu counting lines to determine record count\n", (unsigned long)m_epoch); - m_parser.SetParseMode(ParseLineCount); - m_totalSamples = m_parser.Parse(size_t(-1), NULL, NULL); - m_parser.SetParseMode(ParseNormal); - m_parser.SetFilePosition(0); - m_mbStartSample = 0; - UpdateDataVariables(0); // update all the variables since we read to the end... - if (m_traceLevel > 0) - fprintf(stderr, "\n %lu records found\n", (unsigned long)m_totalSamples); - } - - // make sure we are in the correct location for mid-dataset epochs - size_t mbStartSample = m_epoch * m_epochSize; - - size_t fileRecord = m_totalSamples?mbStartSample % m_totalSamples:0; - fprintf(stderr, "starting epoch %lu at record count %lu, and file position %lu\n", (unsigned long)m_epoch, (unsigned long)mbStartSample, (unsigned long)fileRecord); - size_t currentFileRecord = m_mbStartSample % m_totalSamples; - - // reset the next read sample - m_readNextSample = mbStartSample; - if (currentFileRecord == fileRecord) - { - fprintf(stderr, "already there from last epoch\n"); - - // we have a slight delima here, if we haven't determined the end of the file yet - // and the user told us to find how many records are in the file, we can't distinguish "almost done" - // with a file (a character away) and the middle of the file. So read ahead a record to see if it's there. - bool endReached = m_endReached; - if (!endReached) - { - if (!m_parser.HasMoreData()) - { - endReached = true; - UpdateDataVariables(mbStartSample); - assert(m_endReached); - } - } - // move the read pointer to the end since we have everything already in memory. - if (endReached && m_epochStartSample % m_totalSamples == fileRecord - && m_featureData.size() >= m_epochSize*m_featureCount) - { - m_readNextSample = mbStartSample + m_epochSize; - // write the label file here to make sure we do it somewhere. We know the entire dataset has been read at this point - WriteLabelFile(); - } - - } - // not the right position, need to get there - else - { - // if we are already past the desired record, start at the beginning again - if (currentFileRecord > fileRecord) - { - m_parser.SetFilePosition(0); - currentFileRecord = 0; - } - fprintf(stderr, "reading from record %lu to %lu to be positioned properly for epoch\n", (unsigned long)currentFileRecord, (unsigned long)fileRecord); - m_parser.SetParseMode(ParseLineCount); - m_parser.Parse(fileRecord-currentFileRecord, NULL, NULL); - m_parser.SetParseMode(ParseNormal); - if (!m_labelFileToWrite.empty()) - { - fprintf(stderr, "WARNING: file %ls NOT written to disk, label file will only be written when starting epochs at the beginning of the dataset\n", m_labelFileToWrite.c_str()); - m_labelFileToWrite.clear(); - RuntimeError("LabelMappingFile not provided in config, must be provided if not starting from epoch Zero (0)"); - } - } - m_epochStartSample = m_mbStartSample = mbStartSample; - } -} - -// utility function to round an integer up to a multiple of size -size_t RoundUp(size_t value, size_t size) -{ - return ((value + size -1)/size)*size; -} - -template -void UCIFastReader::SetNumParallelSequences(const size_t sz) -{ - mRequestedNumParallelSequences = sz; - if (mOneLinePerFile) - m_mbSize = mRequestedNumParallelSequences; -}; - -//StartMinibatchLoop - Startup a minibatch loop -// mbSize - [in] size of the minibatch (number of Samples, etc.) -// epoch - [in] epoch number for this loop, if > 0 the requestedEpochSamples must be specified (unless epoch zero was completed this run) -// requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset -// this value must be a multiple of mbSize, if it is not, it will be rounded up to one. -template -void UCIFastReader::StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples /*= requestDataSize */) -{ - m_subsetNum = subsetNum; - m_numSubsets = numSubsets; - if (mOneLinePerFile) - mbSize = mRequestedNumParallelSequences; /// each file has only one observation, therefore the number of data to read is the number of files - - // if we aren't currently caching, see if we can use a cache - if (!m_cachingReader && !m_cachingWriter) - { - InitCache(m_readerConfig); - if (m_cachingReader) - ReleaseMemory(); // free the memory used by the UCIFastReader - } - - // if we are reading from the cache, do so now and return - if (m_cachingReader) - { - m_cachingReader->StartMinibatchLoop(mbSize, epoch, requestedEpochSamples); - return; - } - - // if we are reallocating bigger, release the original - if (mbSize > m_mbSize) - { - m_featuresBuffer = NULL; - m_labelsBuffer = NULL; - m_labelsIdBuffer = NULL; - } - - m_mbSize = mbSize; - if (requestedEpochSamples == requestDataSize) - { - if (!m_endReached) - { - m_epochSize = requestDataSize; - } - } - else - { - m_epochSize = requestedEpochSamples; - if (!m_partialMinibatch) - m_epochSize = RoundUp(requestedEpochSamples, mbSize); - if (m_epochSize != requestedEpochSamples) - fprintf(stderr, "epochSize rounded up to %d to fit an integral number of minibatches\n", (int)m_epochSize); - } - - // set the randomization range for randomizationAuto - // or if it's invalid less than the minibatch size, we need to make it at least minibatch size - if (m_randomizeRange != randomizeNone) - { - if (m_epochSize != requestDataSize && m_randomizeRange == randomizeAuto) - { - m_randomizeRange = m_epochSize; - } - m_randomizeRange = max(m_randomizeRange, m_mbSize); - if (m_randomizeRange != randomizeAuto) - { - if ((m_epochSize != requestDataSize && m_epochSize % m_randomizeRange != 0) || (m_randomizeRange % m_mbSize != 0)) - RuntimeError("randomizeRange must be an even multiple of mbSize and an integral factor of epochSize"); - m_randomordering.Resize(m_randomizeRange, m_randomizeRange); - } - } - - // we use epochSize, which might not be set yet, so use a default value for allocations if not yet set - size_t epochSize = m_epochSize == requestDataSize?1000:m_epochSize; - m_epoch = epoch; - m_mbStartSample = epoch*m_epochSize; - - // allocate room for the data - m_featureData.reserve(m_featureCount*epochSize); - if (m_labelType == labelCategory) - m_labelIdData.reserve(epochSize); - else if (m_labelType != labelNone) - m_labelData.reserve(epochSize); - - SetupEpoch(); -} - -// function to store the LabelType in an ElemType -// required for string labels, which can't be stored in ElemType arrays -template -void UCIFastReader::StoreLabel(ElemType& labelStore, const LabelType& labelValue) -{ - labelStore = (ElemType)m_mapLabelToId[labelValue]; -} - -// GetMinibatch - Get the next minibatch (features and labels) -// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, -// [out] each matrix resized if necessary containing data. -// returns - true if there are more minibatches, false if no more minibatchs remain -template -bool UCIFastReader::GetMinibatch(std::map*>& matrices) -{ - bool minibatchesRemaining = true; - if (m_pendingAsyncGetMinibatch.valid()) - { - // An async GetMinibatch is in flight. Wait for it to finish and swap - // the contents of the m_prefetchedMatrices and parameter matrices - minibatchesRemaining = m_pendingAsyncGetMinibatch.get(); - - // Now swap the m_prefetchedMatrices and parameter matrices - for (auto iter = matrices.begin(); iter != matrices.end(); ++iter) - { - if (m_prefetchMatrices.find(iter->first) == m_prefetchMatrices.end()) - { - LogicError("No mathing prefetch matrix found for matrix named %S!", iter->first.c_str()); - } - - Matrix* prefetchMatrix = m_prefetchMatrices[iter->first].get(); - std::swap(*(iter->second), *prefetchMatrix); - } - } - else - { - minibatchesRemaining = GetMinibatchImpl(matrices); - - // Allocate prefetch matrices if we would be firing an async minibatch prefetch - if (minibatchesRemaining && m_prefetchEnabled) - { - // DeAllocate the existing m_prefetchMatrices - m_prefetchMatrices.clear(); - - for (auto iter = matrices.begin(); iter != matrices.end(); ++iter) - { - m_prefetchMatrices[iter->first].reset(new Matrix(iter->second->GetDeviceId())); - } - } - } - - // Fire a new prefetch if there are any minibatches remaining - if (minibatchesRemaining && m_prefetchEnabled) - { - Matrix& features = *matrices[m_featuresName]; - int deviceId = features.GetDeviceId(); - m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this, deviceId]() - { - // Set the device since this will execute on a new thread - Matrix::SetDevice(deviceId); - - std::map*> prefetchMatrices; - for (auto iter = m_prefetchMatrices.begin(); iter != m_prefetchMatrices.end(); ++iter) - { - prefetchMatrices[iter->first] = iter->second.get(); - } - - return GetMinibatchImpl(prefetchMatrices); - }); - } - - return minibatchesRemaining; -} - -// GetMinibatchImpl - The actual implementation of getting the next minibatch (features and labels) -// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, -// [out] each matrix resized if necessary containing data. -// returns - true if there are more minibatches, false if no more minibatchs remain -template -bool UCIFastReader::GetMinibatchImpl(std::map*>& matrices) -{ - if (m_cachingReader) - { - return m_cachingReader->GetMinibatch(matrices); - } - // get the features array - if (matrices.find(m_featuresName) == matrices.end()) - RuntimeError("Features matrix not found in config file, there should be a section '%ls=[...]' in the configuration file.", m_featuresName.c_str()); - - Matrix& features = *matrices[m_featuresName]; - - // get out if they didn't call StartMinibatchLoop() first - if (m_mbSize == 0) - return false; - - // check to see if we have changed epochs, if so we are done with this one. - if (m_mbStartSample / m_epochSize != m_epoch) - return false; - - bool randomize = Randomize(); - bool moreData = EnsureDataAvailable(m_mbStartSample); - - // figure which sweep of the randomization we are on - size_t epochSample = m_mbStartSample % m_epochSize; // where the minibatch starts in this epoch - //size_t samplesExtra = m_totalSamples % m_epochSize; // extra samples at the end of an epoch - //size_t epochsDS = (m_totalSamples+m_epochSize-1)/m_epochSize; // how many epochs per dataset - size_t randomizeSet = randomize?RandomizeSweep(m_mbStartSample):0; - const auto & tmap = m_randomordering(randomizeSet); - size_t epochEnd = m_epochSize; - size_t recordStart = m_totalSamples?m_mbStartSample%m_totalSamples:m_mbStartSample; - - // actual size is either what requested, or total number of samples read so far - size_t actualmbsize = min(m_totalSamples, m_mbSize); // it may still return less if at end of sweep - - // check for an odd sized last minibatch - if (epochSample + actualmbsize > epochEnd) - { - actualmbsize = epochEnd - epochSample; - } - - // hit the end of the dataset, we should only get here in "one=pass mode" - if (!moreData) - { - // make sure we take into account hitting the end of the dataset (not wrapping around) - actualmbsize = min(m_totalSamples-recordStart,actualmbsize); - } - - if (m_featuresBuffer == NULL) - { - m_featuresBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_mbSize * m_featureCount); - memset(m_featuresBuffer.get(), 0, sizeof(ElemType) * m_mbSize * m_featureCount); - } - - if (m_labelsBuffer == NULL) - { - if (m_labelType == labelCategory) - { - m_labelsBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_labelDim * m_mbSize); - m_labelsIdBuffer = std::shared_ptr(new LabelIdType[m_mbSize], [](LabelIdType* p) { - delete[] p; - }); - } - else if (m_labelType != labelNone) - { - m_labelsBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_mbSize); - m_labelsIdBuffer = NULL; - } - } - - if (m_labelType == labelCategory) - { - memset(m_labelsBuffer.get(), 0, sizeof(ElemType)*m_labelDim*actualmbsize); - memset(m_labelsIdBuffer.get(), 0, sizeof(LabelIdType)*actualmbsize); - } - else if (m_labelType != labelNone) - { - memset(m_labelsBuffer.get(), 0, sizeof(ElemType) * 1 * actualmbsize); - } - - if (actualmbsize > 0) - { - // loop through and copy data to matrix - int j = 0; // vector of vectors of feature data - // determine randomization base index - size_t randBase = 0; // (keep compiler happy) - if (randomize) - randBase = epochSample - epochSample%m_randomizeRange; - - //loop through all the samples - for (size_t jSample = m_mbStartSample; j < actualmbsize; ++j, ++jSample) - { - // pick the right sample with randomization if desired - size_t jRand = randomize?(randBase + tmap[jSample%m_randomizeRange]):jSample; - jRand %= m_epochSize; - - // vector of feature data goes into matrix column - memcpy(&m_featuresBuffer.get()[j*m_featureCount], &m_featureData[jRand*m_featureCount], sizeof(ElemType)*m_featureCount); - - if (m_labelType == labelCategory) - { - m_labelsBuffer.get()[j*m_labelDim + m_labelIdData[jRand]] = (ElemType)1; - m_labelsIdBuffer.get()[j] = m_labelIdData[jRand]; - } - else if (m_labelType != labelNone) - { - if (m_labelType == labelRegression) - { - m_labelsBuffer.get()[j] = (ElemType)atof(m_labelData[jRand].c_str()); - } - else - { - StoreLabel(m_labelsBuffer.get()[j], m_labelData[jRand]); - } - } - } - } - - // There may be multiple parallel trainers reading at the same time in which case - // we will slice the data to only return the share of the current trainer's subset - size_t currSubsetStartCol = (actualmbsize * m_subsetNum) / m_numSubsets; - size_t currSubsetEndCol = (actualmbsize * (m_subsetNum + 1)) / m_numSubsets; - size_t currSubsetSize = currSubsetEndCol - currSubsetStartCol; - // create the respective MBLayout - // Every sample is returned as a sequence of 1 frame. - m_pMBLayout->InitAsFrameMode(currSubsetSize); - - // if we are writing out to the caching writer, do it now - if (m_cachingWriter && (m_subsetNum == 0)) - { - map writeBuffer; - writeBuffer[m_featuresName] = m_featuresBuffer.get(); - if (m_labelType == labelCategory) - { - writeBuffer[m_labelsName] = m_labelsIdBuffer.get(); - if (!m_labelsCategoryName.empty()) - writeBuffer[m_labelsCategoryName] = m_labelsBuffer.get(); - } - else if (m_labelType != labelNone) - { - writeBuffer[m_labelsName] = m_labelsBuffer.get(); - } - - // write out the data, on a second pass compute statistics as needed - bool moreToWrite = m_cachingWriter->SaveData(m_mbStartSample, writeBuffer, actualmbsize, m_totalSamples, 0); - - // done writing - if (!moreToWrite) - { - // write out the mapping table as necessary - if (m_labelType == labelCategory && !m_labelsMapName.empty()) - { - m_cachingWriter->SaveMapping(m_labelsMapName, m_mapIdToLabel); - } - - WriteLabelFile(); - - // now close the cache writer - delete m_cachingWriter; - m_cachingWriter = NULL; - } - } - - // advance to the next minibatch - m_mbStartSample += actualmbsize; - - // if they don't want partial minibatches, skip data transfer and return - if (actualmbsize < m_mbSize && !m_partialMinibatch - || actualmbsize == 0 || currSubsetSize == 0) // no records found (end of minibatch) - { - return false; - } - - // now transfer to the GPU as needed - features.SetValue(m_featureCount, currSubsetSize, features.GetDeviceId(), m_featuresBuffer.get() + (m_featureCount * currSubsetStartCol), matrixFlagNormal); - if (m_labelType == labelCategory) - { - auto labelEntry = matrices.find(m_labelsName); - if (labelEntry != matrices.end()) - { - Matrix* labels = labelEntry->second; - if (labels != nullptr) - labels->SetValue(m_labelDim, currSubsetSize, labels->GetDeviceId(), m_labelsBuffer.get() + (m_labelDim * currSubsetStartCol), matrixFlagNormal); - } - } - else if (m_labelType != labelNone) - { - auto labelEntry = matrices.find(m_labelsName); - if (labelEntry != matrices.end()) - { - Matrix* labels = labelEntry->second; - if (labels != nullptr) - labels->SetValue(1, currSubsetSize, labels->GetDeviceId(), m_labelsBuffer.get() + (1 * currSubsetStartCol), matrixFlagNormal); - } - } - // we read some records, so process them - return true; -} - -// GetLabelMapping - Gets the label mapping from integer index to label type -// returns - a map from numeric datatype to native label type -template -const std::map::LabelIdType, typename IDataReader::LabelType>& UCIFastReader::GetLabelMapping(const std::wstring& sectionName) -{ - if (m_cachingReader) - { - return m_cachingReader->GetLabelMapping(sectionName); - } - return m_mapIdToLabel; -} - -// SetLabelMapping - Sets the label mapping from integer index to label -// labelMapping - mapping table from label values to IDs (must be 0-n) -// note: for tasks with labels, the mapping table must be the same between a training run and a testing run -template -void UCIFastReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, LabelType>& labelMapping) -{ - if (m_cachingReader) - { - RuntimeError("Cannot set mapping table when the caching reader is being used"); - } - m_mapIdToLabel = labelMapping; - m_mapLabelToId.clear(); - for (std::pair var : labelMapping) - { - m_mapLabelToId[var.second] = var.first; - } -} - -// GetData - Gets metadata from the specified section (into CPU memory) -// sectionName - section name to retrieve data from -// numRecords - number of records to read -// data - pointer to data buffer, if NULL, dataBufferSize will be set to size of required buffer to accomidate request -// dataBufferSize - [in] size of the databuffer in bytes -// [out] size of buffer filled with data -// recordStart - record to start reading from, defaults to zero (start of data) -// returns: true if data remains to be read, false if the end of data was reached -template -bool UCIFastReader::GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart) -{ - if (m_cachingReader) - { - return m_cachingReader->GetData(sectionName, numRecords, data, dataBufferSize, recordStart); - } - RuntimeError("GetData not supported in UCIFastReader"); -} - -template -bool UCIFastReader::DataEnd(EndDataType endDataType) -{ - if (m_cachingReader) - { - return m_cachingReader->DataEnd(endDataType); - } - - bool ret = false; - switch (endDataType) - { - case endDataNull: - assert(false); - break; - case endDataEpoch: - ret = (m_mbStartSample / m_epochSize != m_epoch); - break; - case endDataSet: - ret = EnsureDataAvailable(m_mbStartSample, true); - break; - case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true - ret = true; - break; - } - return ret; -} - -template -unique_ptr& UCIFastReader::GetCUDAAllocator(int deviceID) -{ - if (m_cudaAllocator != nullptr) - { - if (m_cudaAllocator->GetDeviceId() != deviceID) - { - m_cudaAllocator.reset(nullptr); - } - } - - if (m_cudaAllocator == nullptr) - { - m_cudaAllocator.reset(new CUDAPageLockedMemAllocator(deviceID)); - } - - return m_cudaAllocator; -} - -template -std::shared_ptr UCIFastReader::AllocateIntermediateBuffer(int deviceID, size_t numElements) -{ - if (deviceID >= 0) - { - // Use pinned memory for GPU devices for better copy performance - size_t totalSize = sizeof(ElemType) * numElements; - return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { - this->GetCUDAAllocator(deviceID)->Free((char*)p); - }); - } - else - { - return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { - delete[] p; - }); - } -} - -// instantiate all the combinations we expect to be used -template class UCIFastReader; -template class UCIFastReader; -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// UCIFastReader.cpp : Defines the exported functions for the DLL application. +// + +#include "stdafx.h" +#define DATAREADER_EXPORTS // creating the exports here +#include "DataReader.h" +#include "UCIFastReader.h" +#ifdef LEAKDETECT +#include // leak detection +#endif +#include "fileutil.h" // for fexists() + +namespace Microsoft { namespace MSR { namespace CNTK { + +template +size_t UCIFastReader::RandomizeSweep(size_t mbStartSample) +{ + //size_t randomRangePerEpoch = (m_epochSize+m_randomizeRange-1)/m_randomizeRange; + //return m_epoch*randomRangePerEpoch + epochSample/m_randomizeRange; + return mbStartSample/m_randomizeRange; +} + +// ReadLine - Read a line +// readSample - sample to read in global sample space +// returns - true if we successfully read a record, otherwise false +template +bool UCIFastReader::ReadRecord(size_t /*readSample*/) +{ + return false; // not used +} + +// RecordsToRead - Determine number of records to read to populate record buffers +// mbStartSample - the starting sample from which to read +// tail - we are checking for possible remainer records to read (default false) +// returns - true if we have more to read, false if we hit the end of the dataset +template +size_t UCIFastReader::RecordsToRead(size_t mbStartSample, bool tail) +{ + assert(mbStartSample >= m_epochStartSample); + // determine how far ahead we need to read + bool randomize = Randomize(); + // need to read to the end of the next minibatch + size_t epochSample = mbStartSample; + epochSample %= m_epochSize; + + // determine number left to read for this epoch + size_t numberToEpoch = m_epochSize - epochSample; + // we will take either a minibatch or the number left in the epoch + size_t numberToRead = min(numberToEpoch, m_mbSize); + if (numberToRead == 0 && !tail) + numberToRead = m_mbSize; + + if (randomize) + { + size_t randomizeSweep = RandomizeSweep(mbStartSample); + // if first read or read takes us to another randomization range + // we need to read at least randomization range records + if (randomizeSweep != m_randomordering.CurrentSeed()) // the range has changed since last time + { + numberToRead = RoundUp(epochSample, m_randomizeRange) - epochSample; + if (numberToRead == 0 && !tail) + numberToRead = m_randomizeRange; + } + } + return numberToRead; +} + +// EnsureDataAvailable - Read enough lines so we can request a minibatch starting as requested +// mbStartSample - the starting sample we are ensureing are good +// endOfDataCheck - check if we are at the end of the dataset (no wraparound) +// returns - true if we have more to read, false if we hit the end of the dataset +template +bool UCIFastReader::EnsureDataAvailable(size_t mbStartSample, bool endOfDataCheck) +{ + assert(mbStartSample >= m_epochStartSample); + // determine how far ahead we need to read + Randomize(); + // need to read to the end of the next minibatch + size_t epochSample = mbStartSample; + epochSample %= m_epochSize; + bool moreToRead = true; + + size_t numberToRead = RecordsToRead(mbStartSample); + + // check to see if we have the proper records read already + if (m_readNextSample >= mbStartSample+numberToRead && mbStartSample >= m_epochStartSample) + return true; + + // truncate the present arrays to the location we are reading from, parser appends on these arrays + if (m_featureData.size() > epochSample*m_featureCount) // should be this size, if not, truncate + m_featureData.resize(epochSample*m_featureCount); + if (m_labelType != labelNone && m_labelData.size() > epochSample) + { + // make sure the labelId array is also the correct size + if (m_labelType == labelCategory) + m_labelIdData.resize(epochSample); + m_labelData.resize(epochSample); + } + + int recordsRead = 0; + do + { + int numRead = m_parser.Parse(numberToRead-recordsRead, &m_featureData, &m_labelData); + + recordsRead += numRead; + if (!m_endReached) + m_totalSamples += numRead; // total number of records in the dataset + + // we should only get less records than requested at when we hit the end of the dataset + if (recordsRead < numberToRead) + { + // update dataset variables + size_t additionalToRead = UpdateDataVariables(mbStartSample+recordsRead); + + m_parser.SetFilePosition(0); // make another pass of the dataset + + // if doing and end of data check, and we are at the end + // or a partial minibatch was found exit now + if ((endOfDataCheck && recordsRead == 0) || + (m_partialMinibatch && recordsRead > 0)) + { + moreToRead = false; + break; + } + + // get the additional number to read + numberToRead = recordsRead + additionalToRead; + } + } + while (recordsRead < numberToRead); + m_readNextSample += recordsRead; + + // for category labels, we need to build up a list of IDs and a mapping table + if (m_labelType == labelCategory) + { + // loop through all the newly read records + for (int numberRead=0; numberRead < recordsRead; numberRead++) + { + LabelType& label = m_labelData[epochSample+numberRead]; + // check to see if we have seen this label before + auto value = m_mapLabelToId.find(label); + LabelIdType labelId; + if (value == m_mapLabelToId.end()) + { + if (m_labelFileToWrite.empty()) + RuntimeError("label found in data not specified in label mapping file: %s", label.c_str()); + // new label so add it to the mapping tables + m_mapLabelToId[label] = m_labelIdMax; + m_mapIdToLabel[m_labelIdMax] = label; + labelId = m_labelIdMax++; + + // if our label dimension is lower than the current labelId then increase it + if (m_labelDim < m_labelIdMax) + m_labelDim = m_labelIdMax; + } + else + { + labelId = value->second; + } + + // now add the label id to the label data array + m_labelIdData.push_back(labelId); + } + } + // if there more to read (always is, unless we want partial minibatches + return moreToRead; +} + +// UpdateDataVariables - Update variables that depend on the dataset being completely read +template +size_t UCIFastReader::UpdateDataVariables(size_t mbStartSample) +{ + // if we haven't been all the way through the file yet + if (!m_endReached) + { + // get the size of the dataset + assert(m_totalSamples*m_featureCount >= m_featureData.size()); + + // if they want us to determine epoch size based on dataset size, do that + if (m_epochSize == requestDataSize) + { + // set the epoch size to be a multiple of mbSize or randomization range + if (m_partialMinibatch) + m_epochSize = m_totalSamples; + else + { + size_t roundUpTo = m_mbSize; + if (m_randomizeRange != randomizeAuto && m_randomizeRange != randomizeNone) + roundUpTo = m_randomizeRange; + m_epochSize = RoundUp(m_totalSamples, roundUpTo); + } + } + + // make sure randomization range is within the sample bounds + if (m_randomizeRange > m_epochSize) + { + m_randomizeRange = m_epochSize; + m_randomordering.Resize(m_randomizeRange,m_randomizeRange); + } + + // write the label file if we hit the end of the file + WriteLabelFile(); + + // we got to the end of the dataset + m_endReached = true; + } + + // update the label dimension if it is not big enough, need it here because m_labelIdMax get's updated in the processing loop (after a read) + if (m_labelType == labelCategory && m_labelIdMax > m_labelDim) + m_labelDim = m_labelIdMax; // update the label dimensions if different + + bool recordsToRead = mbStartSample < m_epochStartSample + m_epochSize; // still some to read after potential epochSize change? + return recordsToRead?RecordsToRead(mbStartSample):0; +} + +template +void UCIFastReader::WriteLabelFile() +{ + // write out the label file if they don't have one + if (!m_labelFileToWrite.empty()) + { + if (m_mapIdToLabel.size() > 0) + { + File labelFile(m_labelFileToWrite, fileOptionsWrite | fileOptionsText); + for (int i=0; i < m_mapIdToLabel.size(); ++i) + { + labelFile << m_mapIdToLabel[i] << '\n'; + } + fprintf(stderr, "label file %ls written to disk\n", m_labelFileToWrite.c_str()); + m_labelFileToWrite.clear(); + } + else if (!m_cachingWriter) + { + fprintf(stderr, "WARNING: file %ls NOT written to disk yet, will be written the first time the end of the entire dataset is found.\n", m_labelFileToWrite.c_str()); + } + } +} + +// Destroy - cleanup and remove this class +// NOTE: this destroys the object, and it can't be used past this point +template +void UCIFastReader::Destroy() +{ + delete this; +} + +// Init - Reader Initialize for multiple data sets +// config - [in] configuration parameters for the datareader +// Sample format below: +//# Parameter values for the reader +//reader=[ +// # reader to use +// readerType=UCIFastReader +// miniBatchMode=Partial +// randomize=None +// features=[ +// dim=784 +// start=1 +// file=c:\speech\mnist\mnist_test.txt +// ] +// labels=[ +// dim=1 +// start=0 +// file=c:\speech\mnist\mnist_test.txt +// labelMappingFile=c:\speech\mnist\labels.txt +// labelDim=10 +// labelType=Category +// ] +//] +template +template +void UCIFastReader::InitFromConfig(const ConfigRecordType & readerConfig) +{ + // See if the user wants caching + m_cachingReader = NULL; + m_cachingWriter = NULL; + + // initialize the cache + InitCache(readerConfig); + + // if we have a cache, no need to parse the text files... + if (m_cachingReader) + return; + + std::vector features; + std::vector labels; + GetFileConfigNames(readerConfig, features, labels); + if (features.size() > 0) + { + m_featuresName = features[0]; + } + if (labels.size() > 0) + { + m_labelsName = labels[0]; + } + if (!readerConfig.Exists(m_featuresName)) + RuntimeError("features file not found, required in configuration: i.e. 'features=[file=c:\\myfile.txt;start=1;dim=123]'"); + bool hasLabels = readerConfig.Exists(m_labelsName); + if (!hasLabels) + fprintf(stderr, "Warning: labels are not specified."); + const ConfigRecordType & configFeatures = readerConfig(m_featuresName.c_str(), ConfigRecordType::Record()); + const ConfigRecordType & configLabels = readerConfig(m_labelsName.c_str(), ConfigRecordType::Record()); + if (configFeatures(L"file", L"") != configLabels(L"file", L"")) + RuntimeError("features and label files must be the same file, use separate readers to define single use files"); + + size_t vdim = configFeatures(L"dim"); + //string name = configFeatures.Name(); // TODO: Aaargh!!! + size_t udim = configLabels(L"labelDim", (size_t)0); + + // initialize all the variables + m_mbStartSample = m_epoch = m_totalSamples = m_epochStartSample = 0; + m_labelIdMax = m_labelDim = 0; + m_partialMinibatch = m_endReached = false; + m_labelType = labelCategory; + m_featureCount = vdim; + m_readNextSample = 0; + m_traceLevel = readerConfig(L"traceLevel", 0); + m_parser.SetTraceLevel(m_traceLevel); + + m_prefetchEnabled = readerConfig(L"prefetch", false); + // set the feature count to at least one (we better have one feature...) + assert (m_featureCount != 0); + + if (readerConfig.Exists(L"randomize")) + { + string randomizeString = readerConfig(L"randomize"); + if (!_stricmp(randomizeString.c_str(), "none")) + { + m_randomizeRange = randomizeNone; + } + else if (!_stricmp(randomizeString.c_str(), "auto")) + { + m_randomizeRange = randomizeAuto; + } + else + { + m_randomizeRange = readerConfig(L"randomize"); + } + } + else + { + m_randomizeRange = randomizeAuto; + } + + // determine if we partial minibatches are desired + std::string minibatchMode(readerConfig(L"minibatchMode","partial")); + m_partialMinibatch = !_stricmp(minibatchMode.c_str(),"partial"); + + // get start and dimensions for labels and features + size_t startLabels = configLabels(L"start", (size_t)0); + size_t dimLabels = configLabels(L"dim", (size_t)0); + + size_t startFeatures = configFeatures(L"start", (size_t)0); + size_t dimFeatures = configFeatures(L"dim", (size_t)0); + + // determine label type desired + std::wstring labelType; + if (!hasLabels) + labelType = L"none"; + else + labelType = (wstring)configLabels(L"labelType", L"category"); + + //convert to lower case for case insensitive comparison + if (!_wcsicmp(labelType.c_str(), L"category")) + { + m_labelType = labelCategory; + } + else if (!_wcsicmp(labelType.c_str(), L"regression")) + { + m_labelType = labelRegression; + } + else if (!_wcsicmp(labelType.c_str(), L"none")) + { + m_labelType = labelNone; + dimLabels = 0; // override for no labels + } + + std::wstring file = configFeatures(L"file"); + if (m_traceLevel > 0) + fprintf(stderr, "Reading UCI file %ls\n", file.c_str()); + + // Simple heuristic to ensure buffer size and avoid breaking existing experiments. + size_t bufSize = max(dimFeatures * 16, (size_t)256 * 1024); + m_parser.ParseInit(file.c_str(), startFeatures, dimFeatures, startLabels, dimLabels, bufSize); + + // if we have labels, we need a label Mapping file, it will be a file with one label per line + if (m_labelType != labelNone) + { + std::wstring labelPath = configLabels(L"labelMappingFile"); + if (fexists(labelPath)) + { + // TODO: We use the old CNTK config reader for this. With BrainScript, we would have to parse the file locally here, which should be easy. + ConfigArray arrayLabels; + arrayLabels.LoadConfigFile(labelPath); + for (int i=0; i < arrayLabels.size(); ++i) + { + LabelType label = arrayLabels[i]; + m_mapIdToLabel[i] = label; + m_mapLabelToId[label] = i; + } + m_labelIdMax = (LabelIdType)arrayLabels.size(); + } + else + { + // only do label creation if we have the allow flag, should only be done as a separate command + // to ensure that the label file will exist for verification step in training + bool allowLabelCreation = readerConfig(L"allowMapCreation", false); + if (allowLabelCreation) + m_labelFileToWrite = labelPath; + else + RuntimeError("label mapping file %ls not found, can be created with a 'createLabelMap' command/action\n", labelPath.c_str()); + } + } + + // if we know the size of the randomization now, resize, otherwise wait until we know the epochSize in StartMinibatchLoop() + if (Randomize() && m_randomizeRange != randomizeAuto) + m_randomordering.Resize(m_randomizeRange, m_randomizeRange); + + // if the value they passed in as udim is not big enough, add something on + if (udim < m_labelIdMax) + udim = m_labelIdMax; + m_labelDim = (LabelIdType)udim; + + mOneLinePerFile = readerConfig(L"oneLinePerFile", false); +} + +// InitCache - Initialize the caching reader if cache files exist, otherwise the writer +// readerConfig - reader configuration +template +void UCIFastReader::InitCache(const ScriptableObjects::IConfigRecord & readerConfig) +{ + // check for a writer tag first (lets us know we are caching) + if (readerConfig.Exists(L"writerType")) + InvalidArgument("UCIFastReader: Caching ('writerType') is currently not supported for BrainScript."); + // BrainScript cannot support this because of the manipulations of ConfigRecords, which the ScriptableObjects interface does not support (IConigRecords are immutable). + // TODO: We could implement an overlay IConfigRecord implementation that fakes the two values that are being added to the interface. + // BrainScript also cannot support this because a copy of the readerConfig is kept. IConfigRecords are not copyable. + // TODO: We could copy the IConfigRecordPtr. That is allowed. Not trivial to do with template magic. +} +template +void UCIFastReader::InitCache(const ConfigParameters& readerConfig) +{ + // first time we are called we cache our parameters + // Note: This is a little ugly. It is an artifact of integrating with BrainScript. Since BrainScript does not allow to copy configs, I moved that copy code here into the ConfigParameters implementation. + if (&readerConfig != &m_readerConfig) + readerConfig.CopyTo(m_readerConfig); + + // check for a writer tag first (lets us know we are caching) + if (!readerConfig.Exists(L"writerType")) + return; + + // first try to open the binary cache + bool found = false; + try + { + // TODO: need to go down to all levels, maybe search for sectionType + ConfigArray filesList(','); + vector names; + if (readerConfig.Exists(L"wfile")) + { + filesList.push_back(readerConfig(L"wfile")); + if (fexists(readerConfig(L"wfile"))) + found = true; + } + FindConfigNames(readerConfig, "wfile", names); + for (const auto & name : names) + { + ConfigParameters config = readerConfig(name); + filesList.push_back(config("wfile")); + if (fexists(config("wfile"))) + found = true; + } + + // if we have a file already, we are going to read the cached files + if (found) + { + ConfigParameters config; + readerConfig.CopyTo(config); + // mmodify the config so the reader types look correct + config["readerType"] = config("writerType"); + config["file"] = filesList; + m_cachingReader = new DataReader(config); + } + else + { + m_cachingWriter = new DataWriter(readerConfig); + + // now get the section names for map and category types + std::map sections; + m_cachingWriter->GetSections(sections); + for (const auto & pair : sections) + { + if (pair.second == sectionTypeCategoryLabel) + { + m_labelsCategoryName = pair.first; + } + else if (pair.second == sectionTypeLabelMapping) + { + m_labelsMapName = pair.first; + } + } + } + } + catch (runtime_error err) + { + // In case caching reader/writer cannot be created, we gracefully fail over and disable caching. + fprintf(stderr,"Error attemping to create Binary%s\n%s\n",found?"Reader":"Writer",err.what()); + delete m_cachingReader; + m_cachingReader = NULL; + delete m_cachingWriter; + m_cachingWriter = NULL; + } + catch (...) + { + // if there is any error, just get rid of the object + fprintf(stderr,"Error attemping to create Binary%s\n",found?"Reader":"Writer"); + delete m_cachingReader; + m_cachingReader = NULL; + delete m_cachingWriter; + m_cachingWriter = NULL; + } +} + +// destructor - virtual so it gets called properly +template +UCIFastReader::~UCIFastReader() +{ + ReleaseMemory(); + delete m_cachingReader; + delete m_cachingWriter; +} + +// ReleaseMemory - release the memory footprint of UCIFastReader +// used when the caching reader is taking over +template +void UCIFastReader::ReleaseMemory() +{ + m_featuresBuffer=NULL; + m_labelsBuffer=NULL; + m_labelsIdBuffer=NULL; + m_featureData.clear(); + m_labelIdData.clear(); + m_labelData.clear(); +} + +//SetupEpoch - Setup the proper position in the file, and other variable settings to start a particular epoch +template +void UCIFastReader::SetupEpoch() +{ + // if we are starting fresh (epoch zero and no data read), init everything + // however if we are using cachingWriter, we need to know record count, so do that first + if (m_epoch == 0 && m_totalSamples == 0 && m_cachingWriter != NULL) + { + m_readNextSample = m_epochStartSample = m_mbStartSample = 0; + m_parser.SetFilePosition(0); + } + else // otherwise, position the read to start at the right location + { + // don't know the total number of samples yet, so count them + if (m_totalSamples == 0) + { + if (m_traceLevel > 0) + fprintf(stderr, "starting at epoch %lu counting lines to determine record count\n", (unsigned long)m_epoch); + m_parser.SetParseMode(ParseLineCount); + m_totalSamples = m_parser.Parse(size_t(-1), NULL, NULL); + m_parser.SetParseMode(ParseNormal); + m_parser.SetFilePosition(0); + m_mbStartSample = 0; + UpdateDataVariables(0); // update all the variables since we read to the end... + if (m_traceLevel > 0) + fprintf(stderr, "\n %lu records found\n", (unsigned long)m_totalSamples); + } + + // make sure we are in the correct location for mid-dataset epochs + size_t mbStartSample = m_epoch * m_epochSize; + + size_t fileRecord = m_totalSamples?mbStartSample % m_totalSamples:0; + fprintf(stderr, "starting epoch %lu at record count %lu, and file position %lu\n", (unsigned long)m_epoch, (unsigned long)mbStartSample, (unsigned long)fileRecord); + size_t currentFileRecord = m_mbStartSample % m_totalSamples; + + // reset the next read sample + m_readNextSample = mbStartSample; + if (currentFileRecord == fileRecord) + { + fprintf(stderr, "already there from last epoch\n"); + + // we have a slight delima here, if we haven't determined the end of the file yet + // and the user told us to find how many records are in the file, we can't distinguish "almost done" + // with a file (a character away) and the middle of the file. So read ahead a record to see if it's there. + bool endReached = m_endReached; + if (!endReached) + { + if (!m_parser.HasMoreData()) + { + endReached = true; + UpdateDataVariables(mbStartSample); + assert(m_endReached); + } + } + // move the read pointer to the end since we have everything already in memory. + if (endReached && m_epochStartSample % m_totalSamples == fileRecord + && m_featureData.size() >= m_epochSize*m_featureCount) + { + m_readNextSample = mbStartSample + m_epochSize; + // write the label file here to make sure we do it somewhere. We know the entire dataset has been read at this point + WriteLabelFile(); + } + + } + // not the right position, need to get there + else + { + // if we are already past the desired record, start at the beginning again + if (currentFileRecord > fileRecord) + { + m_parser.SetFilePosition(0); + currentFileRecord = 0; + } + fprintf(stderr, "reading from record %lu to %lu to be positioned properly for epoch\n", (unsigned long)currentFileRecord, (unsigned long)fileRecord); + m_parser.SetParseMode(ParseLineCount); + m_parser.Parse(fileRecord-currentFileRecord, NULL, NULL); + m_parser.SetParseMode(ParseNormal); + if (!m_labelFileToWrite.empty()) + { + fprintf(stderr, "WARNING: file %ls NOT written to disk, label file will only be written when starting epochs at the beginning of the dataset\n", m_labelFileToWrite.c_str()); + m_labelFileToWrite.clear(); + RuntimeError("LabelMappingFile not provided in config, must be provided if not starting from epoch Zero (0)"); + } + } + m_epochStartSample = m_mbStartSample = mbStartSample; + } +} + +// utility function to round an integer up to a multiple of size +size_t RoundUp(size_t value, size_t size) +{ + return ((value + size -1)/size)*size; +} + +template +void UCIFastReader::SetNumParallelSequences(const size_t sz) +{ + mRequestedNumParallelSequences = sz; + if (mOneLinePerFile) + m_mbSize = mRequestedNumParallelSequences; +}; + +//StartMinibatchLoop - Startup a minibatch loop +// mbSize - [in] size of the minibatch (number of Samples, etc.) +// epoch - [in] epoch number for this loop, if > 0 the requestedEpochSamples must be specified (unless epoch zero was completed this run) +// requestedEpochSamples - [in] number of samples to randomize, defaults to requestDataSize which uses the number of samples there are in the dataset +// this value must be a multiple of mbSize, if it is not, it will be rounded up to one. +template +void UCIFastReader::StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples /*= requestDataSize */) +{ + m_subsetNum = subsetNum; + m_numSubsets = numSubsets; + if (mOneLinePerFile) + mbSize = mRequestedNumParallelSequences; /// each file has only one observation, therefore the number of data to read is the number of files + + // if we aren't currently caching, see if we can use a cache + if (!m_cachingReader && !m_cachingWriter) + { + InitCache(m_readerConfig); + if (m_cachingReader) + ReleaseMemory(); // free the memory used by the UCIFastReader + } + + // if we are reading from the cache, do so now and return + if (m_cachingReader) + { + m_cachingReader->StartMinibatchLoop(mbSize, epoch, requestedEpochSamples); + return; + } + + // if we are reallocating bigger, release the original + if (mbSize > m_mbSize) + { + m_featuresBuffer = NULL; + m_labelsBuffer = NULL; + m_labelsIdBuffer = NULL; + } + + m_mbSize = mbSize; + if (requestedEpochSamples == requestDataSize) + { + if (!m_endReached) + { + m_epochSize = requestDataSize; + } + } + else + { + m_epochSize = requestedEpochSamples; + if (!m_partialMinibatch) + m_epochSize = RoundUp(requestedEpochSamples, mbSize); + if (m_epochSize != requestedEpochSamples) + fprintf(stderr, "epochSize rounded up to %d to fit an integral number of minibatches\n", (int)m_epochSize); + } + + // set the randomization range for randomizationAuto + // or if it's invalid less than the minibatch size, we need to make it at least minibatch size + if (m_randomizeRange != randomizeNone) + { + if (m_epochSize != requestDataSize && m_randomizeRange == randomizeAuto) + { + m_randomizeRange = m_epochSize; + } + m_randomizeRange = max(m_randomizeRange, m_mbSize); + if (m_randomizeRange != randomizeAuto) + { + if ((m_epochSize != requestDataSize && m_epochSize % m_randomizeRange != 0) || (m_randomizeRange % m_mbSize != 0)) + RuntimeError("randomizeRange must be an even multiple of mbSize and an integral factor of epochSize"); + m_randomordering.Resize(m_randomizeRange, m_randomizeRange); + } + } + + // we use epochSize, which might not be set yet, so use a default value for allocations if not yet set + size_t epochSize = m_epochSize == requestDataSize?1000:m_epochSize; + m_epoch = epoch; + m_mbStartSample = epoch*m_epochSize; + + // allocate room for the data + m_featureData.reserve(m_featureCount*epochSize); + if (m_labelType == labelCategory) + m_labelIdData.reserve(epochSize); + else if (m_labelType != labelNone) + m_labelData.reserve(epochSize); + + SetupEpoch(); +} + +// function to store the LabelType in an ElemType +// required for string labels, which can't be stored in ElemType arrays +template +void UCIFastReader::StoreLabel(ElemType& labelStore, const LabelType& labelValue) +{ + labelStore = (ElemType)m_mapLabelToId[labelValue]; +} + +// GetMinibatch - Get the next minibatch (features and labels) +// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, +// [out] each matrix resized if necessary containing data. +// returns - true if there are more minibatches, false if no more minibatchs remain +template +bool UCIFastReader::GetMinibatch(std::map*>& matrices) +{ + bool minibatchesRemaining = true; + if (m_pendingAsyncGetMinibatch.valid()) + { + // An async GetMinibatch is in flight. Wait for it to finish and swap + // the contents of the m_prefetchedMatrices and parameter matrices + minibatchesRemaining = m_pendingAsyncGetMinibatch.get(); + + // Now swap the m_prefetchedMatrices and parameter matrices + for (auto iter = matrices.begin(); iter != matrices.end(); ++iter) + { + if (m_prefetchMatrices.find(iter->first) == m_prefetchMatrices.end()) + { + LogicError("No mathing prefetch matrix found for matrix named %S!", iter->first.c_str()); + } + + Matrix* prefetchMatrix = m_prefetchMatrices[iter->first].get(); + std::swap(*(iter->second), *prefetchMatrix); + } + } + else + { + minibatchesRemaining = GetMinibatchImpl(matrices); + + // Allocate prefetch matrices if we would be firing an async minibatch prefetch + if (minibatchesRemaining && m_prefetchEnabled) + { + // DeAllocate the existing m_prefetchMatrices + m_prefetchMatrices.clear(); + + for (auto iter = matrices.begin(); iter != matrices.end(); ++iter) + { + m_prefetchMatrices[iter->first].reset(new Matrix(iter->second->GetDeviceId())); + } + } + } + + // Fire a new prefetch if there are any minibatches remaining + if (minibatchesRemaining && m_prefetchEnabled) + { + Matrix& features = *matrices[m_featuresName]; + int deviceId = features.GetDeviceId(); + m_pendingAsyncGetMinibatch = std::async(std::launch::async, [this, deviceId]() + { + // Set the device since this will execute on a new thread + Matrix::SetDevice(deviceId); + + std::map*> prefetchMatrices; + for (auto iter = m_prefetchMatrices.begin(); iter != m_prefetchMatrices.end(); ++iter) + { + prefetchMatrices[iter->first] = iter->second.get(); + } + + return GetMinibatchImpl(prefetchMatrices); + }); + } + + return minibatchesRemaining; +} + +// GetMinibatchImpl - The actual implementation of getting the next minibatch (features and labels) +// matrices - [in] a map with named matrix types (i.e. 'features', 'labels') mapped to the corresponing matrix, +// [out] each matrix resized if necessary containing data. +// returns - true if there are more minibatches, false if no more minibatchs remain +template +bool UCIFastReader::GetMinibatchImpl(std::map*>& matrices) +{ + if (m_cachingReader) + { + return m_cachingReader->GetMinibatch(matrices); + } + // get the features array + if (matrices.find(m_featuresName) == matrices.end()) + RuntimeError("Features matrix not found in config file, there should be a section '%ls=[...]' in the configuration file.", m_featuresName.c_str()); + + Matrix& features = *matrices[m_featuresName]; + + // get out if they didn't call StartMinibatchLoop() first + if (m_mbSize == 0) + return false; + + // check to see if we have changed epochs, if so we are done with this one. + if (m_mbStartSample / m_epochSize != m_epoch) + return false; + + bool randomize = Randomize(); + bool moreData = EnsureDataAvailable(m_mbStartSample); + + // figure which sweep of the randomization we are on + size_t epochSample = m_mbStartSample % m_epochSize; // where the minibatch starts in this epoch + //size_t samplesExtra = m_totalSamples % m_epochSize; // extra samples at the end of an epoch + //size_t epochsDS = (m_totalSamples+m_epochSize-1)/m_epochSize; // how many epochs per dataset + size_t randomizeSet = randomize?RandomizeSweep(m_mbStartSample):0; + const auto & tmap = m_randomordering(randomizeSet); + size_t epochEnd = m_epochSize; + size_t recordStart = m_totalSamples?m_mbStartSample%m_totalSamples:m_mbStartSample; + + // actual size is either what requested, or total number of samples read so far + size_t actualmbsize = min(m_totalSamples, m_mbSize); // it may still return less if at end of sweep + + // check for an odd sized last minibatch + if (epochSample + actualmbsize > epochEnd) + { + actualmbsize = epochEnd - epochSample; + } + + // hit the end of the dataset, we should only get here in "one=pass mode" + if (!moreData) + { + // make sure we take into account hitting the end of the dataset (not wrapping around) + actualmbsize = min(m_totalSamples-recordStart,actualmbsize); + } + + if (m_featuresBuffer == NULL) + { + m_featuresBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_mbSize * m_featureCount); + memset(m_featuresBuffer.get(), 0, sizeof(ElemType) * m_mbSize * m_featureCount); + } + + if (m_labelsBuffer == NULL) + { + if (m_labelType == labelCategory) + { + m_labelsBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_labelDim * m_mbSize); + m_labelsIdBuffer = std::shared_ptr(new LabelIdType[m_mbSize], [](LabelIdType* p) { + delete[] p; + }); + } + else if (m_labelType != labelNone) + { + m_labelsBuffer = AllocateIntermediateBuffer(features.GetDeviceId(), m_mbSize); + m_labelsIdBuffer = NULL; + } + } + + if (m_labelType == labelCategory) + { + memset(m_labelsBuffer.get(), 0, sizeof(ElemType)*m_labelDim*actualmbsize); + memset(m_labelsIdBuffer.get(), 0, sizeof(LabelIdType)*actualmbsize); + } + else if (m_labelType != labelNone) + { + memset(m_labelsBuffer.get(), 0, sizeof(ElemType) * 1 * actualmbsize); + } + + if (actualmbsize > 0) + { + // loop through and copy data to matrix + int j = 0; // vector of vectors of feature data + // determine randomization base index + size_t randBase = 0; // (keep compiler happy) + if (randomize) + randBase = epochSample - epochSample%m_randomizeRange; + + //loop through all the samples + for (size_t jSample = m_mbStartSample; j < actualmbsize; ++j, ++jSample) + { + // pick the right sample with randomization if desired + size_t jRand = randomize?(randBase + tmap[jSample%m_randomizeRange]):jSample; + jRand %= m_epochSize; + + // vector of feature data goes into matrix column + memcpy(&m_featuresBuffer.get()[j*m_featureCount], &m_featureData[jRand*m_featureCount], sizeof(ElemType)*m_featureCount); + + if (m_labelType == labelCategory) + { + m_labelsBuffer.get()[j*m_labelDim + m_labelIdData[jRand]] = (ElemType)1; + m_labelsIdBuffer.get()[j] = m_labelIdData[jRand]; + } + else if (m_labelType != labelNone) + { + if (m_labelType == labelRegression) + { + m_labelsBuffer.get()[j] = (ElemType)atof(m_labelData[jRand].c_str()); + } + else + { + StoreLabel(m_labelsBuffer.get()[j], m_labelData[jRand]); + } + } + } + } + + // There may be multiple parallel trainers reading at the same time in which case + // we will slice the data to only return the share of the current trainer's subset + size_t currSubsetStartCol = (actualmbsize * m_subsetNum) / m_numSubsets; + size_t currSubsetEndCol = (actualmbsize * (m_subsetNum + 1)) / m_numSubsets; + size_t currSubsetSize = currSubsetEndCol - currSubsetStartCol; + // create the respective MBLayout + // Every sample is returned as a sequence of 1 frame. + m_pMBLayout->InitAsFrameMode(currSubsetSize); + + // if we are writing out to the caching writer, do it now + if (m_cachingWriter && (m_subsetNum == 0)) + { + map writeBuffer; + writeBuffer[m_featuresName] = m_featuresBuffer.get(); + if (m_labelType == labelCategory) + { + writeBuffer[m_labelsName] = m_labelsIdBuffer.get(); + if (!m_labelsCategoryName.empty()) + writeBuffer[m_labelsCategoryName] = m_labelsBuffer.get(); + } + else if (m_labelType != labelNone) + { + writeBuffer[m_labelsName] = m_labelsBuffer.get(); + } + + // write out the data, on a second pass compute statistics as needed + bool moreToWrite = m_cachingWriter->SaveData(m_mbStartSample, writeBuffer, actualmbsize, m_totalSamples, 0); + + // done writing + if (!moreToWrite) + { + // write out the mapping table as necessary + if (m_labelType == labelCategory && !m_labelsMapName.empty()) + { + m_cachingWriter->SaveMapping(m_labelsMapName, m_mapIdToLabel); + } + + WriteLabelFile(); + + // now close the cache writer + delete m_cachingWriter; + m_cachingWriter = NULL; + } + } + + // advance to the next minibatch + m_mbStartSample += actualmbsize; + + // if they don't want partial minibatches, skip data transfer and return + if (actualmbsize < m_mbSize && !m_partialMinibatch + || actualmbsize == 0 || currSubsetSize == 0) // no records found (end of minibatch) + { + return false; + } + + // now transfer to the GPU as needed + features.SetValue(m_featureCount, currSubsetSize, features.GetDeviceId(), m_featuresBuffer.get() + (m_featureCount * currSubsetStartCol), matrixFlagNormal); + if (m_labelType == labelCategory) + { + auto labelEntry = matrices.find(m_labelsName); + if (labelEntry != matrices.end()) + { + Matrix* labels = labelEntry->second; + if (labels != nullptr) + labels->SetValue(m_labelDim, currSubsetSize, labels->GetDeviceId(), m_labelsBuffer.get() + (m_labelDim * currSubsetStartCol), matrixFlagNormal); + } + } + else if (m_labelType != labelNone) + { + auto labelEntry = matrices.find(m_labelsName); + if (labelEntry != matrices.end()) + { + Matrix* labels = labelEntry->second; + if (labels != nullptr) + labels->SetValue(1, currSubsetSize, labels->GetDeviceId(), m_labelsBuffer.get() + (1 * currSubsetStartCol), matrixFlagNormal); + } + } + // we read some records, so process them + return true; +} + +// GetLabelMapping - Gets the label mapping from integer index to label type +// returns - a map from numeric datatype to native label type +template +const std::map::LabelIdType, typename IDataReader::LabelType>& UCIFastReader::GetLabelMapping(const std::wstring& sectionName) +{ + if (m_cachingReader) + { + return m_cachingReader->GetLabelMapping(sectionName); + } + return m_mapIdToLabel; +} + +// SetLabelMapping - Sets the label mapping from integer index to label +// labelMapping - mapping table from label values to IDs (must be 0-n) +// note: for tasks with labels, the mapping table must be the same between a training run and a testing run +template +void UCIFastReader::SetLabelMapping(const std::wstring& /*sectionName*/, const std::map::LabelIdType, LabelType>& labelMapping) +{ + if (m_cachingReader) + { + RuntimeError("Cannot set mapping table when the caching reader is being used"); + } + m_mapIdToLabel = labelMapping; + m_mapLabelToId.clear(); + for (std::pair var : labelMapping) + { + m_mapLabelToId[var.second] = var.first; + } +} + +// GetData - Gets metadata from the specified section (into CPU memory) +// sectionName - section name to retrieve data from +// numRecords - number of records to read +// data - pointer to data buffer, if NULL, dataBufferSize will be set to size of required buffer to accomidate request +// dataBufferSize - [in] size of the databuffer in bytes +// [out] size of buffer filled with data +// recordStart - record to start reading from, defaults to zero (start of data) +// returns: true if data remains to be read, false if the end of data was reached +template +bool UCIFastReader::GetData(const std::wstring& sectionName, size_t numRecords, void* data, size_t& dataBufferSize, size_t recordStart) +{ + if (m_cachingReader) + { + return m_cachingReader->GetData(sectionName, numRecords, data, dataBufferSize, recordStart); + } + RuntimeError("GetData not supported in UCIFastReader"); +} + +template +bool UCIFastReader::DataEnd(EndDataType endDataType) +{ + if (m_cachingReader) + { + return m_cachingReader->DataEnd(endDataType); + } + + bool ret = false; + switch (endDataType) + { + case endDataNull: + assert(false); + break; + case endDataEpoch: + ret = (m_mbStartSample / m_epochSize != m_epoch); + break; + case endDataSet: + ret = EnsureDataAvailable(m_mbStartSample, true); + break; + case endDataSentence: // for fast reader each minibatch is considered a "sentence", so always true + ret = true; + break; + } + return ret; +} + +template +unique_ptr& UCIFastReader::GetCUDAAllocator(int deviceID) +{ + if (m_cudaAllocator != nullptr) + { + if (m_cudaAllocator->GetDeviceId() != deviceID) + { + m_cudaAllocator.reset(nullptr); + } + } + + if (m_cudaAllocator == nullptr) + { + m_cudaAllocator.reset(new CUDAPageLockedMemAllocator(deviceID)); + } + + return m_cudaAllocator; +} + +template +std::shared_ptr UCIFastReader::AllocateIntermediateBuffer(int deviceID, size_t numElements) +{ + if (deviceID >= 0) + { + // Use pinned memory for GPU devices for better copy performance + size_t totalSize = sizeof(ElemType) * numElements; + return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { + this->GetCUDAAllocator(deviceID)->Free((char*)p); + }); + } + else + { + return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { + delete[] p; + }); + } +} + +// instantiate all the combinations we expect to be used +template class UCIFastReader; +template class UCIFastReader; +}}} diff --git a/Source/Readers/UCIFastReader/UCIFastReader.h b/Source/Readers/UCIFastReader/UCIFastReader.h index 37944a27d413..e314b07ba122 100644 --- a/Source/Readers/UCIFastReader/UCIFastReader.h +++ b/Source/Readers/UCIFastReader/UCIFastReader.h @@ -15,7 +15,7 @@ #include #include #include -#include "CUDAPageLockedMemAllocator.h" +#include "CUDAPageLockedMemAllocator.h" static inline size_t RoundUp(size_t m, size_t n) { @@ -85,7 +85,7 @@ namespace Microsoft { namespace MSR { namespace CNTK { std::vector m_featureData; std::vector m_labelIdData; std::vector m_labelData; - MBLayoutPtr m_pMBLayout; + MBLayoutPtr m_pMBLayout; // map is from ElemType to LabelType // For UCI, we really only need an int for label data, but we have to transmit in Matrix, so use ElemType instead @@ -97,7 +97,7 @@ namespace Microsoft { namespace MSR { namespace CNTK { */ bool mOneLinePerFile; - unique_ptr m_cudaAllocator; + unique_ptr m_cudaAllocator; // caching support DataReader* m_cachingReader; @@ -118,30 +118,30 @@ namespace Microsoft { namespace MSR { namespace CNTK { virtual bool EnsureDataAvailable(size_t mbStartSample, bool endOfDataCheck=false); virtual bool ReadRecord(size_t readSample); - // Helper functions - unique_ptr& GetCUDAAllocator(int deviceID); - std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements); + // Helper functions + unique_ptr& GetCUDAAllocator(int deviceID); + std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements); public: - template void InitFromConfig(const ConfigRecordType &); - virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } + template void InitFromConfig(const ConfigRecordType &); + virtual void Init(const ConfigParameters & config) override { InitFromConfig(config); } virtual void Init(const ScriptableObjects::IConfigRecord & config) override { InitFromConfig(config); } virtual void Destroy(); UCIFastReader() { m_pMBLayout=make_shared(); } virtual ~UCIFastReader(); - virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize) - { - return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); - } - - virtual bool SupportsDistributedMBRead() const override - { - return ((m_cachingReader == nullptr) || m_cachingReader->SupportsDistributedMBRead()); - } - - virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize) override; - + virtual void StartMinibatchLoop(size_t mbSize, size_t epoch, size_t requestedEpochSamples = requestDataSize) + { + return StartDistributedMinibatchLoop(mbSize, epoch, 0, 1, requestedEpochSamples); + } + + virtual bool SupportsDistributedMBRead() const override + { + return ((m_cachingReader == nullptr) || m_cachingReader->SupportsDistributedMBRead()); + } + + virtual void StartDistributedMinibatchLoop(size_t mbSize, size_t epoch, size_t subsetNum, size_t numSubsets, size_t requestedEpochSamples = requestDataSize) override; + virtual bool GetMinibatch(std::map*>& matrices); diff --git a/Source/Readers/UCIFastReader/UCIFastReader.vcxproj b/Source/Readers/UCIFastReader/UCIFastReader.vcxproj index e30dc6b90299..aa899186b817 100644 --- a/Source/Readers/UCIFastReader/UCIFastReader.vcxproj +++ b/Source/Readers/UCIFastReader/UCIFastReader.vcxproj @@ -1,153 +1,153 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {E6646FFE-3588-4276-8A15-8D65C22711C1} - - - - - - - - - Win32Proj - UCIReader - - - - DynamicLibrary - true - v120 - Unicode - - - DynamicLibrary - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - true - - - Console - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) - true - ..\..\common\include;..\..\Math - false - /d2Zi+ %(AdditionalOptions) - true - - - Console - true - true - true - Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) - ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ - true - - - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - false - - - false - - - - - Create - Create - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {E6646FFE-3588-4276-8A15-8D65C22711C1} + + + + + + + + + Win32Proj + UCIReader + + + + DynamicLibrary + true + v120 + Unicode + + + DynamicLibrary + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\common\include;..\..\Math;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + true + + + Console + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + $(SolutionDir)$(Platform)\$(Configuration)\;..\..\Math\$(Platform)\$(Configuration);..\$(Platform)\$(Configuration) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_WINDOWS;_USRDLL;UCIREADER_EXPORTS;%(PreprocessorDefinitions) + true + ..\..\common\include;..\..\Math + false + /d2Zi+ %(AdditionalOptions) + true + + + Console + true + true + true + Math.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) + ..\..\Math\$(Platform)\$(Configuration);$(SolutionDir)$(Platform)\$(Configuration)\ + true + + + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + false + + + false + + + + + Create + Create + + + + + + + \ No newline at end of file diff --git a/Source/Readers/UCIFastReader/UCIFastReader.vcxproj.filters b/Source/Readers/UCIFastReader/UCIFastReader.vcxproj.filters index ccc8fd9b7dab..6f008cbcc73a 100644 --- a/Source/Readers/UCIFastReader/UCIFastReader.vcxproj.filters +++ b/Source/Readers/UCIFastReader/UCIFastReader.vcxproj.filters @@ -1,58 +1,58 @@ - - - - - - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - - - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - {8e18fb2e-ab57-4862-ad16-5e322d6f2fbc} - - - {0dbad9d8-ce99-4f36-b871-3b98a27f58c2} - - + + + + + + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + + + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + {8e18fb2e-ab57-4862-ad16-5e322d6f2fbc} + + + {0dbad9d8-ce99-4f36-b871-3b98a27f58c2} + + \ No newline at end of file diff --git a/Source/SGDLib/DataReaderHelpers.h b/Source/SGDLib/DataReaderHelpers.h index f9477db9fa16..2adb7b7d3e46 100644 --- a/Source/SGDLib/DataReaderHelpers.h +++ b/Source/SGDLib/DataReaderHelpers.h @@ -1,589 +1,589 @@ -// DataReaderHelper.h -- helper functions that understand both DataReader and ComputationNetwork - -#pragma once - -#include "Basics.h" -#include "DataReader.h" -#include "ComputationNetwork.h" -#include "MPIWrapper.h" -#include -#include -#include "TrainingCriterionNodes.h" - - -namespace Microsoft { namespace MSR { namespace CNTK { - - /*static*/ struct DataReaderHelpers - { - - // ------------------------------------------------------------------- - // GetMinibatchIntoNetwork() -- get one minibatch from Reader (this->trainSetDataReader) into Network (this->net) - // Returns false if no data is read. In that case, no other return value can be expected to contain meaningful values (e.g. actualMBSize will be unchanged). - // Sets actualMBSize to the number of matrix columns. Note that 0 is a valid value to be returned for actualMBSize, caller must handle that correctly. - // ------------------------------------------------------------------- - - // Note: This will go away with the redesigned reader interface. - // TODO: callers of this often do ComputationNetwork::BumpEvalTimeStamp(featureNodes) and also for labels; we should eliminate the need for this. - template - static bool GetMinibatchIntoNetwork(IDataReader& trainSetDataReader, - ComputationNetworkPtr net, - ComputationNodeBasePtr criterionNode, - bool useDistributedMBReading, - bool useParallelTrain, - std::map*> & inputMatrices, - size_t & actualMBSize) - { - auto pMBLayout = net->GetMBLayoutPtr(); - // Reading consists of a sequence of Reader API calls: - // - GetMinibatch() --fills the inputMatrices - // - SetActualMiniBatchSizeFromFeatures() --tells Network to resize the nodes' buffers - // - CopyMBLayoutTo() --copies the MBLayout from Reader to Network - // - VerifyActualNumParallelSequences() --(refactoring left-over) verify that MBLayout is consistent with #parallel sequences - // with the special twist that in presence of parallelization, there is some decimation involved. - - bool wasDataRead = trainSetDataReader.GetMinibatch(inputMatrices); // fill in the minibatch data into the Input nodes' buffers directly - // If this returns false, the matrices may contain garbage or not sized to 0 columns. - // On the other hand, if it returns a 0-column matrix, that would be a perfectly cromulent minibatch (in case of data parallelism with distributed reading). - - // reader will have resized input node's m_value directly. Nodes must be notified to do necessary internal state updates from that. - net->NotifyInputNodesFunctionValuesMBSizeModified(); - - if (wasDataRead) - trainSetDataReader.CopyMBLayoutTo(pMBLayout); // get layout meta-data - -#if 0 // (this test is wrong... I think) - if (net->DetermineActualMBSizeFromFeatures() == 0) - wasDataRead = false; -#endif - - // get some additional information when doing sequence training - // TODO: This should not need to be called in case of wasDataRead == false, since in that case, returned values are invalid. - if ((criterionNode != nullptr) && (criterionNode->OperationName() == L"SequenceWithSoftmax")) - { - auto node = dynamic_pointer_cast>(criterionNode); - auto latticeinput = node->getLatticePtr(); - auto uids = node->getuidprt(); - auto boundaries = node->getboundaryprt(); - auto extrauttmap = node->getextrauttmap(); - - trainSetDataReader.GetMinibatch4SE(*latticeinput, *uids, *boundaries, *extrauttmap); - } - - // if no data read then we are done - if (!wasDataRead) - return false; - - // decimate if needed. Decimation happens in-place. - if (!useDistributedMBReading && useParallelTrain) - { - DecimateMinibatch(inputMatrices, g_mpi->NumNodesInUse(), g_mpi->CurrentNodeRank(), net->GetMBLayoutPtr()); - net->NotifyInputNodesFunctionValuesMBSizeModified(); // #matrix columns changes - } - - // get MB size and tell Network to update its nodes' buffers based on what's in the input matrices - // Note: Decimation may have reduced this to 0 frames. We still must return 'true'. - actualMBSize = net->DetermineActualMBSizeFromFeatures(); - - return true; - } - - // ------------------------------------------------------------------- - // DecimateMinibatch - decimate minibatch for parallelization - // ------------------------------------------------------------------- - // non-inplace decimation , to be used in subminibatch implementation - // return [st, en) parallel sequence which has been selected after decimation - template - static pair DecimateMinibatch(const std::map*> MB, // input matrices - std::map*>& decimatedMB, // output decimated matrices. - MBLayoutPtr pMBLayout, // input MBLayout - MBLayoutPtr& pDecimateMBLayout, // output decimated MBLayout (note: cannot work in-place) - int numWorker, int rank) - { - size_t numParallelSequences = pMBLayout->GetNumParallelSequences(); - size_t nT = pMBLayout->GetNumTimeSteps(); - - // decide start column and end column - size_t st = numParallelSequences * (size_t)rank / numWorker; - size_t en = numParallelSequences * (size_t)(rank + 1) / numWorker; - en = en > numParallelSequences ? numParallelSequences : en; // TODO: why are these two tests necessary? - en = (rank == numWorker - 1) ? numParallelSequences : en; - size_t numNewParallelSequence = en - st; - - // begin decimate matrices - size_t rv = 0; - for (const auto& it : MB) - { - wstring name = it.first; - MSR::CNTK::Matrix & mat = *it.second; - size_t numRows = mat.GetNumRows(); - size_t numCols = mat.GetNumCols(); - int devID = mat.GetDeviceId(); - - if (rv == 0) - rv = numCols; - else if (rv != numCols) - LogicError("DecimateMinibatch: Inconsistent number of columns among inputs (found %d and %d).", (int)rv, (int)numCols); - - if (nT != numCols / numParallelSequences) - LogicError("ERROR: MBLayout borked, GetNumTimeSteps() mismatches minibatch number of columns\n"); - - decimatedMB[name] = new Matrix(devID); - decimatedMB[name]->AssignRowSliceValuesOf(mat.Reshaped(numRows*numParallelSequences, nT), st*numRows, (en - st)*numRows); - decimatedMB[name]->Reshape(numRows, numNewParallelSequence*nT); - // If we had a RowSlice function, we would like to write in this way - // decimatedMB[name]->SetValue(mat.Reshaped(nRows*nSequence, nT).RowSlice( st*nRows , (en-st)*nRows).Reshaped(nRows, nNewParallelSequence*nT)); - } - // decimate MBLayout as well - pDecimateMBLayout = make_shared(numNewParallelSequence, nT); -#if 1 - // now copy over all sequence info records that are inside the range, with adjusted 's' - const auto & sequences = pMBLayout->GetAllSequences(); - for (const auto & seq : sequences) - { - if (seq.s >= st && seq.s < en) - { - auto shiftedSeq = seq; - shiftedSeq.s -= st; // these sequences have shifted up by 'st' sequences - pDecimateMBLayout->AddSequence(shiftedSeq); - } - } -#else - for (size_t t = 0; t < nT; t++) for (size_t id = 0; id < numNewParallelSequence; id++) - pDecimateMBLayout->Set(id, t, pMBLayout->Get(id + st, t)); +// DataReaderHelper.h -- helper functions that understand both DataReader and ComputationNetwork + +#pragma once + +#include "Basics.h" +#include "DataReader.h" +#include "ComputationNetwork.h" +#include "MPIWrapper.h" +#include +#include +#include "TrainingCriterionNodes.h" + + +namespace Microsoft { namespace MSR { namespace CNTK { + + /*static*/ struct DataReaderHelpers + { + + // ------------------------------------------------------------------- + // GetMinibatchIntoNetwork() -- get one minibatch from Reader (this->trainSetDataReader) into Network (this->net) + // Returns false if no data is read. In that case, no other return value can be expected to contain meaningful values (e.g. actualMBSize will be unchanged). + // Sets actualMBSize to the number of matrix columns. Note that 0 is a valid value to be returned for actualMBSize, caller must handle that correctly. + // ------------------------------------------------------------------- + + // Note: This will go away with the redesigned reader interface. + // TODO: callers of this often do ComputationNetwork::BumpEvalTimeStamp(featureNodes) and also for labels; we should eliminate the need for this. + template + static bool GetMinibatchIntoNetwork(IDataReader& trainSetDataReader, + ComputationNetworkPtr net, + ComputationNodeBasePtr criterionNode, + bool useDistributedMBReading, + bool useParallelTrain, + std::map*> & inputMatrices, + size_t & actualMBSize) + { + auto pMBLayout = net->GetMBLayoutPtr(); + // Reading consists of a sequence of Reader API calls: + // - GetMinibatch() --fills the inputMatrices + // - SetActualMiniBatchSizeFromFeatures() --tells Network to resize the nodes' buffers + // - CopyMBLayoutTo() --copies the MBLayout from Reader to Network + // - VerifyActualNumParallelSequences() --(refactoring left-over) verify that MBLayout is consistent with #parallel sequences + // with the special twist that in presence of parallelization, there is some decimation involved. + + bool wasDataRead = trainSetDataReader.GetMinibatch(inputMatrices); // fill in the minibatch data into the Input nodes' buffers directly + // If this returns false, the matrices may contain garbage or not sized to 0 columns. + // On the other hand, if it returns a 0-column matrix, that would be a perfectly cromulent minibatch (in case of data parallelism with distributed reading). + + // reader will have resized input node's m_value directly. Nodes must be notified to do necessary internal state updates from that. + net->NotifyInputNodesFunctionValuesMBSizeModified(); + + if (wasDataRead) + trainSetDataReader.CopyMBLayoutTo(pMBLayout); // get layout meta-data + +#if 0 // (this test is wrong... I think) + if (net->DetermineActualMBSizeFromFeatures() == 0) + wasDataRead = false; #endif - - return pair(st, en); - } - - // in-place decimation, for use with data-parallel processing - // return [st, en) parallell sequence which has been selected after decimation - template - static pair DecimateMinibatch(std::map*> &mb, // matrix to be decimated - int numprocs, int rank, // rank info - MBLayoutPtr pMBLayout) // get decimated as well - { - if (numprocs == 1) - return pair(0, pMBLayout->GetNumParallelSequences()); - // no need to do inplace decimation if numproc == 1 - - // allocate space for non-inplace decimation - MBLayoutPtr pDecimatedMB = make_shared(); - std::map*> decimatedMB; - // call in-place decimation - pair selected=DecimateMinibatch(mb, decimatedMB, pMBLayout, pDecimatedMB, numprocs, rank); - // move the data - for (auto k : mb) - { - auto name = k.first; - k.second->SetValue(*decimatedMB[name]); - delete decimatedMB[name]; - decimatedMB[name] = nullptr; - } - pMBLayout->MoveFrom(pDecimatedMB); - return selected; - } - - // =================================================================== - // SubminibatchHelpers -- helper for sub-minibatch implementation - // TODO: Can this just exist inside SGD.cpp? - // =================================================================== - - // A sub-minibathc is a part of a minibatch which helps computing large minibatches that cannot load into GPU memory in one forward-backward computation - // The usage would be : - // SubminibatchHelpers sbhelper; - // for (;;) - // { - // size_t nsb=sb.GetMinibatchIntoCache(...); - // for (size_t i=0; i - class SubminibatchDispatcher - { - private: - typedef std::vector> Lattice; - typedef std::vector Uid; - typedef std::vector ExtrauttMap; - typedef std::vector Boundaries; - - typedef std::vector>* LatticePtr; - typedef std::vector* UidPtr; - typedef std::vector* ExtrauttMapPtr; - typedef std::vector* BoundariesPtr; - typedef std::map*> Matrices; - - // member variables served as caching space - Matrices m_inputMatricesCache; - MBLayoutPtr m_MBLayoutCache; - Lattice m_LatticeCache; - Uid m_uidCache; - ExtrauttMap m_extrauttmapCache; - Boundaries m_BoundariesCache; - shared_ptr> m_NetCriterionAccumulator; - shared_ptr> m_NetEvaluationAccumulator; - std::map>> m_NetStates; // m_NetStatefulNodes[node][i] caches the state of i-th subminibatch of node - bool m_hasLattices; - - Matrices m_cachedGradient; - // we also need to remember where to put into the net - MBLayoutPtr m_NetMBLayoutPtr; - std::map>> m_LearnableNodePtr; - // followings are lattice-related - Matrices m_NetInputMatrixPtr; // TODO: camelCase for all m_Net... - LatticePtr m_NetLatticePtr; - UidPtr m_NetUidPtr; - ExtrauttMapPtr m_NetExtrauttMapPtr; - BoundariesPtr m_NetBoundariesPtr; - // we remember the pointer to the learnable Nodes so that we can accumulate the gradient once a sub-minibatch is done - - - size_t m_numParallelSequences; // number of paralle sequence in the cached matrix and MBLayout - size_t m_numSubminibatches; // how many subminibatches we are going to use ? - - std::vector>> m_NetCriterionNodes; - std::vector>> m_NetEvaluationNodes; - std::map> m_NetStatefulNodes; // we need to Export/Import states of stateful nodes when we swtich subminibatches - - private: - - void EnumerateStatefulNodeWithRoot(ComputationNetwork& net, ComputationNodeBasePtr root, std::map>& statefulnode) - { - const std::list evalorder = net.GetEvalOrder(root); - for (auto& x : evalorder) - { - wstring name = x->GetName(); - if (statefulnode.find(name) != statefulnode.end()) continue; // already in the list - shared_ptr pNode = dynamic_pointer_cast(x); - if (pNode) - { - statefulnode[name] = pNode; - } - } - } - - std::map> EnumerateStatefulNode(ComputationNetwork& net, - const std::vector& criterionNode, - const std::vector& evaluationNode) - { - std::map> statefulNodes; - for (auto& root : criterionNode) - { - EnumerateStatefulNodeWithRoot(net, root, statefulNodes); - } - for (auto& root : evaluationNode) - { - EnumerateStatefulNodeWithRoot(net, root, statefulNodes); - } - return statefulNodes; - } - - public: - SubminibatchDispatcher() : - m_MBLayoutCache(nullptr), m_NetLatticePtr(nullptr), m_NetExtrauttMapPtr(nullptr), m_NetUidPtr(nullptr), m_NetBoundariesPtr(nullptr) - { } - - void Init(ComputationNetworkPtr & net, - const std::list& learnableNodes, - const std::vector& criterionNodes, - const std::vector& evaluationNodes) - { - m_MBLayoutCache = make_shared(); - m_NetCriterionAccumulator = make_shared>(1, 1, net->GetDeviceId()); - m_NetEvaluationAccumulator = make_shared>(1, evaluationNodes.size(), net->GetDeviceId()); - // remember ptrs to learnable nodes - for (auto x : learnableNodes) - { - shared_ptr> pLearnableNode = dynamic_pointer_cast>(x); - wstring nodename = x->NodeName(); - m_LearnableNodePtr[nodename] = pLearnableNode; - } - for (auto& x : criterionNodes) - { - m_NetCriterionNodes.push_back(dynamic_pointer_cast>(x)); - } - for (auto& x : evaluationNodes) - { - m_NetEvaluationNodes.push_back(dynamic_pointer_cast>(x)); - } - m_NetCriterionAccumulator->SetValue((ElemType)0); - m_NetEvaluationAccumulator->SetValue((ElemType)0); - - // emulate all the nodes, find nodes that have state - m_NetStatefulNodes = EnumerateStatefulNode(*net, criterionNodes, evaluationNodes); - for (auto x : m_NetStatefulNodes) - { - wstring name = x.first; - m_NetStates[name] = vector>(); - } - - // for sequence training - if (criterionNodes[0]->OperationName() == L"SequenceWithSoftmax") - { - auto node = dynamic_pointer_cast>(criterionNodes[0]); - assert(node); - m_NetLatticePtr = node->getLatticePtr(); - m_NetExtrauttMapPtr = node->getextrauttmap(); - m_NetUidPtr = node->getuidprt(); - m_NetBoundariesPtr = node->getboundaryprt(); - m_hasLattices = true; - } - else - { - m_NetLatticePtr = nullptr; - m_NetExtrauttMapPtr = nullptr; - m_NetUidPtr = nullptr; - m_NetBoundariesPtr = nullptr; - m_hasLattices = false; - } - } - - ~SubminibatchDispatcher() - { - // TODO: remove these by using shared_ptr - - - for (auto x : m_inputMatricesCache) - { - delete x.second; - } - - for (auto x : m_cachedGradient) - { - delete x.second; - } - } - - size_t GetMinibatchIntoCache(IDataReader& trainSetDataReader, - ComputationNetwork& net, - std::map*> & inputMatrices, - size_t requestedSubminibatches) - { - // first, remember interface to the net - m_NetMBLayoutPtr = net.GetMBLayoutPtr(); - m_NetInputMatrixPtr = inputMatrices; - - // second, get data from reader, stored it in cache - // 1. for each key, allocate the specific matrix on device - for (auto pa : inputMatrices) - { - wstring name = pa.first; - Matrix* M = pa.second; - if (m_inputMatricesCache.find(name) == m_inputMatricesCache.end()) - { - m_inputMatricesCache[name] = new Matrix(*M, M->GetDeviceId()); // deep copy from M - } - else - { - m_inputMatricesCache[name]->SetValue(*M); - } - } - // 2. MBlayout - m_MBLayoutCache->CopyFrom(net.GetMBLayoutPtr()); - size_t nParallelSequences = m_MBLayoutCache->GetNumParallelSequences(); - - - // 3. for bits in seq. training - if (m_hasLattices) - { - m_LatticeCache.clear(); - m_uidCache.clear(); - m_extrauttmapCache.clear(); - m_BoundariesCache.clear(); - - m_LatticeCache = *m_NetLatticePtr; - m_uidCache = *m_NetUidPtr; - m_extrauttmapCache = *m_NetExtrauttMapPtr; - m_BoundariesCache = *m_NetBoundariesPtr; - } - - // subminibatches are cutted at the parallel sequence level; - // if #requested subminibatch is larger than #parallel sequence, - // we cannot split further; instead, each subsequence become a subminibatch - size_t actualnumSubminibatches = requestedSubminibatches > nParallelSequences ? nParallelSequences : requestedSubminibatches; - - // 4. third, allocate space for accumulated gradient - for (auto& n : m_LearnableNodePtr) - { - auto node = n.second; - if (node->IsParameterUpdateRequired()) - { - wstring nodeName = node->GetName(); - shared_ptr> pLearnableNode = node; - auto funvalue = pLearnableNode->Value(); // gradient may not be allocated when this function is first called - size_t nrow = funvalue.GetNumRows(); - size_t ncol = funvalue.GetNumCols(); - if (m_cachedGradient.find(nodeName) == m_cachedGradient.end()) - { - // not allocated yet - m_cachedGradient[nodeName] = new Matrix(nrow, ncol, funvalue.GetDeviceId()); - m_cachedGradient[nodeName]->SetValue((ElemType)0); - } - } - } - // 5. for stateful node - for (auto x : m_NetStatefulNodes) - { - wstring name = x.first; - if (m_NetStates[name].empty()) - { - // this only happens in the first minibatch in an epoch - m_NetStates[name].resize(actualnumSubminibatches); - } - } - - return (m_numSubminibatches = actualnumSubminibatches); - } - - - void DecimateLattices( - LatticePtr decimatedLattices, /* output: lattices after decimation*/ - BoundariesPtr decimatedBoundaryPtr, /* output: boundary after decimation*/ - ExtrauttMapPtr decimatedExtraMapPtr, /* output: extramap after decimation*/ - UidPtr decimatedUidPtr, /* output: Uid after decimation*/ - const Lattice lattices, /* input: lattices to be decimated */ - const Boundaries boundaries, /* input: boundary to be decimated */ - const ExtrauttMap extraMaps, /* input: extra map to be decimated */ - const Uid uids, /* input: uid to be decimated*/ - pair parallelSeqRange /* input: what parallel sequence range we are looking at */ - ) - { - size_t parallelSeqStId = parallelSeqRange.first; - size_t parallelSeqEnId = parallelSeqRange.second; - - decimatedLattices->clear(); - decimatedBoundaryPtr->clear(); - decimatedExtraMapPtr->clear(); - decimatedUidPtr->clear(); - - size_t stFrame = 0; - for (size_t iUtt = 0; iUtt < extraMaps.size(); iUtt++) - { - size_t numFramesInThisUtterance = lattices[iUtt]->getnumframes(); - size_t iParallelSeq = extraMaps[iUtt]; // i-th utterance belongs to iParallelSeq-th parallel sequence - if (iParallelSeq >= parallelSeqStId && iParallelSeq < parallelSeqEnId) - { - // this utterance has been selected - decimatedLattices->push_back(lattices[iUtt]); - decimatedBoundaryPtr->insert(decimatedBoundaryPtr->end(), boundaries.begin()+stFrame, boundaries.begin() + stFrame + numFramesInThisUtterance); - decimatedUidPtr->insert(decimatedUidPtr->end(), uids.begin() + stFrame, uids.begin() + stFrame + numFramesInThisUtterance); - decimatedExtraMapPtr->push_back(extraMaps[iUtt] - parallelSeqStId); - } - stFrame += numFramesInThisUtterance; - } - } - - void GetSubMinibatchToNet(size_t iSubminibatch) - { - Matrices decimatedMatrices; - MBLayoutPtr decimatedLayout; - pair seqRange = DataReaderHelpers::DecimateMinibatch(m_inputMatricesCache, decimatedMatrices, m_MBLayoutCache, decimatedLayout, m_numSubminibatches, iSubminibatch); - // NOTE: deimatedMatrices must be released by caller - - // base on the seqRange, we do the decimation for lattices and related variables - if (m_hasLattices){ - DecimateLattices( - /*output */ - m_NetLatticePtr, m_NetBoundariesPtr, m_NetExtrauttMapPtr, m_NetUidPtr, - /*input to be decimated */ - m_LatticeCache , m_BoundariesCache, m_extrauttmapCache, m_uidCache, - /* what range we want ? */ - seqRange - ); - } - - //m_NetInputMatrixPtr = decimatedMatrices; - for (auto& x : decimatedMatrices) - { - wstring name = x.first; - m_NetInputMatrixPtr[name]->SetValue(*x.second); - delete x.second; // TODO: is it safe to delete here ? Yes! SetValue call cuda memcpy so it is a blocking call - x.second = nullptr; - } - - m_NetMBLayoutPtr->CopyFrom(decimatedLayout); - - for (auto& x : m_NetStatefulNodes) - { - wstring name = x.first; - shared_ptr pNode = x.second; - if (m_NetStates[name][iSubminibatch]) - pNode->ImportState(std::move(m_NetStates[name][iSubminibatch])); - } - } - - // TODO: encapsulate it into a destructor? Note: Cannot throw exceptions in destructor. - void DoneWithCurrentSubMinibatch(size_t iSubminibatch) - { - // accumulate gradient here - for (auto x : m_cachedGradient) - { - wstring nodename = x.first; - if (m_LearnableNodePtr.find(nodename) == m_LearnableNodePtr.end()) - { - RuntimeError("ERROR: in DoneWithCurrentSubMinibatch: node %ls not found in LeanrableNode", nodename.c_str()); - } - shared_ptr> pNode = m_LearnableNodePtr[nodename]; - m_cachedGradient[nodename]->operator+=(pNode->Gradient()); - pNode->Gradient().SetValue((ElemType)0); - } - // accumulate criterion value - Matrix::AddElementToElement(m_NetCriterionNodes[0]->Value(), 0, 0, - *m_NetCriterionAccumulator, 0, 0); - m_NetCriterionNodes[0]->Value().SetValue((ElemType)0); - // accumulate evaluation value - for (size_t i = 0; i < m_NetEvaluationNodes.size(); i++) - { - Matrix::AddElementToElement(m_NetEvaluationNodes[i]->Value(), 0, 0, - *m_NetEvaluationAccumulator, 0, i); - m_NetEvaluationNodes[i]->Value().SetValue((ElemType)0); - } - - // Export node state - for (auto& x : m_NetStatefulNodes) - { - wstring name = x.first; - m_NetStates[name][iSubminibatch] = x.second->ExportState(); - } - } - - void DoneWithCurrentMinibatch() - { - for (auto& x : m_cachedGradient) - { - wstring name = x.first; - Matrix* accumulategrad = x.second; - - if (m_LearnableNodePtr.find(name) == m_LearnableNodePtr.end()) - { - // should never happen, remove this code later - RuntimeError("ERROR: in DoneWithCurrentSubMinibatch: node %ls not found in LearnableNode", name.c_str()); - } - m_LearnableNodePtr[name]->Gradient().SetValue(*accumulategrad); - x.second->SetValue((ElemType)0); - } - // also revert net.m_MBLayoutPtr - m_NetMBLayoutPtr->CopyFrom(m_MBLayoutCache); - - //m_NetCriterionNodes[0]->Value().SetValue((ElemType)0); - Matrix::AddElementToElement(*m_NetCriterionAccumulator, 0, 0, - m_NetCriterionNodes[0]->Value(), 0, 0); - m_NetCriterionAccumulator->SetValue((ElemType)0); - - for (size_t i = 0; i < m_NetEvaluationNodes.size(); i++) - { - //m_NetEvaluationNodes[i]->Value().SetValue((ElemType)0); - Matrix::AddElementToElement(*m_NetEvaluationAccumulator, 0, i, - m_NetEvaluationNodes[i]->Value(), 0, 0); - } - m_NetEvaluationAccumulator->SetValue((ElemType)0); - } - }; - }; - -}}} + + // get some additional information when doing sequence training + // TODO: This should not need to be called in case of wasDataRead == false, since in that case, returned values are invalid. + if ((criterionNode != nullptr) && (criterionNode->OperationName() == L"SequenceWithSoftmax")) + { + auto node = dynamic_pointer_cast>(criterionNode); + auto latticeinput = node->getLatticePtr(); + auto uids = node->getuidprt(); + auto boundaries = node->getboundaryprt(); + auto extrauttmap = node->getextrauttmap(); + + trainSetDataReader.GetMinibatch4SE(*latticeinput, *uids, *boundaries, *extrauttmap); + } + + // if no data read then we are done + if (!wasDataRead) + return false; + + // decimate if needed. Decimation happens in-place. + if (!useDistributedMBReading && useParallelTrain) + { + DecimateMinibatch(inputMatrices, g_mpi->NumNodesInUse(), g_mpi->CurrentNodeRank(), net->GetMBLayoutPtr()); + net->NotifyInputNodesFunctionValuesMBSizeModified(); // #matrix columns changes + } + + // get MB size and tell Network to update its nodes' buffers based on what's in the input matrices + // Note: Decimation may have reduced this to 0 frames. We still must return 'true'. + actualMBSize = net->DetermineActualMBSizeFromFeatures(); + + return true; + } + + // ------------------------------------------------------------------- + // DecimateMinibatch - decimate minibatch for parallelization + // ------------------------------------------------------------------- + // non-inplace decimation , to be used in subminibatch implementation + // return [st, en) parallel sequence which has been selected after decimation + template + static pair DecimateMinibatch(const std::map*> MB, // input matrices + std::map*>& decimatedMB, // output decimated matrices. + MBLayoutPtr pMBLayout, // input MBLayout + MBLayoutPtr& pDecimateMBLayout, // output decimated MBLayout (note: cannot work in-place) + int numWorker, int rank) + { + size_t numParallelSequences = pMBLayout->GetNumParallelSequences(); + size_t nT = pMBLayout->GetNumTimeSteps(); + + // decide start column and end column + size_t st = numParallelSequences * (size_t)rank / numWorker; + size_t en = numParallelSequences * (size_t)(rank + 1) / numWorker; + en = en > numParallelSequences ? numParallelSequences : en; // TODO: why are these two tests necessary? + en = (rank == numWorker - 1) ? numParallelSequences : en; + size_t numNewParallelSequence = en - st; + + // begin decimate matrices + size_t rv = 0; + for (const auto& it : MB) + { + wstring name = it.first; + MSR::CNTK::Matrix & mat = *it.second; + size_t numRows = mat.GetNumRows(); + size_t numCols = mat.GetNumCols(); + int devID = mat.GetDeviceId(); + + if (rv == 0) + rv = numCols; + else if (rv != numCols) + LogicError("DecimateMinibatch: Inconsistent number of columns among inputs (found %d and %d).", (int)rv, (int)numCols); + + if (nT != numCols / numParallelSequences) + LogicError("ERROR: MBLayout borked, GetNumTimeSteps() mismatches minibatch number of columns\n"); + + decimatedMB[name] = new Matrix(devID); + decimatedMB[name]->AssignRowSliceValuesOf(mat.Reshaped(numRows*numParallelSequences, nT), st*numRows, (en - st)*numRows); + decimatedMB[name]->Reshape(numRows, numNewParallelSequence*nT); + // If we had a RowSlice function, we would like to write in this way + // decimatedMB[name]->SetValue(mat.Reshaped(nRows*nSequence, nT).RowSlice( st*nRows , (en-st)*nRows).Reshaped(nRows, nNewParallelSequence*nT)); + } + // decimate MBLayout as well + pDecimateMBLayout = make_shared(numNewParallelSequence, nT); +#if 1 + // now copy over all sequence info records that are inside the range, with adjusted 's' + const auto & sequences = pMBLayout->GetAllSequences(); + for (const auto & seq : sequences) + { + if (seq.s >= st && seq.s < en) + { + auto shiftedSeq = seq; + shiftedSeq.s -= st; // these sequences have shifted up by 'st' sequences + pDecimateMBLayout->AddSequence(shiftedSeq); + } + } +#else + for (size_t t = 0; t < nT; t++) for (size_t id = 0; id < numNewParallelSequence; id++) + pDecimateMBLayout->Set(id, t, pMBLayout->Get(id + st, t)); +#endif + + return pair(st, en); + } + + // in-place decimation, for use with data-parallel processing + // return [st, en) parallell sequence which has been selected after decimation + template + static pair DecimateMinibatch(std::map*> &mb, // matrix to be decimated + int numprocs, int rank, // rank info + MBLayoutPtr pMBLayout) // get decimated as well + { + if (numprocs == 1) + return pair(0, pMBLayout->GetNumParallelSequences()); + // no need to do inplace decimation if numproc == 1 + + // allocate space for non-inplace decimation + MBLayoutPtr pDecimatedMB = make_shared(); + std::map*> decimatedMB; + // call in-place decimation + pair selected=DecimateMinibatch(mb, decimatedMB, pMBLayout, pDecimatedMB, numprocs, rank); + // move the data + for (auto k : mb) + { + auto name = k.first; + k.second->SetValue(*decimatedMB[name]); + delete decimatedMB[name]; + decimatedMB[name] = nullptr; + } + pMBLayout->MoveFrom(pDecimatedMB); + return selected; + } + + // =================================================================== + // SubminibatchHelpers -- helper for sub-minibatch implementation + // TODO: Can this just exist inside SGD.cpp? + // =================================================================== + + // A sub-minibathc is a part of a minibatch which helps computing large minibatches that cannot load into GPU memory in one forward-backward computation + // The usage would be : + // SubminibatchHelpers sbhelper; + // for (;;) + // { + // size_t nsb=sb.GetMinibatchIntoCache(...); + // for (size_t i=0; i + class SubminibatchDispatcher + { + private: + typedef std::vector> Lattice; + typedef std::vector Uid; + typedef std::vector ExtrauttMap; + typedef std::vector Boundaries; + + typedef std::vector>* LatticePtr; + typedef std::vector* UidPtr; + typedef std::vector* ExtrauttMapPtr; + typedef std::vector* BoundariesPtr; + typedef std::map*> Matrices; + + // member variables served as caching space + Matrices m_inputMatricesCache; + MBLayoutPtr m_MBLayoutCache; + Lattice m_LatticeCache; + Uid m_uidCache; + ExtrauttMap m_extrauttmapCache; + Boundaries m_BoundariesCache; + shared_ptr> m_NetCriterionAccumulator; + shared_ptr> m_NetEvaluationAccumulator; + std::map>> m_NetStates; // m_NetStatefulNodes[node][i] caches the state of i-th subminibatch of node + bool m_hasLattices; + + Matrices m_cachedGradient; + // we also need to remember where to put into the net + MBLayoutPtr m_NetMBLayoutPtr; + std::map>> m_LearnableNodePtr; + // followings are lattice-related + Matrices m_NetInputMatrixPtr; // TODO: camelCase for all m_Net... + LatticePtr m_NetLatticePtr; + UidPtr m_NetUidPtr; + ExtrauttMapPtr m_NetExtrauttMapPtr; + BoundariesPtr m_NetBoundariesPtr; + // we remember the pointer to the learnable Nodes so that we can accumulate the gradient once a sub-minibatch is done + + + size_t m_numParallelSequences; // number of paralle sequence in the cached matrix and MBLayout + size_t m_numSubminibatches; // how many subminibatches we are going to use ? + + std::vector>> m_NetCriterionNodes; + std::vector>> m_NetEvaluationNodes; + std::map> m_NetStatefulNodes; // we need to Export/Import states of stateful nodes when we swtich subminibatches + + private: + + void EnumerateStatefulNodeWithRoot(ComputationNetwork& net, ComputationNodeBasePtr root, std::map>& statefulnode) + { + const std::list evalorder = net.GetEvalOrder(root); + for (auto& x : evalorder) + { + wstring name = x->GetName(); + if (statefulnode.find(name) != statefulnode.end()) continue; // already in the list + shared_ptr pNode = dynamic_pointer_cast(x); + if (pNode) + { + statefulnode[name] = pNode; + } + } + } + + std::map> EnumerateStatefulNode(ComputationNetwork& net, + const std::vector& criterionNode, + const std::vector& evaluationNode) + { + std::map> statefulNodes; + for (auto& root : criterionNode) + { + EnumerateStatefulNodeWithRoot(net, root, statefulNodes); + } + for (auto& root : evaluationNode) + { + EnumerateStatefulNodeWithRoot(net, root, statefulNodes); + } + return statefulNodes; + } + + public: + SubminibatchDispatcher() : + m_MBLayoutCache(nullptr), m_NetLatticePtr(nullptr), m_NetExtrauttMapPtr(nullptr), m_NetUidPtr(nullptr), m_NetBoundariesPtr(nullptr) + { } + + void Init(ComputationNetworkPtr & net, + const std::list& learnableNodes, + const std::vector& criterionNodes, + const std::vector& evaluationNodes) + { + m_MBLayoutCache = make_shared(); + m_NetCriterionAccumulator = make_shared>(1, 1, net->GetDeviceId()); + m_NetEvaluationAccumulator = make_shared>(1, evaluationNodes.size(), net->GetDeviceId()); + // remember ptrs to learnable nodes + for (auto x : learnableNodes) + { + shared_ptr> pLearnableNode = dynamic_pointer_cast>(x); + wstring nodename = x->NodeName(); + m_LearnableNodePtr[nodename] = pLearnableNode; + } + for (auto& x : criterionNodes) + { + m_NetCriterionNodes.push_back(dynamic_pointer_cast>(x)); + } + for (auto& x : evaluationNodes) + { + m_NetEvaluationNodes.push_back(dynamic_pointer_cast>(x)); + } + m_NetCriterionAccumulator->SetValue((ElemType)0); + m_NetEvaluationAccumulator->SetValue((ElemType)0); + + // emulate all the nodes, find nodes that have state + m_NetStatefulNodes = EnumerateStatefulNode(*net, criterionNodes, evaluationNodes); + for (auto x : m_NetStatefulNodes) + { + wstring name = x.first; + m_NetStates[name] = vector>(); + } + + // for sequence training + if (criterionNodes[0]->OperationName() == L"SequenceWithSoftmax") + { + auto node = dynamic_pointer_cast>(criterionNodes[0]); + assert(node); + m_NetLatticePtr = node->getLatticePtr(); + m_NetExtrauttMapPtr = node->getextrauttmap(); + m_NetUidPtr = node->getuidprt(); + m_NetBoundariesPtr = node->getboundaryprt(); + m_hasLattices = true; + } + else + { + m_NetLatticePtr = nullptr; + m_NetExtrauttMapPtr = nullptr; + m_NetUidPtr = nullptr; + m_NetBoundariesPtr = nullptr; + m_hasLattices = false; + } + } + + ~SubminibatchDispatcher() + { + // TODO: remove these by using shared_ptr + + + for (auto x : m_inputMatricesCache) + { + delete x.second; + } + + for (auto x : m_cachedGradient) + { + delete x.second; + } + } + + size_t GetMinibatchIntoCache(IDataReader& trainSetDataReader, + ComputationNetwork& net, + std::map*> & inputMatrices, + size_t requestedSubminibatches) + { + // first, remember interface to the net + m_NetMBLayoutPtr = net.GetMBLayoutPtr(); + m_NetInputMatrixPtr = inputMatrices; + + // second, get data from reader, stored it in cache + // 1. for each key, allocate the specific matrix on device + for (auto pa : inputMatrices) + { + wstring name = pa.first; + Matrix* M = pa.second; + if (m_inputMatricesCache.find(name) == m_inputMatricesCache.end()) + { + m_inputMatricesCache[name] = new Matrix(*M, M->GetDeviceId()); // deep copy from M + } + else + { + m_inputMatricesCache[name]->SetValue(*M); + } + } + // 2. MBlayout + m_MBLayoutCache->CopyFrom(net.GetMBLayoutPtr()); + size_t nParallelSequences = m_MBLayoutCache->GetNumParallelSequences(); + + + // 3. for bits in seq. training + if (m_hasLattices) + { + m_LatticeCache.clear(); + m_uidCache.clear(); + m_extrauttmapCache.clear(); + m_BoundariesCache.clear(); + + m_LatticeCache = *m_NetLatticePtr; + m_uidCache = *m_NetUidPtr; + m_extrauttmapCache = *m_NetExtrauttMapPtr; + m_BoundariesCache = *m_NetBoundariesPtr; + } + + // subminibatches are cutted at the parallel sequence level; + // if #requested subminibatch is larger than #parallel sequence, + // we cannot split further; instead, each subsequence become a subminibatch + size_t actualnumSubminibatches = requestedSubminibatches > nParallelSequences ? nParallelSequences : requestedSubminibatches; + + // 4. third, allocate space for accumulated gradient + for (auto& n : m_LearnableNodePtr) + { + auto node = n.second; + if (node->IsParameterUpdateRequired()) + { + wstring nodeName = node->GetName(); + shared_ptr> pLearnableNode = node; + auto funvalue = pLearnableNode->Value(); // gradient may not be allocated when this function is first called + size_t nrow = funvalue.GetNumRows(); + size_t ncol = funvalue.GetNumCols(); + if (m_cachedGradient.find(nodeName) == m_cachedGradient.end()) + { + // not allocated yet + m_cachedGradient[nodeName] = new Matrix(nrow, ncol, funvalue.GetDeviceId()); + m_cachedGradient[nodeName]->SetValue((ElemType)0); + } + } + } + // 5. for stateful node + for (auto x : m_NetStatefulNodes) + { + wstring name = x.first; + if (m_NetStates[name].empty()) + { + // this only happens in the first minibatch in an epoch + m_NetStates[name].resize(actualnumSubminibatches); + } + } + + return (m_numSubminibatches = actualnumSubminibatches); + } + + + void DecimateLattices( + LatticePtr decimatedLattices, /* output: lattices after decimation*/ + BoundariesPtr decimatedBoundaryPtr, /* output: boundary after decimation*/ + ExtrauttMapPtr decimatedExtraMapPtr, /* output: extramap after decimation*/ + UidPtr decimatedUidPtr, /* output: Uid after decimation*/ + const Lattice lattices, /* input: lattices to be decimated */ + const Boundaries boundaries, /* input: boundary to be decimated */ + const ExtrauttMap extraMaps, /* input: extra map to be decimated */ + const Uid uids, /* input: uid to be decimated*/ + pair parallelSeqRange /* input: what parallel sequence range we are looking at */ + ) + { + size_t parallelSeqStId = parallelSeqRange.first; + size_t parallelSeqEnId = parallelSeqRange.second; + + decimatedLattices->clear(); + decimatedBoundaryPtr->clear(); + decimatedExtraMapPtr->clear(); + decimatedUidPtr->clear(); + + size_t stFrame = 0; + for (size_t iUtt = 0; iUtt < extraMaps.size(); iUtt++) + { + size_t numFramesInThisUtterance = lattices[iUtt]->getnumframes(); + size_t iParallelSeq = extraMaps[iUtt]; // i-th utterance belongs to iParallelSeq-th parallel sequence + if (iParallelSeq >= parallelSeqStId && iParallelSeq < parallelSeqEnId) + { + // this utterance has been selected + decimatedLattices->push_back(lattices[iUtt]); + decimatedBoundaryPtr->insert(decimatedBoundaryPtr->end(), boundaries.begin()+stFrame, boundaries.begin() + stFrame + numFramesInThisUtterance); + decimatedUidPtr->insert(decimatedUidPtr->end(), uids.begin() + stFrame, uids.begin() + stFrame + numFramesInThisUtterance); + decimatedExtraMapPtr->push_back(extraMaps[iUtt] - parallelSeqStId); + } + stFrame += numFramesInThisUtterance; + } + } + + void GetSubMinibatchToNet(size_t iSubminibatch) + { + Matrices decimatedMatrices; + MBLayoutPtr decimatedLayout; + pair seqRange = DataReaderHelpers::DecimateMinibatch(m_inputMatricesCache, decimatedMatrices, m_MBLayoutCache, decimatedLayout, m_numSubminibatches, iSubminibatch); + // NOTE: deimatedMatrices must be released by caller + + // base on the seqRange, we do the decimation for lattices and related variables + if (m_hasLattices){ + DecimateLattices( + /*output */ + m_NetLatticePtr, m_NetBoundariesPtr, m_NetExtrauttMapPtr, m_NetUidPtr, + /*input to be decimated */ + m_LatticeCache , m_BoundariesCache, m_extrauttmapCache, m_uidCache, + /* what range we want ? */ + seqRange + ); + } + + //m_NetInputMatrixPtr = decimatedMatrices; + for (auto& x : decimatedMatrices) + { + wstring name = x.first; + m_NetInputMatrixPtr[name]->SetValue(*x.second); + delete x.second; // TODO: is it safe to delete here ? Yes! SetValue call cuda memcpy so it is a blocking call + x.second = nullptr; + } + + m_NetMBLayoutPtr->CopyFrom(decimatedLayout); + + for (auto& x : m_NetStatefulNodes) + { + wstring name = x.first; + shared_ptr pNode = x.second; + if (m_NetStates[name][iSubminibatch]) + pNode->ImportState(std::move(m_NetStates[name][iSubminibatch])); + } + } + + // TODO: encapsulate it into a destructor? Note: Cannot throw exceptions in destructor. + void DoneWithCurrentSubMinibatch(size_t iSubminibatch) + { + // accumulate gradient here + for (auto x : m_cachedGradient) + { + wstring nodename = x.first; + if (m_LearnableNodePtr.find(nodename) == m_LearnableNodePtr.end()) + { + RuntimeError("ERROR: in DoneWithCurrentSubMinibatch: node %ls not found in LeanrableNode", nodename.c_str()); + } + shared_ptr> pNode = m_LearnableNodePtr[nodename]; + m_cachedGradient[nodename]->operator+=(pNode->Gradient()); + pNode->Gradient().SetValue((ElemType)0); + } + // accumulate criterion value + Matrix::AddElementToElement(m_NetCriterionNodes[0]->Value(), 0, 0, + *m_NetCriterionAccumulator, 0, 0); + m_NetCriterionNodes[0]->Value().SetValue((ElemType)0); + // accumulate evaluation value + for (size_t i = 0; i < m_NetEvaluationNodes.size(); i++) + { + Matrix::AddElementToElement(m_NetEvaluationNodes[i]->Value(), 0, 0, + *m_NetEvaluationAccumulator, 0, i); + m_NetEvaluationNodes[i]->Value().SetValue((ElemType)0); + } + + // Export node state + for (auto& x : m_NetStatefulNodes) + { + wstring name = x.first; + m_NetStates[name][iSubminibatch] = x.second->ExportState(); + } + } + + void DoneWithCurrentMinibatch() + { + for (auto& x : m_cachedGradient) + { + wstring name = x.first; + Matrix* accumulategrad = x.second; + + if (m_LearnableNodePtr.find(name) == m_LearnableNodePtr.end()) + { + // should never happen, remove this code later + RuntimeError("ERROR: in DoneWithCurrentSubMinibatch: node %ls not found in LearnableNode", name.c_str()); + } + m_LearnableNodePtr[name]->Gradient().SetValue(*accumulategrad); + x.second->SetValue((ElemType)0); + } + // also revert net.m_MBLayoutPtr + m_NetMBLayoutPtr->CopyFrom(m_MBLayoutCache); + + //m_NetCriterionNodes[0]->Value().SetValue((ElemType)0); + Matrix::AddElementToElement(*m_NetCriterionAccumulator, 0, 0, + m_NetCriterionNodes[0]->Value(), 0, 0); + m_NetCriterionAccumulator->SetValue((ElemType)0); + + for (size_t i = 0; i < m_NetEvaluationNodes.size(); i++) + { + //m_NetEvaluationNodes[i]->Value().SetValue((ElemType)0); + Matrix::AddElementToElement(*m_NetEvaluationAccumulator, 0, i, + m_NetEvaluationNodes[i]->Value(), 0, 0); + } + m_NetEvaluationAccumulator->SetValue((ElemType)0); + } + }; + }; + +}}} diff --git a/Source/SGDLib/MultiNetworksSGD.h b/Source/SGDLib/MultiNetworksSGD.h index a4851fdf828a..6ed7e722ad6d 100644 --- a/Source/SGDLib/MultiNetworksSGD.h +++ b/Source/SGDLib/MultiNetworksSGD.h @@ -1,1271 +1,1271 @@ -// MultiNetworksEvaluator/SGD -- This represents earlier efforts to use CNTK for sequence-to-sequence modeling. This is no longer the intended design. -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - -// TODO: this cannot be instantiated as a whole (compile error), although some function is called from CNTK.cpp--should be fixed - -#include "Basics.h" -#include "ComputationNetwork.h" -#include "IComputationNetBuilder.h" -#include "SimpleEvaluator.h" -#include "MultiNetworksEvaluator.h" -#include "DataReader.h" -#include -#include -#include -#include "fileutil.h" -#include "Config.h" -#include -#include -#include "TimerUtility.h" -#include "SGD.h" - -using namespace std; - -namespace Microsoft { namespace MSR { namespace CNTK { - - extern std::wstring GetEncoderModelNameForEpoch(int epoch, bool b = false); - extern std::wstring GetDecoderModelNameForEpoch(int epoch, bool b = false); - - template - class MultiNetworksSGD : SGD - { - ElemType m_default_activity; - - using SGDBase = SGD; - - public: - // TODO: use a macro similar to class ComputeNode - using SGDBase::m_modelPath; - using SGDBase::m_maxEpochs; - using SGDBase::m_doUnitTest; - using SGDBase::m_learnRateAdjustInterval; - using SGDBase::m_mbSize; - using SGDBase::m_momentumParam; using SGDBase::m_learningRatesParam; - using SGDBase::GetLearningRatePerSample; using SGDBase::GetMomentumPerSample; - using SGDBase::m_dropoutRates; - using SGDBase::m_autoLearnRateSearchType; - using SGDBase::m_minLearnRate; - using SGDBase::m_loadBestModel; - //using SGDBase::m_validateAfterModelReloading; - using SGDBase::m_continueReduce; - using SGDBase::m_reduceLearnRateIfImproveLessThan; - using SGDBase::m_epochSize; - using SGDBase::m_learnRateDecreaseFactor; - using SGDBase::m_increaseLearnRateIfImproveMoreThan; - using SGDBase::m_learnRateIncreaseFactor; - using SGDBase::m_keepCheckPointFiles; - using SGDBase::m_doGradientCheck; - using SGDBase::m_L2RegWeight; - using SGDBase::m_L1RegWeight; - using SGDBase::m_needAveMultiplier; - using SGDBase::m_useNesterovMomentum; - using SGDBase::m_traceLevel; - using SGDBase::m_numMBsToShowResult; - using SGDBase::m_gradientCheckSigDigit; - using SGDBase::m_prevChosenMinibatchSize; - using SGDBase::UpdateWeights; - using SGDBase::GetCheckPointFileNameForEpoch; - using SGDBase::GetTrainCriterionNodes; - using SGDBase::GetEvalCriterionNodes; - - typedef shared_ptr> ComputationNodePtr; - - /// for encoder and decoder nodes pairing - wstring m_decoderModelPath; - wstring m_backwardDecoderModelPath; - wstring m_encoderModelPath; - - list> m_lst_pair_encoder_decode_node_names; - list> m_lst_pair_encoder_decoder_nodes; - - public: - MultiNetworksSGD(const ConfigParameters& configSGD) : SGDBase(configSGD) - { - } - - ~MultiNetworksSGD() - { - } - - void InitTrainEncoderDecoderWithHiddenStates(const ConfigParameters& readerConfig) - { - - m_decoderModelPath = m_modelPath + L".decoder"; - m_backwardDecoderModelPath = m_modelPath + L".backward.decoder"; - m_encoderModelPath = m_modelPath + L".encoder"; - - ConfigArray arrEncoderNodeNames = readerConfig(L"encoderNodes", ""); - vector encoderNodeNames; - m_lst_pair_encoder_decode_node_names.clear(); - - if (arrEncoderNodeNames.size() > 0) - { - /// newer code that explicitly place multiple streams for inputs - foreach_index(i, arrEncoderNodeNames) // inputNames should map to node names - { - wstring nodeName = arrEncoderNodeNames[i]; - encoderNodeNames.push_back(nodeName); - } - } - - ConfigArray arrDecoderNodeNames = readerConfig(L"decoderNodes", ""); - vector decoderNodeNames; - if (arrDecoderNodeNames.size() > 0) - { - /// newer code that explicitly place multiple streams for inputs - foreach_index(i, arrDecoderNodeNames) // inputNames should map to node names - { - wstring nodeName = arrDecoderNodeNames[i]; - decoderNodeNames.push_back(nodeName); - } - } - - assert(encoderNodeNames.size() == decoderNodeNames.size()); - - for (size_t i = 0; i < encoderNodeNames.size(); i++) - { - m_lst_pair_encoder_decode_node_names.push_back(make_pair(encoderNodeNames[i], decoderNodeNames[i])); - fprintf(stderr, "paired %ls <-> %ls\n", encoderNodeNames[i].c_str(), decoderNodeNames[i].c_str()); - } - } - - void EncoderDecoder(vector*> netBuilder, DEVICEID_TYPE deviceId, - vector*> trainSetDataReader, - vector*> validationSetDataReader, - const bool makeMode) - { - if (validationSetDataReader.size() == 0) - InvalidArgument("validation set reader should not be null."); - - int startEpoch = DetermineEncoderDecoderStartEpoch(makeMode); - if (startEpoch == m_maxEpochs) - { - fprintf(stderr, "No further training is necessary.\n"); - return; - } - - size_t iNumNetworks = netBuilder.size(); - vector nets; - ComputationNetworkPtr eachNet; - for (size_t k = 0; k < iNumNetworks; k++) - { - wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1, false, msra::strfun::wstrprintf(L".%d", k)); - fprintf(stderr, "network model FileName=%ls\n", modelFileName.c_str()); - if (startEpoch >= 0) - fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); - if (k == 0) - { - eachNet = - startEpoch < 0 ? netBuilder[k]->BuildNetworkFromDescription() : ComputationNetwork::CreateFromFile(deviceId, modelFileName, FileOptions::fileOptionsBinary, true/*bAllowNoCriterionNode*/); - nets.push_back(eachNet); - } - else - { - eachNet = - startEpoch < 0 ? netBuilder[k]->BuildNetworkFromDescription(nets[k - 1].get()) : ComputationNetwork::CreateFromFile(deviceId, modelFileName, FileOptions::fileOptionsBinary, false/*bAllowNoCriterionNode*/, nets[k - 1].get()); - nets.push_back(eachNet); - } - } - - startEpoch = max(startEpoch, 0); - - if (m_doUnitTest) - { - if (nets[iNumNetworks - 1]->UnitTest() == false) - LogicError("unit test on decoder network not passed"); - - return; - } - - fprintf(stderr, "start training ...\n"); - TrainEncoderDecoderModel(startEpoch, nets, trainSetDataReader, validationSetDataReader); - } - - //return -1 if nothing exists - int DetermineEncoderDecoderStartEpoch(const bool makeMode) - { - if (!makeMode) - return -1; //always start from scratch - - int firstEpoch = -1; - - wstring curEpochFile = GetModelNameForEpoch(int(m_maxEpochs) - 1, false, L".0"); - for (int e = int(m_maxEpochs) - 1; e >= -1; e--) - { - const wstring prevEpochFile = GetModelNameForEpoch(e - 1, false, L".0"); - - if (msra::files::fuptodate(curEpochFile, prevEpochFile, false)) - { - firstEpoch = size_t(e) + 1; - break; - } - else - curEpochFile = prevEpochFile; - } - - return firstEpoch; - } - - wstring GetModelNameForEpoch(const int epoch, bool bLastModel = false, wstring ext = L"") - { - int epoch1Base = epoch + 1; - if (epoch1Base == m_maxEpochs || bLastModel) - return m_modelPath + ext; - else - return msra::strfun::wstrprintf(L"%s%s.%d", m_modelPath.c_str(), ext.c_str(), (int)epoch1Base); - } - - void TrainEncoderDecoderModel(int startEpoch, ComputationNetworkPtr encoderNet, - ComputationNetworkPtr decoderNet, - IDataReader* encoderTrainSetDataReader, - IDataReader* decoderTrainSetDataReader, - IDataReader* encoderValidationSetDataReader, - IDataReader* decoderValidationSetDataReader) - { - std::vector& encoderFeatureNodes = encoderNet->FeatureNodes(); - std::vector& encoderEvaluationNodes = encoderNet->OutputNodes(); - - std::vector& decoderFeatureNodes = decoderNet->FeatureNodes(); - std::vector& decoderLabelNodes = decoderNet->LabelNodes(); - std::vector& decoderCriterionNodes = GetTrainCriterionNodes(*decoderNet); - std::vector& decoderEvaluationNodes = GetEvalCriterionNodes(*decoderNet); - - std::map*> encoderInputMatrices, decoderInputMatrices; - for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(encoderFeatureNodes[i])->Value(); - for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(decoderFeatureNodes[i])->Value(); - for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(decoderLabelNodes[i])->Value(); - - //initializing weights and gradient holder - const std::list & encoderLearnableNodes = encoderNet->LearnableParameterNodes(encoderEvaluationNodes[0]); //only one criterion so far TODO: support multiple ones? - const std::list & decoderLearnableNodes = decoderNet->LearnableParameterNodes(decoderCriterionNodes[0]); - std::list learnableNodes; - for (auto nodeIter = encoderLearnableNodes.begin(); nodeIter != encoderLearnableNodes.end(); nodeIter++) - learnableNodes.push_back(*nodeIter); - for (auto nodeIter = decoderLearnableNodes.begin(); nodeIter != decoderLearnableNodes.end(); nodeIter++) - learnableNodes.push_back(*nodeIter); - - std::list> smoothedGradients; - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - smoothedGradients.push_back(Matrix(node->GetNumRows(), node->GetNumCols(), node->Value().GetDeviceId())); - } - - vector epochCriterion; - double avgCriterion, prevCriterion; - for (size_t i = 0; i < 2; i++) - epochCriterion.push_back(std::numeric_limits::infinity()); - avgCriterion = prevCriterion = std::numeric_limits::infinity(); - - size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; - - std::vector epochEvalErrors(decoderEvaluationNodes.size(), std::numeric_limits::infinity()); - - std::vector evalNodeNames; - for (size_t i = 0; iNodeName()); - - size_t totalSamplesSeen = 0; - double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; - - int m_numPrevLearnRates = 5; //used to control the upper learnining rate in LR search to reduce computation - vector prevLearnRates; - prevLearnRates.resize(m_numPrevLearnRates); - for (int i = 0; i::infinity(); - - //precompute mean and invStdDev nodes and save initial model - if (/// to-do doesn't support pre-compute such as MVN here - /// PreCompute(net, encoderTrainSetDataReader, encoderFeatureNodes, encoderlabelNodes, encoderInputMatrices) || - startEpoch == 0) - { - encoderNet->Save(GetEncoderModelNameForEpoch(int(startEpoch) - 1)); - decoderNet->Save(GetDecoderModelNameForEpoch(int(startEpoch) - 1)); - } - - bool learnRateInitialized = false; - if (startEpoch > 0) - learnRateInitialized = this->LoadCheckPointInfo(startEpoch - 1, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, m_prevChosenMinibatchSize); - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) - InvalidArgument("When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, or an explicit learning rate must be specified in config for the starting epoch."); - - ULONG dropOutSeed = 1; - double prevDropoutRate = 0; - - bool learnRateReduced = false; - - for (int i = int(startEpoch); i(*encoderNet, encoderEvaluationNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); - ComputationNetwork::SetDropoutRate(*decoderNet, decoderCriterionNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); - - //learning rate adjustment - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || (m_learningRatesParam.size() > 0 && m_learningRatesParam.size() > i)) - { - learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, encoderTrainSetDataReader->GetNumParallelSequences()); - } - else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) - { - NOT_IMPLEMENTED; - } - - learnRateInitialized = true; - - if (learnRatePerSample < m_minLearnRate) - { - fprintf(stderr, "Learn Rate Per Sample for Epoch[%lu] = %.8g is less than minLearnRate %.8g. Training stops.\n", i + 1, learnRatePerSample, m_minLearnRate); - break; - } - - TrainOneEpochEncoderDecoderWithHiddenStates(encoderNet, decoderNet, i, - m_epochSize, encoderTrainSetDataReader, - decoderTrainSetDataReader, learnRatePerSample, - encoderFeatureNodes, encoderEvaluationNodes, &encoderInputMatrices, - decoderFeatureNodes, decoderLabelNodes, decoderCriterionNodes, decoderEvaluationNodes, - &decoderInputMatrices, learnableNodes, smoothedGradients, - epochCriterion, epochEvalErrors, totalSamplesSeen); - - - auto t_end_epoch = clock(); - double epochTime = 1.0*(t_end_epoch - t_start_epoch) / (CLOCKS_PER_SEC); - - // fprintf(stderr, "Finished Epoch[%lu]: [Training Set] Train Loss Per Sample = %.8g ", i + 1, epochCriterion); - fprintf(stderr, "Finished Epoch[%lu]: [Training Set] Decoder Train Loss Per Sample = %.8g ", i + 1, epochCriterion[0]); - if (epochEvalErrors.size() == 1) - { - fprintf(stderr, "EvalErr Per Sample = %.8g Ave Learn Rate Per Sample = %.10g Epoch Time=%.8g\n", epochEvalErrors[0], learnRatePerSample, epochTime); - } - else - { - fprintf(stderr, "EvalErr Per Sample "); - for (size_t j = 0; jNodeName().c_str(), epochCriterion[i + 1]); - for (size_t j = 0; j evalforvalidation(*decoderNet); - vector cvEncoderSetTrainAndEvalNodes; - cvEncoderSetTrainAndEvalNodes.push_back(encoderEvaluationNodes[0]->NodeName()); - - vector cvDecoderSetTrainAndEvalNodes; - cvDecoderSetTrainAndEvalNodes.push_back(decoderCriterionNodes[0]->NodeName()); - cvDecoderSetTrainAndEvalNodes.push_back(decoderEvaluationNodes[0]->NodeName()); - - vector vScore = evalforvalidation.EvaluateEncoderDecoderWithHiddenStates( - encoderNet, decoderNet, - encoderValidationSetDataReader, - decoderValidationSetDataReader, cvEncoderSetTrainAndEvalNodes, - cvDecoderSetTrainAndEvalNodes, m_mbSize[i]); - fprintf(stderr, "Finished Epoch[%lu]: [Validation Set] Train Loss Per Sample = %.8g EvalErr Per Sample = %.8g\n", - i + 1, vScore[0], vScore[1]); - - epochCriterion[0] = vScore[0]; //the first one is the decoder training criterion. - } - - bool loadedPrevModel = false; - size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; - if (avgCriterion == std::numeric_limits::infinity()) - avgCriterion = epochCriterion[0]; - else - avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion)* avgCriterion + epochCriterion[0]) / (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) - { - if (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity()) - { - if (m_loadBestModel) - { - encoderNet->RereadPersistableParameters(GetEncoderModelNameForEpoch(i - 1)); - decoderNet->RereadPersistableParameters(GetDecoderModelNameForEpoch(i - 1)); - - size_t dummyMinibatchSize = 0; - this->LoadCheckPointInfo(i - 1, - /*out*/ totalSamplesSeen, - /*out*/ learnRatePerSample, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ dummyMinibatchSize); - fprintf(stderr, "Loaded the previous model which has better training criterion.\n"); - loadedPrevModel = true; - } - } - - if (m_continueReduce) - { - if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - if (learnRateReduced == false) - { - learnRateReduced = true; - } - else - { - decoderNet->Save(GetDecoderModelNameForEpoch(i, true)); - encoderNet->Save(GetEncoderModelNameForEpoch(i, true)); - fprintf(stderr, "Finished training and saved final model\n\n"); - break; - } - } - if (learnRateReduced) - { - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - } - else - { - if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan*prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - learnRatePerSample *= m_learnRateIncreaseFactor; - fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); - } - } - } - - if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) //not loading previous values then set them - { - prevCriterion = avgCriterion; - epochsNotCountedInAvgCriterion = 0; - } - - //persist model and check-point info - decoderNet->Save(GetDecoderModelNameForEpoch(i)); - encoderNet->Save(GetEncoderModelNameForEpoch(i)); - - size_t dummyMinibatchSize = 0; - this->LoadCheckPointInfo(i, - /*out*/ totalSamplesSeen, - /*out*/ learnRatePerSample, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ dummyMinibatchSize); - - if (!m_keepCheckPointFiles) - _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); //delete previous checkpiont file to save space - - if (learnRatePerSample < 1e-12) - fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", learnRatePerSample); - } - } - - void TrainEncoderDecoderModel(int startEpoch, vector nets, - vector*> trainDataReader, - vector*> validationDataReader) - { - size_t iNumNetworks = nets.size(); - vector*> featureNodes; - vector*> outputNodes; - vector*> pairNodes; - vector*> labelNodes; - vector*> criterionNodes; - vector*> evaluationNodes; - vector*>*> inputMatrices; - - for (size_t i = 0; i < iNumNetworks; i++) - { - auto * featPtr = &nets[i]->FeatureNodes(); - auto * lablPtr = &nets[i]->LabelNodes(); - featureNodes.push_back(featPtr); - outputNodes.push_back(&nets[i]->OutputNodes()); - pairNodes.push_back(&nets[i]->PairNodes()); - - labelNodes.push_back(lablPtr); - criterionNodes.push_back(&GetTrainCriterionNodes(nets[i])); - evaluationNodes.push_back(&GetEvalCriterionNodes(nets[i])); - - std::map*> *matrices; - matrices = new std::map*>(); - - for (size_t j = 0; j < featPtr->size(); j++) - { - (*matrices)[(*featPtr)[j]->NodeName()] = - &(dynamic_pointer_cast>((*featPtr)[j])->Value()); - } - - for (size_t j = 0; jsize(); j++) - { - (*matrices)[(*lablPtr)[j]->NodeName()] = - &(dynamic_pointer_cast>((*lablPtr)[j])->Value()); - } - inputMatrices.push_back(matrices); - } - - //initializing weights and gradient holder - std::list learnableNodes; - for (size_t i = 0; i < iNumNetworks; i++) - { - if (criterionNodes[i]->size() == 0) - { - for (auto ptr = evaluationNodes[i]->begin(); ptr != evaluationNodes[i]->end(); ptr++) - { - ComputationNodeBasePtr pptr = *ptr; - - const std::list & eachLearnableNodes = nets[i]->LearnableParameterNodes(pptr); //only one criterion so far TODO: support multiple ones? - for (auto nodeIter = eachLearnableNodes.begin(); nodeIter != eachLearnableNodes.end(); nodeIter++) - { - ComputationNodeBasePtr node = *nodeIter; - learnableNodes.push_back(node); - } - } - } - else - { - for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) - { - ComputationNodeBasePtr pptr = *ptr; - - const std::list & eachLearnableNodes = nets[i]->LearnableParameterNodes(pptr); //only one criterion so far TODO: support multiple ones? - for (auto nodeIter = eachLearnableNodes.begin(); nodeIter != eachLearnableNodes.end(); nodeIter++) - { - ComputationNodeBasePtr node = *nodeIter; - learnableNodes.push_back(node); - } - } - } - - //for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) - // nets[i]->BuildAndValidateSubNetwork(*ptr); - } - - - std::list> smoothedGradients; - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - smoothedGradients.push_back(Matrix(node->GetNumRows(), node->GetNumCols(), node->Value().GetDeviceId())); - } - - double epochCriterion, avgCriterion, prevCriterion; - epochCriterion = std::numeric_limits::infinity(); - avgCriterion = prevCriterion = std::numeric_limits::infinity(); - - size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; - - size_t iNumEvaluations = 0; - for (size_t i = 0; i < iNumNetworks; i++) - { - iNumEvaluations += evaluationNodes[i]->size(); - } - std::vector epochEvalErrors(iNumEvaluations, std::numeric_limits::infinity()); - - std::vector evalNodeNames; - for (size_t k = 0; k < iNumNetworks; k++) - { - for (auto ptr = evaluationNodes[k]->begin(); ptr != evaluationNodes[k]->end(); ptr++) - evalNodeNames.push_back((*ptr)->NodeName()); - } - - size_t totalSamplesSeen = 0; - double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; - - int m_numPrevLearnRates = 5; //used to control the upper learnining rate in LR search to reduce computation - vector prevLearnRates; - prevLearnRates.resize(m_numPrevLearnRates); - for (int i = 0; i::infinity(); - - //precompute mean and invStdDev nodes and save initial model - if (/// to-do doesn't support pre-compute such as MVN here - /// PreCompute(net, encoderTrainSetDataReader, encoderFeatureNodes, encoderlabelNodes, encoderInputMatrices) || - startEpoch == 0) - { - for (size_t k = 0; k < iNumNetworks; k++) - { - wstring tmpstr = msra::strfun::wstrprintf(L".%d", k); - nets[k]->Save(GetModelNameForEpoch(int(startEpoch) - 1, false, tmpstr)); - } - } - - bool learnRateInitialized = false; - if (startEpoch > 0) - { - size_t dummyMinibatchSize = 0; - this->LoadCheckPointInfo(startEpoch - 1, - /*out*/ totalSamplesSeen, - /*out*/ learnRatePerSample, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ dummyMinibatchSize); - } - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) - InvalidArgument("When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, or an explicit learning rate must be specified in config for the starting epoch."); - - ULONG dropOutSeed = 1; - double prevDropoutRate = 0; - - bool learnRateReduced = false; - - for (int i = int(startEpoch); isize() > 0) - ComputationNetwork::SetDropoutRate(nets[k], (*evaluationNodes[k])[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); - if (criterionNodes[k]->size() > 0) - ComputationNetwork::SetDropoutRate(nets[k], (*criterionNodes[k])[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); - } - - //learning rate adjustment - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || (m_learningRatesParam.size() > 0 && m_learningRatesParam.size() > i)) - { - learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, trainDataReader[0]->GetNumParallelSequences()); - } - else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) - { - NOT_IMPLEMENTED; - } - - learnRateInitialized = true; - - if (learnRatePerSample < m_minLearnRate) - { - fprintf(stderr, "Learn Rate Per Sample for Epoch[%d] = %.8g is less than minLearnRate %.8g. Training stops.\n", i + 1, learnRatePerSample, m_minLearnRate); - break; - } - - TrainOneEpochEncoderDecoderWithHiddenStates(i, m_epochSize, nets, - trainDataReader, - featureNodes, - pairNodes, - evaluationNodes, - inputMatrices, - labelNodes, - criterionNodes, - learnableNodes, - learnRatePerSample, - smoothedGradients, - epochCriterion, epochEvalErrors, totalSamplesSeen); - - - auto t_end_epoch = clock(); - double epochTime = 1.0*(t_end_epoch - t_start_epoch) / (CLOCKS_PER_SEC); - - /** - this is hacky. Only allow evaluatio on the first encoder->decoder pair - */ - size_t decoderIdx = iNumNetworks - 1; - IDataReader* decoderValidationSetDataReader = validationDataReader[decoderIdx]; - IDataReader* decoderTrainSetDataReader = trainDataReader[decoderIdx]; - ComputationNetworkPtr decoderNet = nets[decoderIdx]; - - fprintf(stderr, "Finished Epoch[%d]: [Training Set] Decoder Train Loss Per Sample = %.8g ", i + 1, epochCriterion); - if (epochEvalErrors.size() == 1) - { - fprintf(stderr, "EvalErr Per Sample = %.8g Ave Learn Rate Per Sample = %.10g Epoch Time=%.8g\n", epochEvalErrors[0], learnRatePerSample, epochTime); - } - else - { - fprintf(stderr, "EvalErr Per Sample "); - for (size_t j = 0; j evalforvalidation(decoderNet); - - double vScore = evalforvalidation.EvaluateEncoderDecoderWithHiddenStates( - nets, - validationDataReader, - m_mbSize[i]); - - fprintf(stderr, "Finished Epoch[%d]: [Validation Set] Loss Per Sample = %.8g \n ", i+1, vScore ); - - epochCriterion = vScore; - } - - bool loadedPrevModel = false; - size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; - if (avgCriterion == std::numeric_limits::infinity()) - avgCriterion = epochCriterion; - else - avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion)* avgCriterion + epochCriterion) / (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) - { - if (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity()) - { - if (m_loadBestModel) - { - //persist model and check-point info - for (size_t k = 0; k < iNumNetworks; k++) - { - nets[k]->RereadPersistableParameters(GetModelNameForEpoch(i, false, msra::strfun::wstrprintf(L".%d", k))); - nets[k]->ResetEvalTimeStamps(); - } - - size_t dummyLr = 0; - this->LoadCheckPointInfo(i - 1, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, dummyLr); - fprintf(stderr, "Loaded the previous model which has better training criterion.\n"); - loadedPrevModel = true; - } - } - - if (m_continueReduce) - { - if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - if (learnRateReduced == false) - { - learnRateReduced = true; - } - else - { - //persist model and check-point info - for (size_t k = 0; k < iNumNetworks; k++) - { - nets[k]->Save(GetModelNameForEpoch(i, true, msra::strfun::wstrprintf(L".%d", k))); - } - fprintf(stderr, "Finished training and saved final model\n\n"); - break; - } - } - if (learnRateReduced) - { - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - } - else - { - if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan*prevCriterion && prevCriterion != std::numeric_limits::infinity()) - { - learnRatePerSample *= m_learnRateIncreaseFactor; - fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); - } - } - } - - if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) //not loading previous values then set them - { - prevCriterion = avgCriterion; - epochsNotCountedInAvgCriterion = 0; - } - - //persist model and check-point info - for (size_t k = 0; k < iNumNetworks; k++) - { - nets[k]->Save(GetModelNameForEpoch(i, false, msra::strfun::wstrprintf(L".%d", k))); - } - - this->SaveCheckPointInfo(i, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, 0); - if (!m_keepCheckPointFiles) - _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); //delete previous checkpiont file to save space - - if (learnRatePerSample < 1e-12) - fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", learnRatePerSample); - } - - for (size_t i = 0; i < iNumNetworks; i++) - { - delete inputMatrices[i]; - } - } - - /// use hidden states between encoder and decoder to communicate between two networks - void TrainOneEpochEncoderDecoderWithHiddenStates( - const int epochNumber, - const size_t epochSize, - vector nets, /// encoder network - vector*> dataReader, - vector*> featureNodes, - vector*> pairNodes, - vector*> evaluationNodes, - vector*>*> inputMatrices, - vector*> labelNodes, - vector*> criterionNodes, - const std::list& learnableNodes, - const double learnRatePerSample, - std::list>& smoothedGradients, - double& epochCriterion, std::vector& epochEvalErrors, size_t& totalSamplesSeen) - { - ComputationNetworkPtr encoderNet = nets[0]; - ComputationNetworkPtr decoderNet = nets[1]; - DEVICEID_TYPE device = encoderNet->GetDeviceId(); - Matrix historyMat(device); - - double readTimeInMBs = 0, ComputeTimeInMBs = 0; - double epochCriterionLastMBs = 0; - - int numSamplesLastMBs = 0; - std::vector epochEvalErrorsLastMBs(epochEvalErrors.size(), 0); - - clock_t startReadMBTime = 0, startComputeMBTime = 0; - clock_t endReadMBTime = 0, endComputeMBTime = 0; - - //initialize statistics - size_t totalEpochSamples = 0; - - int numMBsRun = 0; - - size_t numEvalNodes = epochEvalErrors.size(); - - // NOTE: the following two local matrices are not used in PTask path - Matrix localEpochCriterion(1, 2, decoderNet->GetDeviceId()); //assume only one training criterion node for each epoch - Matrix localEpochEvalErrors(1, numEvalNodes, decoderNet->GetDeviceId()); - - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - - for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++) - { - (*ptr)->StartMinibatchLoop(m_mbSize[epochNumber], epochNumber, m_epochSize); - } - - startReadMBTime = clock(); - - size_t iNumNetworks = nets.size(); - - unsigned uSeedForDataReader = epochNumber; - - bool bContinueDecoding = true; - while (bContinueDecoding) - { - size_t i = 0; - for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++, i++) - { - IDataReader* pptr = (*ptr); - pptr->SetRandomSeed(uSeedForDataReader); - if (i == 0) - pptr->GetMinibatch(*(inputMatrices[i])); - else - if (pptr->GetMinibatch(*(inputMatrices[i])) == false) - { - bContinueDecoding = false; - break; - } - } - - if (!bContinueDecoding) - break; - - size_t actualMBSize = decoderNet->DetermineActualMBSizeFromFeatures(); - if (actualMBSize == 0) - LogicError("decoderTrainSetDataReader read data but decoderNet reports no data read"); - - - for (size_t i = 0; i < iNumNetworks; i++) - { - ComputationNetwork::BumpEvalTimeStamp(*featureNodes[i]); - if (labelNodes[i]->size() > 0) - ComputationNetwork::BumpEvalTimeStamp(*labelNodes[i]); - } - - endReadMBTime = clock(); - startComputeMBTime = clock(); - - /// not the sentence begining, because the initial hidden layer activity is from the encoder network - // decoderTrainSetDataReader->SetSentenceBegin(false); - // decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->m_mbLayout.m_sentenceBoundaryFlags); - // decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->m_sentenceBegin); - - if (m_doGradientCheck) - { - if (EncoderDecoderGradientCheck(nets, - dataReader, - evaluationNodes, - pairNodes, - featureNodes, - criterionNodes, - localEpochCriterion, localEpochEvalErrors) == false) - { - RuntimeError("SGD::TrainOneEpochEncoderDecoderWithHiddenStates gradient check not passed!"); - } - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - } - - EncoderDecoderWithHiddenStatesForwardPass(nets, - dataReader, pairNodes, evaluationNodes, - featureNodes, criterionNodes, - localEpochCriterion, localEpochEvalErrors); - - EncoderDecoderWithHiddenStatesErrorProp(nets, pairNodes, criterionNodes); - - //update model parameters - if (learnRatePerSample > m_minLearnRate * 0.01) - { - auto smoothedGradientIter = smoothedGradients.begin(); - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++, smoothedGradientIter++) - { - ComputationNodeBasePtr node = *nodeIter; - if (node->IsParameterUpdateRequired()) - { - Matrix& smoothedGradient = (*smoothedGradientIter); - - UpdateWeights(node, smoothedGradient, learnRatePerSample, GetMomentumPerSample(epochNumber/*BUGBUG workaround:*/, dataReader[0]->GetNumParallelSequences()), actualMBSize, m_L2RegWeight, m_L1RegWeight, m_needAveMultiplier, m_useNesterovMomentum); - } - } - } - - endComputeMBTime = clock(); - numMBsRun++; - if (m_traceLevel > 0) - { - double MBReadTime = (double)(endReadMBTime - startReadMBTime) / (CLOCKS_PER_SEC); - double MBComputeTime = (double)(endComputeMBTime - startComputeMBTime) / CLOCKS_PER_SEC; - - readTimeInMBs += MBReadTime; - ComputeTimeInMBs += MBComputeTime; - numSamplesLastMBs += int(actualMBSize); - - if (numMBsRun % m_numMBsToShowResult == 0) - { - - epochCriterion = localEpochCriterion.Get00Element(); - for (size_t i = 0; i< numEvalNodes; i++) - epochEvalErrors[i] = (const double)localEpochEvalErrors(0, i); - - double llk = (epochCriterion - epochCriterionLastMBs) / numSamplesLastMBs; - double ppl = exp(llk); - fprintf(stderr, "Epoch[%d]-Minibatch[%d-%d]: Samples Seen = %d Decoder Train Loss Per Sample = %.8g PPL = %.4e ", epochNumber + 1, numMBsRun - m_numMBsToShowResult + 1, numMBsRun, numSamplesLastMBs, - llk, ppl); - for (size_t i = 0; i= epochSize) - break; - - /// call DataEnd function - /// DataEnd does reader specific process if sentence ending is reached - // encoderTrainSetDataReader->SetSentenceEnd(true); - // decoderTrainSetDataReader->SetSentenceEnd(true); - for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++) - { - (*ptr)->DataEnd(endDataSentence); - } - - uSeedForDataReader++; - } - - localEpochCriterion /= float(totalEpochSamples); - localEpochEvalErrors /= float(totalEpochSamples); - - epochCriterion = localEpochCriterion.Get00Element(); - for (size_t i = 0; i < numEvalNodes; i++) - { - epochEvalErrors[i] = localEpochEvalErrors(0, i); - } - fprintf(stderr, "total samples in epoch[%d] = %zd\n", epochNumber, totalEpochSamples); - } - - bool EncoderDecoderGradientCheck( - vector nets, /// encoder network - vector*> dataReader, - vector*> evaluationNodes, - vector*> pairNodes, - vector*> featureNodes, - vector*> criterionNodes, - Matrix& localEpochCriterion, - Matrix& localEpochEvalErrors - ) - { - size_t iNumNetworks = nets.size(); - vector verror_msgs; - DEVICEID_TYPE deviceId; - - for (int i = iNumNetworks - 1; i >= 0; i--) - { - /// check decoder learnable parameters - const std::list & learnableNodes = - (evaluationNodes[i]->size() == 0 && pairNodes[i]->size() > 0) ? - nets[i]->LearnableParameterNodes((*pairNodes[i])[0]) - : nets[i]->LearnableParameterNodes((*evaluationNodes[i])[0]); - - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - - for (size_t itry = 0; itry < min((size_t)10, node->Value().GetNumElements()); itry++) - { - - int irow = (int)fmod(rand(), node->GetNumRows() - 1); - int icol = (int)fmod(rand(), node->GetNumCols() - 1); - irow = max(0, irow); - icol = max(0, icol); - - fprintf(stderr, "\n###### d%ls######\n", node->NodeName().c_str()); - deviceId = node->Value().GetDeviceId(); // original device id - - node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); - double eOrg = node->Value()(irow, icol); /// warning :: this function will put matrix into CPU - node->Value().TransferToDeviceIfNotThere(deviceId, true); - - /// perturb parameter - double ePos = eOrg + EPSILON; - node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); - node->Value().SetValue(irow, icol, (ElemType)ePos); - node->Value().TransferToDeviceIfNotThere(deviceId, true); - - node->BumpEvalTimeStamp(); - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - - EncoderDecoderWithHiddenStatesForwardPass(nets, - dataReader, pairNodes, evaluationNodes, - featureNodes, criterionNodes, - localEpochCriterion, localEpochEvalErrors); - - double score1 = localEpochCriterion.Get00Element(); - - double eNeg = eOrg - EPSILON; - node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); - node->Value().SetValue(irow, icol, (ElemType)eNeg); - node->Value().TransferToDeviceIfNotThere(deviceId, true); - node->BumpEvalTimeStamp(); - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - - EncoderDecoderWithHiddenStatesForwardPass(nets, - dataReader, pairNodes, evaluationNodes, - featureNodes, criterionNodes, - localEpochCriterion, localEpochEvalErrors); - - double score1r = localEpochCriterion.Get00Element(); - - double grdNum = (score1r - score1) / (eNeg - ePos); - - node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); - node->Value().SetValue(irow, icol, (ElemType)eOrg); - node->Value().TransferToDeviceIfNotThere(deviceId, true); - node->BumpEvalTimeStamp(); - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - - EncoderDecoderWithHiddenStatesForwardPass(nets, - dataReader, pairNodes, evaluationNodes, - featureNodes, criterionNodes, - localEpochCriterion, localEpochEvalErrors); - - EncoderDecoderWithHiddenStatesErrorProp(nets, pairNodes, criterionNodes); - - node->Gradient().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); - double grdErr = node->Gradient()(irow, icol); - node->Gradient().TransferToDeviceIfNotThere(deviceId, true); - - // check if they are consistent - double threshold = pow(10.0, max(0.0, ceil(log10(min(fabs(grdErr), fabs(grdNum))))) - (int)m_gradientCheckSigDigit); - double diff = fabs(grdErr - grdNum); - bool wrong = (std::isnan(diff) || diff > threshold); - if (wrong) - { - char serr[2048]; - sprintf((char*)serr, "Decoder %ls Numeric gradient = %e, Error BP gradient = %e", node->NodeName().c_str(), static_cast(grdNum), static_cast(grdErr)); - fprintf(stdout, "%s\n", serr); - verror_msgs.push_back(serr); - } - } - } - } - - if (verror_msgs.size() > 0) - return false; - return true; - } - - void EncoderDecoderWithHiddenStatesForwardPass( - vector & nets, // TODO: should these vectors all be refs? - vector*> & dataReader, - vector*> & pairNodes, - vector*> & evaluationNodes, - vector*> & /*featureNodes*/, - vector*> & criterionNodes, - Matrix& localEpochCriterion, - Matrix& localEpochEvalErrors - ) - { - size_t iNumNetworks = nets.size(); - - for (size_t i = 0; i < iNumNetworks - 1; i++) - { - size_t j = i + 1; - - EncoderDecoderWithHiddenStatesForwardPass(nets[i], nets[j], - dataReader[i], dataReader[j], - *pairNodes[i], - *criterionNodes[j], - *evaluationNodes[j], - *pairNodes[j], - localEpochCriterion, localEpochEvalErrors); - } - } - - void EncoderDecoderWithHiddenStatesForwardPass( - ComputationNetworkPtr encoderNet, /// encoder network - ComputationNetworkPtr decoderNet, - IDataReader* encoderTrainSetDataReader, - IDataReader* decoderTrainSetDataReader, - vector& encoderEvaluationNodes, - vector& decoderCriterionNodes, - vector& decoderEvaluationNodes, - vector& decoderPairNodes, - Matrix& localEpochCriterion, - Matrix& localEpochEvalErrors - ) - { - //encoderNet->SetActualMiniBatchSizeFromFeatures(); - encoderTrainSetDataReader->CopyMBLayoutTo(encoderNet->GetMBLayoutPtr()); - encoderNet->VerifyActualNumParallelSequences(encoderTrainSetDataReader->GetNumParallelSequences()); - - encoderNet->ForwardProp(encoderEvaluationNodes[0]); - - //decoderNet->SetActualMiniBatchSizeFromFeatures(); - decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->GetMBLayoutPtr()); - decoderNet->VerifyActualNumParallelSequences(decoderTrainSetDataReader->GetNumParallelSequences()); - /// not the sentence begining, because the initial hidden layer activity is from the encoder network - - if (decoderCriterionNodes.size() == 0 && decoderEvaluationNodes.size() == 0) - { - decoderNet->ForwardProp(decoderPairNodes[0]); - } - else - { - decoderNet->ForwardProp(decoderCriterionNodes[0]); - - Matrix::AddElementToElement(dynamic_pointer_cast>(decoderCriterionNodes[0])->Value(), 0, 0, localEpochCriterion, 0, 0); - - size_t numEvalNodes = decoderEvaluationNodes.size(); - std::vectormbEvalErrors(numEvalNodes, 0); - - for (size_t i = 0; i < numEvalNodes; i++) - { - decoderNet->ForwardProp(decoderEvaluationNodes[i]); - Matrix::AddElementToElement(dynamic_pointer_cast>(decoderEvaluationNodes[i])->Value(), 0, 0, localEpochEvalErrors, 0, i); - } -#ifdef DEBUG_DECODER - fprintf(stderr, "ForwardPass score = %.8e\n", localEpochCriterion.Get00Element()); -#endif - } - } - - void EncoderDecoderWithHiddenStatesErrorProp( - vector networks, /// encoder network - vector*> pairNodes, - vector*> criterionNodes) - { - /** - the networks are organized in the forward pass - */ - size_t inetworks = networks.size(); - if (inetworks != criterionNodes.size()) - LogicError("EncoderDecoderWithHiddenStatesErrorProp: number of networks should be the same size as the number of criteron nodes."); - - for (size_t i = 0; i < pairNodes.size(); i++) - { - for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) - networks[i]->ZeroGradients(*ptr); - } - - for (size_t i = 0; i < criterionNodes.size(); i++) - { - for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) - networks[i]->ZeroGradients(*ptr); - } - - for (auto ptr = criterionNodes[inetworks - 1]->begin(); ptr != criterionNodes[inetworks - 1]->end(); ptr++) - { - if (ptr == criterionNodes[inetworks - 1]->begin()) - { - networks[inetworks - 1]->ForwardProp(*ptr); - networks[inetworks - 1]->Backprop(*ptr); - } - else - { - networks[inetworks - 1]->ForwardProp(*ptr); -#if 1 // disable this, so that we can remove the options from Backprop() (trivial to bring back if ever needed) - NOT_IMPLEMENTED; -#else - // This is the old signature of Backprop() +// MultiNetworksEvaluator/SGD -- This represents earlier efforts to use CNTK for sequence-to-sequence modeling. This is no longer the intended design. +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + +// TODO: this cannot be instantiated as a whole (compile error), although some function is called from CNTK.cpp--should be fixed + +#include "Basics.h" +#include "ComputationNetwork.h" +#include "IComputationNetBuilder.h" +#include "SimpleEvaluator.h" +#include "MultiNetworksEvaluator.h" +#include "DataReader.h" +#include +#include +#include +#include "fileutil.h" +#include "Config.h" +#include +#include +#include "TimerUtility.h" +#include "SGD.h" + +using namespace std; + +namespace Microsoft { namespace MSR { namespace CNTK { + + extern std::wstring GetEncoderModelNameForEpoch(int epoch, bool b = false); + extern std::wstring GetDecoderModelNameForEpoch(int epoch, bool b = false); + + template + class MultiNetworksSGD : SGD + { + ElemType m_default_activity; + + using SGDBase = SGD; + + public: + // TODO: use a macro similar to class ComputeNode + using SGDBase::m_modelPath; + using SGDBase::m_maxEpochs; + using SGDBase::m_doUnitTest; + using SGDBase::m_learnRateAdjustInterval; + using SGDBase::m_mbSize; + using SGDBase::m_momentumParam; using SGDBase::m_learningRatesParam; + using SGDBase::GetLearningRatePerSample; using SGDBase::GetMomentumPerSample; + using SGDBase::m_dropoutRates; + using SGDBase::m_autoLearnRateSearchType; + using SGDBase::m_minLearnRate; + using SGDBase::m_loadBestModel; + //using SGDBase::m_validateAfterModelReloading; + using SGDBase::m_continueReduce; + using SGDBase::m_reduceLearnRateIfImproveLessThan; + using SGDBase::m_epochSize; + using SGDBase::m_learnRateDecreaseFactor; + using SGDBase::m_increaseLearnRateIfImproveMoreThan; + using SGDBase::m_learnRateIncreaseFactor; + using SGDBase::m_keepCheckPointFiles; + using SGDBase::m_doGradientCheck; + using SGDBase::m_L2RegWeight; + using SGDBase::m_L1RegWeight; + using SGDBase::m_needAveMultiplier; + using SGDBase::m_useNesterovMomentum; + using SGDBase::m_traceLevel; + using SGDBase::m_numMBsToShowResult; + using SGDBase::m_gradientCheckSigDigit; + using SGDBase::m_prevChosenMinibatchSize; + using SGDBase::UpdateWeights; + using SGDBase::GetCheckPointFileNameForEpoch; + using SGDBase::GetTrainCriterionNodes; + using SGDBase::GetEvalCriterionNodes; + + typedef shared_ptr> ComputationNodePtr; + + /// for encoder and decoder nodes pairing + wstring m_decoderModelPath; + wstring m_backwardDecoderModelPath; + wstring m_encoderModelPath; + + list> m_lst_pair_encoder_decode_node_names; + list> m_lst_pair_encoder_decoder_nodes; + + public: + MultiNetworksSGD(const ConfigParameters& configSGD) : SGDBase(configSGD) + { + } + + ~MultiNetworksSGD() + { + } + + void InitTrainEncoderDecoderWithHiddenStates(const ConfigParameters& readerConfig) + { + + m_decoderModelPath = m_modelPath + L".decoder"; + m_backwardDecoderModelPath = m_modelPath + L".backward.decoder"; + m_encoderModelPath = m_modelPath + L".encoder"; + + ConfigArray arrEncoderNodeNames = readerConfig(L"encoderNodes", ""); + vector encoderNodeNames; + m_lst_pair_encoder_decode_node_names.clear(); + + if (arrEncoderNodeNames.size() > 0) + { + /// newer code that explicitly place multiple streams for inputs + foreach_index(i, arrEncoderNodeNames) // inputNames should map to node names + { + wstring nodeName = arrEncoderNodeNames[i]; + encoderNodeNames.push_back(nodeName); + } + } + + ConfigArray arrDecoderNodeNames = readerConfig(L"decoderNodes", ""); + vector decoderNodeNames; + if (arrDecoderNodeNames.size() > 0) + { + /// newer code that explicitly place multiple streams for inputs + foreach_index(i, arrDecoderNodeNames) // inputNames should map to node names + { + wstring nodeName = arrDecoderNodeNames[i]; + decoderNodeNames.push_back(nodeName); + } + } + + assert(encoderNodeNames.size() == decoderNodeNames.size()); + + for (size_t i = 0; i < encoderNodeNames.size(); i++) + { + m_lst_pair_encoder_decode_node_names.push_back(make_pair(encoderNodeNames[i], decoderNodeNames[i])); + fprintf(stderr, "paired %ls <-> %ls\n", encoderNodeNames[i].c_str(), decoderNodeNames[i].c_str()); + } + } + + void EncoderDecoder(vector*> netBuilder, DEVICEID_TYPE deviceId, + vector*> trainSetDataReader, + vector*> validationSetDataReader, + const bool makeMode) + { + if (validationSetDataReader.size() == 0) + InvalidArgument("validation set reader should not be null."); + + int startEpoch = DetermineEncoderDecoderStartEpoch(makeMode); + if (startEpoch == m_maxEpochs) + { + fprintf(stderr, "No further training is necessary.\n"); + return; + } + + size_t iNumNetworks = netBuilder.size(); + vector nets; + ComputationNetworkPtr eachNet; + for (size_t k = 0; k < iNumNetworks; k++) + { + wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1, false, msra::strfun::wstrprintf(L".%d", k)); + fprintf(stderr, "network model FileName=%ls\n", modelFileName.c_str()); + if (startEpoch >= 0) + fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); + if (k == 0) + { + eachNet = + startEpoch < 0 ? netBuilder[k]->BuildNetworkFromDescription() : ComputationNetwork::CreateFromFile(deviceId, modelFileName, FileOptions::fileOptionsBinary, true/*bAllowNoCriterionNode*/); + nets.push_back(eachNet); + } + else + { + eachNet = + startEpoch < 0 ? netBuilder[k]->BuildNetworkFromDescription(nets[k - 1].get()) : ComputationNetwork::CreateFromFile(deviceId, modelFileName, FileOptions::fileOptionsBinary, false/*bAllowNoCriterionNode*/, nets[k - 1].get()); + nets.push_back(eachNet); + } + } + + startEpoch = max(startEpoch, 0); + + if (m_doUnitTest) + { + if (nets[iNumNetworks - 1]->UnitTest() == false) + LogicError("unit test on decoder network not passed"); + + return; + } + + fprintf(stderr, "start training ...\n"); + TrainEncoderDecoderModel(startEpoch, nets, trainSetDataReader, validationSetDataReader); + } + + //return -1 if nothing exists + int DetermineEncoderDecoderStartEpoch(const bool makeMode) + { + if (!makeMode) + return -1; //always start from scratch + + int firstEpoch = -1; + + wstring curEpochFile = GetModelNameForEpoch(int(m_maxEpochs) - 1, false, L".0"); + for (int e = int(m_maxEpochs) - 1; e >= -1; e--) + { + const wstring prevEpochFile = GetModelNameForEpoch(e - 1, false, L".0"); + + if (msra::files::fuptodate(curEpochFile, prevEpochFile, false)) + { + firstEpoch = size_t(e) + 1; + break; + } + else + curEpochFile = prevEpochFile; + } + + return firstEpoch; + } + + wstring GetModelNameForEpoch(const int epoch, bool bLastModel = false, wstring ext = L"") + { + int epoch1Base = epoch + 1; + if (epoch1Base == m_maxEpochs || bLastModel) + return m_modelPath + ext; + else + return msra::strfun::wstrprintf(L"%s%s.%d", m_modelPath.c_str(), ext.c_str(), (int)epoch1Base); + } + + void TrainEncoderDecoderModel(int startEpoch, ComputationNetworkPtr encoderNet, + ComputationNetworkPtr decoderNet, + IDataReader* encoderTrainSetDataReader, + IDataReader* decoderTrainSetDataReader, + IDataReader* encoderValidationSetDataReader, + IDataReader* decoderValidationSetDataReader) + { + std::vector& encoderFeatureNodes = encoderNet->FeatureNodes(); + std::vector& encoderEvaluationNodes = encoderNet->OutputNodes(); + + std::vector& decoderFeatureNodes = decoderNet->FeatureNodes(); + std::vector& decoderLabelNodes = decoderNet->LabelNodes(); + std::vector& decoderCriterionNodes = GetTrainCriterionNodes(*decoderNet); + std::vector& decoderEvaluationNodes = GetEvalCriterionNodes(*decoderNet); + + std::map*> encoderInputMatrices, decoderInputMatrices; + for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(encoderFeatureNodes[i])->Value(); + for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(decoderFeatureNodes[i])->Value(); + for (size_t i = 0; iNodeName()] = &dynamic_pointer_cast>(decoderLabelNodes[i])->Value(); + + //initializing weights and gradient holder + const std::list & encoderLearnableNodes = encoderNet->LearnableParameterNodes(encoderEvaluationNodes[0]); //only one criterion so far TODO: support multiple ones? + const std::list & decoderLearnableNodes = decoderNet->LearnableParameterNodes(decoderCriterionNodes[0]); + std::list learnableNodes; + for (auto nodeIter = encoderLearnableNodes.begin(); nodeIter != encoderLearnableNodes.end(); nodeIter++) + learnableNodes.push_back(*nodeIter); + for (auto nodeIter = decoderLearnableNodes.begin(); nodeIter != decoderLearnableNodes.end(); nodeIter++) + learnableNodes.push_back(*nodeIter); + + std::list> smoothedGradients; + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + smoothedGradients.push_back(Matrix(node->GetNumRows(), node->GetNumCols(), node->Value().GetDeviceId())); + } + + vector epochCriterion; + double avgCriterion, prevCriterion; + for (size_t i = 0; i < 2; i++) + epochCriterion.push_back(std::numeric_limits::infinity()); + avgCriterion = prevCriterion = std::numeric_limits::infinity(); + + size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; + + std::vector epochEvalErrors(decoderEvaluationNodes.size(), std::numeric_limits::infinity()); + + std::vector evalNodeNames; + for (size_t i = 0; iNodeName()); + + size_t totalSamplesSeen = 0; + double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; + + int m_numPrevLearnRates = 5; //used to control the upper learnining rate in LR search to reduce computation + vector prevLearnRates; + prevLearnRates.resize(m_numPrevLearnRates); + for (int i = 0; i::infinity(); + + //precompute mean and invStdDev nodes and save initial model + if (/// to-do doesn't support pre-compute such as MVN here + /// PreCompute(net, encoderTrainSetDataReader, encoderFeatureNodes, encoderlabelNodes, encoderInputMatrices) || + startEpoch == 0) + { + encoderNet->Save(GetEncoderModelNameForEpoch(int(startEpoch) - 1)); + decoderNet->Save(GetDecoderModelNameForEpoch(int(startEpoch) - 1)); + } + + bool learnRateInitialized = false; + if (startEpoch > 0) + learnRateInitialized = this->LoadCheckPointInfo(startEpoch - 1, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, m_prevChosenMinibatchSize); + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) + InvalidArgument("When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, or an explicit learning rate must be specified in config for the starting epoch."); + + ULONG dropOutSeed = 1; + double prevDropoutRate = 0; + + bool learnRateReduced = false; + + for (int i = int(startEpoch); i(*encoderNet, encoderEvaluationNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); + ComputationNetwork::SetDropoutRate(*decoderNet, decoderCriterionNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); + + //learning rate adjustment + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || (m_learningRatesParam.size() > 0 && m_learningRatesParam.size() > i)) + { + learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, encoderTrainSetDataReader->GetNumParallelSequences()); + } + else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) + { + NOT_IMPLEMENTED; + } + + learnRateInitialized = true; + + if (learnRatePerSample < m_minLearnRate) + { + fprintf(stderr, "Learn Rate Per Sample for Epoch[%lu] = %.8g is less than minLearnRate %.8g. Training stops.\n", i + 1, learnRatePerSample, m_minLearnRate); + break; + } + + TrainOneEpochEncoderDecoderWithHiddenStates(encoderNet, decoderNet, i, + m_epochSize, encoderTrainSetDataReader, + decoderTrainSetDataReader, learnRatePerSample, + encoderFeatureNodes, encoderEvaluationNodes, &encoderInputMatrices, + decoderFeatureNodes, decoderLabelNodes, decoderCriterionNodes, decoderEvaluationNodes, + &decoderInputMatrices, learnableNodes, smoothedGradients, + epochCriterion, epochEvalErrors, totalSamplesSeen); + + + auto t_end_epoch = clock(); + double epochTime = 1.0*(t_end_epoch - t_start_epoch) / (CLOCKS_PER_SEC); + + // fprintf(stderr, "Finished Epoch[%lu]: [Training Set] Train Loss Per Sample = %.8g ", i + 1, epochCriterion); + fprintf(stderr, "Finished Epoch[%lu]: [Training Set] Decoder Train Loss Per Sample = %.8g ", i + 1, epochCriterion[0]); + if (epochEvalErrors.size() == 1) + { + fprintf(stderr, "EvalErr Per Sample = %.8g Ave Learn Rate Per Sample = %.10g Epoch Time=%.8g\n", epochEvalErrors[0], learnRatePerSample, epochTime); + } + else + { + fprintf(stderr, "EvalErr Per Sample "); + for (size_t j = 0; jNodeName().c_str(), epochCriterion[i + 1]); + for (size_t j = 0; j evalforvalidation(*decoderNet); + vector cvEncoderSetTrainAndEvalNodes; + cvEncoderSetTrainAndEvalNodes.push_back(encoderEvaluationNodes[0]->NodeName()); + + vector cvDecoderSetTrainAndEvalNodes; + cvDecoderSetTrainAndEvalNodes.push_back(decoderCriterionNodes[0]->NodeName()); + cvDecoderSetTrainAndEvalNodes.push_back(decoderEvaluationNodes[0]->NodeName()); + + vector vScore = evalforvalidation.EvaluateEncoderDecoderWithHiddenStates( + encoderNet, decoderNet, + encoderValidationSetDataReader, + decoderValidationSetDataReader, cvEncoderSetTrainAndEvalNodes, + cvDecoderSetTrainAndEvalNodes, m_mbSize[i]); + fprintf(stderr, "Finished Epoch[%lu]: [Validation Set] Train Loss Per Sample = %.8g EvalErr Per Sample = %.8g\n", + i + 1, vScore[0], vScore[1]); + + epochCriterion[0] = vScore[0]; //the first one is the decoder training criterion. + } + + bool loadedPrevModel = false; + size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; + if (avgCriterion == std::numeric_limits::infinity()) + avgCriterion = epochCriterion[0]; + else + avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion)* avgCriterion + epochCriterion[0]) / (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) + { + if (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity()) + { + if (m_loadBestModel) + { + encoderNet->RereadPersistableParameters(GetEncoderModelNameForEpoch(i - 1)); + decoderNet->RereadPersistableParameters(GetDecoderModelNameForEpoch(i - 1)); + + size_t dummyMinibatchSize = 0; + this->LoadCheckPointInfo(i - 1, + /*out*/ totalSamplesSeen, + /*out*/ learnRatePerSample, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ dummyMinibatchSize); + fprintf(stderr, "Loaded the previous model which has better training criterion.\n"); + loadedPrevModel = true; + } + } + + if (m_continueReduce) + { + if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + if (learnRateReduced == false) + { + learnRateReduced = true; + } + else + { + decoderNet->Save(GetDecoderModelNameForEpoch(i, true)); + encoderNet->Save(GetEncoderModelNameForEpoch(i, true)); + fprintf(stderr, "Finished training and saved final model\n\n"); + break; + } + } + if (learnRateReduced) + { + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + } + else + { + if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan*prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + learnRatePerSample *= m_learnRateIncreaseFactor; + fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); + } + } + } + + if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) //not loading previous values then set them + { + prevCriterion = avgCriterion; + epochsNotCountedInAvgCriterion = 0; + } + + //persist model and check-point info + decoderNet->Save(GetDecoderModelNameForEpoch(i)); + encoderNet->Save(GetEncoderModelNameForEpoch(i)); + + size_t dummyMinibatchSize = 0; + this->LoadCheckPointInfo(i, + /*out*/ totalSamplesSeen, + /*out*/ learnRatePerSample, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ dummyMinibatchSize); + + if (!m_keepCheckPointFiles) + _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); //delete previous checkpiont file to save space + + if (learnRatePerSample < 1e-12) + fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", learnRatePerSample); + } + } + + void TrainEncoderDecoderModel(int startEpoch, vector nets, + vector*> trainDataReader, + vector*> validationDataReader) + { + size_t iNumNetworks = nets.size(); + vector*> featureNodes; + vector*> outputNodes; + vector*> pairNodes; + vector*> labelNodes; + vector*> criterionNodes; + vector*> evaluationNodes; + vector*>*> inputMatrices; + + for (size_t i = 0; i < iNumNetworks; i++) + { + auto * featPtr = &nets[i]->FeatureNodes(); + auto * lablPtr = &nets[i]->LabelNodes(); + featureNodes.push_back(featPtr); + outputNodes.push_back(&nets[i]->OutputNodes()); + pairNodes.push_back(&nets[i]->PairNodes()); + + labelNodes.push_back(lablPtr); + criterionNodes.push_back(&GetTrainCriterionNodes(nets[i])); + evaluationNodes.push_back(&GetEvalCriterionNodes(nets[i])); + + std::map*> *matrices; + matrices = new std::map*>(); + + for (size_t j = 0; j < featPtr->size(); j++) + { + (*matrices)[(*featPtr)[j]->NodeName()] = + &(dynamic_pointer_cast>((*featPtr)[j])->Value()); + } + + for (size_t j = 0; jsize(); j++) + { + (*matrices)[(*lablPtr)[j]->NodeName()] = + &(dynamic_pointer_cast>((*lablPtr)[j])->Value()); + } + inputMatrices.push_back(matrices); + } + + //initializing weights and gradient holder + std::list learnableNodes; + for (size_t i = 0; i < iNumNetworks; i++) + { + if (criterionNodes[i]->size() == 0) + { + for (auto ptr = evaluationNodes[i]->begin(); ptr != evaluationNodes[i]->end(); ptr++) + { + ComputationNodeBasePtr pptr = *ptr; + + const std::list & eachLearnableNodes = nets[i]->LearnableParameterNodes(pptr); //only one criterion so far TODO: support multiple ones? + for (auto nodeIter = eachLearnableNodes.begin(); nodeIter != eachLearnableNodes.end(); nodeIter++) + { + ComputationNodeBasePtr node = *nodeIter; + learnableNodes.push_back(node); + } + } + } + else + { + for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) + { + ComputationNodeBasePtr pptr = *ptr; + + const std::list & eachLearnableNodes = nets[i]->LearnableParameterNodes(pptr); //only one criterion so far TODO: support multiple ones? + for (auto nodeIter = eachLearnableNodes.begin(); nodeIter != eachLearnableNodes.end(); nodeIter++) + { + ComputationNodeBasePtr node = *nodeIter; + learnableNodes.push_back(node); + } + } + } + + //for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) + // nets[i]->BuildAndValidateSubNetwork(*ptr); + } + + + std::list> smoothedGradients; + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + smoothedGradients.push_back(Matrix(node->GetNumRows(), node->GetNumCols(), node->Value().GetDeviceId())); + } + + double epochCriterion, avgCriterion, prevCriterion; + epochCriterion = std::numeric_limits::infinity(); + avgCriterion = prevCriterion = std::numeric_limits::infinity(); + + size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; + + size_t iNumEvaluations = 0; + for (size_t i = 0; i < iNumNetworks; i++) + { + iNumEvaluations += evaluationNodes[i]->size(); + } + std::vector epochEvalErrors(iNumEvaluations, std::numeric_limits::infinity()); + + std::vector evalNodeNames; + for (size_t k = 0; k < iNumNetworks; k++) + { + for (auto ptr = evaluationNodes[k]->begin(); ptr != evaluationNodes[k]->end(); ptr++) + evalNodeNames.push_back((*ptr)->NodeName()); + } + + size_t totalSamplesSeen = 0; + double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; + + int m_numPrevLearnRates = 5; //used to control the upper learnining rate in LR search to reduce computation + vector prevLearnRates; + prevLearnRates.resize(m_numPrevLearnRates); + for (int i = 0; i::infinity(); + + //precompute mean and invStdDev nodes and save initial model + if (/// to-do doesn't support pre-compute such as MVN here + /// PreCompute(net, encoderTrainSetDataReader, encoderFeatureNodes, encoderlabelNodes, encoderInputMatrices) || + startEpoch == 0) + { + for (size_t k = 0; k < iNumNetworks; k++) + { + wstring tmpstr = msra::strfun::wstrprintf(L".%d", k); + nets[k]->Save(GetModelNameForEpoch(int(startEpoch) - 1, false, tmpstr)); + } + } + + bool learnRateInitialized = false; + if (startEpoch > 0) + { + size_t dummyMinibatchSize = 0; + this->LoadCheckPointInfo(startEpoch - 1, + /*out*/ totalSamplesSeen, + /*out*/ learnRatePerSample, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ dummyMinibatchSize); + } + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) + InvalidArgument("When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, or an explicit learning rate must be specified in config for the starting epoch."); + + ULONG dropOutSeed = 1; + double prevDropoutRate = 0; + + bool learnRateReduced = false; + + for (int i = int(startEpoch); isize() > 0) + ComputationNetwork::SetDropoutRate(nets[k], (*evaluationNodes[k])[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); + if (criterionNodes[k]->size() > 0) + ComputationNetwork::SetDropoutRate(nets[k], (*criterionNodes[k])[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); + } + + //learning rate adjustment + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || (m_learningRatesParam.size() > 0 && m_learningRatesParam.size() > i)) + { + learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, trainDataReader[0]->GetNumParallelSequences()); + } + else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) + { + NOT_IMPLEMENTED; + } + + learnRateInitialized = true; + + if (learnRatePerSample < m_minLearnRate) + { + fprintf(stderr, "Learn Rate Per Sample for Epoch[%d] = %.8g is less than minLearnRate %.8g. Training stops.\n", i + 1, learnRatePerSample, m_minLearnRate); + break; + } + + TrainOneEpochEncoderDecoderWithHiddenStates(i, m_epochSize, nets, + trainDataReader, + featureNodes, + pairNodes, + evaluationNodes, + inputMatrices, + labelNodes, + criterionNodes, + learnableNodes, + learnRatePerSample, + smoothedGradients, + epochCriterion, epochEvalErrors, totalSamplesSeen); + + + auto t_end_epoch = clock(); + double epochTime = 1.0*(t_end_epoch - t_start_epoch) / (CLOCKS_PER_SEC); + + /** + this is hacky. Only allow evaluatio on the first encoder->decoder pair + */ + size_t decoderIdx = iNumNetworks - 1; + IDataReader* decoderValidationSetDataReader = validationDataReader[decoderIdx]; + IDataReader* decoderTrainSetDataReader = trainDataReader[decoderIdx]; + ComputationNetworkPtr decoderNet = nets[decoderIdx]; + + fprintf(stderr, "Finished Epoch[%d]: [Training Set] Decoder Train Loss Per Sample = %.8g ", i + 1, epochCriterion); + if (epochEvalErrors.size() == 1) + { + fprintf(stderr, "EvalErr Per Sample = %.8g Ave Learn Rate Per Sample = %.10g Epoch Time=%.8g\n", epochEvalErrors[0], learnRatePerSample, epochTime); + } + else + { + fprintf(stderr, "EvalErr Per Sample "); + for (size_t j = 0; j evalforvalidation(decoderNet); + + double vScore = evalforvalidation.EvaluateEncoderDecoderWithHiddenStates( + nets, + validationDataReader, + m_mbSize[i]); + + fprintf(stderr, "Finished Epoch[%d]: [Validation Set] Loss Per Sample = %.8g \n ", i+1, vScore ); + + epochCriterion = vScore; + } + + bool loadedPrevModel = false; + size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; + if (avgCriterion == std::numeric_limits::infinity()) + avgCriterion = epochCriterion; + else + avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion)* avgCriterion + epochCriterion) / (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) + { + if (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity()) + { + if (m_loadBestModel) + { + //persist model and check-point info + for (size_t k = 0; k < iNumNetworks; k++) + { + nets[k]->RereadPersistableParameters(GetModelNameForEpoch(i, false, msra::strfun::wstrprintf(L".%d", k))); + nets[k]->ResetEvalTimeStamps(); + } + + size_t dummyLr = 0; + this->LoadCheckPointInfo(i - 1, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, dummyLr); + fprintf(stderr, "Loaded the previous model which has better training criterion.\n"); + loadedPrevModel = true; + } + } + + if (m_continueReduce) + { + if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + if (learnRateReduced == false) + { + learnRateReduced = true; + } + else + { + //persist model and check-point info + for (size_t k = 0; k < iNumNetworks; k++) + { + nets[k]->Save(GetModelNameForEpoch(i, true, msra::strfun::wstrprintf(L".%d", k))); + } + fprintf(stderr, "Finished training and saved final model\n\n"); + break; + } + } + if (learnRateReduced) + { + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + } + else + { + if (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan*prevCriterion && prevCriterion != std::numeric_limits::infinity()) + { + learnRatePerSample *= m_learnRateIncreaseFactor; + fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); + } + } + } + + if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) //not loading previous values then set them + { + prevCriterion = avgCriterion; + epochsNotCountedInAvgCriterion = 0; + } + + //persist model and check-point info + for (size_t k = 0; k < iNumNetworks; k++) + { + nets[k]->Save(GetModelNameForEpoch(i, false, msra::strfun::wstrprintf(L".%d", k))); + } + + this->SaveCheckPointInfo(i, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, 0); + if (!m_keepCheckPointFiles) + _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); //delete previous checkpiont file to save space + + if (learnRatePerSample < 1e-12) + fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", learnRatePerSample); + } + + for (size_t i = 0; i < iNumNetworks; i++) + { + delete inputMatrices[i]; + } + } + + /// use hidden states between encoder and decoder to communicate between two networks + void TrainOneEpochEncoderDecoderWithHiddenStates( + const int epochNumber, + const size_t epochSize, + vector nets, /// encoder network + vector*> dataReader, + vector*> featureNodes, + vector*> pairNodes, + vector*> evaluationNodes, + vector*>*> inputMatrices, + vector*> labelNodes, + vector*> criterionNodes, + const std::list& learnableNodes, + const double learnRatePerSample, + std::list>& smoothedGradients, + double& epochCriterion, std::vector& epochEvalErrors, size_t& totalSamplesSeen) + { + ComputationNetworkPtr encoderNet = nets[0]; + ComputationNetworkPtr decoderNet = nets[1]; + DEVICEID_TYPE device = encoderNet->GetDeviceId(); + Matrix historyMat(device); + + double readTimeInMBs = 0, ComputeTimeInMBs = 0; + double epochCriterionLastMBs = 0; + + int numSamplesLastMBs = 0; + std::vector epochEvalErrorsLastMBs(epochEvalErrors.size(), 0); + + clock_t startReadMBTime = 0, startComputeMBTime = 0; + clock_t endReadMBTime = 0, endComputeMBTime = 0; + + //initialize statistics + size_t totalEpochSamples = 0; + + int numMBsRun = 0; + + size_t numEvalNodes = epochEvalErrors.size(); + + // NOTE: the following two local matrices are not used in PTask path + Matrix localEpochCriterion(1, 2, decoderNet->GetDeviceId()); //assume only one training criterion node for each epoch + Matrix localEpochEvalErrors(1, numEvalNodes, decoderNet->GetDeviceId()); + + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + + for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++) + { + (*ptr)->StartMinibatchLoop(m_mbSize[epochNumber], epochNumber, m_epochSize); + } + + startReadMBTime = clock(); + + size_t iNumNetworks = nets.size(); + + unsigned uSeedForDataReader = epochNumber; + + bool bContinueDecoding = true; + while (bContinueDecoding) + { + size_t i = 0; + for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++, i++) + { + IDataReader* pptr = (*ptr); + pptr->SetRandomSeed(uSeedForDataReader); + if (i == 0) + pptr->GetMinibatch(*(inputMatrices[i])); + else + if (pptr->GetMinibatch(*(inputMatrices[i])) == false) + { + bContinueDecoding = false; + break; + } + } + + if (!bContinueDecoding) + break; + + size_t actualMBSize = decoderNet->DetermineActualMBSizeFromFeatures(); + if (actualMBSize == 0) + LogicError("decoderTrainSetDataReader read data but decoderNet reports no data read"); + + + for (size_t i = 0; i < iNumNetworks; i++) + { + ComputationNetwork::BumpEvalTimeStamp(*featureNodes[i]); + if (labelNodes[i]->size() > 0) + ComputationNetwork::BumpEvalTimeStamp(*labelNodes[i]); + } + + endReadMBTime = clock(); + startComputeMBTime = clock(); + + /// not the sentence begining, because the initial hidden layer activity is from the encoder network + // decoderTrainSetDataReader->SetSentenceBegin(false); + // decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->m_mbLayout.m_sentenceBoundaryFlags); + // decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->m_sentenceBegin); + + if (m_doGradientCheck) + { + if (EncoderDecoderGradientCheck(nets, + dataReader, + evaluationNodes, + pairNodes, + featureNodes, + criterionNodes, + localEpochCriterion, localEpochEvalErrors) == false) + { + RuntimeError("SGD::TrainOneEpochEncoderDecoderWithHiddenStates gradient check not passed!"); + } + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + } + + EncoderDecoderWithHiddenStatesForwardPass(nets, + dataReader, pairNodes, evaluationNodes, + featureNodes, criterionNodes, + localEpochCriterion, localEpochEvalErrors); + + EncoderDecoderWithHiddenStatesErrorProp(nets, pairNodes, criterionNodes); + + //update model parameters + if (learnRatePerSample > m_minLearnRate * 0.01) + { + auto smoothedGradientIter = smoothedGradients.begin(); + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++, smoothedGradientIter++) + { + ComputationNodeBasePtr node = *nodeIter; + if (node->IsParameterUpdateRequired()) + { + Matrix& smoothedGradient = (*smoothedGradientIter); + + UpdateWeights(node, smoothedGradient, learnRatePerSample, GetMomentumPerSample(epochNumber/*BUGBUG workaround:*/, dataReader[0]->GetNumParallelSequences()), actualMBSize, m_L2RegWeight, m_L1RegWeight, m_needAveMultiplier, m_useNesterovMomentum); + } + } + } + + endComputeMBTime = clock(); + numMBsRun++; + if (m_traceLevel > 0) + { + double MBReadTime = (double)(endReadMBTime - startReadMBTime) / (CLOCKS_PER_SEC); + double MBComputeTime = (double)(endComputeMBTime - startComputeMBTime) / CLOCKS_PER_SEC; + + readTimeInMBs += MBReadTime; + ComputeTimeInMBs += MBComputeTime; + numSamplesLastMBs += int(actualMBSize); + + if (numMBsRun % m_numMBsToShowResult == 0) + { + + epochCriterion = localEpochCriterion.Get00Element(); + for (size_t i = 0; i< numEvalNodes; i++) + epochEvalErrors[i] = (const double)localEpochEvalErrors(0, i); + + double llk = (epochCriterion - epochCriterionLastMBs) / numSamplesLastMBs; + double ppl = exp(llk); + fprintf(stderr, "Epoch[%d]-Minibatch[%d-%d]: Samples Seen = %d Decoder Train Loss Per Sample = %.8g PPL = %.4e ", epochNumber + 1, numMBsRun - m_numMBsToShowResult + 1, numMBsRun, numSamplesLastMBs, + llk, ppl); + for (size_t i = 0; i= epochSize) + break; + + /// call DataEnd function + /// DataEnd does reader specific process if sentence ending is reached + // encoderTrainSetDataReader->SetSentenceEnd(true); + // decoderTrainSetDataReader->SetSentenceEnd(true); + for (auto ptr = dataReader.begin(); ptr != dataReader.end(); ptr++) + { + (*ptr)->DataEnd(endDataSentence); + } + + uSeedForDataReader++; + } + + localEpochCriterion /= float(totalEpochSamples); + localEpochEvalErrors /= float(totalEpochSamples); + + epochCriterion = localEpochCriterion.Get00Element(); + for (size_t i = 0; i < numEvalNodes; i++) + { + epochEvalErrors[i] = localEpochEvalErrors(0, i); + } + fprintf(stderr, "total samples in epoch[%d] = %zd\n", epochNumber, totalEpochSamples); + } + + bool EncoderDecoderGradientCheck( + vector nets, /// encoder network + vector*> dataReader, + vector*> evaluationNodes, + vector*> pairNodes, + vector*> featureNodes, + vector*> criterionNodes, + Matrix& localEpochCriterion, + Matrix& localEpochEvalErrors + ) + { + size_t iNumNetworks = nets.size(); + vector verror_msgs; + DEVICEID_TYPE deviceId; + + for (int i = iNumNetworks - 1; i >= 0; i--) + { + /// check decoder learnable parameters + const std::list & learnableNodes = + (evaluationNodes[i]->size() == 0 && pairNodes[i]->size() > 0) ? + nets[i]->LearnableParameterNodes((*pairNodes[i])[0]) + : nets[i]->LearnableParameterNodes((*evaluationNodes[i])[0]); + + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + + for (size_t itry = 0; itry < min((size_t)10, node->Value().GetNumElements()); itry++) + { + + int irow = (int)fmod(rand(), node->GetNumRows() - 1); + int icol = (int)fmod(rand(), node->GetNumCols() - 1); + irow = max(0, irow); + icol = max(0, icol); + + fprintf(stderr, "\n###### d%ls######\n", node->NodeName().c_str()); + deviceId = node->Value().GetDeviceId(); // original device id + + node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); + double eOrg = node->Value()(irow, icol); /// warning :: this function will put matrix into CPU + node->Value().TransferToDeviceIfNotThere(deviceId, true); + + /// perturb parameter + double ePos = eOrg + EPSILON; + node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); + node->Value().SetValue(irow, icol, (ElemType)ePos); + node->Value().TransferToDeviceIfNotThere(deviceId, true); + + node->BumpEvalTimeStamp(); + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + + EncoderDecoderWithHiddenStatesForwardPass(nets, + dataReader, pairNodes, evaluationNodes, + featureNodes, criterionNodes, + localEpochCriterion, localEpochEvalErrors); + + double score1 = localEpochCriterion.Get00Element(); + + double eNeg = eOrg - EPSILON; + node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); + node->Value().SetValue(irow, icol, (ElemType)eNeg); + node->Value().TransferToDeviceIfNotThere(deviceId, true); + node->BumpEvalTimeStamp(); + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + + EncoderDecoderWithHiddenStatesForwardPass(nets, + dataReader, pairNodes, evaluationNodes, + featureNodes, criterionNodes, + localEpochCriterion, localEpochEvalErrors); + + double score1r = localEpochCriterion.Get00Element(); + + double grdNum = (score1r - score1) / (eNeg - ePos); + + node->Value().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); + node->Value().SetValue(irow, icol, (ElemType)eOrg); + node->Value().TransferToDeviceIfNotThere(deviceId, true); + node->BumpEvalTimeStamp(); + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + + EncoderDecoderWithHiddenStatesForwardPass(nets, + dataReader, pairNodes, evaluationNodes, + featureNodes, criterionNodes, + localEpochCriterion, localEpochEvalErrors); + + EncoderDecoderWithHiddenStatesErrorProp(nets, pairNodes, criterionNodes); + + node->Gradient().TransferFromDeviceToDevice(deviceId, CPUDEVICE, true, false, false); + double grdErr = node->Gradient()(irow, icol); + node->Gradient().TransferToDeviceIfNotThere(deviceId, true); + + // check if they are consistent + double threshold = pow(10.0, max(0.0, ceil(log10(min(fabs(grdErr), fabs(grdNum))))) - (int)m_gradientCheckSigDigit); + double diff = fabs(grdErr - grdNum); + bool wrong = (std::isnan(diff) || diff > threshold); + if (wrong) + { + char serr[2048]; + sprintf((char*)serr, "Decoder %ls Numeric gradient = %e, Error BP gradient = %e", node->NodeName().c_str(), static_cast(grdNum), static_cast(grdErr)); + fprintf(stdout, "%s\n", serr); + verror_msgs.push_back(serr); + } + } + } + } + + if (verror_msgs.size() > 0) + return false; + return true; + } + + void EncoderDecoderWithHiddenStatesForwardPass( + vector & nets, // TODO: should these vectors all be refs? + vector*> & dataReader, + vector*> & pairNodes, + vector*> & evaluationNodes, + vector*> & /*featureNodes*/, + vector*> & criterionNodes, + Matrix& localEpochCriterion, + Matrix& localEpochEvalErrors + ) + { + size_t iNumNetworks = nets.size(); + + for (size_t i = 0; i < iNumNetworks - 1; i++) + { + size_t j = i + 1; + + EncoderDecoderWithHiddenStatesForwardPass(nets[i], nets[j], + dataReader[i], dataReader[j], + *pairNodes[i], + *criterionNodes[j], + *evaluationNodes[j], + *pairNodes[j], + localEpochCriterion, localEpochEvalErrors); + } + } + + void EncoderDecoderWithHiddenStatesForwardPass( + ComputationNetworkPtr encoderNet, /// encoder network + ComputationNetworkPtr decoderNet, + IDataReader* encoderTrainSetDataReader, + IDataReader* decoderTrainSetDataReader, + vector& encoderEvaluationNodes, + vector& decoderCriterionNodes, + vector& decoderEvaluationNodes, + vector& decoderPairNodes, + Matrix& localEpochCriterion, + Matrix& localEpochEvalErrors + ) + { + //encoderNet->SetActualMiniBatchSizeFromFeatures(); + encoderTrainSetDataReader->CopyMBLayoutTo(encoderNet->GetMBLayoutPtr()); + encoderNet->VerifyActualNumParallelSequences(encoderTrainSetDataReader->GetNumParallelSequences()); + + encoderNet->ForwardProp(encoderEvaluationNodes[0]); + + //decoderNet->SetActualMiniBatchSizeFromFeatures(); + decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->GetMBLayoutPtr()); + decoderNet->VerifyActualNumParallelSequences(decoderTrainSetDataReader->GetNumParallelSequences()); + /// not the sentence begining, because the initial hidden layer activity is from the encoder network + + if (decoderCriterionNodes.size() == 0 && decoderEvaluationNodes.size() == 0) + { + decoderNet->ForwardProp(decoderPairNodes[0]); + } + else + { + decoderNet->ForwardProp(decoderCriterionNodes[0]); + + Matrix::AddElementToElement(dynamic_pointer_cast>(decoderCriterionNodes[0])->Value(), 0, 0, localEpochCriterion, 0, 0); + + size_t numEvalNodes = decoderEvaluationNodes.size(); + std::vectormbEvalErrors(numEvalNodes, 0); + + for (size_t i = 0; i < numEvalNodes; i++) + { + decoderNet->ForwardProp(decoderEvaluationNodes[i]); + Matrix::AddElementToElement(dynamic_pointer_cast>(decoderEvaluationNodes[i])->Value(), 0, 0, localEpochEvalErrors, 0, i); + } +#ifdef DEBUG_DECODER + fprintf(stderr, "ForwardPass score = %.8e\n", localEpochCriterion.Get00Element()); +#endif + } + } + + void EncoderDecoderWithHiddenStatesErrorProp( + vector networks, /// encoder network + vector*> pairNodes, + vector*> criterionNodes) + { + /** + the networks are organized in the forward pass + */ + size_t inetworks = networks.size(); + if (inetworks != criterionNodes.size()) + LogicError("EncoderDecoderWithHiddenStatesErrorProp: number of networks should be the same size as the number of criteron nodes."); + + for (size_t i = 0; i < pairNodes.size(); i++) + { + for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) + networks[i]->ZeroGradients(*ptr); + } + + for (size_t i = 0; i < criterionNodes.size(); i++) + { + for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) + networks[i]->ZeroGradients(*ptr); + } + + for (auto ptr = criterionNodes[inetworks - 1]->begin(); ptr != criterionNodes[inetworks - 1]->end(); ptr++) + { + if (ptr == criterionNodes[inetworks - 1]->begin()) + { + networks[inetworks - 1]->ForwardProp(*ptr); + networks[inetworks - 1]->Backprop(*ptr); + } + else + { + networks[inetworks - 1]->ForwardProp(*ptr); +#if 1 // disable this, so that we can remove the options from Backprop() (trivial to bring back if ever needed) + NOT_IMPLEMENTED; +#else + // This is the old signature of Backprop() // void Backprop(const ComputationNodeBasePtr rootNode, bool /*bResetToOne*/, bool /*bClearGradient*/) - networks[inetworks - 1]->Backprop(*ptr, false, false); -#endif - } - } - - for (int i = inetworks - 2; i >= 0; i--) - { - if (criterionNodes[i]->size() > 0) - { - /// has criterion - /// no need to compute gradients from pairnodes, because the gradients are added from pair nodes already - for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) - { - networks[i]->ForwardProp(*ptr); -#if 1 - NOT_IMPLEMENTED; -#else - networks[i]->Backprop(*ptr, true, false); -#endif - } - } - else if (pairNodes[i]->size() > 0) - { - /// no criterion, so use pair-node gradients - for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) - { - networks[i]->ForwardProp(*ptr); -#if 1 - NOT_IMPLEMENTED; -#else - networks[i]->Backprop(*ptr, false, false); -#endif - } - } - } - } - }; - -}}} + networks[inetworks - 1]->Backprop(*ptr, false, false); +#endif + } + } + + for (int i = inetworks - 2; i >= 0; i--) + { + if (criterionNodes[i]->size() > 0) + { + /// has criterion + /// no need to compute gradients from pairnodes, because the gradients are added from pair nodes already + for (auto ptr = criterionNodes[i]->begin(); ptr != criterionNodes[i]->end(); ptr++) + { + networks[i]->ForwardProp(*ptr); +#if 1 + NOT_IMPLEMENTED; +#else + networks[i]->Backprop(*ptr, true, false); +#endif + } + } + else if (pairNodes[i]->size() > 0) + { + /// no criterion, so use pair-node gradients + for (auto ptr = pairNodes[i]->begin(); ptr != pairNodes[i]->end(); ptr++) + { + networks[i]->ForwardProp(*ptr); +#if 1 + NOT_IMPLEMENTED; +#else + networks[i]->Backprop(*ptr, false, false); +#endif + } + } + } + } + }; + +}}} diff --git a/Source/SGDLib/SGD.cpp b/Source/SGDLib/SGD.cpp index 284a9e3add23..274662b4aedd 100644 --- a/Source/SGDLib/SGD.cpp +++ b/Source/SGDLib/SGD.cpp @@ -1,2756 +1,2756 @@ -// SGD.cpp -- implements SGD with all bells and whistles, parallelization, randomizatiom, etc. - -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings - -#include "Basics.h" -#include "SGD.h" -#include "DataReaderHelpers.h" - -#include "MatrixQuantizerImpl.h" - -#ifdef QUANTIZED_GRADIENT_AGGREGATION -#include "AllReduceDistGradAggregator.h" -#endif - -#include "SimpleDistGradAggregator.h" -#include "ProgressTracing.h" - -#include -#include - -namespace Microsoft { namespace MSR { namespace CNTK { - - using namespace std; - - // ======================================================================= - // class SGD - // ======================================================================= - - template SGD::SGD(const ConfigParameters &); - template SGD::SGD(const ConfigParameters &); - template SGD::SGD(const ScriptableObjects::IConfigRecord &); - template SGD::SGD(const ScriptableObjects::IConfigRecord &); - - // ----------------------------------------------------------------------- - // Train() -- perform a multi-epoch training end-to-end with checkpointing - // ----------------------------------------------------------------------- - - template - void SGD::Train(function createNetworkFn, DEVICEID_TYPE deviceId, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader, - const bool makeMode) - { - // determine which epoch to start with, including recoveing a checkpoint if any and 'makeMode' enabled - int startEpoch = DetermineStartEpoch(makeMode); - if (startEpoch == m_maxEpochs) - { - fprintf(stderr, "No further training is necessary.\n"); - return; - } - - wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); - if (startEpoch >= 0) - fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); - - // create or load from checkpoint - shared_ptr net = startEpoch < 0 ? createNetworkFn(deviceId) : ComputationNetwork::CreateFromFile(deviceId, modelFileName); - - // log the device we are computing on - if (net->GetDeviceId() < 0) - fprintf(stderr, "\nSGD using CPU.\n"); - else - fprintf(stderr, "\nSGD using GPU %d.\n", (int)net->GetDeviceId()); - - // TODO: BUGBUG: if not starting from checkpoint, need to synchronize initial model - // strategy should be to run the initializer above on mpiRank==0, and then broadcast parameters. - - startEpoch = max(startEpoch, 0); - m_needAdaptRegularization = false; - - TrainOrAdaptModel(startEpoch, net, net, nullptr, trainSetDataReader, validationSetDataReader); - } - - // ----------------------------------------------------------------------- - // Adapt() -- similar to Train(), but for purpose of adapting - // ----------------------------------------------------------------------- - - template - void SGD::Adapt(wstring origModelFileName, wstring refNodeName, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader, - const DEVICEID_TYPE deviceId, const bool makeMode) - { - int startEpoch = DetermineStartEpoch(makeMode); - if (startEpoch == m_maxEpochs) - { - fprintf(stderr, "No further training is necessary.\n"); - return; - } - - ComputationNetworkPtr net; - if (startEpoch >= 0) - { - wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); - fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); - net = ComputationNetwork::CreateFromFile(deviceId, modelFileName); - } - else - { - fprintf(stderr, "Load Network From the original model file %ls.\n", origModelFileName.c_str()); - net = ComputationNetwork::CreateFromFile(deviceId, origModelFileName); - } - - startEpoch = max(startEpoch, 0); - - ComputationNetworkPtr refNet; - m_needAdaptRegularization = m_adaptationRegType != AdaptationRegType::None && m_adaptationRegWeight > 0; - if (m_needAdaptRegularization) - { - fprintf(stderr, "Load reference Network From the original model file %ls.\n", origModelFileName.c_str()); - refNet = ComputationNetwork::CreateFromFile(deviceId, origModelFileName); - } - - ComputationNodeBasePtr refNode; - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL) - { - fprintf(stderr, "Checking refNodeName %ls.\n", origModelFileName.c_str()); - if (refNodeName == L"") - InvalidArgument("refNodeName does not exist and is needed when adaptationRegType is KL."); - refNode = refNet->GetNodeFromName(refNodeName); - } - - TrainOrAdaptModel(startEpoch, net, refNet, refNode, trainSetDataReader, validationSetDataReader); - } - - // ----------------------------------------------------------------------- - // TrainOrAdaptModel() -- main training end-to-end, given a start model - // ----------------------------------------------------------------------- - - static double MomentumPerMB(double momentumPerSample, size_t minibatchSize); - - template - void SGD::TrainOrAdaptModel(int startEpoch, ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - ComputationNodeBasePtr refNode, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader) - { - auto & featureNodes = net->FeatureNodes(); - auto & labelNodes = net->LabelNodes(); - auto & criterionNodes = GetTrainCriterionNodes(net); - - fprintf(stderr, "\nTraining criterion node(s):\n"); - for (const auto & node : criterionNodes) - fprintf(stderr, "\t%ls = %ls\n", node->NodeName().c_str(), node->OperationName().c_str()); - - // determine evaluationNodes from GetEvalCriterionNodes(), ensuring each criterion is only logged once - std::vector evaluationNodes; - { - auto originalEvaluationNodes = GetEvalCriterionNodes(net); - set criteriaLogged; // set to make sure we don't double-log criteria - for (const auto & node : criterionNodes) - criteriaLogged.insert(node); - for (const auto & node : originalEvaluationNodes) - if (criteriaLogged.insert(node).second) - evaluationNodes.push_back(node); - - if (!evaluationNodes.empty()) - { - fprintf(stderr, "\nEvaluation criterion node(s):\n"); - for (const auto & node : evaluationNodes) - fprintf(stderr, "\t%ls = %ls\n", node->NodeName().c_str(), node->OperationName().c_str()); - } - } - - std::vector additionalNodesToEvaluate; - auto& outputNodes = net->OutputNodes(); - additionalNodesToEvaluate.insert(additionalNodesToEvaluate.end(), outputNodes.cbegin(), outputNodes.cend()); - - auto preComputeNodesList = net->GetNodesRequiringPreComputation(); - additionalNodesToEvaluate.insert(additionalNodesToEvaluate.end(), preComputeNodesList.cbegin(), preComputeNodesList.cend()); - - // allocate memory for forward and backward computation - net->AllocateAllMatrices(evaluationNodes, additionalNodesToEvaluate, criterionNodes[0]); - - // get feature and label nodes into an array of matrices that will be passed to GetMinibatch() - // TODO: instead, remember the nodes directly, to be able to handle both float and double nodes; current version will crash for mixed networks - std::map*>* inputMatrices = new std::map*>(); - for (size_t pass = 0; pass < 2; pass++) - { - auto & nodes = (pass == 0) ? featureNodes : labelNodes; - for (size_t i = 0; i < nodes.size(); i++) - { - auto & node = nodes[i]; - auto * functionValues = &dynamic_pointer_cast>(node)->Value(); - assert(functionValues->GetNumCols() == net->GetMBLayoutPtr()->GetNumTimeSteps()); - (*inputMatrices)[node->NodeName()] = functionValues; - } - } - - //get hmm file for sequence training - bool isSequenceTrainingCriterion = (criterionNodes[0]->OperationName() == L"SequenceWithSoftmax"); - if (isSequenceTrainingCriterion) - { - //SequenceWithSoftmaxNode* node = static_cast*>(criterionNodes[0]); - auto node = dynamic_pointer_cast>(criterionNodes[0]); - auto hmm = node->gethmm(); - trainSetDataReader->GetHmmData(hmm); - } - - // used for KLD regularized adaptation. For all other adaptation techniques - // use MEL to edit the model and using normal training algorithm - // TODO: Should this be done in SGD::Adapt()? - // TODO: Redo this leveraging that we now have shared_ptrs. It is probably even OK if both networks share feature nodes. - // TODO: Then we can also share the MBLayout; which currently is copied by value. - std::vector refFeatureNodes; - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode != nullptr) - { - // replace input nodes in ref network by input nodes of the main network - refFeatureNodes.resize(featureNodes.size()); - for (size_t i = 0; i < featureNodes.size(); i++) - { - // we need to keep this info to undo this later - // TODO: After the change to shared_ptrs, this may no longer be necessary. - refFeatureNodes[i] = refNet->GetNodeFromName(featureNodes[i]->NodeName()); - refNet->ChangeNode(featureNodes[i]->NodeName(), featureNodes[i]); - } - refNet->InvalidateCompiledNetwork(); // prepare to re-compile - refNet->CompileNetwork(); - - // allocate memory for forward computation - refNet->AllocateAllMatrices({ refNode }, {}, nullptr); - } - - // initializing weights and gradient holder - // only one criterion so far TODO: support multiple ones? - auto & learnableNodes = net->LearnableParameterNodes(criterionNodes[0]); - std::list> smoothedGradients; - - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - smoothedGradients.push_back(Matrix(node->GetNumRows(), - node->GetNumCols(), - net->GetDeviceId())); - } - - double epochCriterion, avgCriterion, prevCriterion, lrControlCriterion; - lrControlCriterion = epochCriterion = avgCriterion = prevCriterion = std::numeric_limits::infinity(); - size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; - - std::vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); - - std::vector evalNodeNames; - for (size_t i = 0; i < evaluationNodes.size(); i++) - { - evalNodeNames.push_back(evaluationNodes[i]->NodeName()); - } - - size_t totalSamplesSeen = 0; - double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; - - double learningRateAdjustmentFactor = 1.0f; - vector prevLearnRates; - prevLearnRates.resize(m_numPrevLearnRates); - for (int i = 0; i < m_numPrevLearnRates; i++) - { - prevLearnRates[i] = -1.0; - } - - if (m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) - { - InitDistGradAgg(evaluationNodes.size(), m_traceLevel); - } - //precompute mean and invStdDev nodes and save initial model - if (PreCompute(net, trainSetDataReader, featureNodes, labelNodes, inputMatrices) || startEpoch == 0) - { - // Synchronize all ranks before writing the model to ensure that - // everyone is done loading the model - if (g_mpi != nullptr) - { - g_mpi->WaitAll(); - } - - net->Save(GetModelNameForEpoch(int(startEpoch) - 1)); - } - - bool learnRateInitialized = false; - if (startEpoch > 0) - { - learnRateInitialized = LoadCheckPointInfo(startEpoch - 1, - /*out*/ totalSamplesSeen, - /*out*/ learnRatePerSample, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ m_prevChosenMinibatchSize); - if (learnRateInitialized) - prevLearnRates[startEpoch % m_numPrevLearnRates] = learnRatePerSample; - } - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && - !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) - { - InvalidArgument( - "When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, " - "or an explicit learning rate must be specified in config for the starting epoch."); - } - - unsigned long dropOutSeed = 1; - double prevDropoutRate = 0; - - bool learnRateReduced = false; - - // pass user config on memory allocation for convolution operations to the Network - ComputationNetwork::SetMaxTempMemSizeForCNN(net, criterionNodes[0], m_maxTempMemSizeInSamplesForCNN); - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) - { - ComputationNetwork::SetMaxTempMemSizeForCNN(refNet, refNode, m_maxTempMemSizeInSamplesForCNN); - } - - // likewise for sequence training parameters - if (isSequenceTrainingCriterion) - { - ComputationNetwork::SetSeqParam(net, criterionNodes[0], m_hSmoothingWeight, m_frameDropThresh, m_doReferenceAlign, - m_seqGammarCalcAMF, m_seqGammarCalcLMF, m_seqGammarCalcWP, m_seqGammarCalcbMMIFactor, m_seqGammarCalcUsesMBR ); - } - - // --- MAIN EPOCH LOOP - for (int i = startEpoch; i < (int)m_maxEpochs; i++) // TODO: why is this an int, and not a size_t? - { - // Synchronize all ranks before proceeding to ensure that - // rank 0 has finished writing the previous model file - if (g_mpi != nullptr) - { - g_mpi->WaitAll(); - } - - Timer timer; - timer.Start(); - - // set dropout rate for this epoch - ComputationNetwork::SetDropoutRate(net, criterionNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); - - // learning rate adjustment - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || i < m_learningRatesParam.size()) - { - // BUGBUG: GetNumParallelSequences() returns 1 under certain situations; it seems when restarting from checkpoint - learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); - } - else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) - { - double largestPrevLearnRatePerSample = prevLearnRates[0]; - for (int j = 1; j < m_numPrevLearnRates; j++) - { - largestPrevLearnRatePerSample = max(largestPrevLearnRatePerSample, prevLearnRates[j]); - } - - // return a reasonable learning rate based on the initial minibatchSize - double newLearningRatePerSample = SearchForBestLearnRate(net, refNet, refNode, i, learnRatePerSample, - trainSetDataReader, featureNodes, labelNodes, - criterionNodes, evaluationNodes, inputMatrices, - learnableNodes, smoothedGradients, - learnRateInitialized, largestPrevLearnRatePerSample); - learningRateAdjustmentFactor = newLearningRatePerSample / learnRatePerSample; - learnRatePerSample = newLearningRatePerSample; - - // save per sample learn rate to support changeable minibatchSize - prevLearnRates[i % m_numPrevLearnRates] = learnRatePerSample; - } - - learnRateInitialized = true; - - if (learnRatePerSample < m_minLearnRate) - { - fprintf(stderr, "Learn Rate Per Sample for Epoch[%d] = %.8g is less than minLearnRate %.8g. Training complete.\n", - i + 1, learnRatePerSample, m_minLearnRate); - if (m_autoLearnRateSearchType != LearningRateSearchAlgorithm::None) - { - net->Save(m_modelPath); - } - break; - } - - size_t chosenMinibatchSize; - size_t actualMinibatchSize; - - // Through the command line or config file the user can set minibatch sizes on a per epoch - // basis for a set number of epochs. For epochs after that point, m_mbSize.size(), either - // we just keep using - // the last minibatch size, or we use tuning to try and find a better one. - if (m_autoAdjustMinibatch && i >= m_mbSize.size()) - { - size_t numFramesToUseInSearch = m_numMiniBatch4LRSearch[i] * m_mbSize[i]; - if (m_epochSize != requestDataSize) - { - // ensure the numFramesToUseInSearch does not exceed the total number of frames in the epoch - numFramesToUseInSearch = min(numFramesToUseInSearch, m_epochSize); - } - - // Use tuning to try and find a better minibatch size - chosenMinibatchSize = AdaptiveMinibatchSizing(net, refNet, refNode, i, - numFramesToUseInSearch, - trainSetDataReader, learnRatePerSample, - m_mbSize[i], featureNodes, labelNodes, - criterionNodes, evaluationNodes, - inputMatrices, learnableNodes, - smoothedGradients, learningRateAdjustmentFactor); - m_prevChosenMinibatchSize = chosenMinibatchSize; - } - else - { - // use the explicitly set minibatch size - chosenMinibatchSize = m_mbSize[i]; - } - - actualMinibatchSize = FixUpEffectiveMBSize(chosenMinibatchSize/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); - - double momentumPerSample = GetMomentumPerSample(i/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); - // time constant = number of samples after which a contribution has been reduced to e^-1 - double momentumAsTimeConstant = momentumPerSample == 0.0 ? 0.0 - : momentumPerSample >= 1.0 ? 0.0 - : -1.0 / log(momentumPerSample); - fprintf(stderr, "Starting Epoch %d: learning rate per sample = %f effective momentum = %f momentum as time constant = %.1f samples\n", - i + 1, learnRatePerSample, MomentumPerMB(momentumPerSample, actualMinibatchSize), momentumAsTimeConstant); - - TrainOneEpoch(net, - refNet, - refNode, - i, - m_epochSize, - trainSetDataReader, - learnRatePerSample, - chosenMinibatchSize, - featureNodes, - labelNodes, - criterionNodes, - evaluationNodes, - inputMatrices, - learnableNodes, smoothedGradients, - epochCriterion, epochEvalErrors, totalSamplesSeen); - - timer.Stop(); - double epochTime = timer.ElapsedSeconds(); - - if (m_useEvalCriterionControlLR && epochEvalErrors.size() > 0) - { - lrControlCriterion = epochEvalErrors[0]; - } - else - { - lrControlCriterion = epochCriterion; - } - - fprintf(stderr, - "Finished Epoch[%2d of %d]: [Training Set] TrainLossPerSample = %.8g; ", - i + 1, (int) m_maxEpochs, epochCriterion); - m_lastFinishedEpochTrainLoss = epochCriterion; - if (epochEvalErrors.size() == 0) // no eval criterion, only train criterion itself - { - fprintf(stderr, - "AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", - learnRatePerSample, epochTime); - - } - else if (epochEvalErrors.size() == 1) - { - fprintf(stderr, - "EvalErrPerSample = %.8g; AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", - epochEvalErrors[0], learnRatePerSample, epochTime); - } - else - { - fprintf(stderr, "EvalErrPerSample "); - for (size_t j = 0; j < epochEvalErrors.size(); j++) - { - fprintf(stderr, "[%lu]=%.8g; ", j, epochEvalErrors[j]); - } - - fprintf(stderr, "AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", - learnRatePerSample, epochTime); - - // TODO: why these extra log messages here and not for 1 eval criterion? - fprintf(stderr, "Finished Epoch[%2d of %d]: Criterion Node [%ls] Per Sample = %.8g\n", - i + 1, (int) m_maxEpochs, criterionNodes[0]->NodeName().c_str(), epochCriterion); - - for (size_t j = 0; j < epochEvalErrors.size(); j++) - { - fprintf(stderr, "Finished Epoch[%2d of %d]: Evaluation Node [%ls] Per Sample = %.8g\n", - i + 1, (int) m_maxEpochs, evalNodeNames[j].c_str(), epochEvalErrors[j]); - } - } - - if ((g_mpi == nullptr) || g_mpi->IsMainNode()) - { - if (validationSetDataReader != trainSetDataReader && validationSetDataReader != nullptr) - { - SimpleEvaluator evalforvalidation(net); - vector cvSetTrainAndEvalNodes; - if (criterionNodes.size() > 0) - { - cvSetTrainAndEvalNodes.push_back(criterionNodes[0]->NodeName()); - } - if (evaluationNodes.size() > 0) - { - cvSetTrainAndEvalNodes.push_back(evaluationNodes[0]->NodeName()); - } - - vector vScore = evalforvalidation.Evaluate(validationSetDataReader, cvSetTrainAndEvalNodes, m_mbSize[i]); - fprintf(stderr, "Finished Epoch[%2d of %d]: [Validation Set] TrainLossPerSample = %.8g", i + 1, (int) m_maxEpochs, vScore[0]); - if (vScore.size() > 1) - { - fprintf(stderr, "; EvalErrPerSample = %.8g", vScore[1]); - } - fprintf(stderr, "\n"); - - if (m_useCVSetControlLRIfCVExists) - { - if (m_useEvalCriterionControlLR && vScore.size() > 1) - { - lrControlCriterion = vScore[1]; - } - else - { - lrControlCriterion = vScore[0]; // the first one is the training criterion - } - } - } - } - - // broadcast epochCriterion to make sure each processor will have the same learning rate schedule - if ((m_parallelizationMethod == ParallelizationMethod::ModelAveragingSGD) && (g_mpi->NumNodesInUse() > 1)) - { - g_mpi->Bcast(&epochCriterion, 1, g_mpi->MainNodeRank()); - g_mpi->Bcast(&lrControlCriterion, 1, g_mpi->MainNodeRank()); - } - - bool loadedPrevModel = false; - size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; - if (avgCriterion == std::numeric_limits::infinity()) - { - avgCriterion = lrControlCriterion; - } - else - { - avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion) * - avgCriterion + lrControlCriterion) / - (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); - } - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && - m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) - { - if (std::isnan(avgCriterion) || (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity())) - { - if (m_loadBestModel) - { - auto bestModelPath = GetModelNameForEpoch(i - m_learnRateAdjustInterval); - fprintf(stderr, "Loading previous model with best training-criterion value: %ls.\n", bestModelPath.c_str()); - net->RereadPersistableParameters(bestModelPath); - LoadCheckPointInfo(i - m_learnRateAdjustInterval, - /*out*/ totalSamplesSeen, - /*out*/ learnRatePerSample, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ m_prevChosenMinibatchSize); - loadedPrevModel = true; - } - } - - if (m_continueReduce) - { - if (std::isnan(avgCriterion) || - (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && - prevCriterion != std::numeric_limits::infinity())) - { - if (learnRateReduced == false) - { - learnRateReduced = true; - } - else - { - net->Save(GetModelNameForEpoch(i, true)); - - fprintf(stderr, "Finished training and saved final model\n\n"); - break; - } - } - - if (learnRateReduced) - { - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - } - else - { - if (std::isnan(avgCriterion) || - (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && - prevCriterion != std::numeric_limits::infinity())) - { - - learnRatePerSample *= m_learnRateDecreaseFactor; - fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); - } - else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan * prevCriterion && - prevCriterion != std::numeric_limits::infinity()) - { - learnRatePerSample *= m_learnRateIncreaseFactor; - fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); - } - } - } - else - { - if (std::isnan(avgCriterion)) - RuntimeError("The training criterion is not a number (NAN). Stop\n"); - } - - // not loading previous values then set them - if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) - { - prevCriterion = avgCriterion; - epochsNotCountedInAvgCriterion = 0; - } - - // Synchronize all ranks before proceeding to ensure that - // nobody tries reading the checkpoint file at the same time - // as rank 0 deleting it below - if (g_mpi != nullptr) - { - g_mpi->WaitAll(); - } - - // persist model and check-point info - if ((g_mpi == nullptr) || g_mpi->IsMainNode()) - { - net->Save(GetModelNameForEpoch(i)); - SaveCheckPointInfo(i, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, chosenMinibatchSize); - if (!m_keepCheckPointFiles) - { - // delete previous checkpoint file to save space - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_loadBestModel) - { - if (epochsSinceLastLearnRateAdjust != 1) - { - _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); - } - if (epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) - { - _wunlink(GetCheckPointFileNameForEpoch(i - m_learnRateAdjustInterval).c_str()); - } - } - else - { - _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); - } - } - } - - if (learnRatePerSample < 1e-12) - { - fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", - learnRatePerSample); - } - } - // --- END OF MAIN EPOCH LOOP - - // Synchronize all ranks before proceeding to ensure that - // rank 0 has finished writing the model file - if (g_mpi != nullptr) - { - g_mpi->WaitAll(); - } - - // progress tracing for compute cluster management - ProgressTracing::TraceProgressPercentage(m_maxEpochs, 0.0, true); - ProgressTracing::TraceTrainLoss(m_lastFinishedEpochTrainLoss); - - // since we linked feature nodes. we need to remove it from the deletion - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode != nullptr) - { - for (size_t i = 0; i < refFeatureNodes.size(); i++) - { - // note we need to handle deletion carefully - refNet->ChangeNode(refFeatureNodes[i]->NodeName(), refFeatureNodes[i]); - } - } - - delete inputMatrices; - } - - // ----------------------------------------------------------------------- - // TrainOneEpoch() -- train one epoch - // ----------------------------------------------------------------------- - - static string GeneratePaddedFloatOrExpFormat(int padSize, int precision, double value); - - template - size_t SGD::TrainOneEpoch(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t epochSize, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - size_t tunedMBSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, // TODO: why is this a pointer? - const std::list & learnableNodes, - std::list>& smoothedGradients, - /*out*/ double& epochCriterion, - /*out*/ std::vector& epochEvalErrors, - /*out*/ size_t& totalSamplesSeen, - std::string prefixMsg) - { - double totalTimeInMBs = 0; // use double since timer has sub-microsecond time resolution - double epochCriterionLastMBs = 0; - - int numSamplesLastMBs = 0; - std::vector epochEvalErrorsLastMBs(epochEvalErrors.size(), 0); - - // initialize statistics - size_t totalEpochSamples = 0; - - int numMBsRun = 0; - - // NOTE: the following two local matrices are not used in distGradAgg path - // assume only one training criterion node for each epoch. - // The criterion values are accumulated here over the minibatches (without having to pull them off the GPU). - Matrix localEpochCriterion(1, 1, net->GetDeviceId()); - Matrix localEpochEvalErrors(1, epochEvalErrors.size(), net->GetDeviceId()); - - localEpochCriterion.SetValue(0); - localEpochEvalErrors.SetValue(0); - - bool useGradientAggregation = ((m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) && - (epochNumber >= m_parallelizationStartEpochNum)); - bool useModelAveraging = ((m_parallelizationMethod == ParallelizationMethod::ModelAveragingSGD) && - (epochNumber >= m_parallelizationStartEpochNum)); - bool useParallelTrain = useGradientAggregation || useModelAveraging; - - // MA-related variables - size_t nSamplesSinceLastModelSync = 0; - size_t nSynced = 0; - float nSecondsOnMASync = 0; - float nSecondsSinceLastMAPerfReport = 0; - - std::vector*> learnParamsGradients; - if (useGradientAggregation) - { - epochCriterion = double(0.0); - epochEvalErrors.assign(epochEvalErrors.size(), double(0.0)); - } - - Profiler profiler(m_numMBsToCUDAProfile); - - // resetting this, so profiling is performed for one epoch only - m_numMBsToCUDAProfile = 0; - - bool useDistributedMBReading = useParallelTrain && - m_enableDistributedMBReading && - trainSetDataReader->SupportsDistributedMBRead(); - if (useDistributedMBReading) - { - trainSetDataReader->StartDistributedMinibatchLoop(tunedMBSize, epochNumber, g_mpi->CurrentNodeRank(), - g_mpi->NumNodesInUse(), epochSize); - } - else - { - trainSetDataReader->StartMinibatchLoop(tunedMBSize, epochNumber, epochSize); - } - - net->StartEvaluateMinibatchLoop(evaluationNodes); - net->StartEvaluateMinibatchLoop(criterionNodes); - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) - { - refNet->StartEvaluateMinibatchLoop(refNode); - } - - // prepare for sub-minibatching - // Sub-minibatching is used if a single minibatch is too large to fit into GPU RAM. - DataReaderHelpers::SubminibatchDispatcher smbDispatcher; - size_t numSubminibatchesNeeded = 0; - if (m_maxSamplesInRAM < SIZE_MAX || m_numSubminiBatches > 1) // user-specified maximum number of samples that fit into GPU RAM; or 0 if not enabled - { - if (m_maxSamplesInRAM < SIZE_MAX) - { - // into how many pieces would we need to break the minibatch? - // TODO: The following calculation relies on the ill-devised definition of "minibatch" of the current truncated BPTT implementation. Adapt this once fixed. - size_t numParallelSequences = trainSetDataReader->GetNumParallelSequences(); - size_t estimatedMBSize = tunedMBSize * numParallelSequences; - numSubminibatchesNeeded = (size_t)std::ceil((float)estimatedMBSize / m_maxSamplesInRAM); - } - if (m_numSubminiBatches > 1) - { - numSubminibatchesNeeded = m_numSubminiBatches; - } - } - // this is non-trivial, we need a manager object to handle this - if (numSubminibatchesNeeded > 1) - smbDispatcher.Init(net, learnableNodes, criterionNodes, evaluationNodes); - - // The following is a special feature only supported by the Kaldi2Reader for more efficient sequence training. - // This attemps to compute the error signal for the whole utterance, which will - // be fed to the neural network as features. Currently it is a workaround - // for the two-forward-pass sequence and ctc training, which allows - // processing more utterances at the same time. - // TODO: move the two-forward-pass support out of the reader, make a first-class citizen. - AttemptUtteranceDerivativeFeatures(net, trainSetDataReader, featureNodes, inputMatrices); - - fprintf(stderr, "\nStarting minibatch loop"); - if (useGradientAggregation) - { - fprintf(stderr, ", DataParallelSGD training (MyRank = %d, NumNodes = %d, NumGradientBits = %d)", - (int)g_mpi->CurrentNodeRank(), (int)g_mpi->NumNodesInUse(), (int)m_numGradientBits); - if (m_bufferedAsyncGradientAggregation) - { - fprintf(stderr, ", BufferedAsyncGradientAggregation is ENABLED"); - } - } - if (useDistributedMBReading) - { - fprintf(stderr, ", distributed reading is ENABLED"); - } - if (numSubminibatchesNeeded > 1) - { - if (m_maxSamplesInRAM < SIZE_MAX) - fprintf(stderr, ", with maximum %d samples in RAM", (int)m_maxSamplesInRAM); - else - fprintf(stderr, ", with %d subminibatch", (int)numSubminibatchesNeeded); - } - fprintf(stderr, ".\n"); - - Timer timer; - timer.Start(); - - // --- MAIN MINIBATCH LOOP - - bool noMoreSamplesToProcess = false; - for (;;) - { - // get minibatch - // TODO: is it guaranteed that the GPU is already completed at this point, is it safe to overwrite the buffers? - size_t actualMBSize = 0; - bool wasDataRead = DataReaderHelpers::GetMinibatchIntoNetwork(*trainSetDataReader, net, criterionNodes[0], - useDistributedMBReading, useParallelTrain, *inputMatrices, actualMBSize); - if (!wasDataRead && (!useDistributedMBReading || noMoreSamplesToProcess)) // in case of distributed reading, we do a few more loops until all ranks have completed - break; // end of epoch - - // Note: If !wasDataRead then the data that GetMinibatchIntoNetwork() was supposed to full in are undefined. - // Must not touch them. - - if (!wasDataRead) - actualMBSize = 0; // (undefined if !wasDataRead) - - nSamplesSinceLastModelSync += actualMBSize; - - // node data was changed - // TODO: move this to that function as well--just tired to pass everything as arguments - // TODO: We should do this right after the GetMinibatch() call, since that's where these changed. - // Need to check whether that would cause unintended side effects. - // TODO: original code did not call this for actualMBSize == 0 - ComputationNetwork::BumpEvalTimeStamp(featureNodes); - ComputationNetwork::BumpEvalTimeStamp(labelNodes); - - if (actualMBSize > 0) - { - assert(wasDataRead); -#ifndef EVALDLL - if (m_doGradientCheck && GradientCheck(net, criterionNodes, learnableNodes, 0) == false) - LogicError("cannot pass gradient checker"); -#endif - // TODO: currently we only support one node for regularization - if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) - { -#if 0 // TODO: where does refNet get its features from? - refNet->ResizeAllFeatureNodes(actualMBSize); -#endif - //size_t actualMBSize2 = refNet->SetActualMiniBatchSizeFromFeatures(); - size_t actualMBSize2 = refNet->DetermineActualMBSizeFromFeatures(); - refNet->GetMBLayoutPtr()->CopyFrom(net->GetMBLayoutPtr()); // TODO: This is UNTESTED (before this was missing, seemingly inconsistently) - refNet->VerifyActualNumParallelSequences(trainSetDataReader->GetNumParallelSequences()); - - if (actualMBSize2 != actualMBSize) - LogicError("TrainOneEpoch: refNet has different MB size than main net??"); - - refNet->ForwardProp(refNode); - Matrix::ScaleAndAdd((ElemType)m_adaptationRegWeight, - dynamic_pointer_cast>(refNode)->Value(), - (ElemType)(1.0 - m_adaptationRegWeight), - dynamic_pointer_cast>(labelNodes[0])->Value()); - } - - // do forward and back propagation - - // We optionally break the minibatch into sub-minibatches. - // This, when enabled, is used when a full minibatch does not fit into GPU RAM. - size_t actualNumSubminibatches = numSubminibatchesNeeded <= 1 ? 1 : smbDispatcher.GetMinibatchIntoCache(*trainSetDataReader, *net, *inputMatrices, numSubminibatchesNeeded); - for (size_t ismb = 0; ismb < actualNumSubminibatches; ismb++) - { - if (actualNumSubminibatches > 1) - { - smbDispatcher.GetSubMinibatchToNet(ismb); // get sub-minibatch from full-size one - ComputationNetwork::BumpEvalTimeStamp(featureNodes); - ComputationNetwork::BumpEvalTimeStamp(labelNodes); - } - - // =========================================================== - // forward prop for evaluate eval nodes - // =========================================================== - - // compute eval node first since when gradient is computed the forward function values - // may be changed and need to be recomputed when gradient and function value share the same matrix - net->ForwardProp(evaluationNodes); // the bulk of this evaluation is reused in ComputeGradient() below - - // =========================================================== - // forward prop for training criterion - // =========================================================== - - net->ForwardProp(criterionNodes[0]); - - // =========================================================== - // backprop - // =========================================================== - - if (learnRatePerSample > 0.01 * m_minLearnRate) // only compute gradient when learning rate is large enough - net->Backprop(criterionNodes[0]); - - // house-keeping for sub-minibatching - if (actualNumSubminibatches > 1) - smbDispatcher.DoneWithCurrentSubMinibatch(ismb); // page state out - } // end sub-minibatch loop - if (actualNumSubminibatches > 1) - smbDispatcher.DoneWithCurrentMinibatch(); - } // if (actualMBSize > 0) - - // for progress and statistics, we should only count frames that are not gaps - size_t numSamplesWithLabel = wasDataRead ? net->GetNumSamplesWithLabel(actualMBSize) : 0; - - // Sum of actualMBSize across all nodes when using parallel training - size_t aggregateNumSamples = actualMBSize; - size_t aggregateNumSamplesWithLabel = numSamplesWithLabel; - - if (!useGradientAggregation) - { - // accumulate criterion values (objective, eval) - if (actualMBSize != 0) - { - assert(wasDataRead); - // criteria are in Value()(0,0), we accumulate into another 1x1 Matrix (to avoid having to pull the values off the GPU) - Matrix::AddElementToElement(dynamic_pointer_cast>(criterionNodes[0])->Value(), - 0, 0, localEpochCriterion, 0, 0); - for (size_t i = 0; i < evaluationNodes.size(); i++) - { - Matrix::AddElementToElement(dynamic_pointer_cast>(evaluationNodes[i])->Value(), - 0, 0, localEpochEvalErrors, 0, i); - } - } - } - else - { - // distributed gradient aggregation - if (learnParamsGradients.size() == 0) - { - learnParamsGradients.reserve(learnableNodes.size()); - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - if (node->IsParameterUpdateRequired()) - { - Matrix* currParamsGradient = &(node->Gradient()); - - // Sometimes, in parallel training, the current node may not get any samples to process - // In this case, the gradient matrix may not have been sized yet. If so, lets size it. - if (currParamsGradient->GetNumCols() == 0) - { - Matrix* currParamsValues = &(node->Value()); - currParamsGradient->Resize(currParamsValues->GetNumRows(), currParamsValues->GetNumCols()); - } - - learnParamsGradients.push_back(currParamsGradient); - } - } - } - - // prepare the header - m_gradHeader->numEvalNode = evaluationNodes.size(); - m_gradHeader->numSamples = actualMBSize; - m_gradHeader->numSamplesWithLabel = numSamplesWithLabel; - m_gradHeader->criterion = actualMBSize > 0 ? criterionNodes[0]->Get00Element() : 0.0; - for (size_t i = 0; i < evaluationNodes.size(); i++) - m_gradHeader->evalErrors[i] = actualMBSize > 0 ? evaluationNodes[i]->Get00Element() : 0.0; - - bool samplesProcessed = m_distGradAgg->AggregateGradients(learnParamsGradients, m_gradHeader, epochNumber); - noMoreSamplesToProcess = !samplesProcessed; - - aggregateNumSamples = m_gradHeader->numSamples; - aggregateNumSamplesWithLabel = m_gradHeader->numSamplesWithLabel; - epochCriterion += m_gradHeader->criterion; - for (size_t i = 0; ievalErrors[i]; - } - - // update model parameters - if ((aggregateNumSamples > 0) && (learnRatePerSample > m_minLearnRate * 0.01)) - { - auto smoothedGradientIter = smoothedGradients.begin(); - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++, smoothedGradientIter++) - { - ComputationNodeBasePtr node = *nodeIter; - if (node->IsParameterUpdateRequired()) - { - Matrix& smoothedGradient = *smoothedGradientIter; -#ifdef _DEBUG - if (smoothedGradient.HasNan("TrainOneEpoch/UpdateWeights(): ")) - LogicError("%ls %ls operation has NaNs in smoothedGradient.", node->NodeName().c_str(), node->OperationName().c_str()); -#endif - UpdateWeights(node, smoothedGradient, learnRatePerSample, - GetMomentumPerSample(epochNumber/*BUGBUG workaround:*/, net->GetMBLayoutPtr()->GetNumParallelSequences()), aggregateNumSamples, - m_L2RegWeight, m_L1RegWeight, - m_needAveMultiplier, m_useNesterovMomentum); -#ifdef _DEBUG - if (dynamic_pointer_cast>(node)->Value().HasNan("TrainOneEpoch/UpdateWeights(): ")) - LogicError("%ls %ls operation has NaNs in functionValues after parameter update.", node->NodeName().c_str(), node->OperationName().c_str()); -#endif - } - } - } - - // aggregation by model averaging - // TODO: this does not happen each MB, does it? - if (useModelAveraging) - { - // Determine if any samples were processed across any of the ranks - if (useDistributedMBReading) - { - std::array numNodesWithDataToProcess; - numNodesWithDataToProcess[0] = wasDataRead ? 1 : 0; - g_mpi->AllReduce(numNodesWithDataToProcess); - - if (numNodesWithDataToProcess[0] == 0) - noMoreSamplesToProcess = true; - } - - if (g_mpi->NumNodesInUse() > 1) - { - size_t processedSamples = 0; - float secondsSinceLastSyncFinished = 0; - float secondsSpentOnSync = 0; - if (ModelAveragingProcessing(nSamplesSinceLastModelSync, learnableNodes, processedSamples, - secondsSinceLastSyncFinished, secondsSpentOnSync)) - { - // if a sync happens, do some extra work - nSamplesSinceLastModelSync = 0; - nSynced++; - - nSecondsOnMASync += secondsSpentOnSync; - nSecondsSinceLastMAPerfReport += secondsSinceLastSyncFinished; - - if (m_syncStatsTrace > 0) - { - if (nSynced % m_syncStatsTrace == 0) - { - fprintf(stderr, "\t\t-----(model averaging stats) %d-th sync, %8.2f seconds since last report, %5.2f seconds on communication\n", - (int)nSynced, nSecondsSinceLastMAPerfReport, nSecondsOnMASync); - nSecondsOnMASync = 0; - nSecondsSinceLastMAPerfReport = 0; - } - } - } - aggregateNumSamplesWithLabel = processedSamples; - } - } - - timer.Stop(); - numMBsRun++; - - totalTimeInMBs += timer.ElapsedSeconds(); - numSamplesLastMBs += useModelAveraging ? int(actualMBSize) : int(aggregateNumSamplesWithLabel); - - if (numMBsRun % m_numMBsToShowResult == 0) - { - // get the epoch Values updated - if (!useGradientAggregation) - { - timer.Restart(); - epochCriterion = localEpochCriterion.Get00Element(); - for (size_t i = 0; i < epochEvalErrors.size(); i++) - { - epochEvalErrors[i] = localEpochEvalErrors(0, i); - } - timer.Stop(); - - // Add the last trailing compute - totalTimeInMBs += timer.ElapsedSeconds(); - } - - double trainLossPerSample = (numSamplesLastMBs != 0) ? ((epochCriterion - epochCriterionLastMBs) / numSamplesLastMBs) : 0.0; - bool wasProgressPrinted = false; - - if (epochNumber > 0 || (int) epochSize > 0) - { - // progress tracing for compute cluster management - double mbProg = 0.0; - int mbProgNumPrecision = 2; - if (m_maxComputedEpochSize != 0) - { - double numMBPerEpoch = (double)m_maxComputedEpochSize / (double)tunedMBSize; - mbProg = (double)numMBsRun / numMBPerEpoch; - mbProgNumPrecision = (int)ceil(log10(numMBPerEpoch / (double)m_numMBsToShowResult)); - mbProgNumPrecision = max(mbProgNumPrecision - 2, 2); - } - wasProgressPrinted = ProgressTracing::TraceProgressPercentage(epochNumber, mbProg, false); - - // progress tracing for regular log - string formatString = "%s Epoch[%2d of %d]-Minibatch[%4d-%4d, %2." + std::to_string(mbProgNumPrecision) + "f%%]: SamplesSeen = %d; TrainLossPerSample = " + - GeneratePaddedFloatOrExpFormat(11, 8, trainLossPerSample) + "; "; - SGDTrace(stderr, formatString.c_str(), - prefixMsg.c_str(), epochNumber + 1, m_maxEpochs, numMBsRun - m_numMBsToShowResult + 1, - numMBsRun, mbProg * 100, numSamplesLastMBs, trainLossPerSample); - } - else - { - string formatString = "%s Epoch[%2d of %d]-Minibatch[%4d-%4d]: SamplesSeen = %d; TrainLossPerSample = " + - GeneratePaddedFloatOrExpFormat(11, 8, trainLossPerSample) + "; "; - SGDTrace(stderr, formatString.c_str(), - prefixMsg.c_str(), epochNumber + 1, m_maxEpochs, numMBsRun - m_numMBsToShowResult + 1, - numMBsRun, numSamplesLastMBs, trainLossPerSample); - m_maxComputedEpochSize = numMBsRun * numSamplesLastMBs / m_numMBsToShowResult; - } - - double evalError = 0.0; - for (size_t i = 0; i < epochEvalErrors.size(); i++) - { - evalError = (epochEvalErrors[i] - epochEvalErrorsLastMBs[i]) / numSamplesLastMBs; - string formatString = "EvalErr[%lu]PerSample = " + GeneratePaddedFloatOrExpFormat(0, 8, evalError) + "; "; - SGDTrace(stderr, formatString.c_str(), i, evalError); - } - - string formatString = "TotalTime = " + GeneratePaddedFloatOrExpFormat(0, 4, totalTimeInMBs) + "s; SamplesPerSecond = %.1f\n"; - SGDTrace(stderr, formatString.c_str(), totalTimeInMBs, numSamplesLastMBs / totalTimeInMBs); - - // progress tracing for compute cluster management - if (wasProgressPrinted) - { - ProgressTracing::TraceTrainLoss(trainLossPerSample); - } - - if (m_traceLevel > 0) - { - fflush(stderr); - } - - // reset statistics - totalTimeInMBs = 0; - numSamplesLastMBs = 0; - - epochCriterionLastMBs = epochCriterion; - for (size_t i = 0; i < epochEvalErrorsLastMBs.size(); i++) - { - epochEvalErrorsLastMBs[i] = epochEvalErrors[i]; - } - - if (std::isnan(epochCriterion)) - { - RuntimeError("The training criterion is not a number (NAN). Stop\n"); - } - } - - timer.Restart(); - totalEpochSamples += aggregateNumSamplesWithLabel; - totalSamplesSeen += aggregateNumSamplesWithLabel; - - // call DataEnd function - // This signals something from SGD to the reader. - // DataEnd does reader specific process if sentence ending is reached - trainSetDataReader->DataEnd(EndDataType::endDataSentence); - - // Attempts to compute the error signal for the whole utterance, which will - // be fed to the neural network as features. Currently it is a workaround - // for the two-forward-pass sequence and ctc training, which allows - // processing more utterances at the same time. Only used in Kaldi2Reader. - // TODO: move the two-forward-pass support out of the reader. - AttemptUtteranceDerivativeFeatures(net, trainSetDataReader, featureNodes, inputMatrices); - - profiler.NextSample(); - } - - // --- END MAIN MINIBATCH LOOP - - if (useModelAveraging && (g_mpi->NumNodesInUse() > 1) ) - { - // may not be synced after epoch finished, so do the sync here - int residualSampels = (int)nSamplesSinceLastModelSync; - g_mpi->AllReduce(&residualSampels, 1); - totalSamplesSeen += residualSampels; - totalEpochSamples += residualSampels; - ModelAveragingSync(nSamplesSinceLastModelSync, learnableNodes); - nSynced++; - nSamplesSinceLastModelSync = 0; - } - - // compute final criterion values - if (useGradientAggregation) - { - // with parallelization, we have them in regular variables - epochCriterion /= float(totalEpochSamples); - for (size_t i = 0; i< epochEvalErrors.size(); i++) - { - epochEvalErrors[i] /= totalEpochSamples; - } - } - else - { - // without, we have them in Matrix objects that possibly live on the GPU--get them over now - localEpochCriterion /= float(totalEpochSamples); - localEpochEvalErrors /= float(totalEpochSamples); - - epochCriterion = localEpochCriterion.Get00Element(); - for (size_t i = 0; i < epochEvalErrors.size(); i++) - { - epochEvalErrors[i] = localEpochEvalErrors(0, i); - } - } - - // in case of model averaging, do one more final aggregation of criteria - if (useModelAveraging && (g_mpi->NumNodesInUse() > 1)) - { - // merge epochCriterion and epochEvalErrors over nodes - g_mpi->AllReduce(&epochCriterion, 1); - g_mpi->AllReduce(epochEvalErrors); - } - return totalEpochSamples; - } - - // ----------------------------------------------------------------------- - // sub-routines and helpers follow below - // ----------------------------------------------------------------------- - -#if 0 - // TODO: per discussion with Dong Yu, Guoguo Chen, and Yu Zhang, this function can be removed. - template - void SGD::SequenceTrain(IComputationNetBuilder* netBuilder, wstring origModelFileName, - IDataReader* trainSetDataReader, IDataReader* validationSetDataReader, - const DEVICEID_TYPE deviceId, const bool makeMode) - { - if (netBuilder == nullptr || origModelFileName == L"" || trainSetDataReader == nullptr) - { - InvalidArgument("netBuilder, origModel and trainSetDataReader should not be null."); - } - - int startEpoch = DetermineStartEpoch(makeMode); - if (startEpoch == m_maxEpochs) - { - fprintf(stderr, "No further training is necessary.\n"); - return; - } - - // Initializes the model from original model. - // TODO: Comment what this does! - auto origNet = make_shared(deviceId); - ComputationNetworkPtr sequenceNet = - (startEpoch < 0) ? netBuilder->BuildNetworkFromDescription() : origNet; - std::vector addedFeatureNodes; - std::vector replacedCriterionNodes; - if (startEpoch < 0) - { - // Loads models. - origNet->Load(origModelFileName); - - // Processes feature nodes. - std::vector & sequenceFeatureNodes = sequenceNet->FeatureNodes(); - for (size_t i = 0; i < sequenceFeatureNodes.size(); ++i) - { - if (!origNet->NodeNameExists(sequenceFeatureNodes[i]->NodeName())) - { - addedFeatureNodes.push_back(sequenceFeatureNodes[i]); - origNet->AddFeatureNode(sequenceFeatureNodes[i]); - } - } - - // Processes criterion nodes. - auto & origCriterionNodes = GetTrainCriterionNodes(origNet); - auto & sequenceCriterionNodes = GetTrainCriterionNodes(sequenceNet); - if (origCriterionNodes.size() == 0 || sequenceCriterionNodes.size() == 0) - { - RuntimeError("Training criterion node does not exist."); - } - replacedCriterionNodes.push_back(origCriterionNodes[0]); - origNet->ReplaceFinalCriterionNode(origCriterionNodes[0]->NodeName(), sequenceCriterionNodes[0]); - origNet->ResetEvalTimeStamps(); - } - - wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); - if (startEpoch >= 0) - { - fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); - } - else - { - fprintf(stderr, "Load Network From the original model file %ls.\n", origModelFileName.c_str()); - } - ComputationNetworkPtr net = (startEpoch < 0) ? origNet : ComputationNetwork::CreateFromFile(deviceId, modelFileName); - - startEpoch = max(startEpoch, 0); - - TrainOrAdaptModel(startEpoch, net, net, nullptr, trainSetDataReader, validationSetDataReader); - - // Handles deletions carefully here. - // TODO: This is no longer needed since we own our networks and deal with shared_ptrs now. - if (startEpoch < 0) - { - for (size_t i = 0; i < addedFeatureNodes.size(); ++i) - { - origNet->RemoveFeatureNode(addedFeatureNodes[i]); - } - auto & origCriterionNodes = GetTrainCriterionNodes(origNet); - origNet->ReplaceFinalCriterionNode(origCriterionNodes[0]->NodeName(), replacedCriterionNodes[0]); - } - } -#endif - - static double MomentumPerMB(double momentumPerSample, size_t minibatchSize) - { - return pow(momentumPerSample, minibatchSize); - } - - // Get{Train,Eval}CriterionNodes() return a reference that is, unfortunately, dependent on the network. - // So we hold those inside here. Not very nice. Also not thread-safe. This may go away once we fix sequence-to-sequence models properly. - // TODO: merge them into one. - static map> tmpCriterionNodeSets; - // TODO: test this, then remove this comment - - template - std::vector & SGD::GetTrainCriterionNodes(ComputationNetworkPtr net) - { - if (!m_trainCriterionNodeName.empty()) - { - tmpCriterionNodeSets[net] = net->CriterionNodesFrom(m_trainCriterionNodeName); - return tmpCriterionNodeSets[net]; - } - else - return net->FinalCriterionNodes(); - } - - template - std::vector & SGD::GetEvalCriterionNodes(ComputationNetworkPtr net) - { - if (!m_evalCriterionNodeName.empty()) - { - tmpCriterionNodeSets[net] = net->CriterionNodesFrom(m_evalCriterionNodeName); - return tmpCriterionNodeSets[net]; - } - else - return net->EvaluationNodes(); - } - - // return true if precomputation is executed. - template - bool SGD::PreCompute(ComputationNetworkPtr net, - IDataReader* trainSetDataReader, - std::vector & featureNodes, - std::vector & labelNodes, - std::map*>* inputMatrices) - { - std::list nodes = net->GetNodesRequiringPreComputation(); // this tests all HasComputed() flags - - if (nodes.size() == 0) - { - fprintf(stderr, "No PreCompute nodes found, skipping PreCompute step\n"); - return false; - } - - fprintf(stderr, "\nPrecomputing --> %lu PreCompute nodes found.\n\n", nodes.size()); - for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) - { - auto node = static_pointer_cast>(*nodeIter); - fprintf(stderr, "\tNodeName: %ls\n", (node->NodeName()).c_str()); - } - - //compute - //trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0 , requestDataSize); - // trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0 , m_epochSize); // only based on one epoch - // [1/12/2015 erw] to support large dataset, we usually partition whole dataset into several epoch's, - // so we need to use all the data to do precomputing - if (m_useAllDataForPreComputedNode) // using all the data - trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0); - else // using only one epoch - trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0, m_epochSize); - net->StartEvaluateMinibatchLoop(nodes); - - // initialize - for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) - { - auto node = static_pointer_cast>(*nodeIter); - node->MarkComputed(false/*begin accumulating*/); - } - size_t actualMBSizeDummy; - while (DataReaderHelpers::GetMinibatchIntoNetwork(*trainSetDataReader, net, nullptr, false, false, *inputMatrices, actualMBSizeDummy)) - { - // TODO: move these into GetMinibatchIntoNetwork() --but those are passed around; necessary? Can't we get them from 'net'? - ComputationNetwork::BumpEvalTimeStamp(featureNodes); - ComputationNetwork::BumpEvalTimeStamp(labelNodes); - - net->ForwardProp(nodes); - } - // finalize - for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) - { - auto node = static_pointer_cast>(*nodeIter); - node->MarkComputed(true/*done accumulating*/); - } - fprintf(stderr, "\nPrecomputing --> Completed.\n\n"); - - return true; - } - - // return a reasonable initial learning rate based on the initial mbsize - template - double SGD::SearchForBestLearnRate(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, const int epochNumber, - const double curLearnRate, - IDataReader* trainSetDataReader, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const bool learnRateInitialized, - const double largestPrevLearnRatePerSample) - { - double epochCriterion = std::numeric_limits::infinity(); - double prevCriterion = std::numeric_limits::infinity(); - vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); - - size_t totalSamplesSeen = 0; - double bestLearnRatePerSample = curLearnRate; - - size_t numFramesToUseInSearch = m_numMiniBatch4LRSearch[epochNumber] * m_mbSize[epochNumber]; - if (m_epochSize != requestDataSize) - { - // ensure the numFramesToUseInSearch does not exceed the total number of frames in the epoch - numFramesToUseInSearch = min(numFramesToUseInSearch, m_epochSize); - } - - double baseCriterion; - - double minLearnRate = m_minLearnRate * 0.3f; - double learnRatePerSample = 1.0f / 8.0f / 0.618f / sqrt((double)m_mbSize[epochNumber]); - - if (learnRateInitialized && largestPrevLearnRatePerSample > 0) - { - // largestPrevLearnRatePerSample is per sample, first 0.618f is for compensation, second one is for safety - learnRatePerSample = largestPrevLearnRatePerSample / 0.618f / 0.618f; - } - - int baseModelEpoch = epochNumber - 1; - net->RereadPersistableParameters(GetModelNameForEpoch(baseModelEpoch)); - - double learnRate = learnRatePerSample; - size_t dummyMinibatchSize = 0; - LoadCheckPointInfo(baseModelEpoch, - /*out*/ totalSamplesSeen, - /*out*/ learnRate, - smoothedGradients, - /*out*/ prevCriterion, - /*out*/ dummyMinibatchSize); - - // if model is not changed this is what we will get - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, - numFramesToUseInSearch, trainSetDataReader, 0, m_mbSize[epochNumber], - featureNodes, labelNodes, - criterionNodes, evaluationNodes, - inputMatrices, learnableNodes, - smoothedGradients, /*out*/ baseCriterion, - /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, - "BaseAdaptiveLearnRateSearch:"); - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) - { - if (prevCriterion == std::numeric_limits::infinity()) - prevCriterion = baseCriterion; - - double ratio = 0.3; - - if (m_epochSize != requestDataSize) - ratio = pow(((double)numFramesToUseInSearch) / m_epochSize, 1.0f / 2); - - baseCriterion = max(ratio * prevCriterion + (1 - ratio) * baseCriterion, baseCriterion); - } - - do - { - learnRatePerSample *= 0.618; - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, - numFramesToUseInSearch, trainSetDataReader, - learnRatePerSample, m_mbSize[epochNumber], featureNodes, - labelNodes, criterionNodes, - evaluationNodes, inputMatrices, - learnableNodes, smoothedGradients, - /*out*/ epochCriterion, /*out*/ epochEvalErrors, - /*out*/ totalSamplesSeen, "AdaptiveLearnRateSearch:"); - - } while (std::isnan(epochCriterion) || (epochCriterion > baseCriterion && learnRatePerSample > minLearnRate)); - - bestLearnRatePerSample = learnRatePerSample; - - //grid search for the first m_numBestSearchEpoch epochs - if (epochNumber < m_numBestSearchEpoch) - { - double leftLearnRatePerSample = 0.01 / m_mbSize[epochNumber]; - double rightLearnRatePerSample = learnRatePerSample; - double leftCriterion, rightCriterion = epochCriterion; - - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, - numFramesToUseInSearch, trainSetDataReader, - leftLearnRatePerSample, m_mbSize[epochNumber], - featureNodes, labelNodes, - criterionNodes, evaluationNodes, - inputMatrices, learnableNodes, - smoothedGradients, /*out*/ leftCriterion, - /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, - "DetailBaseAdaptiveLearnRateSearch:"); - - while (rightLearnRatePerSample > leftLearnRatePerSample * 1.2) - { - if (rightCriterion > leftCriterion) - { - rightLearnRatePerSample *= 0.618; - - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, - epochNumber, numFramesToUseInSearch, - trainSetDataReader, - rightLearnRatePerSample, m_mbSize[epochNumber], - featureNodes, labelNodes, - criterionNodes, - evaluationNodes, - inputMatrices, - learnableNodes, - smoothedGradients, - /*out*/ rightCriterion, - /*out*/ epochEvalErrors, - /*out*/ totalSamplesSeen, - "DetailRightAdaptiveLearnRateSearch:"); - } - else - { - leftLearnRatePerSample /= 0.618; - - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, - epochNumber, numFramesToUseInSearch, - trainSetDataReader, - leftLearnRatePerSample, m_mbSize[epochNumber], - featureNodes, labelNodes, - criterionNodes, - evaluationNodes, - inputMatrices, - learnableNodes, - smoothedGradients, - /*out*/ leftCriterion, - /*out*/ epochEvalErrors, - /*out*/ totalSamplesSeen, - "DetailLeftAdaptiveLearnRateSearch:"); - } - } - - bestLearnRatePerSample = (leftCriterion < rightCriterion) ? leftLearnRatePerSample : - rightLearnRatePerSample; - } - - fprintf(stderr, "Best Learn Rate Per Sample for Epoch[%d] = %.10g baseCriterion=%.10g\n", - epochNumber + 1, bestLearnRatePerSample, baseCriterion); - - return bestLearnRatePerSample; - } - - template - void SGD::TrainOneMiniEpochAndReloadModel(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, const int epochNumber, - const size_t epochSize, IDataReader* trainSetDataReader, - const double learnRatePerSample, - const size_t minibatchSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - /*out*/ double& epochCriterion, - /*out*/ std::vector& epochEvalErrors, - /*out*/ size_t& totalSamplesSeen, - std::string prefixMsg) - { - TrainOneEpoch(net, refNet, refNode, epochNumber, epochSize, - trainSetDataReader, learnRatePerSample, minibatchSize, featureNodes, - labelNodes, criterionNodes, evaluationNodes, - inputMatrices, learnableNodes, smoothedGradients, - /*out*/ epochCriterion, /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, - prefixMsg); - - fprintf(stderr, "Finished Mini-Epoch For LearnRate Selection: TrainLossPerSample = %.8g;", epochCriterion); - - if (epochEvalErrors.size() == 1) - fprintf(stderr, "EvalErrPerSample = %.8g; AvgLearningRatePerSample = %.8g\n", epochEvalErrors[0], learnRatePerSample); - else - { - fprintf(stderr, "EvalErrPerSample "); - for (size_t i = 0; i < epochEvalErrors.size(); i++) - { - fprintf(stderr, "[%lu] = %.8g; ", i, epochEvalErrors[i]); - } - fprintf(stderr, "AvgLearningRatePerSample = %.8g\n", learnRatePerSample); - } - - int baseModelEpoch = epochNumber - 1; - net->RereadPersistableParameters(GetModelNameForEpoch(baseModelEpoch)); - - double dummyLearnRate; - double dummtPrevCriterion; - size_t dummyMinibatchSize = 0; - LoadCheckPointInfo(baseModelEpoch, - /*out*/ totalSamplesSeen, - /*out*/ dummyLearnRate, - smoothedGradients, - /*out*/ dummtPrevCriterion, - /*out*/ dummyMinibatchSize); - } - - // AdaptiveMinibatchSizing() -- choose the largest feasible minibatch size - // This is necessary for data-parallel operation. The aim is to minimize model updates, and hence bandwidth - // This implements - // F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu: - // "On Parallelizability of Stochastic Gradient Descent for Speech DNNs" - // In Proc. ICASSP 2014. - template - size_t SGD::AdaptiveMinibatchSizing(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t numFramesToUseInSearch, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - const size_t initialMinibatchSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const double learningRateAdjustmentFactor) - { - size_t minMinibatchSize = initialMinibatchSize; - size_t chosenMinibatchSize = initialMinibatchSize; - - // do some pre-adjustment based on LR - // Basically we assume that the LR for epoch 1 is safe for mbsize. - // If LR control led to a smaller LR, then we can safely increase the lower bound of the MB size. - double learningRateChangeSoFar = GetLearningRatePerSample(epochNumber/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()) / GetLearningRatePerSample(0/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); - learningRateChangeSoFar *= learningRateAdjustmentFactor; - - // increasing by the full factor is found to be too aggressive; sqrt() seems more robust - learningRateChangeSoFar = sqrt(learningRateChangeSoFar); - - // LR was indeed reduced - if (learningRateChangeSoFar < 1.0f) - { - // we can safely increase MB size (note: this may be bigger than our max) - minMinibatchSize = (size_t)(minMinibatchSize / learningRateChangeSoFar); - } - - if (epochNumber < 2 && m_prevChosenMinibatchSize != 0) - { - // newly started training: any previous MB size stored in the model is to be ignored - fprintf(stderr, "before epoch .2, previous minibatchSize %zd is " - "considered invalid -> resetting\n", m_prevChosenMinibatchSize); - m_prevChosenMinibatchSize = 0; - } - - // check if we need to skip - if (m_prevChosenMinibatchSize != 0 && - (epochNumber + 1) > m_minibatchSizeTuningFrequency && - (epochNumber + 1) % m_minibatchSizeTuningFrequency != 0) - { - fprintf(stderr, "AdaptiveMinibatchSearch: Search for a better minibatchSize " - "in epoch %d skipped, keeping minibatchSize of %zd\n", - epochNumber + 1, m_prevChosenMinibatchSize); - chosenMinibatchSize = m_prevChosenMinibatchSize; - } - else - { - if (m_prevChosenMinibatchSize != 0) - { - // if m_prevChosenMinibatchSize (the chosen minibatch size for the previous epoch) div 2 - // is higher than initialMinibatchSize (the minibatch size we start with for this epoch), - // then start the search with m_prevChosenMinibatchSize/2 instead of initialMinibatchSize. - fprintf(stderr, "AdaptiveMinibatchSearch: Limiting minMinibatchSize to " - "largest of previous minibatchSize = (%d / 2) or %d\n", - (int) m_prevChosenMinibatchSize, (int) minMinibatchSize); - minMinibatchSize = max(minMinibatchSize, m_prevChosenMinibatchSize / 2); - } - - size_t maxMinibatchSize = m_minibatchSizeTuningMax; - - // only grow at most 2 x compared to previous step - if (m_prevChosenMinibatchSize != 0.0f) - { - assert(m_prevChosenMinibatchSize >= chosenMinibatchSize); - - fprintf(stderr, "AdaptiveMinibatchSearch: Limiting maxMinibatchSize to " - "previous minibatchSize %zd*2\n", m_prevChosenMinibatchSize); - maxMinibatchSize = min(maxMinibatchSize, m_prevChosenMinibatchSize * 2); - } - - chosenMinibatchSize = SearchForBestMinibatchSize(net, refNet, refNode, epochNumber, - numFramesToUseInSearch, trainSetDataReader, - learnRatePerSample, featureNodes, - labelNodes, criterionNodes, - evaluationNodes, inputMatrices, - learnableNodes, smoothedGradients, - minMinibatchSize, maxMinibatchSize); - } - - return chosenMinibatchSize; - } - - static size_t RoundToMultipleOf64(float val) - { - return 64 * (size_t)((val + 32) / 64); - } - - static size_t RoundToMultipleOf64(size_t val) - { - return 64 * ((val + 32) / 64); - } - - // uses a small percentage of training data of minibatch to - // speculatively train with various MB sizes; then picks the best - template - size_t SGD::SearchForBestMinibatchSize(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t numFramesToUseInSearch, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const size_t minMinibatchSize, const size_t maxMinibatchSize) - { - // may happen for automatically reduced learning rates - if (minMinibatchSize > maxMinibatchSize) - { - return maxMinibatchSize; - } - - size_t trialMinibatchSize = 0; - bool isFirstIteration = true; - double baseCriterion = 0; - - // increase the minibatch size by a factor of sqrt(2) in each step. - const float minibatchSizeTuningFactor = sqrtf(2.0f); - - size_t lastTriedTrialMinibatchSize = 0; - double lastTriedTrialEpochCriterion = 0; - for (float trialMinibatchSizeFloat = (float)minMinibatchSize; - trialMinibatchSizeFloat <= maxMinibatchSize; - trialMinibatchSizeFloat *= minibatchSizeTuningFactor) - { - // round mbsize to something meaningful - trialMinibatchSize = RoundToMultipleOf64(trialMinibatchSizeFloat); - - fprintf(stderr, "\nAdaptiveMinibatchSearch: Evaluating trial minibatchSize=%zd out of range %zd..%zd ...\n\n", - trialMinibatchSize, RoundToMultipleOf64(minMinibatchSize), RoundToMultipleOf64(maxMinibatchSize)); - - size_t totalSamplesSeen; - std::vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); - double epochCriterion = std::numeric_limits::infinity(); - - // Train on a few minibatches and so we can observe the epochCriterion as we try increasing - // minibatches with iteration of this loop. - TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, - numFramesToUseInSearch, trainSetDataReader, - learnRatePerSample, trialMinibatchSize, featureNodes, - labelNodes, criterionNodes, - evaluationNodes, inputMatrices, - learnableNodes, smoothedGradients, - /*out*/ epochCriterion, /*out*/ epochEvalErrors, - /*out*/ totalSamplesSeen, - isFirstIteration ? "BaseAdaptiveMinibatchSearch:" : - "AdaptiveMinibatchSearch:"); - - if (isFirstIteration) - { - // for the first iteration of the loop only, set baseCriterion - // to the result we got from TrainOneMiniEpochAndReloadModel(). - baseCriterion = epochCriterion; - lastTriedTrialMinibatchSize = trialMinibatchSize; - lastTriedTrialEpochCriterion = baseCriterion; - isFirstIteration = false; - - fprintf(stderr, "AdaptiveMinibatchSearch: Computed BaseCriterion %.10g\n", baseCriterion); - } - else if (!std::isnan(epochCriterion) && - (epochCriterion > (baseCriterion * (1.0 + ( m_minibatchSearchCriterionErrorMargin / 100.0))))) - { - // As soon as we see the Criterion (a measure of error) start to get larger than the - // Criterion we started with, we stop. - // TODO: if this is too sensitive, we can add a margin on the bases of percentage of - // baseCriterion. - break; - } - else - { - lastTriedTrialMinibatchSize = trialMinibatchSize; - lastTriedTrialEpochCriterion = epochCriterion; - if (trialMinibatchSizeFloat * minibatchSizeTuningFactor <= maxMinibatchSize) - { - fprintf(stderr, "AdaptiveMinibatchSearch: Keep searching... " - "EpochCriterion = %.10g vs BaseCriterion = %.10g\n", - epochCriterion, baseCriterion); - } - } - } - fprintf(stderr, "AdaptiveMinibatchSearch: Search successful!!! Chose new minibatchSize of %d. " - "EpochCriterion = %.10g vs BaseCriterion = %.10g\n\n", - (int) lastTriedTrialMinibatchSize, lastTriedTrialEpochCriterion, baseCriterion); - - - return lastTriedTrialMinibatchSize; - } - - // Attemps to compute the error signal for the whole utterance, which will - // be fed to the neural network as features. Currently it is a workaround - // for the two-forward-pass sequence and ctc training, which allows - // processing more utterances at the same time. Only used in Kaldi2Reader. - // TODO: move the two-forward-pass support out of the reader. - template - void SGD::AttemptUtteranceDerivativeFeatures(ComputationNetworkPtr net, - IDataReader* trainSetDataReader, - const std::vector & featureNodes, - std::map*>* inputMatrices) - { - assert(trainSetDataReader != NULL); - std::vector>> uttInfo; - auto pMBLayout = make_shared(); - // TODO: use GetMinibatchIntoNetwork(). - while (trainSetDataReader->GetMinibatchCopy(uttInfo, *inputMatrices, pMBLayout)) - { - ComputationNetwork::BumpEvalTimeStamp(featureNodes); - - auto & outputNodes = net->OutputNodes(); - if (outputNodes.empty()) - LogicError("no output node was found."); - - //net->SetActualMiniBatchSizeFromFeatures(); - trainSetDataReader->CopyMBLayoutTo(net->GetMBLayoutPtr()); - net->VerifyActualNumParallelSequences(trainSetDataReader->GetNumParallelSequences()); - net->ForwardProp(outputNodes[0]); // Only evaluate the first output - trainSetDataReader->SetNetOutput(uttInfo, - dynamic_pointer_cast>(outputNodes[0])->Value(), - pMBLayout); - } - } - - static string GeneratePaddedFloatOrExpFormat(int padSize, int precision, double value) - { - char format[16]; - char buffer[512]; - - sprintf(format, "%%.%dg", precision); - sprintf(buffer, format, value); - - for (int i = 0; i < strlen(buffer); i++) - { - if (buffer[i] == 'e' || buffer[i] == 'E') - { - sprintf(format, "%%%d.%de", padSize, precision); - return format; - } - } - sprintf(format, "%%%d.%df", padSize, precision); - return format; - } - - template - int SGD::SGDTrace(FILE *__restrict __stream, const char *__restrict __format, ...) - { - int result = 0; - if (m_traceLevel > 0) - { - va_list args; - va_start(args, __format); - result = vfprintf(__stream, __format, args); - va_end(args); - } - return result; - } - - template - void SGD::InitDistGradAgg(int numEvalNodes, int traceLevel) - { - if (m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) - { - if (m_distGradAgg == nullptr) - { -#ifdef QUANTIZED_GRADIENT_AGGREGATION - m_distGradAgg = new AllReduceDistGradAggregator(g_mpi, m_numGradientBits, m_zeroThresholdFor1Bit, true /*useQuantizationForSelfStripe*/, m_bufferedAsyncGradientAggregation, traceLevel, m_syncStatsTrace); -#else - if (m_numGradientBits != (8 * sizeof(ElemType))) - { - RuntimeError("Gradient quantization is unsupported in CNTK binaries built without quantized gradient aggregation support!"); - } - - m_distGradAgg = new SimpleDistGradAggregator(g_mpi, m_bufferedAsyncGradientAggregation, m_syncStatsTrace); -#endif // !QUANTIZED_GRADIENT_AGGREGATION - - } - - if (m_gradHeader == nullptr) - { - m_gradHeader = DistGradHeader::Create(numEvalNodes); - } - } - } - - template - bool SGD::ModelAveragingProcessing(size_t nSamplesSinceLastSync, const std::list& learnableNodes, size_t& nProcessedFrames, - float& SecondsSinceLastSyncFinished, float& SecondsSpentOnSync) - { - ////////////////////////////////////////////////////////////////////////// - // the current strategy is that after each minibatch, we will sync between processors - // to decide whether a sync need to be performed. This is definitely not optimal, - // which we will fix it later. - - // TODO: the way we handle timer is not very good - ////////////////////////////////////////////////////////////////////////// - static bool first = true ; - static Timer MAtimer; - if (first) - { - MAtimer.Start(); - first = false; - } - - char bNeedToSync = (char)0; // use char for bool - if (g_mpi->IsMainNode() && nSamplesSinceLastSync >= m_nFramesBetweenMASync) - { - // only the main node can decide whether a sync need to be performed - bNeedToSync = (char)1; - } - g_mpi->Bcast(&bNeedToSync, 1, g_mpi->MainNodeRank()); - if (bNeedToSync) - { - MAtimer.Stop(); - double elapsedsec = MAtimer.ElapsedSeconds(); - SecondsSinceLastSyncFinished = first ? 0 : (float) elapsedsec ; - MAtimer.Start(); - nProcessedFrames = ModelAveragingSync((int)nSamplesSinceLastSync, learnableNodes); - MAtimer.Stop(); - SecondsSpentOnSync = (float)MAtimer.ElapsedSeconds(); - - MAtimer.Start(); - } - else - { - nProcessedFrames = 0; - return false; - } - return true; - } - - template - size_t SGD::ModelAveragingSync(int nSamplesSinceLastSync, const std::list& learnableNodes) - { - if (g_mpi->NumNodesInUse() <= 1) - { - return nSamplesSinceLastSync; - } - - //======================================== - // Sec. 1 calculate factor - //======================================== - float factor = 0; - int nTotalSamples = nSamplesSinceLastSync; - g_mpi->AllReduce(&nTotalSamples, 1); - if (nTotalSamples <= 0) - { - // prepare for overflow - factor = 1.0f / g_mpi->NumNodesInUse(); - } - else - { - factor = (nSamplesSinceLastSync + 0.0f) / nTotalSamples; - } - - //======================================== - // Sec. 2 sync models based on factor - // Note: this is suboptimal at the moment: - // we do the averaging for each node in a sequence manner, i.e., - // (node1) GPU->CPU->MPI_AllReduce -> (node2)GPU->CPU->MPI_AllReduce - // we can improve it by using a pipeline - // (node1) GPU -> CPU -> MPI_AllReduce - // (node2) GPU -> CPU -> MPI_AllReduce - // (node3) GPU -> CPU -> MPI_AllReduce - //======================================== - for (auto iter = learnableNodes.begin(); iter != learnableNodes.end(); iter++) - { - ComputationNodeBasePtr pNode = *iter; - if (!pNode->IsParameterUpdateRequired()) - continue; - - Matrix& mat = dynamic_pointer_cast>(pNode)->Value(); - // 1. normalize the weight matrix - Matrix::Scale(factor, mat); - // 2. send weight matrix over MPI nodes; - ElemType* px = mat.CopyToArray(); - size_t nx = mat.GetNumElements(); - - // 3. inplace sum - g_mpi->AllReduce(px, nx); - mat.SetValue(mat.GetNumRows(), mat.GetNumCols(), mat.GetDeviceId(), px); - // 4. clean up - delete []px; - } - - return nTotalSamples; - } - -// public: - // UpdateWeightsS - static version of UpdateWeights() - // not static since it wants to access protected methods on the SGD object - template - /*static*/ void SGD::UpdateWeightsS(const SGD* sgd, Matrix& functionValues, - Matrix& gradientValues, - Matrix& smoothedGradient, - const double learnRatePerSample, - const double momentumPerSample, - size_t actualMBSize, - const double L2RegWeight, - const double L1RegWeight, - const bool needAveMultiplier, - const bool useNesterovMomentum - ) - { - // we use simple linear (instead of log linear) scaling here - const double momentum = MomentumPerMB(momentumPerSample, actualMBSize); -#if DUMPOUTPUT - fprintf(stderr, "learnRatePerSample=%0.8f, momentum=%0.8f, actualMBSize=%ld\n", - learnRatePerSample, momentum, actualMBSize); - fprintf(stderr, "sgd->GradUpdateType()=%d, sgd->GradientUpdateNoiseStd()=%0.8f\n", - sgd->GradUpdateType(), sgd->GradientUpdateNoiseStd()); - gradientValues.Print("Gradient Input"); - smoothedGradient.Print("Smoothed Gradient Input"); -#endif - - // make actualMBSize is a valid value - assert(actualMBSize > 0); - - //clipping gradients to prevent outliers - sgd->ClipGradient(gradientValues, actualMBSize); - - GradientsUpdateType adpType = sgd->GradUpdateType(); - double noiseStd = sgd->GradientUpdateNoiseStd(); - Matrix sgdUpdateNoise((DEVICEID_TYPE)functionValues.GetDeviceId()); - if (noiseStd > 0) - { - // get the gradient structure since gradient is sparse - sgdUpdateNoise.SetValue(gradientValues); - - // reset its value to random - sgdUpdateNoise.SetGaussianRandomValue(0, (ElemType)noiseStd); - } - - // L2 regularizer - if (L2RegWeight > 0) - { - // multiply by actualMBSize so that it's invariant to minibatch size since learning rate is per sample - Matrix::ScaleAndAdd((ElemType)(L2RegWeight * actualMBSize), functionValues, gradientValues); - } - - if (adpType == GradientsUpdateType::None) - { - smoothedGradient.NormalGrad(gradientValues, functionValues, - (ElemType)learnRatePerSample, (ElemType)momentum, useNesterovMomentum); - } - else if (adpType == GradientsUpdateType::AdaGrad || - (adpType == GradientsUpdateType::RmsProp && gradientValues.GetMatrixType() == MatrixType::SPARSE) || - (adpType == GradientsUpdateType::FSAdaGrad && gradientValues.GetMatrixType() == MatrixType::SPARSE)) - { - //rmsprop for sparse is not implemented yet, delegate it with adagrad - - double aveMultiplier = smoothedGradient.Adagrad(gradientValues, needAveMultiplier); - Matrix::ScaleAndAdd((ElemType)(-learnRatePerSample / aveMultiplier), gradientValues, functionValues); - } - else if (adpType == GradientsUpdateType::FSAdaGrad) - { - smoothedGradient.FSAdagrad(actualMBSize, gradientValues, functionValues, (ElemType)learnRatePerSample, (ElemType)momentum); - } - else if (adpType == GradientsUpdateType::RmsProp) - { - double aveMultiplier = smoothedGradient.RmsProp(gradientValues, (ElemType)sgd->m_rpi.gamma, - (ElemType)sgd->m_rpi.inc, (ElemType)sgd->m_rpi.max, - (ElemType)sgd->m_rpi.dec, (ElemType)sgd->m_rpi.min, needAveMultiplier); - Matrix::ScaleAndAdd((ElemType)(-learnRatePerSample / aveMultiplier), gradientValues, functionValues); - } - - if (noiseStd > 0) - { - Matrix::ScaleAndAdd(1.0, sgdUpdateNoise, functionValues); - } - - // L1 regularizer with proximal gradient descent method - if (L1RegWeight > 0) - { - // multiply by actualMBSize so that it's invariant to minibatch size since learning rate is per sample - functionValues.InplaceSoftThreshold((ElemType)(learnRatePerSample * L1RegWeight * actualMBSize)); - } - -#if DUMPOUTPUT - functionValues.Print("Parameter Update"); -#endif - } - -// protected: - - // UpdateWeights - update the weights in - template - void SGD::UpdateWeights(const ComputationNodeBasePtr& node, - Matrix& smoothedGradient, - const double learnRatePerSample, - const double momentumPerSample, - const size_t actualMBSize, - const double L2RegWeight, const double L1RegWeight, - const bool needAveMultiplier, - const bool useNesterovMomentum - ) const - { -#if DUMPOUTPUT - fprintf(stderr, "Update_%ls\n", node->NodeName().c_str()); -#endif - if (!node->IsParameterUpdateRequired()) - LogicError("UpdateWeights() called for a learnable ComputationNode which has m_parameterUpdateRequired == false!"); - - UpdateWeightsS(this, dynamic_pointer_cast>(node)->Value(), dynamic_pointer_cast>(node)->Gradient(), - smoothedGradient, learnRatePerSample, momentumPerSample, - actualMBSize, L2RegWeight, L1RegWeight, - needAveMultiplier, m_useNesterovMomentum); - node->BumpEvalTimeStamp(); - } - - template - void SGD::ClipGradient(Matrix& gradient, const size_t actualMBSize) const - { - if (m_clippingThresholdPerSample != std::numeric_limits::infinity()) - { - double maxGradientPerMB = m_clippingThresholdPerSample * actualMBSize; - if (m_gradientClippingWithTruncation) - gradient.InplaceTruncate((ElemType)(maxGradientPerMB)); - else - { - // norm2 normalized - double gradientNorm = gradient.FrobeniusNorm(); - if (gradientNorm > maxGradientPerMB) - { - double normFactor = maxGradientPerMB / gradientNorm; - gradient *= (ElemType)normFactor; - } - } - } - } - - template - void SGD::SaveCheckPointInfo(const size_t epoch, const size_t totalSamplesSeen, - const double learnRatePerSample, - const std::list>& smoothedGradients, - const double prevCriterion, - const size_t minibatchSize) - { - // In case of parallel training only the main node should we saving the checkpoint to prevent - // the parallel training nodes from colliding to write the same file - if ((g_mpi == nullptr) || g_mpi->IsMainNode()) - { - wstring checkPointFileName = GetCheckPointFileNameForEpoch(int(epoch)); - // Saving into temporary file and then renaming it to the checkPointFileName - // This is a standard trick to avoid havign corrupted checkpoints files if process dies during writing - wstring tempFileName = checkPointFileName + L".tmp"; - - { - File fstream(tempFileName, FileOptions::fileOptionsBinary | FileOptions::fileOptionsWrite); - fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BCKP"); - - fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BLearnRate"); - fstream << totalSamplesSeen << learnRatePerSample << prevCriterion; - fstream.PutMarker(FileMarker::fileMarkerEndSection, L"ELearnRate"); - - fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BMinibatchSize"); - fstream << minibatchSize; - fstream.PutMarker(FileMarker::fileMarkerEndSection, L"EMinibatchSize"); - - fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BGradient"); - - for (auto smoothedGradientIter = smoothedGradients.begin(); smoothedGradientIter != smoothedGradients.end(); smoothedGradientIter++) - { - const Matrix& smoothedGradient = *smoothedGradientIter; - fstream << smoothedGradient; - } - - fstream.PutMarker(FileMarker::fileMarkerEndSection, L"EGradient"); - - fstream.PutMarker(FileMarker::fileMarkerEndSection, L"ECKP"); - - // Ensuring that data is written - fstream.Flush(); - } - - renameOrDie(tempFileName, checkPointFileName); - } - } - - template - bool SGD::LoadCheckPointInfo(const size_t epochNumber, - /*out*/ size_t& totalSamplesSeen, - /*out*/ double& learnRatePerSample, - std::list>& smoothedGradients, - /*out*/ double& prevCriterion, - /*out*/ size_t& minibatchSize) - { - wstring checkPointFileName = GetCheckPointFileNameForEpoch(int(epochNumber)); - if (!fexists(checkPointFileName.c_str())) - { - fprintf(stderr, "Warning: checkpoint file is missing. learning parameters will be initialized from 0\n"); - return false; - } - - File fstream(checkPointFileName, - FileOptions::fileOptionsBinary | FileOptions::fileOptionsRead); - fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BCKP"); - - fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BLearnRate"); - fstream >> totalSamplesSeen >> learnRatePerSample >> prevCriterion; - fstream.GetMarker(FileMarker::fileMarkerEndSection, L"ELearnRate"); - - if (fstream.TryGetMarker(FileMarker::fileMarkerBeginSection, L"BMinibatchSize")) - { - fstream >> minibatchSize; - fstream.GetMarker(FileMarker::fileMarkerEndSection, L"EMinibatchSize"); - } - else - { - minibatchSize = m_mbSize[epochNumber]; - } - - fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BGradient"); - - for (auto smoothedGradientIter = smoothedGradients.begin(); smoothedGradientIter != smoothedGradients.end(); smoothedGradientIter++) - { - Matrix& smoothedGradient = *smoothedGradientIter; - fstream >> smoothedGradient; - } - fstream.GetMarker(FileMarker::fileMarkerEndSection, L"EGradient"); - - fstream.GetMarker(FileMarker::fileMarkerEndSection, L"ECKP"); - - return true; - } - - template - wstring SGD::GetCheckPointFileNameForEpoch(const int epoch) - { - return GetModelNameForEpoch(epoch) + L".ckp"; - } - - template - wstring SGD::GetModelNameForEpoch(const int epoch, bool bLastModel) - { - int epoch1Base = epoch + 1; - if (epoch1Base == m_maxEpochs || bLastModel) - { - return m_modelPath; - } - else - { - wstring w = msra::strfun::wstrprintf(L"%ls.%d", m_modelPath.c_str(), (int)epoch1Base); - return w; - } - - } - - // return -1 if nothing exists - template // TODO: needed? - int SGD::DetermineStartEpoch(const bool makeMode) - { - if (!makeMode) - { - // always start from scratch - return -1; - } - - int firstEpoch = -1; - - wstring curEpochFile = GetModelNameForEpoch(int(m_maxEpochs) - 1); - for (int e = int(m_maxEpochs) - 1; e >= -1; e--) - { - const wstring prevEpochFile = GetModelNameForEpoch(e - 1); - - if (msra::files::fuptodate(curEpochFile, prevEpochFile, false)) - { - firstEpoch = e + 1; - break; - } - else - { - curEpochFile = prevEpochFile; - } - } - if (firstEpoch == m_maxEpochs) - fprintf(stderr, "Final model exists: %ls\n", GetModelNameForEpoch(firstEpoch - 1).c_str()); - - return firstEpoch; - } - -#define EPSILON 1e-5 - - // this probes the automatic gradient computation with random inputs - template - bool SGD::GradientCheck(ComputationNetworkPtr net, - const std::vector & criterionNodes, - const std::list & learnableNodes, - int npos) - { - vector errMsgs; - - net->StartEvaluateMinibatchLoop(criterionNodes[npos]); - - // gradient checking - for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) - { - ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); - char wstrtmp[2048]; - - for (size_t itry = 0; itry < min((size_t)50, node->Value().GetNumElements()); itry++) - { - /// no support to sparse matrix yet - int irow = (int) fmod(rand(), node->GetNumRows() - 1); - int icol = (int) fmod(rand(), node->GetNumCols() - 1); - irow = max(0, irow); - icol = max(0, icol); - - fprintf(stderr, "\n###### d%ls######\n", node->NodeName().c_str()); - - double eOrg = node->Value()(irow, icol); - node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); - - node->BumpEvalTimeStamp(); - - net->ForwardProp(criterionNodes[npos]); - net->Backprop(criterionNodes[npos]); - - if (node->Gradient().GetMatrixType() == MatrixType::SPARSE) - { - break; - } - - //double mbEvalCri = - //criterionNode should be a scalar - // TODO: why is this value not used? - criterionNodes[npos]->Get00Element(); - double eGradErr = node->Gradient()(irow, icol); - node->Gradient().TransferToDeviceIfNotThere(net->GetDeviceId(), true); - - double ePos = eOrg + EPSILON; - double eNeg = eOrg - EPSILON; - - node->Value()(irow, icol) = (ElemType)ePos; - node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); - - node->BumpEvalTimeStamp(); - net->ForwardProp(criterionNodes[npos]); - //criterionNode should be a scalar - - double mbEvalCriPos = criterionNodes[npos]->Get00Element(); // TODO: make Get00Element() a function of ComputationNodeBase - - node->Value()(irow, icol) = (ElemType)eNeg; - node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); - - node->BumpEvalTimeStamp(); - net->ForwardProp(criterionNodes[npos]); - - // criterionNode should be a scalar - double mbEvalCriNeg = criterionNodes[npos]->Get00Element(); - - // back to its original parameter value - node->Value()(irow, icol) = (ElemType)eOrg; - node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); - - // check if they are consistent - double eGradNum = ((mbEvalCriPos - mbEvalCriNeg) / (ePos - eNeg)); - double threshold = pow(10.0, - max(0.0, - ceil(log10(min(fabs(eGradErr), - fabs(eGradNum))))) - (int)m_gradientCheckSigDigit); - double diff = fabs(eGradErr - eGradNum); - bool wrong = (std::isnan(diff) || diff > threshold); - if (wrong) - { - fprintf(stderr, "\nd%ls Numeric gradient = %e, Error BP gradient = %e\n", - node->NodeName().c_str(), eGradNum, eGradErr); - sprintf(wstrtmp, "\nd%ls Numeric gradient = %e, Error BP gradient = %e\n", - node->NodeName().c_str(), eGradNum, eGradErr); - errMsgs.push_back(wstrtmp); - } - } - } - - return errMsgs.size() == 0; - } - - template class SGD; - template class SGD; - - // ======================================================================= - // class SGDParams - // ======================================================================= - - static AdaptationRegType ParseAdaptationRegType(const wstring & s) - { - if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none")) - return AdaptationRegType::None; - else if (!_wcsicmp(s.c_str(), L"kl") || !_wcsicmp(s.c_str(), L"klReg")) - return AdaptationRegType::KL; - else - InvalidArgument("ParseAdaptationRegType: Invalid Adaptation Regularization Type. Valid values are (none | kl)"); - } - - static GradientsUpdateType ParseGradUpdateType(const wstring & s) - { - if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none") || !_wcsicmp(s.c_str(), L"normal") || !_wcsicmp(s.c_str(), L"simple")) - return GradientsUpdateType::None; - else if (!_wcsicmp(s.c_str(), L"adagrad")) - return GradientsUpdateType::AdaGrad; - else if (!_wcsicmp(s.c_str(), L"rmsProp")) - return GradientsUpdateType::RmsProp; - else if (!_wcsicmp(s.c_str(), L"fsAdagrad")) - return GradientsUpdateType::FSAdaGrad; - else - InvalidArgument("ParseGradUpdateType: Invalid Gradient Updating Type. Valid values are (none | adagrad | rmsProp | fsAdagrad )"); - } - - static ParallelizationMethod ParseParallelizationMethod(const wstring & s) - { - if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none")) - return ParallelizationMethod::None; - else if (!_wcsicmp(s.c_str(), L"DataParallelSGD")) - return ParallelizationMethod::DataParallelSGD; - else if (!_wcsicmp(s.c_str(), L"ModelAveragingSGD")) - return ParallelizationMethod::ModelAveragingSGD; - else - InvalidArgument("ParseParallelizationMethod: Invalid Parallelization Method. Valid values are (none | dataParallelSGD | modelAveragingSGD)"); - } - - static LearningRateSearchAlgorithm ParseLearningRateSearchType(const wstring & s) - { - // TODO: why allow so many variants? - if (!_wcsicmp(s.c_str(), L"false") || !_wcsicmp(s.c_str(), L"none")) - return LearningRateSearchAlgorithm::None; - else if (!_wcsicmp(s.c_str(), L"searchBeforeEpoch") || !_wcsicmp(s.c_str(), L"beforeEpoch"/*legacy, deprecated*/) || !_wcsicmp(s.c_str(), L"before"/*legacy, deprecated*/)) - return LearningRateSearchAlgorithm::SearchBeforeEpoch; - else if (!_wcsicmp(s.c_str(), L"adjustAfterEpoch") || !_wcsicmp(s.c_str(), L"afterEpoch"/*legacy, deprecated*/) || !_wcsicmp(s.c_str(), L"after"/*legacy, deprecated*/)) - return LearningRateSearchAlgorithm::AdjustAfterEpoch; - else - InvalidArgument("autoAdjustLR: Invalid learning rate search type. Valid values are (none | searchBeforeEpoch | adjustAfterEpoch)"); - } - - template - SGDParams::SGDParams(const ConfigRecordType& configSGD, size_t sizeofElemType) - { - floatargvector learningRatesPerMB = configSGD(L"learningRatesPerMB", ConfigRecordType::Array(floatargvector())); - - floatargvector learningRatesPerSample = configSGD(L"learningRatesPerSample", ConfigRecordType::Array(floatargvector())); - - string executionEngineValue = configSGD(L"executionEngine", "synchronous"); - - // AutoAdjust Parameters - const ConfigRecordType & configAALR(configSGD(L"AutoAdjust", ConfigRecordType::Record())); - m_autoLearnRateSearchType = ParseLearningRateSearchType(configAALR(L"autoAdjustLR", L"None")); - m_reduceLearnRateIfImproveLessThan = configAALR(L"reduceLearnRateIfImproveLessThan", 0.0); - m_continueReduce = configAALR(L"continueReduce", false); - m_learnRateAdjustInterval = configAALR(L"learnRateAdjustInterval", (size_t)1); - m_learnRateAdjustInterval = max((size_t)1, m_learnRateAdjustInterval); //minimum interval is 1 epoch - m_learnRateDecreaseFactor = configAALR(L"learnRateDecreaseFactor", 0.618); - m_increaseLearnRateIfImproveMoreThan = configAALR(L"increaseLearnRateIfImproveMoreThan", numeric_limits::infinity()); - m_learnRateIncreaseFactor = configAALR(L"learnRateIncreaseFactor", 1.382); - - // AutoAdjust Auto Adjust Minibatch Parameters - m_autoAdjustMinibatch = configAALR(L"autoAdjustMinibatch", false); - m_minibatchSizeTuningFrequency = configAALR(L"minibatchSizeTuningFrequency", (size_t)1); - m_minibatchSizeTuningMax = configAALR(L"minibatchSizeTuningMax", (size_t)1048576); - m_minibatchSearchCriterionErrorMargin = configAALR(L"minibatchSearchCriterionErrorMargin", (size_t)1); - - // the number of minibatches used to search - // the learning rate. It’s typically set to 10-20% of - // the total minibatches in an epoch. - m_numMiniBatch4LRSearch = configAALR(L"numMiniBatch4LRSearch", ConfigRecordType::Array(intargvector(vector{ 500 }))); - - m_numPrevLearnRates = configAALR(L"numPrevLearnRates", (size_t)5); - m_numBestSearchEpoch = configAALR(L"numBestSearchEpoch", (size_t)1); - m_loadBestModel = configAALR(L"loadBestModel", true); - m_useCVSetControlLRIfCVExists = configAALR(L"UseCVSetControlLRIfCVExists", true); - m_useEvalCriterionControlLR = configAALR(L"UseEvalCriterionControlLR", false); - - // TODO: mbSize and truncated should be specified differently for truncated BPTT: - // mbSize = total number of samples after which a model update should happen - // truncated = truncation length - m_mbSize = configSGD(L"minibatchSize", ConfigRecordType::Array(intargvector(vector{ 256 }))); - m_truncated = configSGD(L"truncated", false); - m_maxSamplesInRAM = configSGD(L"maxSamplesInRAM", (size_t)SIZE_MAX); - m_numSubminiBatches = configSGD(L"numSubminibatches", (size_t)1); - - // the number of samples in each epoch (0 means, use all the samples in each epoch). - m_epochSize = configSGD(L"epochSize", (size_t)0); - // the number of samples in each epoch (0 means, use all the samples in each epoch). - if (m_epochSize == 0) - m_epochSize = requestDataSize; - m_maxComputedEpochSize = m_epochSize; - - // the total number of epochs to run. - m_maxEpochs = configSGD(L"maxEpochs"); - - // Note: Momentum is best specified as a MB-size agnostic fashion. - // Because momentum per sample is a number very close to 1, it is more handy to use a logarithmic specification. - // We use 'momentumAsTimeConstant' to specify the time constant of the low-pass filter that momentum really is. - // To convert a typical per-MB momentum value of 'm' used with a MB size of 'N', use momentumAsTimeConstant = -N/ln(m). - // For the common configuration of momentum 0.9 at MB size of 256, that is momentumAsTimeConstant = 2429.8. - floatargvector momentumPerMB = configSGD(L"momentumPerMB", ConfigRecordType::Array(floatargvector())); - floatargvector momentumPerSample = configSGD(L"momentumPerSample", ConfigRecordType::Array(floatargvector())); - floatargvector momentumAsTimeConstant = configSGD(L"momentumAsTimeConstant", ConfigRecordType::Array(floatargvector())); - bool useNesterovMomentum = configSGD(L"useNAG", false); - - - m_maxTempMemSizeInSamplesForCNN = configSGD(L"maxTempMemSizeInSamplesForCNN", (size_t)0); - - m_traceLevel = configSGD(L"traceLevel", (int)0); - m_numMBsToShowResult = configSGD(L"numMBsToShowResult", (size_t)10); - m_numMBsToCUDAProfile = configSGD(L"numMBsToCUDAProfile", (size_t)0); - - m_gradientClippingWithTruncation = configSGD(L"gradientClippingWithTruncation", true); - m_clippingThresholdPerSample = configSGD(L"clippingThresholdPerSample", numeric_limits::infinity()); - - // sequence-training parameters - m_hSmoothingWeight = configSGD(L"hSmoothingWeight", 0.95); - m_frameDropThresh = configSGD(L"frameDropThresh", 1e-10); - m_doReferenceAlign = configSGD(L"doReferenceAlign", false); - m_seqGammarCalcUsesMBR = configSGD(L"seqGammarUsesMBR", false); - m_seqGammarCalcAMF = configSGD(L"seqGammarAMF", 14.0); - m_seqGammarCalcLMF = configSGD(L"seqGammarLMF", 14.0); - m_seqGammarCalcbMMIFactor = configSGD(L"seqGammarBMMIFactor", 0.0); - m_seqGammarCalcWP = configSGD(L"seqGammarWordPen", 0.0); - - m_dropoutRates = configSGD(L"dropoutRate", ConfigRecordType::Array(floatargvector(vector{ 0.0f }))); - - GradientsUpdateType gradUpdateType = ParseGradUpdateType(configSGD(L"gradUpdateType", L"None")); - double gaussianNoiseInjecStd = configSGD(L"gaussianNoiseInjectStd", 0.0); - m_gradType.mType = gradUpdateType; - m_gradType.mGaussianNoiseInjectStd = (float) gaussianNoiseInjecStd; - - // extract RMSProp parameters from config, if they exist. Default to reasonable values. - m_rpi.dec = configSGD(L"rms_wgt_dec", 0.75); - m_rpi.inc = configSGD(L"rms_wgt_inc", 1.2); - m_rpi.min = configSGD(L"rms_wgt_min", 0.1); - m_rpi.max = configSGD(L"rms_wgt_max", 10.0); - m_rpi.gamma = configSGD(L"rms_gamma", 0.99); - - m_needAveMultiplier = configSGD(L"normWithAveMultiplier", true); - m_L2RegWeight = configSGD(L"L2RegWeight", 0.0); - m_L1RegWeight = configSGD(L"L1RegWeight", 0.0); - - /// for backward support. future setup should use gradUpdateType=AdaGrad, instead of - /// useAdagrad=true - bool useAdagrad = configSGD(L"useAdagrad", false); - if (useAdagrad) - { - gradUpdateType = GradientsUpdateType::AdaGrad; - m_gradType.mType = gradUpdateType; - } - - m_adaptationRegType = ParseAdaptationRegType(configSGD(L"adaptationRegType", L"None")); - m_adaptationRegWeight = configSGD(L"adaptationRegWeight", 0.0); - - /// gradient check setup - m_doGradientCheck = configSGD(L"gradientcheck", false); - m_gradientCheckSigDigit = configSGD(L"sigFigs", 6.0); // TODO: why is this a double? - - if (m_doGradientCheck && sizeofElemType != sizeof(double)) - { - LogicError("Gradient check needs to use precision = 'double'."); - } - - m_useAllDataForPreComputedNode = configSGD(L"UseAllDataForPreComputedNode", true); - - // consistency checks - for (size_t i = 0; i < m_mbSize.size(); i++) - { - if (m_epochSize != requestDataSize && m_epochSize < m_mbSize[i]) - { - InvalidArgument("epoch size must be larger than mbsize."); - } - } - - if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None && - (learningRatesPerSample.size() == 0 && learningRatesPerMB.size() == 0)) - { - InvalidArgument("If autoLearnRateSearchType is false you must specify the learningRatesPerSample or learningRatesPerMB parameter."); - } - - if (learningRatesPerSample.size() > 0 && learningRatesPerMB.size() > 0) - { - InvalidArgument("You specified both learningRatesPerSample and learningRatesPerMB. Please comment out one of them."); - } - - if (learningRatesPerSample.size() > 0) - { - m_learningRatesParam = learningRatesPerSample; - m_learningRatesSpecifiedForMBSize = intargvector(L"1"); - } - else if (learningRatesPerMB.size() > 0) // this actually means per specified minibatch size - { - m_learningRatesParam = learningRatesPerMB; - m_learningRatesSpecifiedForMBSize = m_mbSize; - } - - if ((int)(momentumPerSample.size() > 0) + (int)(momentumPerMB.size() > 0) + (int)(momentumAsTimeConstant.size() > 0) > 1) - { - InvalidArgument("You specified more than one of momentumPerSample, momentumPerMB, and momentumAsTimeConstant. Please only specify one."); - } - - if (momentumPerSample.size() > 0) // note: noone would ever use this; use momentumAsTimeConstant instead - { - m_momentumParam = momentumPerSample; - m_momentumSpecifiedForMBSize = intargvector(L"1"); - } - else if (momentumAsTimeConstant.size() > 0) - { - vector momentumPerSampleVec; - for (int i = 0; i < momentumAsTimeConstant.size(); i++) - { - double momTC = momentumAsTimeConstant[i]; - double momPS = momTC == 0.0 ? 0 : exp(-1.0 / momTC); - momentumPerSampleVec.push_back((float)momPS); - } - m_momentumParam = momentumPerSampleVec; - m_momentumSpecifiedForMBSize = intargvector(L"1"); - } - else if (momentumPerMB.size() > 0) - { - m_momentumParam = momentumPerMB; - m_momentumSpecifiedForMBSize = m_mbSize; - } - else // default: momentumPerMB = 0.9 per MB - { - m_momentumParam = floatargvector(L"0.9"); - m_momentumSpecifiedForMBSize = m_mbSize; - } - m_useNesterovMomentum = useNesterovMomentum; - - for (int i = 0; i < m_momentumParam.size(); i++) - { - if (m_momentumParam[i] >= 1.0 || m_momentumParam[i] < 0.0) - { - InvalidArgument("Momentum parameter must be in [0, 1)."); - } - } - - if (m_learnRateDecreaseFactor > 1 || m_learnRateIncreaseFactor < 1) - { - InvalidArgument("learnRateIncreaseFactor must be >= 1 and learnRateDecreaseFactor must be <= 1."); - } - - for (size_t i = 0; i < m_dropoutRates.size(); i++) - { - if (m_dropoutRates[i] >= 1 || m_dropoutRates[i] < 0) - { - InvalidArgument("dropoutRate must be >= 0 and < 1."); - } - } - - if (m_adaptationRegWeight > 1 || m_adaptationRegWeight < 0) - InvalidArgument("adaptationRegWeight must be in [0 1]"); - - m_minLearnRate = 1e-9f; - - m_needAdaptRegularization = false; - - // BUGBUG: these are not passed to Init() - m_doUnitTest = configSGD(L"unitTest", false); - - // parallel training - m_parallelizationMethod = ParallelizationMethod::None; - m_numGradientBits = 32; - m_zeroThresholdFor1Bit = true; - m_bufferedAsyncGradientAggregation = false; - m_enableDistributedMBReading = false; - m_parallelizationStartEpochNum = 0; - m_nFramesBetweenMASync = 40000; // default 40k frames - - if ((g_mpi != nullptr) && configSGD.Exists(L"ParallelTrain")) - { - const ConfigRecordType & configParallelTrain(configSGD(L"ParallelTrain", ConfigRecordType::Record())); - m_parallelizationMethod = ParseParallelizationMethod(configParallelTrain(L"parallelizationMethod", L"none")); - m_parallelizationStartEpochNum = configParallelTrain(L"parallelizationStartEpoch", (int)1) - 1; // Epoch numbers internally are 0 based - m_enableDistributedMBReading = configParallelTrain(L"distributedMBReading", false); - m_syncStatsTrace = configParallelTrain(L"syncPerfStats", (int)0); - - if (configParallelTrain.Exists(L"DataParallelSGD")) - { - const ConfigRecordType & configDataParallelSGD(configParallelTrain(L"DataParallelSGD", ConfigRecordType::Record())); - size_t defaultGradientBits = 8 * sizeofElemType; - m_numGradientBits = configDataParallelSGD(L"gradientBits", defaultGradientBits); - m_zeroThresholdFor1Bit = configDataParallelSGD(L"useZeroThresholdFor1BitQuantization", true); - m_bufferedAsyncGradientAggregation = configDataParallelSGD(L"useBufferedAsyncGradientAggregation", false); - if ((m_numGradientBits < 1) || (m_numGradientBits > (8 * sizeofElemType))) - { - InvalidArgument("gradientBits must be in the range [1, 32] when using precision=float and in range [1, 64] when using precision=double!"); - } - } - - if (configParallelTrain.Exists(L"ModelAveragingSGD") ) - { - const ConfigRecordType & configMASGD(configParallelTrain(L"ModelAveragingSGD", ConfigRecordType::Record())); - m_nFramesBetweenMASync = configMASGD(L"syncFrequencyInFrames", (size_t)40000); - } - } - } - - static size_t GetSizeOfPrecision(const ScriptableObjects::IConfigRecordPtr configp) - { - wstring precision = configp->Get(L"precision"); - if (precision == L"float") - return sizeof(float); - else if (precision == L"double") - return sizeof(double); - else - RuntimeError("invalid value '%ls' for 'precision', must be 'float' or 'double'", precision.c_str()); - } - - SGDParams::SGDParams(const ScriptableObjects::IConfigRecordPtr configp) : - SGDParams(*configp, GetSizeOfPrecision(configp)) - { } - - // register SGD<> with the ScriptableObject system - ScriptableObjects::ConfigurableRuntimeTypeRegister::AddFloatDouble, SGD> registerSGDOptimizer(L"SGDOptimizer"); - -}}} +// SGD.cpp -- implements SGD with all bells and whistles, parallelization, randomizatiom, etc. + +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings + +#include "Basics.h" +#include "SGD.h" +#include "DataReaderHelpers.h" + +#include "MatrixQuantizerImpl.h" + +#ifdef QUANTIZED_GRADIENT_AGGREGATION +#include "AllReduceDistGradAggregator.h" +#endif + +#include "SimpleDistGradAggregator.h" +#include "ProgressTracing.h" + +#include +#include + +namespace Microsoft { namespace MSR { namespace CNTK { + + using namespace std; + + // ======================================================================= + // class SGD + // ======================================================================= + + template SGD::SGD(const ConfigParameters &); + template SGD::SGD(const ConfigParameters &); + template SGD::SGD(const ScriptableObjects::IConfigRecord &); + template SGD::SGD(const ScriptableObjects::IConfigRecord &); + + // ----------------------------------------------------------------------- + // Train() -- perform a multi-epoch training end-to-end with checkpointing + // ----------------------------------------------------------------------- + + template + void SGD::Train(function createNetworkFn, DEVICEID_TYPE deviceId, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader, + const bool makeMode) + { + // determine which epoch to start with, including recoveing a checkpoint if any and 'makeMode' enabled + int startEpoch = DetermineStartEpoch(makeMode); + if (startEpoch == m_maxEpochs) + { + fprintf(stderr, "No further training is necessary.\n"); + return; + } + + wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); + if (startEpoch >= 0) + fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); + + // create or load from checkpoint + shared_ptr net = startEpoch < 0 ? createNetworkFn(deviceId) : ComputationNetwork::CreateFromFile(deviceId, modelFileName); + + // log the device we are computing on + if (net->GetDeviceId() < 0) + fprintf(stderr, "\nSGD using CPU.\n"); + else + fprintf(stderr, "\nSGD using GPU %d.\n", (int)net->GetDeviceId()); + + // TODO: BUGBUG: if not starting from checkpoint, need to synchronize initial model + // strategy should be to run the initializer above on mpiRank==0, and then broadcast parameters. + + startEpoch = max(startEpoch, 0); + m_needAdaptRegularization = false; + + TrainOrAdaptModel(startEpoch, net, net, nullptr, trainSetDataReader, validationSetDataReader); + } + + // ----------------------------------------------------------------------- + // Adapt() -- similar to Train(), but for purpose of adapting + // ----------------------------------------------------------------------- + + template + void SGD::Adapt(wstring origModelFileName, wstring refNodeName, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader, + const DEVICEID_TYPE deviceId, const bool makeMode) + { + int startEpoch = DetermineStartEpoch(makeMode); + if (startEpoch == m_maxEpochs) + { + fprintf(stderr, "No further training is necessary.\n"); + return; + } + + ComputationNetworkPtr net; + if (startEpoch >= 0) + { + wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); + fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); + net = ComputationNetwork::CreateFromFile(deviceId, modelFileName); + } + else + { + fprintf(stderr, "Load Network From the original model file %ls.\n", origModelFileName.c_str()); + net = ComputationNetwork::CreateFromFile(deviceId, origModelFileName); + } + + startEpoch = max(startEpoch, 0); + + ComputationNetworkPtr refNet; + m_needAdaptRegularization = m_adaptationRegType != AdaptationRegType::None && m_adaptationRegWeight > 0; + if (m_needAdaptRegularization) + { + fprintf(stderr, "Load reference Network From the original model file %ls.\n", origModelFileName.c_str()); + refNet = ComputationNetwork::CreateFromFile(deviceId, origModelFileName); + } + + ComputationNodeBasePtr refNode; + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL) + { + fprintf(stderr, "Checking refNodeName %ls.\n", origModelFileName.c_str()); + if (refNodeName == L"") + InvalidArgument("refNodeName does not exist and is needed when adaptationRegType is KL."); + refNode = refNet->GetNodeFromName(refNodeName); + } + + TrainOrAdaptModel(startEpoch, net, refNet, refNode, trainSetDataReader, validationSetDataReader); + } + + // ----------------------------------------------------------------------- + // TrainOrAdaptModel() -- main training end-to-end, given a start model + // ----------------------------------------------------------------------- + + static double MomentumPerMB(double momentumPerSample, size_t minibatchSize); + + template + void SGD::TrainOrAdaptModel(int startEpoch, ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + ComputationNodeBasePtr refNode, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader) + { + auto & featureNodes = net->FeatureNodes(); + auto & labelNodes = net->LabelNodes(); + auto & criterionNodes = GetTrainCriterionNodes(net); + + fprintf(stderr, "\nTraining criterion node(s):\n"); + for (const auto & node : criterionNodes) + fprintf(stderr, "\t%ls = %ls\n", node->NodeName().c_str(), node->OperationName().c_str()); + + // determine evaluationNodes from GetEvalCriterionNodes(), ensuring each criterion is only logged once + std::vector evaluationNodes; + { + auto originalEvaluationNodes = GetEvalCriterionNodes(net); + set criteriaLogged; // set to make sure we don't double-log criteria + for (const auto & node : criterionNodes) + criteriaLogged.insert(node); + for (const auto & node : originalEvaluationNodes) + if (criteriaLogged.insert(node).second) + evaluationNodes.push_back(node); + + if (!evaluationNodes.empty()) + { + fprintf(stderr, "\nEvaluation criterion node(s):\n"); + for (const auto & node : evaluationNodes) + fprintf(stderr, "\t%ls = %ls\n", node->NodeName().c_str(), node->OperationName().c_str()); + } + } + + std::vector additionalNodesToEvaluate; + auto& outputNodes = net->OutputNodes(); + additionalNodesToEvaluate.insert(additionalNodesToEvaluate.end(), outputNodes.cbegin(), outputNodes.cend()); + + auto preComputeNodesList = net->GetNodesRequiringPreComputation(); + additionalNodesToEvaluate.insert(additionalNodesToEvaluate.end(), preComputeNodesList.cbegin(), preComputeNodesList.cend()); + + // allocate memory for forward and backward computation + net->AllocateAllMatrices(evaluationNodes, additionalNodesToEvaluate, criterionNodes[0]); + + // get feature and label nodes into an array of matrices that will be passed to GetMinibatch() + // TODO: instead, remember the nodes directly, to be able to handle both float and double nodes; current version will crash for mixed networks + std::map*>* inputMatrices = new std::map*>(); + for (size_t pass = 0; pass < 2; pass++) + { + auto & nodes = (pass == 0) ? featureNodes : labelNodes; + for (size_t i = 0; i < nodes.size(); i++) + { + auto & node = nodes[i]; + auto * functionValues = &dynamic_pointer_cast>(node)->Value(); + assert(functionValues->GetNumCols() == net->GetMBLayoutPtr()->GetNumTimeSteps()); + (*inputMatrices)[node->NodeName()] = functionValues; + } + } + + //get hmm file for sequence training + bool isSequenceTrainingCriterion = (criterionNodes[0]->OperationName() == L"SequenceWithSoftmax"); + if (isSequenceTrainingCriterion) + { + //SequenceWithSoftmaxNode* node = static_cast*>(criterionNodes[0]); + auto node = dynamic_pointer_cast>(criterionNodes[0]); + auto hmm = node->gethmm(); + trainSetDataReader->GetHmmData(hmm); + } + + // used for KLD regularized adaptation. For all other adaptation techniques + // use MEL to edit the model and using normal training algorithm + // TODO: Should this be done in SGD::Adapt()? + // TODO: Redo this leveraging that we now have shared_ptrs. It is probably even OK if both networks share feature nodes. + // TODO: Then we can also share the MBLayout; which currently is copied by value. + std::vector refFeatureNodes; + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode != nullptr) + { + // replace input nodes in ref network by input nodes of the main network + refFeatureNodes.resize(featureNodes.size()); + for (size_t i = 0; i < featureNodes.size(); i++) + { + // we need to keep this info to undo this later + // TODO: After the change to shared_ptrs, this may no longer be necessary. + refFeatureNodes[i] = refNet->GetNodeFromName(featureNodes[i]->NodeName()); + refNet->ChangeNode(featureNodes[i]->NodeName(), featureNodes[i]); + } + refNet->InvalidateCompiledNetwork(); // prepare to re-compile + refNet->CompileNetwork(); + + // allocate memory for forward computation + refNet->AllocateAllMatrices({ refNode }, {}, nullptr); + } + + // initializing weights and gradient holder + // only one criterion so far TODO: support multiple ones? + auto & learnableNodes = net->LearnableParameterNodes(criterionNodes[0]); + std::list> smoothedGradients; + + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + smoothedGradients.push_back(Matrix(node->GetNumRows(), + node->GetNumCols(), + net->GetDeviceId())); + } + + double epochCriterion, avgCriterion, prevCriterion, lrControlCriterion; + lrControlCriterion = epochCriterion = avgCriterion = prevCriterion = std::numeric_limits::infinity(); + size_t epochsNotCountedInAvgCriterion = startEpoch % m_learnRateAdjustInterval; + + std::vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); + + std::vector evalNodeNames; + for (size_t i = 0; i < evaluationNodes.size(); i++) + { + evalNodeNames.push_back(evaluationNodes[i]->NodeName()); + } + + size_t totalSamplesSeen = 0; + double learnRatePerSample = 0.5f / m_mbSize[startEpoch]; + + double learningRateAdjustmentFactor = 1.0f; + vector prevLearnRates; + prevLearnRates.resize(m_numPrevLearnRates); + for (int i = 0; i < m_numPrevLearnRates; i++) + { + prevLearnRates[i] = -1.0; + } + + if (m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) + { + InitDistGradAgg(evaluationNodes.size(), m_traceLevel); + } + //precompute mean and invStdDev nodes and save initial model + if (PreCompute(net, trainSetDataReader, featureNodes, labelNodes, inputMatrices) || startEpoch == 0) + { + // Synchronize all ranks before writing the model to ensure that + // everyone is done loading the model + if (g_mpi != nullptr) + { + g_mpi->WaitAll(); + } + + net->Save(GetModelNameForEpoch(int(startEpoch) - 1)); + } + + bool learnRateInitialized = false; + if (startEpoch > 0) + { + learnRateInitialized = LoadCheckPointInfo(startEpoch - 1, + /*out*/ totalSamplesSeen, + /*out*/ learnRatePerSample, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ m_prevChosenMinibatchSize); + if (learnRateInitialized) + prevLearnRates[startEpoch % m_numPrevLearnRates] = learnRatePerSample; + } + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && + !learnRateInitialized && m_learningRatesParam.size() <= startEpoch) + { + InvalidArgument( + "When using \"AdjustAfterEpoch\", there must either exist a checkpoint file, " + "or an explicit learning rate must be specified in config for the starting epoch."); + } + + unsigned long dropOutSeed = 1; + double prevDropoutRate = 0; + + bool learnRateReduced = false; + + // pass user config on memory allocation for convolution operations to the Network + ComputationNetwork::SetMaxTempMemSizeForCNN(net, criterionNodes[0], m_maxTempMemSizeInSamplesForCNN); + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) + { + ComputationNetwork::SetMaxTempMemSizeForCNN(refNet, refNode, m_maxTempMemSizeInSamplesForCNN); + } + + // likewise for sequence training parameters + if (isSequenceTrainingCriterion) + { + ComputationNetwork::SetSeqParam(net, criterionNodes[0], m_hSmoothingWeight, m_frameDropThresh, m_doReferenceAlign, + m_seqGammarCalcAMF, m_seqGammarCalcLMF, m_seqGammarCalcWP, m_seqGammarCalcbMMIFactor, m_seqGammarCalcUsesMBR ); + } + + // --- MAIN EPOCH LOOP + for (int i = startEpoch; i < (int)m_maxEpochs; i++) // TODO: why is this an int, and not a size_t? + { + // Synchronize all ranks before proceeding to ensure that + // rank 0 has finished writing the previous model file + if (g_mpi != nullptr) + { + g_mpi->WaitAll(); + } + + Timer timer; + timer.Start(); + + // set dropout rate for this epoch + ComputationNetwork::SetDropoutRate(net, criterionNodes[0], m_dropoutRates[i], prevDropoutRate, dropOutSeed); + + // learning rate adjustment + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None || i < m_learningRatesParam.size()) + { + // BUGBUG: GetNumParallelSequences() returns 1 under certain situations; it seems when restarting from checkpoint + learnRatePerSample = GetLearningRatePerSample(i/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); + } + else if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) + { + double largestPrevLearnRatePerSample = prevLearnRates[0]; + for (int j = 1; j < m_numPrevLearnRates; j++) + { + largestPrevLearnRatePerSample = max(largestPrevLearnRatePerSample, prevLearnRates[j]); + } + + // return a reasonable learning rate based on the initial minibatchSize + double newLearningRatePerSample = SearchForBestLearnRate(net, refNet, refNode, i, learnRatePerSample, + trainSetDataReader, featureNodes, labelNodes, + criterionNodes, evaluationNodes, inputMatrices, + learnableNodes, smoothedGradients, + learnRateInitialized, largestPrevLearnRatePerSample); + learningRateAdjustmentFactor = newLearningRatePerSample / learnRatePerSample; + learnRatePerSample = newLearningRatePerSample; + + // save per sample learn rate to support changeable minibatchSize + prevLearnRates[i % m_numPrevLearnRates] = learnRatePerSample; + } + + learnRateInitialized = true; + + if (learnRatePerSample < m_minLearnRate) + { + fprintf(stderr, "Learn Rate Per Sample for Epoch[%d] = %.8g is less than minLearnRate %.8g. Training complete.\n", + i + 1, learnRatePerSample, m_minLearnRate); + if (m_autoLearnRateSearchType != LearningRateSearchAlgorithm::None) + { + net->Save(m_modelPath); + } + break; + } + + size_t chosenMinibatchSize; + size_t actualMinibatchSize; + + // Through the command line or config file the user can set minibatch sizes on a per epoch + // basis for a set number of epochs. For epochs after that point, m_mbSize.size(), either + // we just keep using + // the last minibatch size, or we use tuning to try and find a better one. + if (m_autoAdjustMinibatch && i >= m_mbSize.size()) + { + size_t numFramesToUseInSearch = m_numMiniBatch4LRSearch[i] * m_mbSize[i]; + if (m_epochSize != requestDataSize) + { + // ensure the numFramesToUseInSearch does not exceed the total number of frames in the epoch + numFramesToUseInSearch = min(numFramesToUseInSearch, m_epochSize); + } + + // Use tuning to try and find a better minibatch size + chosenMinibatchSize = AdaptiveMinibatchSizing(net, refNet, refNode, i, + numFramesToUseInSearch, + trainSetDataReader, learnRatePerSample, + m_mbSize[i], featureNodes, labelNodes, + criterionNodes, evaluationNodes, + inputMatrices, learnableNodes, + smoothedGradients, learningRateAdjustmentFactor); + m_prevChosenMinibatchSize = chosenMinibatchSize; + } + else + { + // use the explicitly set minibatch size + chosenMinibatchSize = m_mbSize[i]; + } + + actualMinibatchSize = FixUpEffectiveMBSize(chosenMinibatchSize/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); + + double momentumPerSample = GetMomentumPerSample(i/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); + // time constant = number of samples after which a contribution has been reduced to e^-1 + double momentumAsTimeConstant = momentumPerSample == 0.0 ? 0.0 + : momentumPerSample >= 1.0 ? 0.0 + : -1.0 / log(momentumPerSample); + fprintf(stderr, "Starting Epoch %d: learning rate per sample = %f effective momentum = %f momentum as time constant = %.1f samples\n", + i + 1, learnRatePerSample, MomentumPerMB(momentumPerSample, actualMinibatchSize), momentumAsTimeConstant); + + TrainOneEpoch(net, + refNet, + refNode, + i, + m_epochSize, + trainSetDataReader, + learnRatePerSample, + chosenMinibatchSize, + featureNodes, + labelNodes, + criterionNodes, + evaluationNodes, + inputMatrices, + learnableNodes, smoothedGradients, + epochCriterion, epochEvalErrors, totalSamplesSeen); + + timer.Stop(); + double epochTime = timer.ElapsedSeconds(); + + if (m_useEvalCriterionControlLR && epochEvalErrors.size() > 0) + { + lrControlCriterion = epochEvalErrors[0]; + } + else + { + lrControlCriterion = epochCriterion; + } + + fprintf(stderr, + "Finished Epoch[%2d of %d]: [Training Set] TrainLossPerSample = %.8g; ", + i + 1, (int) m_maxEpochs, epochCriterion); + m_lastFinishedEpochTrainLoss = epochCriterion; + if (epochEvalErrors.size() == 0) // no eval criterion, only train criterion itself + { + fprintf(stderr, + "AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", + learnRatePerSample, epochTime); + + } + else if (epochEvalErrors.size() == 1) + { + fprintf(stderr, + "EvalErrPerSample = %.8g; AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", + epochEvalErrors[0], learnRatePerSample, epochTime); + } + else + { + fprintf(stderr, "EvalErrPerSample "); + for (size_t j = 0; j < epochEvalErrors.size(); j++) + { + fprintf(stderr, "[%lu]=%.8g; ", j, epochEvalErrors[j]); + } + + fprintf(stderr, "AvgLearningRatePerSample = %.8g; EpochTime=%.6g\n", + learnRatePerSample, epochTime); + + // TODO: why these extra log messages here and not for 1 eval criterion? + fprintf(stderr, "Finished Epoch[%2d of %d]: Criterion Node [%ls] Per Sample = %.8g\n", + i + 1, (int) m_maxEpochs, criterionNodes[0]->NodeName().c_str(), epochCriterion); + + for (size_t j = 0; j < epochEvalErrors.size(); j++) + { + fprintf(stderr, "Finished Epoch[%2d of %d]: Evaluation Node [%ls] Per Sample = %.8g\n", + i + 1, (int) m_maxEpochs, evalNodeNames[j].c_str(), epochEvalErrors[j]); + } + } + + if ((g_mpi == nullptr) || g_mpi->IsMainNode()) + { + if (validationSetDataReader != trainSetDataReader && validationSetDataReader != nullptr) + { + SimpleEvaluator evalforvalidation(net); + vector cvSetTrainAndEvalNodes; + if (criterionNodes.size() > 0) + { + cvSetTrainAndEvalNodes.push_back(criterionNodes[0]->NodeName()); + } + if (evaluationNodes.size() > 0) + { + cvSetTrainAndEvalNodes.push_back(evaluationNodes[0]->NodeName()); + } + + vector vScore = evalforvalidation.Evaluate(validationSetDataReader, cvSetTrainAndEvalNodes, m_mbSize[i]); + fprintf(stderr, "Finished Epoch[%2d of %d]: [Validation Set] TrainLossPerSample = %.8g", i + 1, (int) m_maxEpochs, vScore[0]); + if (vScore.size() > 1) + { + fprintf(stderr, "; EvalErrPerSample = %.8g", vScore[1]); + } + fprintf(stderr, "\n"); + + if (m_useCVSetControlLRIfCVExists) + { + if (m_useEvalCriterionControlLR && vScore.size() > 1) + { + lrControlCriterion = vScore[1]; + } + else + { + lrControlCriterion = vScore[0]; // the first one is the training criterion + } + } + } + } + + // broadcast epochCriterion to make sure each processor will have the same learning rate schedule + if ((m_parallelizationMethod == ParallelizationMethod::ModelAveragingSGD) && (g_mpi->NumNodesInUse() > 1)) + { + g_mpi->Bcast(&epochCriterion, 1, g_mpi->MainNodeRank()); + g_mpi->Bcast(&lrControlCriterion, 1, g_mpi->MainNodeRank()); + } + + bool loadedPrevModel = false; + size_t epochsSinceLastLearnRateAdjust = i % m_learnRateAdjustInterval + 1; + if (avgCriterion == std::numeric_limits::infinity()) + { + avgCriterion = lrControlCriterion; + } + else + { + avgCriterion = ((epochsSinceLastLearnRateAdjust - 1 - epochsNotCountedInAvgCriterion) * + avgCriterion + lrControlCriterion) / + (epochsSinceLastLearnRateAdjust - epochsNotCountedInAvgCriterion); + } + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && + m_learningRatesParam.size() <= i && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) + { + if (std::isnan(avgCriterion) || (prevCriterion - avgCriterion < 0 && prevCriterion != std::numeric_limits::infinity())) + { + if (m_loadBestModel) + { + auto bestModelPath = GetModelNameForEpoch(i - m_learnRateAdjustInterval); + fprintf(stderr, "Loading previous model with best training-criterion value: %ls.\n", bestModelPath.c_str()); + net->RereadPersistableParameters(bestModelPath); + LoadCheckPointInfo(i - m_learnRateAdjustInterval, + /*out*/ totalSamplesSeen, + /*out*/ learnRatePerSample, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ m_prevChosenMinibatchSize); + loadedPrevModel = true; + } + } + + if (m_continueReduce) + { + if (std::isnan(avgCriterion) || + (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && + prevCriterion != std::numeric_limits::infinity())) + { + if (learnRateReduced == false) + { + learnRateReduced = true; + } + else + { + net->Save(GetModelNameForEpoch(i, true)); + + fprintf(stderr, "Finished training and saved final model\n\n"); + break; + } + } + + if (learnRateReduced) + { + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + } + else + { + if (std::isnan(avgCriterion) || + (prevCriterion - avgCriterion <= m_reduceLearnRateIfImproveLessThan * prevCriterion && + prevCriterion != std::numeric_limits::infinity())) + { + + learnRatePerSample *= m_learnRateDecreaseFactor; + fprintf(stderr, "learnRatePerSample reduced to %.8g\n", learnRatePerSample); + } + else if (prevCriterion - avgCriterion > m_increaseLearnRateIfImproveMoreThan * prevCriterion && + prevCriterion != std::numeric_limits::infinity()) + { + learnRatePerSample *= m_learnRateIncreaseFactor; + fprintf(stderr, "learnRatePerSample increased to %.8g\n", learnRatePerSample); + } + } + } + else + { + if (std::isnan(avgCriterion)) + RuntimeError("The training criterion is not a number (NAN). Stop\n"); + } + + // not loading previous values then set them + if (!loadedPrevModel && epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) + { + prevCriterion = avgCriterion; + epochsNotCountedInAvgCriterion = 0; + } + + // Synchronize all ranks before proceeding to ensure that + // nobody tries reading the checkpoint file at the same time + // as rank 0 deleting it below + if (g_mpi != nullptr) + { + g_mpi->WaitAll(); + } + + // persist model and check-point info + if ((g_mpi == nullptr) || g_mpi->IsMainNode()) + { + net->Save(GetModelNameForEpoch(i)); + SaveCheckPointInfo(i, totalSamplesSeen, learnRatePerSample, smoothedGradients, prevCriterion, chosenMinibatchSize); + if (!m_keepCheckPointFiles) + { + // delete previous checkpoint file to save space + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::AdjustAfterEpoch && m_loadBestModel) + { + if (epochsSinceLastLearnRateAdjust != 1) + { + _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); + } + if (epochsSinceLastLearnRateAdjust == m_learnRateAdjustInterval) + { + _wunlink(GetCheckPointFileNameForEpoch(i - m_learnRateAdjustInterval).c_str()); + } + } + else + { + _wunlink(GetCheckPointFileNameForEpoch(i - 1).c_str()); + } + } + } + + if (learnRatePerSample < 1e-12) + { + fprintf(stderr, "learnRate per sample is reduced to %.8g which is below 1e-12. stop training.\n", + learnRatePerSample); + } + } + // --- END OF MAIN EPOCH LOOP + + // Synchronize all ranks before proceeding to ensure that + // rank 0 has finished writing the model file + if (g_mpi != nullptr) + { + g_mpi->WaitAll(); + } + + // progress tracing for compute cluster management + ProgressTracing::TraceProgressPercentage(m_maxEpochs, 0.0, true); + ProgressTracing::TraceTrainLoss(m_lastFinishedEpochTrainLoss); + + // since we linked feature nodes. we need to remove it from the deletion + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode != nullptr) + { + for (size_t i = 0; i < refFeatureNodes.size(); i++) + { + // note we need to handle deletion carefully + refNet->ChangeNode(refFeatureNodes[i]->NodeName(), refFeatureNodes[i]); + } + } + + delete inputMatrices; + } + + // ----------------------------------------------------------------------- + // TrainOneEpoch() -- train one epoch + // ----------------------------------------------------------------------- + + static string GeneratePaddedFloatOrExpFormat(int padSize, int precision, double value); + + template + size_t SGD::TrainOneEpoch(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t epochSize, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + size_t tunedMBSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, // TODO: why is this a pointer? + const std::list & learnableNodes, + std::list>& smoothedGradients, + /*out*/ double& epochCriterion, + /*out*/ std::vector& epochEvalErrors, + /*out*/ size_t& totalSamplesSeen, + std::string prefixMsg) + { + double totalTimeInMBs = 0; // use double since timer has sub-microsecond time resolution + double epochCriterionLastMBs = 0; + + int numSamplesLastMBs = 0; + std::vector epochEvalErrorsLastMBs(epochEvalErrors.size(), 0); + + // initialize statistics + size_t totalEpochSamples = 0; + + int numMBsRun = 0; + + // NOTE: the following two local matrices are not used in distGradAgg path + // assume only one training criterion node for each epoch. + // The criterion values are accumulated here over the minibatches (without having to pull them off the GPU). + Matrix localEpochCriterion(1, 1, net->GetDeviceId()); + Matrix localEpochEvalErrors(1, epochEvalErrors.size(), net->GetDeviceId()); + + localEpochCriterion.SetValue(0); + localEpochEvalErrors.SetValue(0); + + bool useGradientAggregation = ((m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) && + (epochNumber >= m_parallelizationStartEpochNum)); + bool useModelAveraging = ((m_parallelizationMethod == ParallelizationMethod::ModelAveragingSGD) && + (epochNumber >= m_parallelizationStartEpochNum)); + bool useParallelTrain = useGradientAggregation || useModelAveraging; + + // MA-related variables + size_t nSamplesSinceLastModelSync = 0; + size_t nSynced = 0; + float nSecondsOnMASync = 0; + float nSecondsSinceLastMAPerfReport = 0; + + std::vector*> learnParamsGradients; + if (useGradientAggregation) + { + epochCriterion = double(0.0); + epochEvalErrors.assign(epochEvalErrors.size(), double(0.0)); + } + + Profiler profiler(m_numMBsToCUDAProfile); + + // resetting this, so profiling is performed for one epoch only + m_numMBsToCUDAProfile = 0; + + bool useDistributedMBReading = useParallelTrain && + m_enableDistributedMBReading && + trainSetDataReader->SupportsDistributedMBRead(); + if (useDistributedMBReading) + { + trainSetDataReader->StartDistributedMinibatchLoop(tunedMBSize, epochNumber, g_mpi->CurrentNodeRank(), + g_mpi->NumNodesInUse(), epochSize); + } + else + { + trainSetDataReader->StartMinibatchLoop(tunedMBSize, epochNumber, epochSize); + } + + net->StartEvaluateMinibatchLoop(evaluationNodes); + net->StartEvaluateMinibatchLoop(criterionNodes); + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) + { + refNet->StartEvaluateMinibatchLoop(refNode); + } + + // prepare for sub-minibatching + // Sub-minibatching is used if a single minibatch is too large to fit into GPU RAM. + DataReaderHelpers::SubminibatchDispatcher smbDispatcher; + size_t numSubminibatchesNeeded = 0; + if (m_maxSamplesInRAM < SIZE_MAX || m_numSubminiBatches > 1) // user-specified maximum number of samples that fit into GPU RAM; or 0 if not enabled + { + if (m_maxSamplesInRAM < SIZE_MAX) + { + // into how many pieces would we need to break the minibatch? + // TODO: The following calculation relies on the ill-devised definition of "minibatch" of the current truncated BPTT implementation. Adapt this once fixed. + size_t numParallelSequences = trainSetDataReader->GetNumParallelSequences(); + size_t estimatedMBSize = tunedMBSize * numParallelSequences; + numSubminibatchesNeeded = (size_t)std::ceil((float)estimatedMBSize / m_maxSamplesInRAM); + } + if (m_numSubminiBatches > 1) + { + numSubminibatchesNeeded = m_numSubminiBatches; + } + } + // this is non-trivial, we need a manager object to handle this + if (numSubminibatchesNeeded > 1) + smbDispatcher.Init(net, learnableNodes, criterionNodes, evaluationNodes); + + // The following is a special feature only supported by the Kaldi2Reader for more efficient sequence training. + // This attemps to compute the error signal for the whole utterance, which will + // be fed to the neural network as features. Currently it is a workaround + // for the two-forward-pass sequence and ctc training, which allows + // processing more utterances at the same time. + // TODO: move the two-forward-pass support out of the reader, make a first-class citizen. + AttemptUtteranceDerivativeFeatures(net, trainSetDataReader, featureNodes, inputMatrices); + + fprintf(stderr, "\nStarting minibatch loop"); + if (useGradientAggregation) + { + fprintf(stderr, ", DataParallelSGD training (MyRank = %d, NumNodes = %d, NumGradientBits = %d)", + (int)g_mpi->CurrentNodeRank(), (int)g_mpi->NumNodesInUse(), (int)m_numGradientBits); + if (m_bufferedAsyncGradientAggregation) + { + fprintf(stderr, ", BufferedAsyncGradientAggregation is ENABLED"); + } + } + if (useDistributedMBReading) + { + fprintf(stderr, ", distributed reading is ENABLED"); + } + if (numSubminibatchesNeeded > 1) + { + if (m_maxSamplesInRAM < SIZE_MAX) + fprintf(stderr, ", with maximum %d samples in RAM", (int)m_maxSamplesInRAM); + else + fprintf(stderr, ", with %d subminibatch", (int)numSubminibatchesNeeded); + } + fprintf(stderr, ".\n"); + + Timer timer; + timer.Start(); + + // --- MAIN MINIBATCH LOOP + + bool noMoreSamplesToProcess = false; + for (;;) + { + // get minibatch + // TODO: is it guaranteed that the GPU is already completed at this point, is it safe to overwrite the buffers? + size_t actualMBSize = 0; + bool wasDataRead = DataReaderHelpers::GetMinibatchIntoNetwork(*trainSetDataReader, net, criterionNodes[0], + useDistributedMBReading, useParallelTrain, *inputMatrices, actualMBSize); + if (!wasDataRead && (!useDistributedMBReading || noMoreSamplesToProcess)) // in case of distributed reading, we do a few more loops until all ranks have completed + break; // end of epoch + + // Note: If !wasDataRead then the data that GetMinibatchIntoNetwork() was supposed to full in are undefined. + // Must not touch them. + + if (!wasDataRead) + actualMBSize = 0; // (undefined if !wasDataRead) + + nSamplesSinceLastModelSync += actualMBSize; + + // node data was changed + // TODO: move this to that function as well--just tired to pass everything as arguments + // TODO: We should do this right after the GetMinibatch() call, since that's where these changed. + // Need to check whether that would cause unintended side effects. + // TODO: original code did not call this for actualMBSize == 0 + ComputationNetwork::BumpEvalTimeStamp(featureNodes); + ComputationNetwork::BumpEvalTimeStamp(labelNodes); + + if (actualMBSize > 0) + { + assert(wasDataRead); +#ifndef EVALDLL + if (m_doGradientCheck && GradientCheck(net, criterionNodes, learnableNodes, 0) == false) + LogicError("cannot pass gradient checker"); +#endif + // TODO: currently we only support one node for regularization + if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode) + { +#if 0 // TODO: where does refNet get its features from? + refNet->ResizeAllFeatureNodes(actualMBSize); +#endif + //size_t actualMBSize2 = refNet->SetActualMiniBatchSizeFromFeatures(); + size_t actualMBSize2 = refNet->DetermineActualMBSizeFromFeatures(); + refNet->GetMBLayoutPtr()->CopyFrom(net->GetMBLayoutPtr()); // TODO: This is UNTESTED (before this was missing, seemingly inconsistently) + refNet->VerifyActualNumParallelSequences(trainSetDataReader->GetNumParallelSequences()); + + if (actualMBSize2 != actualMBSize) + LogicError("TrainOneEpoch: refNet has different MB size than main net??"); + + refNet->ForwardProp(refNode); + Matrix::ScaleAndAdd((ElemType)m_adaptationRegWeight, + dynamic_pointer_cast>(refNode)->Value(), + (ElemType)(1.0 - m_adaptationRegWeight), + dynamic_pointer_cast>(labelNodes[0])->Value()); + } + + // do forward and back propagation + + // We optionally break the minibatch into sub-minibatches. + // This, when enabled, is used when a full minibatch does not fit into GPU RAM. + size_t actualNumSubminibatches = numSubminibatchesNeeded <= 1 ? 1 : smbDispatcher.GetMinibatchIntoCache(*trainSetDataReader, *net, *inputMatrices, numSubminibatchesNeeded); + for (size_t ismb = 0; ismb < actualNumSubminibatches; ismb++) + { + if (actualNumSubminibatches > 1) + { + smbDispatcher.GetSubMinibatchToNet(ismb); // get sub-minibatch from full-size one + ComputationNetwork::BumpEvalTimeStamp(featureNodes); + ComputationNetwork::BumpEvalTimeStamp(labelNodes); + } + + // =========================================================== + // forward prop for evaluate eval nodes + // =========================================================== + + // compute eval node first since when gradient is computed the forward function values + // may be changed and need to be recomputed when gradient and function value share the same matrix + net->ForwardProp(evaluationNodes); // the bulk of this evaluation is reused in ComputeGradient() below + + // =========================================================== + // forward prop for training criterion + // =========================================================== + + net->ForwardProp(criterionNodes[0]); + + // =========================================================== + // backprop + // =========================================================== + + if (learnRatePerSample > 0.01 * m_minLearnRate) // only compute gradient when learning rate is large enough + net->Backprop(criterionNodes[0]); + + // house-keeping for sub-minibatching + if (actualNumSubminibatches > 1) + smbDispatcher.DoneWithCurrentSubMinibatch(ismb); // page state out + } // end sub-minibatch loop + if (actualNumSubminibatches > 1) + smbDispatcher.DoneWithCurrentMinibatch(); + } // if (actualMBSize > 0) + + // for progress and statistics, we should only count frames that are not gaps + size_t numSamplesWithLabel = wasDataRead ? net->GetNumSamplesWithLabel(actualMBSize) : 0; + + // Sum of actualMBSize across all nodes when using parallel training + size_t aggregateNumSamples = actualMBSize; + size_t aggregateNumSamplesWithLabel = numSamplesWithLabel; + + if (!useGradientAggregation) + { + // accumulate criterion values (objective, eval) + if (actualMBSize != 0) + { + assert(wasDataRead); + // criteria are in Value()(0,0), we accumulate into another 1x1 Matrix (to avoid having to pull the values off the GPU) + Matrix::AddElementToElement(dynamic_pointer_cast>(criterionNodes[0])->Value(), + 0, 0, localEpochCriterion, 0, 0); + for (size_t i = 0; i < evaluationNodes.size(); i++) + { + Matrix::AddElementToElement(dynamic_pointer_cast>(evaluationNodes[i])->Value(), + 0, 0, localEpochEvalErrors, 0, i); + } + } + } + else + { + // distributed gradient aggregation + if (learnParamsGradients.size() == 0) + { + learnParamsGradients.reserve(learnableNodes.size()); + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + if (node->IsParameterUpdateRequired()) + { + Matrix* currParamsGradient = &(node->Gradient()); + + // Sometimes, in parallel training, the current node may not get any samples to process + // In this case, the gradient matrix may not have been sized yet. If so, lets size it. + if (currParamsGradient->GetNumCols() == 0) + { + Matrix* currParamsValues = &(node->Value()); + currParamsGradient->Resize(currParamsValues->GetNumRows(), currParamsValues->GetNumCols()); + } + + learnParamsGradients.push_back(currParamsGradient); + } + } + } + + // prepare the header + m_gradHeader->numEvalNode = evaluationNodes.size(); + m_gradHeader->numSamples = actualMBSize; + m_gradHeader->numSamplesWithLabel = numSamplesWithLabel; + m_gradHeader->criterion = actualMBSize > 0 ? criterionNodes[0]->Get00Element() : 0.0; + for (size_t i = 0; i < evaluationNodes.size(); i++) + m_gradHeader->evalErrors[i] = actualMBSize > 0 ? evaluationNodes[i]->Get00Element() : 0.0; + + bool samplesProcessed = m_distGradAgg->AggregateGradients(learnParamsGradients, m_gradHeader, epochNumber); + noMoreSamplesToProcess = !samplesProcessed; + + aggregateNumSamples = m_gradHeader->numSamples; + aggregateNumSamplesWithLabel = m_gradHeader->numSamplesWithLabel; + epochCriterion += m_gradHeader->criterion; + for (size_t i = 0; ievalErrors[i]; + } + + // update model parameters + if ((aggregateNumSamples > 0) && (learnRatePerSample > m_minLearnRate * 0.01)) + { + auto smoothedGradientIter = smoothedGradients.begin(); + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++, smoothedGradientIter++) + { + ComputationNodeBasePtr node = *nodeIter; + if (node->IsParameterUpdateRequired()) + { + Matrix& smoothedGradient = *smoothedGradientIter; +#ifdef _DEBUG + if (smoothedGradient.HasNan("TrainOneEpoch/UpdateWeights(): ")) + LogicError("%ls %ls operation has NaNs in smoothedGradient.", node->NodeName().c_str(), node->OperationName().c_str()); +#endif + UpdateWeights(node, smoothedGradient, learnRatePerSample, + GetMomentumPerSample(epochNumber/*BUGBUG workaround:*/, net->GetMBLayoutPtr()->GetNumParallelSequences()), aggregateNumSamples, + m_L2RegWeight, m_L1RegWeight, + m_needAveMultiplier, m_useNesterovMomentum); +#ifdef _DEBUG + if (dynamic_pointer_cast>(node)->Value().HasNan("TrainOneEpoch/UpdateWeights(): ")) + LogicError("%ls %ls operation has NaNs in functionValues after parameter update.", node->NodeName().c_str(), node->OperationName().c_str()); +#endif + } + } + } + + // aggregation by model averaging + // TODO: this does not happen each MB, does it? + if (useModelAveraging) + { + // Determine if any samples were processed across any of the ranks + if (useDistributedMBReading) + { + std::array numNodesWithDataToProcess; + numNodesWithDataToProcess[0] = wasDataRead ? 1 : 0; + g_mpi->AllReduce(numNodesWithDataToProcess); + + if (numNodesWithDataToProcess[0] == 0) + noMoreSamplesToProcess = true; + } + + if (g_mpi->NumNodesInUse() > 1) + { + size_t processedSamples = 0; + float secondsSinceLastSyncFinished = 0; + float secondsSpentOnSync = 0; + if (ModelAveragingProcessing(nSamplesSinceLastModelSync, learnableNodes, processedSamples, + secondsSinceLastSyncFinished, secondsSpentOnSync)) + { + // if a sync happens, do some extra work + nSamplesSinceLastModelSync = 0; + nSynced++; + + nSecondsOnMASync += secondsSpentOnSync; + nSecondsSinceLastMAPerfReport += secondsSinceLastSyncFinished; + + if (m_syncStatsTrace > 0) + { + if (nSynced % m_syncStatsTrace == 0) + { + fprintf(stderr, "\t\t-----(model averaging stats) %d-th sync, %8.2f seconds since last report, %5.2f seconds on communication\n", + (int)nSynced, nSecondsSinceLastMAPerfReport, nSecondsOnMASync); + nSecondsOnMASync = 0; + nSecondsSinceLastMAPerfReport = 0; + } + } + } + aggregateNumSamplesWithLabel = processedSamples; + } + } + + timer.Stop(); + numMBsRun++; + + totalTimeInMBs += timer.ElapsedSeconds(); + numSamplesLastMBs += useModelAveraging ? int(actualMBSize) : int(aggregateNumSamplesWithLabel); + + if (numMBsRun % m_numMBsToShowResult == 0) + { + // get the epoch Values updated + if (!useGradientAggregation) + { + timer.Restart(); + epochCriterion = localEpochCriterion.Get00Element(); + for (size_t i = 0; i < epochEvalErrors.size(); i++) + { + epochEvalErrors[i] = localEpochEvalErrors(0, i); + } + timer.Stop(); + + // Add the last trailing compute + totalTimeInMBs += timer.ElapsedSeconds(); + } + + double trainLossPerSample = (numSamplesLastMBs != 0) ? ((epochCriterion - epochCriterionLastMBs) / numSamplesLastMBs) : 0.0; + bool wasProgressPrinted = false; + + if (epochNumber > 0 || (int) epochSize > 0) + { + // progress tracing for compute cluster management + double mbProg = 0.0; + int mbProgNumPrecision = 2; + if (m_maxComputedEpochSize != 0) + { + double numMBPerEpoch = (double)m_maxComputedEpochSize / (double)tunedMBSize; + mbProg = (double)numMBsRun / numMBPerEpoch; + mbProgNumPrecision = (int)ceil(log10(numMBPerEpoch / (double)m_numMBsToShowResult)); + mbProgNumPrecision = max(mbProgNumPrecision - 2, 2); + } + wasProgressPrinted = ProgressTracing::TraceProgressPercentage(epochNumber, mbProg, false); + + // progress tracing for regular log + string formatString = "%s Epoch[%2d of %d]-Minibatch[%4d-%4d, %2." + std::to_string(mbProgNumPrecision) + "f%%]: SamplesSeen = %d; TrainLossPerSample = " + + GeneratePaddedFloatOrExpFormat(11, 8, trainLossPerSample) + "; "; + SGDTrace(stderr, formatString.c_str(), + prefixMsg.c_str(), epochNumber + 1, m_maxEpochs, numMBsRun - m_numMBsToShowResult + 1, + numMBsRun, mbProg * 100, numSamplesLastMBs, trainLossPerSample); + } + else + { + string formatString = "%s Epoch[%2d of %d]-Minibatch[%4d-%4d]: SamplesSeen = %d; TrainLossPerSample = " + + GeneratePaddedFloatOrExpFormat(11, 8, trainLossPerSample) + "; "; + SGDTrace(stderr, formatString.c_str(), + prefixMsg.c_str(), epochNumber + 1, m_maxEpochs, numMBsRun - m_numMBsToShowResult + 1, + numMBsRun, numSamplesLastMBs, trainLossPerSample); + m_maxComputedEpochSize = numMBsRun * numSamplesLastMBs / m_numMBsToShowResult; + } + + double evalError = 0.0; + for (size_t i = 0; i < epochEvalErrors.size(); i++) + { + evalError = (epochEvalErrors[i] - epochEvalErrorsLastMBs[i]) / numSamplesLastMBs; + string formatString = "EvalErr[%lu]PerSample = " + GeneratePaddedFloatOrExpFormat(0, 8, evalError) + "; "; + SGDTrace(stderr, formatString.c_str(), i, evalError); + } + + string formatString = "TotalTime = " + GeneratePaddedFloatOrExpFormat(0, 4, totalTimeInMBs) + "s; SamplesPerSecond = %.1f\n"; + SGDTrace(stderr, formatString.c_str(), totalTimeInMBs, numSamplesLastMBs / totalTimeInMBs); + + // progress tracing for compute cluster management + if (wasProgressPrinted) + { + ProgressTracing::TraceTrainLoss(trainLossPerSample); + } + + if (m_traceLevel > 0) + { + fflush(stderr); + } + + // reset statistics + totalTimeInMBs = 0; + numSamplesLastMBs = 0; + + epochCriterionLastMBs = epochCriterion; + for (size_t i = 0; i < epochEvalErrorsLastMBs.size(); i++) + { + epochEvalErrorsLastMBs[i] = epochEvalErrors[i]; + } + + if (std::isnan(epochCriterion)) + { + RuntimeError("The training criterion is not a number (NAN). Stop\n"); + } + } + + timer.Restart(); + totalEpochSamples += aggregateNumSamplesWithLabel; + totalSamplesSeen += aggregateNumSamplesWithLabel; + + // call DataEnd function + // This signals something from SGD to the reader. + // DataEnd does reader specific process if sentence ending is reached + trainSetDataReader->DataEnd(EndDataType::endDataSentence); + + // Attempts to compute the error signal for the whole utterance, which will + // be fed to the neural network as features. Currently it is a workaround + // for the two-forward-pass sequence and ctc training, which allows + // processing more utterances at the same time. Only used in Kaldi2Reader. + // TODO: move the two-forward-pass support out of the reader. + AttemptUtteranceDerivativeFeatures(net, trainSetDataReader, featureNodes, inputMatrices); + + profiler.NextSample(); + } + + // --- END MAIN MINIBATCH LOOP + + if (useModelAveraging && (g_mpi->NumNodesInUse() > 1) ) + { + // may not be synced after epoch finished, so do the sync here + int residualSampels = (int)nSamplesSinceLastModelSync; + g_mpi->AllReduce(&residualSampels, 1); + totalSamplesSeen += residualSampels; + totalEpochSamples += residualSampels; + ModelAveragingSync(nSamplesSinceLastModelSync, learnableNodes); + nSynced++; + nSamplesSinceLastModelSync = 0; + } + + // compute final criterion values + if (useGradientAggregation) + { + // with parallelization, we have them in regular variables + epochCriterion /= float(totalEpochSamples); + for (size_t i = 0; i< epochEvalErrors.size(); i++) + { + epochEvalErrors[i] /= totalEpochSamples; + } + } + else + { + // without, we have them in Matrix objects that possibly live on the GPU--get them over now + localEpochCriterion /= float(totalEpochSamples); + localEpochEvalErrors /= float(totalEpochSamples); + + epochCriterion = localEpochCriterion.Get00Element(); + for (size_t i = 0; i < epochEvalErrors.size(); i++) + { + epochEvalErrors[i] = localEpochEvalErrors(0, i); + } + } + + // in case of model averaging, do one more final aggregation of criteria + if (useModelAveraging && (g_mpi->NumNodesInUse() > 1)) + { + // merge epochCriterion and epochEvalErrors over nodes + g_mpi->AllReduce(&epochCriterion, 1); + g_mpi->AllReduce(epochEvalErrors); + } + return totalEpochSamples; + } + + // ----------------------------------------------------------------------- + // sub-routines and helpers follow below + // ----------------------------------------------------------------------- + +#if 0 + // TODO: per discussion with Dong Yu, Guoguo Chen, and Yu Zhang, this function can be removed. + template + void SGD::SequenceTrain(IComputationNetBuilder* netBuilder, wstring origModelFileName, + IDataReader* trainSetDataReader, IDataReader* validationSetDataReader, + const DEVICEID_TYPE deviceId, const bool makeMode) + { + if (netBuilder == nullptr || origModelFileName == L"" || trainSetDataReader == nullptr) + { + InvalidArgument("netBuilder, origModel and trainSetDataReader should not be null."); + } + + int startEpoch = DetermineStartEpoch(makeMode); + if (startEpoch == m_maxEpochs) + { + fprintf(stderr, "No further training is necessary.\n"); + return; + } + + // Initializes the model from original model. + // TODO: Comment what this does! + auto origNet = make_shared(deviceId); + ComputationNetworkPtr sequenceNet = + (startEpoch < 0) ? netBuilder->BuildNetworkFromDescription() : origNet; + std::vector addedFeatureNodes; + std::vector replacedCriterionNodes; + if (startEpoch < 0) + { + // Loads models. + origNet->Load(origModelFileName); + + // Processes feature nodes. + std::vector & sequenceFeatureNodes = sequenceNet->FeatureNodes(); + for (size_t i = 0; i < sequenceFeatureNodes.size(); ++i) + { + if (!origNet->NodeNameExists(sequenceFeatureNodes[i]->NodeName())) + { + addedFeatureNodes.push_back(sequenceFeatureNodes[i]); + origNet->AddFeatureNode(sequenceFeatureNodes[i]); + } + } + + // Processes criterion nodes. + auto & origCriterionNodes = GetTrainCriterionNodes(origNet); + auto & sequenceCriterionNodes = GetTrainCriterionNodes(sequenceNet); + if (origCriterionNodes.size() == 0 || sequenceCriterionNodes.size() == 0) + { + RuntimeError("Training criterion node does not exist."); + } + replacedCriterionNodes.push_back(origCriterionNodes[0]); + origNet->ReplaceFinalCriterionNode(origCriterionNodes[0]->NodeName(), sequenceCriterionNodes[0]); + origNet->ResetEvalTimeStamps(); + } + + wstring modelFileName = GetModelNameForEpoch(int(startEpoch) - 1); + if (startEpoch >= 0) + { + fprintf(stderr, "Starting from checkpoint. Load Network From File %ls.\n", modelFileName.c_str()); + } + else + { + fprintf(stderr, "Load Network From the original model file %ls.\n", origModelFileName.c_str()); + } + ComputationNetworkPtr net = (startEpoch < 0) ? origNet : ComputationNetwork::CreateFromFile(deviceId, modelFileName); + + startEpoch = max(startEpoch, 0); + + TrainOrAdaptModel(startEpoch, net, net, nullptr, trainSetDataReader, validationSetDataReader); + + // Handles deletions carefully here. + // TODO: This is no longer needed since we own our networks and deal with shared_ptrs now. + if (startEpoch < 0) + { + for (size_t i = 0; i < addedFeatureNodes.size(); ++i) + { + origNet->RemoveFeatureNode(addedFeatureNodes[i]); + } + auto & origCriterionNodes = GetTrainCriterionNodes(origNet); + origNet->ReplaceFinalCriterionNode(origCriterionNodes[0]->NodeName(), replacedCriterionNodes[0]); + } + } +#endif + + static double MomentumPerMB(double momentumPerSample, size_t minibatchSize) + { + return pow(momentumPerSample, minibatchSize); + } + + // Get{Train,Eval}CriterionNodes() return a reference that is, unfortunately, dependent on the network. + // So we hold those inside here. Not very nice. Also not thread-safe. This may go away once we fix sequence-to-sequence models properly. + // TODO: merge them into one. + static map> tmpCriterionNodeSets; + // TODO: test this, then remove this comment + + template + std::vector & SGD::GetTrainCriterionNodes(ComputationNetworkPtr net) + { + if (!m_trainCriterionNodeName.empty()) + { + tmpCriterionNodeSets[net] = net->CriterionNodesFrom(m_trainCriterionNodeName); + return tmpCriterionNodeSets[net]; + } + else + return net->FinalCriterionNodes(); + } + + template + std::vector & SGD::GetEvalCriterionNodes(ComputationNetworkPtr net) + { + if (!m_evalCriterionNodeName.empty()) + { + tmpCriterionNodeSets[net] = net->CriterionNodesFrom(m_evalCriterionNodeName); + return tmpCriterionNodeSets[net]; + } + else + return net->EvaluationNodes(); + } + + // return true if precomputation is executed. + template + bool SGD::PreCompute(ComputationNetworkPtr net, + IDataReader* trainSetDataReader, + std::vector & featureNodes, + std::vector & labelNodes, + std::map*>* inputMatrices) + { + std::list nodes = net->GetNodesRequiringPreComputation(); // this tests all HasComputed() flags + + if (nodes.size() == 0) + { + fprintf(stderr, "No PreCompute nodes found, skipping PreCompute step\n"); + return false; + } + + fprintf(stderr, "\nPrecomputing --> %lu PreCompute nodes found.\n\n", nodes.size()); + for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) + { + auto node = static_pointer_cast>(*nodeIter); + fprintf(stderr, "\tNodeName: %ls\n", (node->NodeName()).c_str()); + } + + //compute + //trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0 , requestDataSize); + // trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0 , m_epochSize); // only based on one epoch + // [1/12/2015 erw] to support large dataset, we usually partition whole dataset into several epoch's, + // so we need to use all the data to do precomputing + if (m_useAllDataForPreComputedNode) // using all the data + trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0); + else // using only one epoch + trainSetDataReader->StartMinibatchLoop(m_mbSize[0], 0, m_epochSize); + net->StartEvaluateMinibatchLoop(nodes); + + // initialize + for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) + { + auto node = static_pointer_cast>(*nodeIter); + node->MarkComputed(false/*begin accumulating*/); + } + size_t actualMBSizeDummy; + while (DataReaderHelpers::GetMinibatchIntoNetwork(*trainSetDataReader, net, nullptr, false, false, *inputMatrices, actualMBSizeDummy)) + { + // TODO: move these into GetMinibatchIntoNetwork() --but those are passed around; necessary? Can't we get them from 'net'? + ComputationNetwork::BumpEvalTimeStamp(featureNodes); + ComputationNetwork::BumpEvalTimeStamp(labelNodes); + + net->ForwardProp(nodes); + } + // finalize + for (auto nodeIter = nodes.begin(); nodeIter != nodes.end(); nodeIter++) + { + auto node = static_pointer_cast>(*nodeIter); + node->MarkComputed(true/*done accumulating*/); + } + fprintf(stderr, "\nPrecomputing --> Completed.\n\n"); + + return true; + } + + // return a reasonable initial learning rate based on the initial mbsize + template + double SGD::SearchForBestLearnRate(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, const int epochNumber, + const double curLearnRate, + IDataReader* trainSetDataReader, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const bool learnRateInitialized, + const double largestPrevLearnRatePerSample) + { + double epochCriterion = std::numeric_limits::infinity(); + double prevCriterion = std::numeric_limits::infinity(); + vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); + + size_t totalSamplesSeen = 0; + double bestLearnRatePerSample = curLearnRate; + + size_t numFramesToUseInSearch = m_numMiniBatch4LRSearch[epochNumber] * m_mbSize[epochNumber]; + if (m_epochSize != requestDataSize) + { + // ensure the numFramesToUseInSearch does not exceed the total number of frames in the epoch + numFramesToUseInSearch = min(numFramesToUseInSearch, m_epochSize); + } + + double baseCriterion; + + double minLearnRate = m_minLearnRate * 0.3f; + double learnRatePerSample = 1.0f / 8.0f / 0.618f / sqrt((double)m_mbSize[epochNumber]); + + if (learnRateInitialized && largestPrevLearnRatePerSample > 0) + { + // largestPrevLearnRatePerSample is per sample, first 0.618f is for compensation, second one is for safety + learnRatePerSample = largestPrevLearnRatePerSample / 0.618f / 0.618f; + } + + int baseModelEpoch = epochNumber - 1; + net->RereadPersistableParameters(GetModelNameForEpoch(baseModelEpoch)); + + double learnRate = learnRatePerSample; + size_t dummyMinibatchSize = 0; + LoadCheckPointInfo(baseModelEpoch, + /*out*/ totalSamplesSeen, + /*out*/ learnRate, + smoothedGradients, + /*out*/ prevCriterion, + /*out*/ dummyMinibatchSize); + + // if model is not changed this is what we will get + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, + numFramesToUseInSearch, trainSetDataReader, 0, m_mbSize[epochNumber], + featureNodes, labelNodes, + criterionNodes, evaluationNodes, + inputMatrices, learnableNodes, + smoothedGradients, /*out*/ baseCriterion, + /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, + "BaseAdaptiveLearnRateSearch:"); + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::SearchBeforeEpoch) + { + if (prevCriterion == std::numeric_limits::infinity()) + prevCriterion = baseCriterion; + + double ratio = 0.3; + + if (m_epochSize != requestDataSize) + ratio = pow(((double)numFramesToUseInSearch) / m_epochSize, 1.0f / 2); + + baseCriterion = max(ratio * prevCriterion + (1 - ratio) * baseCriterion, baseCriterion); + } + + do + { + learnRatePerSample *= 0.618; + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, + numFramesToUseInSearch, trainSetDataReader, + learnRatePerSample, m_mbSize[epochNumber], featureNodes, + labelNodes, criterionNodes, + evaluationNodes, inputMatrices, + learnableNodes, smoothedGradients, + /*out*/ epochCriterion, /*out*/ epochEvalErrors, + /*out*/ totalSamplesSeen, "AdaptiveLearnRateSearch:"); + + } while (std::isnan(epochCriterion) || (epochCriterion > baseCriterion && learnRatePerSample > minLearnRate)); + + bestLearnRatePerSample = learnRatePerSample; + + //grid search for the first m_numBestSearchEpoch epochs + if (epochNumber < m_numBestSearchEpoch) + { + double leftLearnRatePerSample = 0.01 / m_mbSize[epochNumber]; + double rightLearnRatePerSample = learnRatePerSample; + double leftCriterion, rightCriterion = epochCriterion; + + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, + numFramesToUseInSearch, trainSetDataReader, + leftLearnRatePerSample, m_mbSize[epochNumber], + featureNodes, labelNodes, + criterionNodes, evaluationNodes, + inputMatrices, learnableNodes, + smoothedGradients, /*out*/ leftCriterion, + /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, + "DetailBaseAdaptiveLearnRateSearch:"); + + while (rightLearnRatePerSample > leftLearnRatePerSample * 1.2) + { + if (rightCriterion > leftCriterion) + { + rightLearnRatePerSample *= 0.618; + + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, + epochNumber, numFramesToUseInSearch, + trainSetDataReader, + rightLearnRatePerSample, m_mbSize[epochNumber], + featureNodes, labelNodes, + criterionNodes, + evaluationNodes, + inputMatrices, + learnableNodes, + smoothedGradients, + /*out*/ rightCriterion, + /*out*/ epochEvalErrors, + /*out*/ totalSamplesSeen, + "DetailRightAdaptiveLearnRateSearch:"); + } + else + { + leftLearnRatePerSample /= 0.618; + + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, + epochNumber, numFramesToUseInSearch, + trainSetDataReader, + leftLearnRatePerSample, m_mbSize[epochNumber], + featureNodes, labelNodes, + criterionNodes, + evaluationNodes, + inputMatrices, + learnableNodes, + smoothedGradients, + /*out*/ leftCriterion, + /*out*/ epochEvalErrors, + /*out*/ totalSamplesSeen, + "DetailLeftAdaptiveLearnRateSearch:"); + } + } + + bestLearnRatePerSample = (leftCriterion < rightCriterion) ? leftLearnRatePerSample : + rightLearnRatePerSample; + } + + fprintf(stderr, "Best Learn Rate Per Sample for Epoch[%d] = %.10g baseCriterion=%.10g\n", + epochNumber + 1, bestLearnRatePerSample, baseCriterion); + + return bestLearnRatePerSample; + } + + template + void SGD::TrainOneMiniEpochAndReloadModel(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, const int epochNumber, + const size_t epochSize, IDataReader* trainSetDataReader, + const double learnRatePerSample, + const size_t minibatchSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + /*out*/ double& epochCriterion, + /*out*/ std::vector& epochEvalErrors, + /*out*/ size_t& totalSamplesSeen, + std::string prefixMsg) + { + TrainOneEpoch(net, refNet, refNode, epochNumber, epochSize, + trainSetDataReader, learnRatePerSample, minibatchSize, featureNodes, + labelNodes, criterionNodes, evaluationNodes, + inputMatrices, learnableNodes, smoothedGradients, + /*out*/ epochCriterion, /*out*/ epochEvalErrors, /*out*/ totalSamplesSeen, + prefixMsg); + + fprintf(stderr, "Finished Mini-Epoch For LearnRate Selection: TrainLossPerSample = %.8g;", epochCriterion); + + if (epochEvalErrors.size() == 1) + fprintf(stderr, "EvalErrPerSample = %.8g; AvgLearningRatePerSample = %.8g\n", epochEvalErrors[0], learnRatePerSample); + else + { + fprintf(stderr, "EvalErrPerSample "); + for (size_t i = 0; i < epochEvalErrors.size(); i++) + { + fprintf(stderr, "[%lu] = %.8g; ", i, epochEvalErrors[i]); + } + fprintf(stderr, "AvgLearningRatePerSample = %.8g\n", learnRatePerSample); + } + + int baseModelEpoch = epochNumber - 1; + net->RereadPersistableParameters(GetModelNameForEpoch(baseModelEpoch)); + + double dummyLearnRate; + double dummtPrevCriterion; + size_t dummyMinibatchSize = 0; + LoadCheckPointInfo(baseModelEpoch, + /*out*/ totalSamplesSeen, + /*out*/ dummyLearnRate, + smoothedGradients, + /*out*/ dummtPrevCriterion, + /*out*/ dummyMinibatchSize); + } + + // AdaptiveMinibatchSizing() -- choose the largest feasible minibatch size + // This is necessary for data-parallel operation. The aim is to minimize model updates, and hence bandwidth + // This implements + // F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu: + // "On Parallelizability of Stochastic Gradient Descent for Speech DNNs" + // In Proc. ICASSP 2014. + template + size_t SGD::AdaptiveMinibatchSizing(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t numFramesToUseInSearch, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + const size_t initialMinibatchSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const double learningRateAdjustmentFactor) + { + size_t minMinibatchSize = initialMinibatchSize; + size_t chosenMinibatchSize = initialMinibatchSize; + + // do some pre-adjustment based on LR + // Basically we assume that the LR for epoch 1 is safe for mbsize. + // If LR control led to a smaller LR, then we can safely increase the lower bound of the MB size. + double learningRateChangeSoFar = GetLearningRatePerSample(epochNumber/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()) / GetLearningRatePerSample(0/*BUGBUG workaround:*/, trainSetDataReader->GetNumParallelSequences()); + learningRateChangeSoFar *= learningRateAdjustmentFactor; + + // increasing by the full factor is found to be too aggressive; sqrt() seems more robust + learningRateChangeSoFar = sqrt(learningRateChangeSoFar); + + // LR was indeed reduced + if (learningRateChangeSoFar < 1.0f) + { + // we can safely increase MB size (note: this may be bigger than our max) + minMinibatchSize = (size_t)(minMinibatchSize / learningRateChangeSoFar); + } + + if (epochNumber < 2 && m_prevChosenMinibatchSize != 0) + { + // newly started training: any previous MB size stored in the model is to be ignored + fprintf(stderr, "before epoch .2, previous minibatchSize %zd is " + "considered invalid -> resetting\n", m_prevChosenMinibatchSize); + m_prevChosenMinibatchSize = 0; + } + + // check if we need to skip + if (m_prevChosenMinibatchSize != 0 && + (epochNumber + 1) > m_minibatchSizeTuningFrequency && + (epochNumber + 1) % m_minibatchSizeTuningFrequency != 0) + { + fprintf(stderr, "AdaptiveMinibatchSearch: Search for a better minibatchSize " + "in epoch %d skipped, keeping minibatchSize of %zd\n", + epochNumber + 1, m_prevChosenMinibatchSize); + chosenMinibatchSize = m_prevChosenMinibatchSize; + } + else + { + if (m_prevChosenMinibatchSize != 0) + { + // if m_prevChosenMinibatchSize (the chosen minibatch size for the previous epoch) div 2 + // is higher than initialMinibatchSize (the minibatch size we start with for this epoch), + // then start the search with m_prevChosenMinibatchSize/2 instead of initialMinibatchSize. + fprintf(stderr, "AdaptiveMinibatchSearch: Limiting minMinibatchSize to " + "largest of previous minibatchSize = (%d / 2) or %d\n", + (int) m_prevChosenMinibatchSize, (int) minMinibatchSize); + minMinibatchSize = max(minMinibatchSize, m_prevChosenMinibatchSize / 2); + } + + size_t maxMinibatchSize = m_minibatchSizeTuningMax; + + // only grow at most 2 x compared to previous step + if (m_prevChosenMinibatchSize != 0.0f) + { + assert(m_prevChosenMinibatchSize >= chosenMinibatchSize); + + fprintf(stderr, "AdaptiveMinibatchSearch: Limiting maxMinibatchSize to " + "previous minibatchSize %zd*2\n", m_prevChosenMinibatchSize); + maxMinibatchSize = min(maxMinibatchSize, m_prevChosenMinibatchSize * 2); + } + + chosenMinibatchSize = SearchForBestMinibatchSize(net, refNet, refNode, epochNumber, + numFramesToUseInSearch, trainSetDataReader, + learnRatePerSample, featureNodes, + labelNodes, criterionNodes, + evaluationNodes, inputMatrices, + learnableNodes, smoothedGradients, + minMinibatchSize, maxMinibatchSize); + } + + return chosenMinibatchSize; + } + + static size_t RoundToMultipleOf64(float val) + { + return 64 * (size_t)((val + 32) / 64); + } + + static size_t RoundToMultipleOf64(size_t val) + { + return 64 * ((val + 32) / 64); + } + + // uses a small percentage of training data of minibatch to + // speculatively train with various MB sizes; then picks the best + template + size_t SGD::SearchForBestMinibatchSize(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t numFramesToUseInSearch, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const size_t minMinibatchSize, const size_t maxMinibatchSize) + { + // may happen for automatically reduced learning rates + if (minMinibatchSize > maxMinibatchSize) + { + return maxMinibatchSize; + } + + size_t trialMinibatchSize = 0; + bool isFirstIteration = true; + double baseCriterion = 0; + + // increase the minibatch size by a factor of sqrt(2) in each step. + const float minibatchSizeTuningFactor = sqrtf(2.0f); + + size_t lastTriedTrialMinibatchSize = 0; + double lastTriedTrialEpochCriterion = 0; + for (float trialMinibatchSizeFloat = (float)minMinibatchSize; + trialMinibatchSizeFloat <= maxMinibatchSize; + trialMinibatchSizeFloat *= minibatchSizeTuningFactor) + { + // round mbsize to something meaningful + trialMinibatchSize = RoundToMultipleOf64(trialMinibatchSizeFloat); + + fprintf(stderr, "\nAdaptiveMinibatchSearch: Evaluating trial minibatchSize=%zd out of range %zd..%zd ...\n\n", + trialMinibatchSize, RoundToMultipleOf64(minMinibatchSize), RoundToMultipleOf64(maxMinibatchSize)); + + size_t totalSamplesSeen; + std::vector epochEvalErrors(evaluationNodes.size(), std::numeric_limits::infinity()); + double epochCriterion = std::numeric_limits::infinity(); + + // Train on a few minibatches and so we can observe the epochCriterion as we try increasing + // minibatches with iteration of this loop. + TrainOneMiniEpochAndReloadModel(net, refNet, refNode, epochNumber, + numFramesToUseInSearch, trainSetDataReader, + learnRatePerSample, trialMinibatchSize, featureNodes, + labelNodes, criterionNodes, + evaluationNodes, inputMatrices, + learnableNodes, smoothedGradients, + /*out*/ epochCriterion, /*out*/ epochEvalErrors, + /*out*/ totalSamplesSeen, + isFirstIteration ? "BaseAdaptiveMinibatchSearch:" : + "AdaptiveMinibatchSearch:"); + + if (isFirstIteration) + { + // for the first iteration of the loop only, set baseCriterion + // to the result we got from TrainOneMiniEpochAndReloadModel(). + baseCriterion = epochCriterion; + lastTriedTrialMinibatchSize = trialMinibatchSize; + lastTriedTrialEpochCriterion = baseCriterion; + isFirstIteration = false; + + fprintf(stderr, "AdaptiveMinibatchSearch: Computed BaseCriterion %.10g\n", baseCriterion); + } + else if (!std::isnan(epochCriterion) && + (epochCriterion > (baseCriterion * (1.0 + ( m_minibatchSearchCriterionErrorMargin / 100.0))))) + { + // As soon as we see the Criterion (a measure of error) start to get larger than the + // Criterion we started with, we stop. + // TODO: if this is too sensitive, we can add a margin on the bases of percentage of + // baseCriterion. + break; + } + else + { + lastTriedTrialMinibatchSize = trialMinibatchSize; + lastTriedTrialEpochCriterion = epochCriterion; + if (trialMinibatchSizeFloat * minibatchSizeTuningFactor <= maxMinibatchSize) + { + fprintf(stderr, "AdaptiveMinibatchSearch: Keep searching... " + "EpochCriterion = %.10g vs BaseCriterion = %.10g\n", + epochCriterion, baseCriterion); + } + } + } + fprintf(stderr, "AdaptiveMinibatchSearch: Search successful!!! Chose new minibatchSize of %d. " + "EpochCriterion = %.10g vs BaseCriterion = %.10g\n\n", + (int) lastTriedTrialMinibatchSize, lastTriedTrialEpochCriterion, baseCriterion); + + + return lastTriedTrialMinibatchSize; + } + + // Attemps to compute the error signal for the whole utterance, which will + // be fed to the neural network as features. Currently it is a workaround + // for the two-forward-pass sequence and ctc training, which allows + // processing more utterances at the same time. Only used in Kaldi2Reader. + // TODO: move the two-forward-pass support out of the reader. + template + void SGD::AttemptUtteranceDerivativeFeatures(ComputationNetworkPtr net, + IDataReader* trainSetDataReader, + const std::vector & featureNodes, + std::map*>* inputMatrices) + { + assert(trainSetDataReader != NULL); + std::vector>> uttInfo; + auto pMBLayout = make_shared(); + // TODO: use GetMinibatchIntoNetwork(). + while (trainSetDataReader->GetMinibatchCopy(uttInfo, *inputMatrices, pMBLayout)) + { + ComputationNetwork::BumpEvalTimeStamp(featureNodes); + + auto & outputNodes = net->OutputNodes(); + if (outputNodes.empty()) + LogicError("no output node was found."); + + //net->SetActualMiniBatchSizeFromFeatures(); + trainSetDataReader->CopyMBLayoutTo(net->GetMBLayoutPtr()); + net->VerifyActualNumParallelSequences(trainSetDataReader->GetNumParallelSequences()); + net->ForwardProp(outputNodes[0]); // Only evaluate the first output + trainSetDataReader->SetNetOutput(uttInfo, + dynamic_pointer_cast>(outputNodes[0])->Value(), + pMBLayout); + } + } + + static string GeneratePaddedFloatOrExpFormat(int padSize, int precision, double value) + { + char format[16]; + char buffer[512]; + + sprintf(format, "%%.%dg", precision); + sprintf(buffer, format, value); + + for (int i = 0; i < strlen(buffer); i++) + { + if (buffer[i] == 'e' || buffer[i] == 'E') + { + sprintf(format, "%%%d.%de", padSize, precision); + return format; + } + } + sprintf(format, "%%%d.%df", padSize, precision); + return format; + } + + template + int SGD::SGDTrace(FILE *__restrict __stream, const char *__restrict __format, ...) + { + int result = 0; + if (m_traceLevel > 0) + { + va_list args; + va_start(args, __format); + result = vfprintf(__stream, __format, args); + va_end(args); + } + return result; + } + + template + void SGD::InitDistGradAgg(int numEvalNodes, int traceLevel) + { + if (m_parallelizationMethod == ParallelizationMethod::DataParallelSGD) + { + if (m_distGradAgg == nullptr) + { +#ifdef QUANTIZED_GRADIENT_AGGREGATION + m_distGradAgg = new AllReduceDistGradAggregator(g_mpi, m_numGradientBits, m_zeroThresholdFor1Bit, true /*useQuantizationForSelfStripe*/, m_bufferedAsyncGradientAggregation, traceLevel, m_syncStatsTrace); +#else + if (m_numGradientBits != (8 * sizeof(ElemType))) + { + RuntimeError("Gradient quantization is unsupported in CNTK binaries built without quantized gradient aggregation support!"); + } + + m_distGradAgg = new SimpleDistGradAggregator(g_mpi, m_bufferedAsyncGradientAggregation, m_syncStatsTrace); +#endif // !QUANTIZED_GRADIENT_AGGREGATION + + } + + if (m_gradHeader == nullptr) + { + m_gradHeader = DistGradHeader::Create(numEvalNodes); + } + } + } + + template + bool SGD::ModelAveragingProcessing(size_t nSamplesSinceLastSync, const std::list& learnableNodes, size_t& nProcessedFrames, + float& SecondsSinceLastSyncFinished, float& SecondsSpentOnSync) + { + ////////////////////////////////////////////////////////////////////////// + // the current strategy is that after each minibatch, we will sync between processors + // to decide whether a sync need to be performed. This is definitely not optimal, + // which we will fix it later. + + // TODO: the way we handle timer is not very good + ////////////////////////////////////////////////////////////////////////// + static bool first = true ; + static Timer MAtimer; + if (first) + { + MAtimer.Start(); + first = false; + } + + char bNeedToSync = (char)0; // use char for bool + if (g_mpi->IsMainNode() && nSamplesSinceLastSync >= m_nFramesBetweenMASync) + { + // only the main node can decide whether a sync need to be performed + bNeedToSync = (char)1; + } + g_mpi->Bcast(&bNeedToSync, 1, g_mpi->MainNodeRank()); + if (bNeedToSync) + { + MAtimer.Stop(); + double elapsedsec = MAtimer.ElapsedSeconds(); + SecondsSinceLastSyncFinished = first ? 0 : (float) elapsedsec ; + MAtimer.Start(); + nProcessedFrames = ModelAveragingSync((int)nSamplesSinceLastSync, learnableNodes); + MAtimer.Stop(); + SecondsSpentOnSync = (float)MAtimer.ElapsedSeconds(); + + MAtimer.Start(); + } + else + { + nProcessedFrames = 0; + return false; + } + return true; + } + + template + size_t SGD::ModelAveragingSync(int nSamplesSinceLastSync, const std::list& learnableNodes) + { + if (g_mpi->NumNodesInUse() <= 1) + { + return nSamplesSinceLastSync; + } + + //======================================== + // Sec. 1 calculate factor + //======================================== + float factor = 0; + int nTotalSamples = nSamplesSinceLastSync; + g_mpi->AllReduce(&nTotalSamples, 1); + if (nTotalSamples <= 0) + { + // prepare for overflow + factor = 1.0f / g_mpi->NumNodesInUse(); + } + else + { + factor = (nSamplesSinceLastSync + 0.0f) / nTotalSamples; + } + + //======================================== + // Sec. 2 sync models based on factor + // Note: this is suboptimal at the moment: + // we do the averaging for each node in a sequence manner, i.e., + // (node1) GPU->CPU->MPI_AllReduce -> (node2)GPU->CPU->MPI_AllReduce + // we can improve it by using a pipeline + // (node1) GPU -> CPU -> MPI_AllReduce + // (node2) GPU -> CPU -> MPI_AllReduce + // (node3) GPU -> CPU -> MPI_AllReduce + //======================================== + for (auto iter = learnableNodes.begin(); iter != learnableNodes.end(); iter++) + { + ComputationNodeBasePtr pNode = *iter; + if (!pNode->IsParameterUpdateRequired()) + continue; + + Matrix& mat = dynamic_pointer_cast>(pNode)->Value(); + // 1. normalize the weight matrix + Matrix::Scale(factor, mat); + // 2. send weight matrix over MPI nodes; + ElemType* px = mat.CopyToArray(); + size_t nx = mat.GetNumElements(); + + // 3. inplace sum + g_mpi->AllReduce(px, nx); + mat.SetValue(mat.GetNumRows(), mat.GetNumCols(), mat.GetDeviceId(), px); + // 4. clean up + delete []px; + } + + return nTotalSamples; + } + +// public: + // UpdateWeightsS - static version of UpdateWeights() + // not static since it wants to access protected methods on the SGD object + template + /*static*/ void SGD::UpdateWeightsS(const SGD* sgd, Matrix& functionValues, + Matrix& gradientValues, + Matrix& smoothedGradient, + const double learnRatePerSample, + const double momentumPerSample, + size_t actualMBSize, + const double L2RegWeight, + const double L1RegWeight, + const bool needAveMultiplier, + const bool useNesterovMomentum + ) + { + // we use simple linear (instead of log linear) scaling here + const double momentum = MomentumPerMB(momentumPerSample, actualMBSize); +#if DUMPOUTPUT + fprintf(stderr, "learnRatePerSample=%0.8f, momentum=%0.8f, actualMBSize=%ld\n", + learnRatePerSample, momentum, actualMBSize); + fprintf(stderr, "sgd->GradUpdateType()=%d, sgd->GradientUpdateNoiseStd()=%0.8f\n", + sgd->GradUpdateType(), sgd->GradientUpdateNoiseStd()); + gradientValues.Print("Gradient Input"); + smoothedGradient.Print("Smoothed Gradient Input"); +#endif + + // make actualMBSize is a valid value + assert(actualMBSize > 0); + + //clipping gradients to prevent outliers + sgd->ClipGradient(gradientValues, actualMBSize); + + GradientsUpdateType adpType = sgd->GradUpdateType(); + double noiseStd = sgd->GradientUpdateNoiseStd(); + Matrix sgdUpdateNoise((DEVICEID_TYPE)functionValues.GetDeviceId()); + if (noiseStd > 0) + { + // get the gradient structure since gradient is sparse + sgdUpdateNoise.SetValue(gradientValues); + + // reset its value to random + sgdUpdateNoise.SetGaussianRandomValue(0, (ElemType)noiseStd); + } + + // L2 regularizer + if (L2RegWeight > 0) + { + // multiply by actualMBSize so that it's invariant to minibatch size since learning rate is per sample + Matrix::ScaleAndAdd((ElemType)(L2RegWeight * actualMBSize), functionValues, gradientValues); + } + + if (adpType == GradientsUpdateType::None) + { + smoothedGradient.NormalGrad(gradientValues, functionValues, + (ElemType)learnRatePerSample, (ElemType)momentum, useNesterovMomentum); + } + else if (adpType == GradientsUpdateType::AdaGrad || + (adpType == GradientsUpdateType::RmsProp && gradientValues.GetMatrixType() == MatrixType::SPARSE) || + (adpType == GradientsUpdateType::FSAdaGrad && gradientValues.GetMatrixType() == MatrixType::SPARSE)) + { + //rmsprop for sparse is not implemented yet, delegate it with adagrad + + double aveMultiplier = smoothedGradient.Adagrad(gradientValues, needAveMultiplier); + Matrix::ScaleAndAdd((ElemType)(-learnRatePerSample / aveMultiplier), gradientValues, functionValues); + } + else if (adpType == GradientsUpdateType::FSAdaGrad) + { + smoothedGradient.FSAdagrad(actualMBSize, gradientValues, functionValues, (ElemType)learnRatePerSample, (ElemType)momentum); + } + else if (adpType == GradientsUpdateType::RmsProp) + { + double aveMultiplier = smoothedGradient.RmsProp(gradientValues, (ElemType)sgd->m_rpi.gamma, + (ElemType)sgd->m_rpi.inc, (ElemType)sgd->m_rpi.max, + (ElemType)sgd->m_rpi.dec, (ElemType)sgd->m_rpi.min, needAveMultiplier); + Matrix::ScaleAndAdd((ElemType)(-learnRatePerSample / aveMultiplier), gradientValues, functionValues); + } + + if (noiseStd > 0) + { + Matrix::ScaleAndAdd(1.0, sgdUpdateNoise, functionValues); + } + + // L1 regularizer with proximal gradient descent method + if (L1RegWeight > 0) + { + // multiply by actualMBSize so that it's invariant to minibatch size since learning rate is per sample + functionValues.InplaceSoftThreshold((ElemType)(learnRatePerSample * L1RegWeight * actualMBSize)); + } + +#if DUMPOUTPUT + functionValues.Print("Parameter Update"); +#endif + } + +// protected: + + // UpdateWeights - update the weights in + template + void SGD::UpdateWeights(const ComputationNodeBasePtr& node, + Matrix& smoothedGradient, + const double learnRatePerSample, + const double momentumPerSample, + const size_t actualMBSize, + const double L2RegWeight, const double L1RegWeight, + const bool needAveMultiplier, + const bool useNesterovMomentum + ) const + { +#if DUMPOUTPUT + fprintf(stderr, "Update_%ls\n", node->NodeName().c_str()); +#endif + if (!node->IsParameterUpdateRequired()) + LogicError("UpdateWeights() called for a learnable ComputationNode which has m_parameterUpdateRequired == false!"); + + UpdateWeightsS(this, dynamic_pointer_cast>(node)->Value(), dynamic_pointer_cast>(node)->Gradient(), + smoothedGradient, learnRatePerSample, momentumPerSample, + actualMBSize, L2RegWeight, L1RegWeight, + needAveMultiplier, m_useNesterovMomentum); + node->BumpEvalTimeStamp(); + } + + template + void SGD::ClipGradient(Matrix& gradient, const size_t actualMBSize) const + { + if (m_clippingThresholdPerSample != std::numeric_limits::infinity()) + { + double maxGradientPerMB = m_clippingThresholdPerSample * actualMBSize; + if (m_gradientClippingWithTruncation) + gradient.InplaceTruncate((ElemType)(maxGradientPerMB)); + else + { + // norm2 normalized + double gradientNorm = gradient.FrobeniusNorm(); + if (gradientNorm > maxGradientPerMB) + { + double normFactor = maxGradientPerMB / gradientNorm; + gradient *= (ElemType)normFactor; + } + } + } + } + + template + void SGD::SaveCheckPointInfo(const size_t epoch, const size_t totalSamplesSeen, + const double learnRatePerSample, + const std::list>& smoothedGradients, + const double prevCriterion, + const size_t minibatchSize) + { + // In case of parallel training only the main node should we saving the checkpoint to prevent + // the parallel training nodes from colliding to write the same file + if ((g_mpi == nullptr) || g_mpi->IsMainNode()) + { + wstring checkPointFileName = GetCheckPointFileNameForEpoch(int(epoch)); + // Saving into temporary file and then renaming it to the checkPointFileName + // This is a standard trick to avoid havign corrupted checkpoints files if process dies during writing + wstring tempFileName = checkPointFileName + L".tmp"; + + { + File fstream(tempFileName, FileOptions::fileOptionsBinary | FileOptions::fileOptionsWrite); + fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BCKP"); + + fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BLearnRate"); + fstream << totalSamplesSeen << learnRatePerSample << prevCriterion; + fstream.PutMarker(FileMarker::fileMarkerEndSection, L"ELearnRate"); + + fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BMinibatchSize"); + fstream << minibatchSize; + fstream.PutMarker(FileMarker::fileMarkerEndSection, L"EMinibatchSize"); + + fstream.PutMarker(FileMarker::fileMarkerBeginSection, L"BGradient"); + + for (auto smoothedGradientIter = smoothedGradients.begin(); smoothedGradientIter != smoothedGradients.end(); smoothedGradientIter++) + { + const Matrix& smoothedGradient = *smoothedGradientIter; + fstream << smoothedGradient; + } + + fstream.PutMarker(FileMarker::fileMarkerEndSection, L"EGradient"); + + fstream.PutMarker(FileMarker::fileMarkerEndSection, L"ECKP"); + + // Ensuring that data is written + fstream.Flush(); + } + + renameOrDie(tempFileName, checkPointFileName); + } + } + + template + bool SGD::LoadCheckPointInfo(const size_t epochNumber, + /*out*/ size_t& totalSamplesSeen, + /*out*/ double& learnRatePerSample, + std::list>& smoothedGradients, + /*out*/ double& prevCriterion, + /*out*/ size_t& minibatchSize) + { + wstring checkPointFileName = GetCheckPointFileNameForEpoch(int(epochNumber)); + if (!fexists(checkPointFileName.c_str())) + { + fprintf(stderr, "Warning: checkpoint file is missing. learning parameters will be initialized from 0\n"); + return false; + } + + File fstream(checkPointFileName, + FileOptions::fileOptionsBinary | FileOptions::fileOptionsRead); + fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BCKP"); + + fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BLearnRate"); + fstream >> totalSamplesSeen >> learnRatePerSample >> prevCriterion; + fstream.GetMarker(FileMarker::fileMarkerEndSection, L"ELearnRate"); + + if (fstream.TryGetMarker(FileMarker::fileMarkerBeginSection, L"BMinibatchSize")) + { + fstream >> minibatchSize; + fstream.GetMarker(FileMarker::fileMarkerEndSection, L"EMinibatchSize"); + } + else + { + minibatchSize = m_mbSize[epochNumber]; + } + + fstream.GetMarker(FileMarker::fileMarkerBeginSection, L"BGradient"); + + for (auto smoothedGradientIter = smoothedGradients.begin(); smoothedGradientIter != smoothedGradients.end(); smoothedGradientIter++) + { + Matrix& smoothedGradient = *smoothedGradientIter; + fstream >> smoothedGradient; + } + fstream.GetMarker(FileMarker::fileMarkerEndSection, L"EGradient"); + + fstream.GetMarker(FileMarker::fileMarkerEndSection, L"ECKP"); + + return true; + } + + template + wstring SGD::GetCheckPointFileNameForEpoch(const int epoch) + { + return GetModelNameForEpoch(epoch) + L".ckp"; + } + + template + wstring SGD::GetModelNameForEpoch(const int epoch, bool bLastModel) + { + int epoch1Base = epoch + 1; + if (epoch1Base == m_maxEpochs || bLastModel) + { + return m_modelPath; + } + else + { + wstring w = msra::strfun::wstrprintf(L"%ls.%d", m_modelPath.c_str(), (int)epoch1Base); + return w; + } + + } + + // return -1 if nothing exists + template // TODO: needed? + int SGD::DetermineStartEpoch(const bool makeMode) + { + if (!makeMode) + { + // always start from scratch + return -1; + } + + int firstEpoch = -1; + + wstring curEpochFile = GetModelNameForEpoch(int(m_maxEpochs) - 1); + for (int e = int(m_maxEpochs) - 1; e >= -1; e--) + { + const wstring prevEpochFile = GetModelNameForEpoch(e - 1); + + if (msra::files::fuptodate(curEpochFile, prevEpochFile, false)) + { + firstEpoch = e + 1; + break; + } + else + { + curEpochFile = prevEpochFile; + } + } + if (firstEpoch == m_maxEpochs) + fprintf(stderr, "Final model exists: %ls\n", GetModelNameForEpoch(firstEpoch - 1).c_str()); + + return firstEpoch; + } + +#define EPSILON 1e-5 + + // this probes the automatic gradient computation with random inputs + template + bool SGD::GradientCheck(ComputationNetworkPtr net, + const std::vector & criterionNodes, + const std::list & learnableNodes, + int npos) + { + vector errMsgs; + + net->StartEvaluateMinibatchLoop(criterionNodes[npos]); + + // gradient checking + for (auto nodeIter = learnableNodes.begin(); nodeIter != learnableNodes.end(); nodeIter++) + { + ComputationNodePtr node = dynamic_pointer_cast>(*nodeIter); + char wstrtmp[2048]; + + for (size_t itry = 0; itry < min((size_t)50, node->Value().GetNumElements()); itry++) + { + /// no support to sparse matrix yet + int irow = (int) fmod(rand(), node->GetNumRows() - 1); + int icol = (int) fmod(rand(), node->GetNumCols() - 1); + irow = max(0, irow); + icol = max(0, icol); + + fprintf(stderr, "\n###### d%ls######\n", node->NodeName().c_str()); + + double eOrg = node->Value()(irow, icol); + node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); + + node->BumpEvalTimeStamp(); + + net->ForwardProp(criterionNodes[npos]); + net->Backprop(criterionNodes[npos]); + + if (node->Gradient().GetMatrixType() == MatrixType::SPARSE) + { + break; + } + + //double mbEvalCri = + //criterionNode should be a scalar + // TODO: why is this value not used? + criterionNodes[npos]->Get00Element(); + double eGradErr = node->Gradient()(irow, icol); + node->Gradient().TransferToDeviceIfNotThere(net->GetDeviceId(), true); + + double ePos = eOrg + EPSILON; + double eNeg = eOrg - EPSILON; + + node->Value()(irow, icol) = (ElemType)ePos; + node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); + + node->BumpEvalTimeStamp(); + net->ForwardProp(criterionNodes[npos]); + //criterionNode should be a scalar + + double mbEvalCriPos = criterionNodes[npos]->Get00Element(); // TODO: make Get00Element() a function of ComputationNodeBase + + node->Value()(irow, icol) = (ElemType)eNeg; + node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); + + node->BumpEvalTimeStamp(); + net->ForwardProp(criterionNodes[npos]); + + // criterionNode should be a scalar + double mbEvalCriNeg = criterionNodes[npos]->Get00Element(); + + // back to its original parameter value + node->Value()(irow, icol) = (ElemType)eOrg; + node->Value().TransferToDeviceIfNotThere(net->GetDeviceId(), true); + + // check if they are consistent + double eGradNum = ((mbEvalCriPos - mbEvalCriNeg) / (ePos - eNeg)); + double threshold = pow(10.0, + max(0.0, + ceil(log10(min(fabs(eGradErr), + fabs(eGradNum))))) - (int)m_gradientCheckSigDigit); + double diff = fabs(eGradErr - eGradNum); + bool wrong = (std::isnan(diff) || diff > threshold); + if (wrong) + { + fprintf(stderr, "\nd%ls Numeric gradient = %e, Error BP gradient = %e\n", + node->NodeName().c_str(), eGradNum, eGradErr); + sprintf(wstrtmp, "\nd%ls Numeric gradient = %e, Error BP gradient = %e\n", + node->NodeName().c_str(), eGradNum, eGradErr); + errMsgs.push_back(wstrtmp); + } + } + } + + return errMsgs.size() == 0; + } + + template class SGD; + template class SGD; + + // ======================================================================= + // class SGDParams + // ======================================================================= + + static AdaptationRegType ParseAdaptationRegType(const wstring & s) + { + if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none")) + return AdaptationRegType::None; + else if (!_wcsicmp(s.c_str(), L"kl") || !_wcsicmp(s.c_str(), L"klReg")) + return AdaptationRegType::KL; + else + InvalidArgument("ParseAdaptationRegType: Invalid Adaptation Regularization Type. Valid values are (none | kl)"); + } + + static GradientsUpdateType ParseGradUpdateType(const wstring & s) + { + if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none") || !_wcsicmp(s.c_str(), L"normal") || !_wcsicmp(s.c_str(), L"simple")) + return GradientsUpdateType::None; + else if (!_wcsicmp(s.c_str(), L"adagrad")) + return GradientsUpdateType::AdaGrad; + else if (!_wcsicmp(s.c_str(), L"rmsProp")) + return GradientsUpdateType::RmsProp; + else if (!_wcsicmp(s.c_str(), L"fsAdagrad")) + return GradientsUpdateType::FSAdaGrad; + else + InvalidArgument("ParseGradUpdateType: Invalid Gradient Updating Type. Valid values are (none | adagrad | rmsProp | fsAdagrad )"); + } + + static ParallelizationMethod ParseParallelizationMethod(const wstring & s) + { + if (!_wcsicmp(s.c_str(), L"") || !_wcsicmp(s.c_str(), L"none")) + return ParallelizationMethod::None; + else if (!_wcsicmp(s.c_str(), L"DataParallelSGD")) + return ParallelizationMethod::DataParallelSGD; + else if (!_wcsicmp(s.c_str(), L"ModelAveragingSGD")) + return ParallelizationMethod::ModelAveragingSGD; + else + InvalidArgument("ParseParallelizationMethod: Invalid Parallelization Method. Valid values are (none | dataParallelSGD | modelAveragingSGD)"); + } + + static LearningRateSearchAlgorithm ParseLearningRateSearchType(const wstring & s) + { + // TODO: why allow so many variants? + if (!_wcsicmp(s.c_str(), L"false") || !_wcsicmp(s.c_str(), L"none")) + return LearningRateSearchAlgorithm::None; + else if (!_wcsicmp(s.c_str(), L"searchBeforeEpoch") || !_wcsicmp(s.c_str(), L"beforeEpoch"/*legacy, deprecated*/) || !_wcsicmp(s.c_str(), L"before"/*legacy, deprecated*/)) + return LearningRateSearchAlgorithm::SearchBeforeEpoch; + else if (!_wcsicmp(s.c_str(), L"adjustAfterEpoch") || !_wcsicmp(s.c_str(), L"afterEpoch"/*legacy, deprecated*/) || !_wcsicmp(s.c_str(), L"after"/*legacy, deprecated*/)) + return LearningRateSearchAlgorithm::AdjustAfterEpoch; + else + InvalidArgument("autoAdjustLR: Invalid learning rate search type. Valid values are (none | searchBeforeEpoch | adjustAfterEpoch)"); + } + + template + SGDParams::SGDParams(const ConfigRecordType& configSGD, size_t sizeofElemType) + { + floatargvector learningRatesPerMB = configSGD(L"learningRatesPerMB", ConfigRecordType::Array(floatargvector())); + + floatargvector learningRatesPerSample = configSGD(L"learningRatesPerSample", ConfigRecordType::Array(floatargvector())); + + string executionEngineValue = configSGD(L"executionEngine", "synchronous"); + + // AutoAdjust Parameters + const ConfigRecordType & configAALR(configSGD(L"AutoAdjust", ConfigRecordType::Record())); + m_autoLearnRateSearchType = ParseLearningRateSearchType(configAALR(L"autoAdjustLR", L"None")); + m_reduceLearnRateIfImproveLessThan = configAALR(L"reduceLearnRateIfImproveLessThan", 0.0); + m_continueReduce = configAALR(L"continueReduce", false); + m_learnRateAdjustInterval = configAALR(L"learnRateAdjustInterval", (size_t)1); + m_learnRateAdjustInterval = max((size_t)1, m_learnRateAdjustInterval); //minimum interval is 1 epoch + m_learnRateDecreaseFactor = configAALR(L"learnRateDecreaseFactor", 0.618); + m_increaseLearnRateIfImproveMoreThan = configAALR(L"increaseLearnRateIfImproveMoreThan", numeric_limits::infinity()); + m_learnRateIncreaseFactor = configAALR(L"learnRateIncreaseFactor", 1.382); + + // AutoAdjust Auto Adjust Minibatch Parameters + m_autoAdjustMinibatch = configAALR(L"autoAdjustMinibatch", false); + m_minibatchSizeTuningFrequency = configAALR(L"minibatchSizeTuningFrequency", (size_t)1); + m_minibatchSizeTuningMax = configAALR(L"minibatchSizeTuningMax", (size_t)1048576); + m_minibatchSearchCriterionErrorMargin = configAALR(L"minibatchSearchCriterionErrorMargin", (size_t)1); + + // the number of minibatches used to search + // the learning rate. It’s typically set to 10-20% of + // the total minibatches in an epoch. + m_numMiniBatch4LRSearch = configAALR(L"numMiniBatch4LRSearch", ConfigRecordType::Array(intargvector(vector{ 500 }))); + + m_numPrevLearnRates = configAALR(L"numPrevLearnRates", (size_t)5); + m_numBestSearchEpoch = configAALR(L"numBestSearchEpoch", (size_t)1); + m_loadBestModel = configAALR(L"loadBestModel", true); + m_useCVSetControlLRIfCVExists = configAALR(L"UseCVSetControlLRIfCVExists", true); + m_useEvalCriterionControlLR = configAALR(L"UseEvalCriterionControlLR", false); + + // TODO: mbSize and truncated should be specified differently for truncated BPTT: + // mbSize = total number of samples after which a model update should happen + // truncated = truncation length + m_mbSize = configSGD(L"minibatchSize", ConfigRecordType::Array(intargvector(vector{ 256 }))); + m_truncated = configSGD(L"truncated", false); + m_maxSamplesInRAM = configSGD(L"maxSamplesInRAM", (size_t)SIZE_MAX); + m_numSubminiBatches = configSGD(L"numSubminibatches", (size_t)1); + + // the number of samples in each epoch (0 means, use all the samples in each epoch). + m_epochSize = configSGD(L"epochSize", (size_t)0); + // the number of samples in each epoch (0 means, use all the samples in each epoch). + if (m_epochSize == 0) + m_epochSize = requestDataSize; + m_maxComputedEpochSize = m_epochSize; + + // the total number of epochs to run. + m_maxEpochs = configSGD(L"maxEpochs"); + + // Note: Momentum is best specified as a MB-size agnostic fashion. + // Because momentum per sample is a number very close to 1, it is more handy to use a logarithmic specification. + // We use 'momentumAsTimeConstant' to specify the time constant of the low-pass filter that momentum really is. + // To convert a typical per-MB momentum value of 'm' used with a MB size of 'N', use momentumAsTimeConstant = -N/ln(m). + // For the common configuration of momentum 0.9 at MB size of 256, that is momentumAsTimeConstant = 2429.8. + floatargvector momentumPerMB = configSGD(L"momentumPerMB", ConfigRecordType::Array(floatargvector())); + floatargvector momentumPerSample = configSGD(L"momentumPerSample", ConfigRecordType::Array(floatargvector())); + floatargvector momentumAsTimeConstant = configSGD(L"momentumAsTimeConstant", ConfigRecordType::Array(floatargvector())); + bool useNesterovMomentum = configSGD(L"useNAG", false); + + + m_maxTempMemSizeInSamplesForCNN = configSGD(L"maxTempMemSizeInSamplesForCNN", (size_t)0); + + m_traceLevel = configSGD(L"traceLevel", (int)0); + m_numMBsToShowResult = configSGD(L"numMBsToShowResult", (size_t)10); + m_numMBsToCUDAProfile = configSGD(L"numMBsToCUDAProfile", (size_t)0); + + m_gradientClippingWithTruncation = configSGD(L"gradientClippingWithTruncation", true); + m_clippingThresholdPerSample = configSGD(L"clippingThresholdPerSample", numeric_limits::infinity()); + + // sequence-training parameters + m_hSmoothingWeight = configSGD(L"hSmoothingWeight", 0.95); + m_frameDropThresh = configSGD(L"frameDropThresh", 1e-10); + m_doReferenceAlign = configSGD(L"doReferenceAlign", false); + m_seqGammarCalcUsesMBR = configSGD(L"seqGammarUsesMBR", false); + m_seqGammarCalcAMF = configSGD(L"seqGammarAMF", 14.0); + m_seqGammarCalcLMF = configSGD(L"seqGammarLMF", 14.0); + m_seqGammarCalcbMMIFactor = configSGD(L"seqGammarBMMIFactor", 0.0); + m_seqGammarCalcWP = configSGD(L"seqGammarWordPen", 0.0); + + m_dropoutRates = configSGD(L"dropoutRate", ConfigRecordType::Array(floatargvector(vector{ 0.0f }))); + + GradientsUpdateType gradUpdateType = ParseGradUpdateType(configSGD(L"gradUpdateType", L"None")); + double gaussianNoiseInjecStd = configSGD(L"gaussianNoiseInjectStd", 0.0); + m_gradType.mType = gradUpdateType; + m_gradType.mGaussianNoiseInjectStd = (float) gaussianNoiseInjecStd; + + // extract RMSProp parameters from config, if they exist. Default to reasonable values. + m_rpi.dec = configSGD(L"rms_wgt_dec", 0.75); + m_rpi.inc = configSGD(L"rms_wgt_inc", 1.2); + m_rpi.min = configSGD(L"rms_wgt_min", 0.1); + m_rpi.max = configSGD(L"rms_wgt_max", 10.0); + m_rpi.gamma = configSGD(L"rms_gamma", 0.99); + + m_needAveMultiplier = configSGD(L"normWithAveMultiplier", true); + m_L2RegWeight = configSGD(L"L2RegWeight", 0.0); + m_L1RegWeight = configSGD(L"L1RegWeight", 0.0); + + /// for backward support. future setup should use gradUpdateType=AdaGrad, instead of + /// useAdagrad=true + bool useAdagrad = configSGD(L"useAdagrad", false); + if (useAdagrad) + { + gradUpdateType = GradientsUpdateType::AdaGrad; + m_gradType.mType = gradUpdateType; + } + + m_adaptationRegType = ParseAdaptationRegType(configSGD(L"adaptationRegType", L"None")); + m_adaptationRegWeight = configSGD(L"adaptationRegWeight", 0.0); + + /// gradient check setup + m_doGradientCheck = configSGD(L"gradientcheck", false); + m_gradientCheckSigDigit = configSGD(L"sigFigs", 6.0); // TODO: why is this a double? + + if (m_doGradientCheck && sizeofElemType != sizeof(double)) + { + LogicError("Gradient check needs to use precision = 'double'."); + } + + m_useAllDataForPreComputedNode = configSGD(L"UseAllDataForPreComputedNode", true); + + // consistency checks + for (size_t i = 0; i < m_mbSize.size(); i++) + { + if (m_epochSize != requestDataSize && m_epochSize < m_mbSize[i]) + { + InvalidArgument("epoch size must be larger than mbsize."); + } + } + + if (m_autoLearnRateSearchType == LearningRateSearchAlgorithm::None && + (learningRatesPerSample.size() == 0 && learningRatesPerMB.size() == 0)) + { + InvalidArgument("If autoLearnRateSearchType is false you must specify the learningRatesPerSample or learningRatesPerMB parameter."); + } + + if (learningRatesPerSample.size() > 0 && learningRatesPerMB.size() > 0) + { + InvalidArgument("You specified both learningRatesPerSample and learningRatesPerMB. Please comment out one of them."); + } + + if (learningRatesPerSample.size() > 0) + { + m_learningRatesParam = learningRatesPerSample; + m_learningRatesSpecifiedForMBSize = intargvector(L"1"); + } + else if (learningRatesPerMB.size() > 0) // this actually means per specified minibatch size + { + m_learningRatesParam = learningRatesPerMB; + m_learningRatesSpecifiedForMBSize = m_mbSize; + } + + if ((int)(momentumPerSample.size() > 0) + (int)(momentumPerMB.size() > 0) + (int)(momentumAsTimeConstant.size() > 0) > 1) + { + InvalidArgument("You specified more than one of momentumPerSample, momentumPerMB, and momentumAsTimeConstant. Please only specify one."); + } + + if (momentumPerSample.size() > 0) // note: noone would ever use this; use momentumAsTimeConstant instead + { + m_momentumParam = momentumPerSample; + m_momentumSpecifiedForMBSize = intargvector(L"1"); + } + else if (momentumAsTimeConstant.size() > 0) + { + vector momentumPerSampleVec; + for (int i = 0; i < momentumAsTimeConstant.size(); i++) + { + double momTC = momentumAsTimeConstant[i]; + double momPS = momTC == 0.0 ? 0 : exp(-1.0 / momTC); + momentumPerSampleVec.push_back((float)momPS); + } + m_momentumParam = momentumPerSampleVec; + m_momentumSpecifiedForMBSize = intargvector(L"1"); + } + else if (momentumPerMB.size() > 0) + { + m_momentumParam = momentumPerMB; + m_momentumSpecifiedForMBSize = m_mbSize; + } + else // default: momentumPerMB = 0.9 per MB + { + m_momentumParam = floatargvector(L"0.9"); + m_momentumSpecifiedForMBSize = m_mbSize; + } + m_useNesterovMomentum = useNesterovMomentum; + + for (int i = 0; i < m_momentumParam.size(); i++) + { + if (m_momentumParam[i] >= 1.0 || m_momentumParam[i] < 0.0) + { + InvalidArgument("Momentum parameter must be in [0, 1)."); + } + } + + if (m_learnRateDecreaseFactor > 1 || m_learnRateIncreaseFactor < 1) + { + InvalidArgument("learnRateIncreaseFactor must be >= 1 and learnRateDecreaseFactor must be <= 1."); + } + + for (size_t i = 0; i < m_dropoutRates.size(); i++) + { + if (m_dropoutRates[i] >= 1 || m_dropoutRates[i] < 0) + { + InvalidArgument("dropoutRate must be >= 0 and < 1."); + } + } + + if (m_adaptationRegWeight > 1 || m_adaptationRegWeight < 0) + InvalidArgument("adaptationRegWeight must be in [0 1]"); + + m_minLearnRate = 1e-9f; + + m_needAdaptRegularization = false; + + // BUGBUG: these are not passed to Init() + m_doUnitTest = configSGD(L"unitTest", false); + + // parallel training + m_parallelizationMethod = ParallelizationMethod::None; + m_numGradientBits = 32; + m_zeroThresholdFor1Bit = true; + m_bufferedAsyncGradientAggregation = false; + m_enableDistributedMBReading = false; + m_parallelizationStartEpochNum = 0; + m_nFramesBetweenMASync = 40000; // default 40k frames + + if ((g_mpi != nullptr) && configSGD.Exists(L"ParallelTrain")) + { + const ConfigRecordType & configParallelTrain(configSGD(L"ParallelTrain", ConfigRecordType::Record())); + m_parallelizationMethod = ParseParallelizationMethod(configParallelTrain(L"parallelizationMethod", L"none")); + m_parallelizationStartEpochNum = configParallelTrain(L"parallelizationStartEpoch", (int)1) - 1; // Epoch numbers internally are 0 based + m_enableDistributedMBReading = configParallelTrain(L"distributedMBReading", false); + m_syncStatsTrace = configParallelTrain(L"syncPerfStats", (int)0); + + if (configParallelTrain.Exists(L"DataParallelSGD")) + { + const ConfigRecordType & configDataParallelSGD(configParallelTrain(L"DataParallelSGD", ConfigRecordType::Record())); + size_t defaultGradientBits = 8 * sizeofElemType; + m_numGradientBits = configDataParallelSGD(L"gradientBits", defaultGradientBits); + m_zeroThresholdFor1Bit = configDataParallelSGD(L"useZeroThresholdFor1BitQuantization", true); + m_bufferedAsyncGradientAggregation = configDataParallelSGD(L"useBufferedAsyncGradientAggregation", false); + if ((m_numGradientBits < 1) || (m_numGradientBits > (8 * sizeofElemType))) + { + InvalidArgument("gradientBits must be in the range [1, 32] when using precision=float and in range [1, 64] when using precision=double!"); + } + } + + if (configParallelTrain.Exists(L"ModelAveragingSGD") ) + { + const ConfigRecordType & configMASGD(configParallelTrain(L"ModelAveragingSGD", ConfigRecordType::Record())); + m_nFramesBetweenMASync = configMASGD(L"syncFrequencyInFrames", (size_t)40000); + } + } + } + + static size_t GetSizeOfPrecision(const ScriptableObjects::IConfigRecordPtr configp) + { + wstring precision = configp->Get(L"precision"); + if (precision == L"float") + return sizeof(float); + else if (precision == L"double") + return sizeof(double); + else + RuntimeError("invalid value '%ls' for 'precision', must be 'float' or 'double'", precision.c_str()); + } + + SGDParams::SGDParams(const ScriptableObjects::IConfigRecordPtr configp) : + SGDParams(*configp, GetSizeOfPrecision(configp)) + { } + + // register SGD<> with the ScriptableObject system + ScriptableObjects::ConfigurableRuntimeTypeRegister::AddFloatDouble, SGD> registerSGDOptimizer(L"SGDOptimizer"); + +}}} diff --git a/Source/SGDLib/SGD.h b/Source/SGDLib/SGD.h index a014ec1d2b13..b499c54ef502 100644 --- a/Source/SGDLib/SGD.h +++ b/Source/SGDLib/SGD.h @@ -1,515 +1,515 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#pragma once - -#include "Basics.h" -#include "ComputationNetwork.h" -#include "NonlinearityNodes.h" // for DropoutNode -#include "CompositeComputationNodes.h" // for PrecomputeNode -#include "SimpleEvaluator.h" -#include "DataReader.h" -#include "IComputationNetBuilder.h" -#include "ScriptableObjects.h" -#include -#include -#include -#include "fileutil.h" -#include "Config.h" -#include -#include -#include "Profiler.h" - -using namespace std; // ugh! TODO: get rid of this from .h files!!! - -namespace Microsoft { namespace MSR { namespace CNTK { - -enum class LearningRateSearchAlgorithm : int -{ - None, - AdjustAfterEpoch, - SearchBeforeEpoch -}; - -enum class AdaptationRegType : int -{ - None, - KL -}; - -enum class GradientsUpdateType : int -{ - None, - AdaGrad, - RmsProp, - FSAdaGrad -}; - -// TODO: While currently combining these methods is not supported, -// these are not mutually exclusive and we can/should support combinations of these -// in the future -enum class ParallelizationMethod : int -{ - None = 0, - DataParallelSGD = 1, - ModelAveragingSGD = (1 << 1), - ModelParallelSGD = (1 << 2), // Currently unsupported -}; - -// configuration parameters associated with RMSProp learning algorithm -struct RMSPropInfo -{ - double gamma; - double inc; - double dec; - double max; - double min; - - RMSPropInfo() - { - gamma = 0.99; - inc = 1.2; - dec = 0.75; - max = 10.0; - min = 0.1; - } -}; - -struct GradientUpdateInfo -{ - GradientsUpdateType mType; - float mGaussianNoiseInjectStd; - - GradientUpdateInfo() - { - mType = GradientsUpdateType::AdaGrad; - mGaussianNoiseInjectStd = 0.0075f; - } -}; - -// --------------------------------------------------------------------------- -// SGDParams -- parameters for SGD -// -// TODO: This should keep everything that is configured by the config. -// Currently it does not store which matrices are used. -// --------------------------------------------------------------------------- - -struct SGDParams : public ScriptableObjects::Object -{ - template // (needed for default value of m_gradientBits) - SGDParams(const ConfigRecord& configSGD, size_t sizeofElemType); - - SGDParams(const ScriptableObjects::IConfigRecordPtr configp); - - //SGDParams(SGDParams&&) = default; // (does not compile in VS 2013; not critical) - -protected: - // learning rate per sample provided outside - floatargvector m_learningRatesParam; - intargvector m_learningRatesSpecifiedForMBSize; // 1 for per sample, m_mbSize[] for per MB - floatargvector m_momentumParam; - intargvector m_momentumSpecifiedForMBSize; - bool m_useNesterovMomentum; - - // Determine the MB size used for mapping a given learning-rate or momentum parameter to a per-sample value. - // MB size is the number of samples across all time steps and parallel sequences. - // This function exists to post-fix a design bug in SGD: - // In the case of BPTT, the 'minibatchSize' parameter given to the SGD module really means the truncation size, - // while the MB size to be used is (truncation size * number of parallel sequences). - // SGD also does not know #parallel sequences upfront. - size_t FixUpEffectiveMBSize(size_t specifiedMBSize, size_t numParallelSequences) const - { - // remedy the bug that truncation size is incorrectly passed as MB size - if (m_truncated && specifiedMBSize > 1) // currently only happens in this mode - specifiedMBSize *= numParallelSequences; // assume 'specifiedMBSize' refers to truncation size - // end bug post-fix - // TODO: This ^^ should go away once SGD gets fixed to take the truncation size as a parameter. - - return specifiedMBSize; - } - - // helpers to convert learning rates to per-sample values used in the actual algorithms - // 'numParallelSequences' must be specified because of the definitional MB-size bug in SGD mentioned above, and should go away once that is sorted out. - double GetLearningRatePerSample(size_t epoch/*BUGBUG workaround:*/, size_t numParallelSequences) const - { - return m_learningRatesParam[epoch] / FixUpEffectiveMBSize(m_learningRatesSpecifiedForMBSize[epoch], numParallelSequences); - } - double GetMomentumPerSample(size_t epoch/*BUGBUG workaround:*/, size_t numParallelSequences) const - { - return pow(m_momentumParam[epoch], 1.0 / FixUpEffectiveMBSize(m_momentumSpecifiedForMBSize[epoch], numParallelSequences)); - } - - // only true when the user specify LearningRatePerMB and the number of parallel utterances in Reader > 1 - //bool m_needToNormalizeLRByParallUtterance; // TODO: should go away - //bool m_needToNormalizeMomentumByParallUtterance; - - intargvector m_mbSize; - bool m_truncated; // do BPTT - // BUGBUG: The 'Truncated' option is duplicated in the reader and must be set to the same there (e.g. by defining in the config on an outer enclosing level, like current samples). - // We really should only read it in SGD and pass it ourselves on to the Reader, instead of it being a Reader parameter. - // BUGBUG: If m_truncated, then m_mbSize is interpreted as truncation length; the actual MB size is a combination of that and the #parallel sequences specified in the reader. - // TODO: do not specify 'Truncated' but 'TruncatedLength', set m_truncated so given, and let m_mbSize control how many #parallel sequences the reader is allowed to pack into an MB. - size_t m_maxSamplesInRAM; - // This is related with subminibatch implementation - // maxSamplesInRAM denotes how many samples we used in forward-backward on net. - // Due to the GPU memory limitations, it is sometime not possible to hold the m_mbSize in RAM. - // To mitigate this issue, we adopt the sub-minibatch implementation, where - // each m_mbSize[epoch] is divided by a few sub-minibatch of which size will be no more than m_maxSamplesInRAM - // a forward-backward is performed for each sub-minibathch; a model update is performed after each minibatch - size_t m_numSubminiBatches; - // alternative method to specify how to split minibatches into subminibatches - // default is 1, which means no subminibatch is used - // if m_maxTempMemSizeInSamples = SIZE_MAX (which means users do not specify the option) and m_numSubminiBatches > 1 - // we divide one minibatch to m_numSubminiBatches subMinibatches - - // the number of samples in each epoch (0 means, use all the samples in each epoch). - size_t m_epochSize; - size_t m_maxComputedEpochSize; - - // the total number of epochs to run. - size_t m_maxEpochs; - - bool m_gradientClippingWithTruncation; - double m_clippingThresholdPerSample; - - intargvector m_numMiniBatch4LRSearch; - size_t m_numBestSearchEpoch; - - LearningRateSearchAlgorithm m_autoLearnRateSearchType; - - AdaptationRegType m_adaptationRegType; - double m_adaptationRegWeight; - bool m_needAdaptRegularization; - - bool m_loadBestModel; - double m_reduceLearnRateIfImproveLessThan; - bool m_continueReduce; - - // determine after how many epochs the learning rate should be auto adjusted. - size_t m_learnRateAdjustInterval; - - bool m_useCVSetControlLRIfCVExists; - bool m_useEvalCriterionControlLR; - - double m_increaseLearnRateIfImproveMoreThan; - double m_learnRateIncreaseFactor; - double m_learnRateDecreaseFactor; - bool m_autoAdjustMinibatch; - size_t m_minibatchSearchCriterionErrorMargin; - size_t m_minibatchSizeTuningFrequency; - size_t m_minibatchSizeTuningMax; - - floatargvector m_dropoutRates; - size_t m_maxTempMemSizeInSamplesForCNN; - - int m_traceLevel; - - size_t m_numPrevLearnRates; - - double m_minLearnRate; - - GradientUpdateInfo m_gradType; - RMSPropInfo m_rpi; - - int m_numMBsToShowResult; - int m_numMBsToCUDAProfile; - - bool m_doGradientCheck; - double m_gradientCheckSigDigit; - - bool m_doUnitTest; - - bool m_useAllDataForPreComputedNode; - - // Parallel training - ParallelizationMethod m_parallelizationMethod; - bool m_enableDistributedMBReading; - int m_parallelizationStartEpochNum; - - // decide if/how often we measure and show sync performance stats (seconds spend on sync, seconds since last sync etc.) ? - // 0: No sync perfomance stats - // 1: Show stats on every sync - // n > 1: Show stats after every n sync - int m_syncStatsTrace; - - // Data parallel SGD training parameters - int m_numGradientBits; - bool m_bufferedAsyncGradientAggregation; - bool m_zeroThresholdFor1Bit; - - // Parallel training related with MA - size_t m_nFramesBetweenMASync; - - bool m_needAveMultiplier; - double m_L2RegWeight; - double m_L1RegWeight; - - //sequence training - double m_hSmoothingWeight; - double m_frameDropThresh; - bool m_doReferenceAlign; - double m_seqGammarCalcAMF; - double m_seqGammarCalcLMF; - double m_seqGammarCalcWP; - double m_seqGammarCalcbMMIFactor; - bool m_seqGammarCalcUsesMBR; -}; - -template class IDistGradAggregator; - -// ----------------------------------------------------------------------- -// class SGD -// ----------------------------------------------------------------------- - -// TODO: make this independent of ElemType. Then these repeated dynamic_pointer_casts will go away -// TODO: why is this a class, and not just a procedure? Then we wouldn't have to include the massive header -template -class SGD : public SGDParams -{ -protected: - typedef shared_ptr> ComputationNodePtr; - typedef ClassBasedCrossEntropyWithSoftmaxNode* ClassBasedCrossEntropyWithSoftmaxNodePtr; - -public: - // constructor from old CNTK config. This is a function template that is also used to get the config from Scripting. - template - SGD(const ConfigRecordType & configSGD) : - SGDParams(configSGD, sizeof(ElemType)), - // TODO: The next few do not belong into SGD any more than the network or reader we operate on. Either move network and reader in here, or move these out. - m_modelPath((const wstring &)configSGD(L"modelPath")), - m_keepCheckPointFiles(configSGD(L"keepCheckPointFiles", false)), - //m_validateAfterModelReloading(configSGD(L"validateAfterModelReloading", true)), - m_trainCriterionNodeName((const wstring &)configSGD(L"trainCriterionNodeName", L"")), - m_evalCriterionNodeName((const wstring &)configSGD(L"evalCriterionNodeName", L"")), - m_prevChosenMinibatchSize(0), - m_lastFinishedEpochTrainLoss(0.0), - m_distGradAgg(nullptr), - m_gradHeader(nullptr) - { - msra::files::make_intermediate_dirs(m_modelPath); - } - // note: This must be in the header, as we cannot properly specialize this constructor in the CPP to make sure all versions are generated. - - // constructor from Scripting - SGD(const ScriptableObjects::IConfigRecordPtr configp) : - SGD(*configp) - { } - - void Train(function createNetworkFn, DEVICEID_TYPE deviceId, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader, - const bool makeMode = true); - void Adapt(wstring origModelFileName, wstring refNodeName, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader, - const DEVICEID_TYPE deviceID, const bool makeMode = true); -#if 0 - // TODO: per discussion with Dong Yu, Guoguo Chen, and Yu Zhang, this function can be removed. - void SequenceTrain(IComputationNetBuilder* netBuilder, wstring origModelFileName, - IDataReader* trainSetDataReader, IDataReader* validationSetDataReader, - const DEVICEID_TYPE deviceID, const bool makeMode = true); -#endif - -protected: - std::vector & GetTrainCriterionNodes(ComputationNetworkPtr net); - std::vector & GetEvalCriterionNodes(ComputationNetworkPtr net); - - void TrainOrAdaptModel(int startEpoch, ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - ComputationNodeBasePtr refNode, - IDataReader* trainSetDataReader, - IDataReader* validationSetDataReader); - -protected: - // return true if precomputation is executed. - bool PreCompute(ComputationNetworkPtr net, - IDataReader* trainSetDataReader, - std::vector & featureNodes, - std::vector & labelNodes, - std::map*>* inputMatrices); - - // return a reasonable initial learning rate based on the initial mbsize - double SearchForBestLearnRate(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, const int epochNumber, - const double curLearnRate, - IDataReader* trainSetDataReader, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const bool learnRateInitialized, - const double largestPrevLearnRatePerSample); - - void TrainOneMiniEpochAndReloadModel(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, const int epochNumber, - const size_t epochSize, IDataReader* trainSetDataReader, - const double learnRatePerSample, - const size_t minibatchSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - /*out*/ double& epochCriterion, - /*out*/ std::vector& epochEvalErrors, - /*out*/ size_t& totalSamplesSeen, - std::string prefixMsg = ""); - - size_t AdaptiveMinibatchSizing(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t numFramesToUseInSearch, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - const size_t initialMinibatchSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const double learningRateAdjustmentFactor); - - // uses a small percentage of training data of minibatch to - // speculatively train with various MB sizes; then picks the best - size_t SearchForBestMinibatchSize(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t numFramesToUseInSearch, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - const size_t minMinibatchSize, const size_t maxMinibatchSize); - - // Attemps to compute the error signal for the whole utterance, which will - // be fed to the neural network as features. Currently it is a workaround - // for the two-forward-pass sequence and ctc training, which allows - // processing more utterances at the same time. Only used in Kaldi2Reader. - // TODO: move the two-forward-pass support out of the reader. - void AttemptUtteranceDerivativeFeatures(ComputationNetworkPtr net, - IDataReader* trainSetDataReader, - const std::vector & featureNodes, - std::map*>* inputMatrices); - - size_t TrainOneEpoch(ComputationNetworkPtr net, - ComputationNetworkPtr refNet, - const ComputationNodeBasePtr& refNode, - const int epochNumber, - const size_t epochSize, - IDataReader* trainSetDataReader, - const double learnRatePerSample, - size_t tunedMBSize, - const std::vector & featureNodes, - const std::vector & labelNodes, - const std::vector & criterionNodes, - const std::vector & evaluationNodes, - std::map*>* inputMatrices, - const std::list & learnableNodes, - std::list>& smoothedGradients, - /*out*/ double& epochCriterion, - /*out*/ std::vector& epochEvalErrors, - /*out*/ size_t& totalSamplesSeen, - std::string prefixMsg = ""); - - void InitDistGradAgg(int numEvalNodes, int traceLevel); - - bool ModelAveragingProcessing(size_t nSamplesSinceLastSync, const std::list& learnableNodes, size_t& nProcessedFrames, - float& SecondsSinceLastSyncFinished, float& SecondsSpentOnSync); - - size_t ModelAveragingSync(int nSamplesSinceLastSync, const std::list& learnableNodes); - -public: - // UpdateWeightsS - static version of UpdateWeights() - static void UpdateWeightsS(const SGD* sgd, Matrix& functionValues, - Matrix& gradientValues, - Matrix& smoothedGradient, - const double learnRatePerSample, - const double momentumPerSample, - size_t actualMBSize, - const double L2RegWeight, - const double L1RegWeight, - const bool needAveMultiplier, - const bool useNesterovMomentum - ); - -protected: - // UpdateWeights - update the weights in - void UpdateWeights(const ComputationNodeBasePtr& node, - Matrix& smoothedGradient, - const double learnRatePerSample, - const double momentumPerSample, - const size_t actualMBSize, - const double L2RegWeight, const double L1RegWeight, - const bool needAveMultiplier, - const bool useNesterovMomentum) const; - - void ClipGradient(Matrix& gradient, const size_t actualMBSize) const; - - void SaveCheckPointInfo(const size_t epoch, const size_t totalSamplesSeen, - const double learnRatePerSample, - const std::list>& smoothedGradients, - const double prevCriterion, - const size_t minibatchSize); - - bool LoadCheckPointInfo(const size_t epochNumber, - /*out*/ size_t& totalSamplesSeen, - /*out*/ double& learnRatePerSample, - std::list>& smoothedGradients, - /*out*/ double& prevCriterion, - /*out*/ size_t& minibatchSize); - - wstring GetCheckPointFileNameForEpoch(const int epoch); - wstring GetModelNameForEpoch(const int epoch, bool bLastModel = false); - - // return -1 if nothing exists - int DetermineStartEpoch(const bool makeMode); - - GradientsUpdateType GradUpdateType() const { return m_gradType.mType; } - double GradientUpdateNoiseStd() const { return m_gradType.mGaussianNoiseInjectStd; } - -public: - -#define EPSILON 1e-5 - - bool GradientCheck(ComputationNetworkPtr net, - const std::vector & criterionNodes, - const std::list & learnableNodes, - int npos); - -protected: - wstring m_modelPath; - bool m_keepCheckPointFiles; - //bool m_validateAfterModelReloading; // TODO: remove this. Why would one not validate a model? - - wstring m_trainCriterionNodeName; - wstring m_evalCriterionNodeName; - - size_t m_prevChosenMinibatchSize; - double m_lastFinishedEpochTrainLoss; - - IDistGradAggregator* m_distGradAgg; - struct DistGradHeader* m_gradHeader; - -private: - int SGDTrace(FILE *__restrict __stream, const char *__restrict __format, ...); -}; - -}}} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#pragma once + +#include "Basics.h" +#include "ComputationNetwork.h" +#include "NonlinearityNodes.h" // for DropoutNode +#include "CompositeComputationNodes.h" // for PrecomputeNode +#include "SimpleEvaluator.h" +#include "DataReader.h" +#include "IComputationNetBuilder.h" +#include "ScriptableObjects.h" +#include +#include +#include +#include "fileutil.h" +#include "Config.h" +#include +#include +#include "Profiler.h" + +using namespace std; // ugh! TODO: get rid of this from .h files!!! + +namespace Microsoft { namespace MSR { namespace CNTK { + +enum class LearningRateSearchAlgorithm : int +{ + None, + AdjustAfterEpoch, + SearchBeforeEpoch +}; + +enum class AdaptationRegType : int +{ + None, + KL +}; + +enum class GradientsUpdateType : int +{ + None, + AdaGrad, + RmsProp, + FSAdaGrad +}; + +// TODO: While currently combining these methods is not supported, +// these are not mutually exclusive and we can/should support combinations of these +// in the future +enum class ParallelizationMethod : int +{ + None = 0, + DataParallelSGD = 1, + ModelAveragingSGD = (1 << 1), + ModelParallelSGD = (1 << 2), // Currently unsupported +}; + +// configuration parameters associated with RMSProp learning algorithm +struct RMSPropInfo +{ + double gamma; + double inc; + double dec; + double max; + double min; + + RMSPropInfo() + { + gamma = 0.99; + inc = 1.2; + dec = 0.75; + max = 10.0; + min = 0.1; + } +}; + +struct GradientUpdateInfo +{ + GradientsUpdateType mType; + float mGaussianNoiseInjectStd; + + GradientUpdateInfo() + { + mType = GradientsUpdateType::AdaGrad; + mGaussianNoiseInjectStd = 0.0075f; + } +}; + +// --------------------------------------------------------------------------- +// SGDParams -- parameters for SGD +// +// TODO: This should keep everything that is configured by the config. +// Currently it does not store which matrices are used. +// --------------------------------------------------------------------------- + +struct SGDParams : public ScriptableObjects::Object +{ + template // (needed for default value of m_gradientBits) + SGDParams(const ConfigRecord& configSGD, size_t sizeofElemType); + + SGDParams(const ScriptableObjects::IConfigRecordPtr configp); + + //SGDParams(SGDParams&&) = default; // (does not compile in VS 2013; not critical) + +protected: + // learning rate per sample provided outside + floatargvector m_learningRatesParam; + intargvector m_learningRatesSpecifiedForMBSize; // 1 for per sample, m_mbSize[] for per MB + floatargvector m_momentumParam; + intargvector m_momentumSpecifiedForMBSize; + bool m_useNesterovMomentum; + + // Determine the MB size used for mapping a given learning-rate or momentum parameter to a per-sample value. + // MB size is the number of samples across all time steps and parallel sequences. + // This function exists to post-fix a design bug in SGD: + // In the case of BPTT, the 'minibatchSize' parameter given to the SGD module really means the truncation size, + // while the MB size to be used is (truncation size * number of parallel sequences). + // SGD also does not know #parallel sequences upfront. + size_t FixUpEffectiveMBSize(size_t specifiedMBSize, size_t numParallelSequences) const + { + // remedy the bug that truncation size is incorrectly passed as MB size + if (m_truncated && specifiedMBSize > 1) // currently only happens in this mode + specifiedMBSize *= numParallelSequences; // assume 'specifiedMBSize' refers to truncation size + // end bug post-fix + // TODO: This ^^ should go away once SGD gets fixed to take the truncation size as a parameter. + + return specifiedMBSize; + } + + // helpers to convert learning rates to per-sample values used in the actual algorithms + // 'numParallelSequences' must be specified because of the definitional MB-size bug in SGD mentioned above, and should go away once that is sorted out. + double GetLearningRatePerSample(size_t epoch/*BUGBUG workaround:*/, size_t numParallelSequences) const + { + return m_learningRatesParam[epoch] / FixUpEffectiveMBSize(m_learningRatesSpecifiedForMBSize[epoch], numParallelSequences); + } + double GetMomentumPerSample(size_t epoch/*BUGBUG workaround:*/, size_t numParallelSequences) const + { + return pow(m_momentumParam[epoch], 1.0 / FixUpEffectiveMBSize(m_momentumSpecifiedForMBSize[epoch], numParallelSequences)); + } + + // only true when the user specify LearningRatePerMB and the number of parallel utterances in Reader > 1 + //bool m_needToNormalizeLRByParallUtterance; // TODO: should go away + //bool m_needToNormalizeMomentumByParallUtterance; + + intargvector m_mbSize; + bool m_truncated; // do BPTT + // BUGBUG: The 'Truncated' option is duplicated in the reader and must be set to the same there (e.g. by defining in the config on an outer enclosing level, like current samples). + // We really should only read it in SGD and pass it ourselves on to the Reader, instead of it being a Reader parameter. + // BUGBUG: If m_truncated, then m_mbSize is interpreted as truncation length; the actual MB size is a combination of that and the #parallel sequences specified in the reader. + // TODO: do not specify 'Truncated' but 'TruncatedLength', set m_truncated so given, and let m_mbSize control how many #parallel sequences the reader is allowed to pack into an MB. + size_t m_maxSamplesInRAM; + // This is related with subminibatch implementation + // maxSamplesInRAM denotes how many samples we used in forward-backward on net. + // Due to the GPU memory limitations, it is sometime not possible to hold the m_mbSize in RAM. + // To mitigate this issue, we adopt the sub-minibatch implementation, where + // each m_mbSize[epoch] is divided by a few sub-minibatch of which size will be no more than m_maxSamplesInRAM + // a forward-backward is performed for each sub-minibathch; a model update is performed after each minibatch + size_t m_numSubminiBatches; + // alternative method to specify how to split minibatches into subminibatches + // default is 1, which means no subminibatch is used + // if m_maxTempMemSizeInSamples = SIZE_MAX (which means users do not specify the option) and m_numSubminiBatches > 1 + // we divide one minibatch to m_numSubminiBatches subMinibatches + + // the number of samples in each epoch (0 means, use all the samples in each epoch). + size_t m_epochSize; + size_t m_maxComputedEpochSize; + + // the total number of epochs to run. + size_t m_maxEpochs; + + bool m_gradientClippingWithTruncation; + double m_clippingThresholdPerSample; + + intargvector m_numMiniBatch4LRSearch; + size_t m_numBestSearchEpoch; + + LearningRateSearchAlgorithm m_autoLearnRateSearchType; + + AdaptationRegType m_adaptationRegType; + double m_adaptationRegWeight; + bool m_needAdaptRegularization; + + bool m_loadBestModel; + double m_reduceLearnRateIfImproveLessThan; + bool m_continueReduce; + + // determine after how many epochs the learning rate should be auto adjusted. + size_t m_learnRateAdjustInterval; + + bool m_useCVSetControlLRIfCVExists; + bool m_useEvalCriterionControlLR; + + double m_increaseLearnRateIfImproveMoreThan; + double m_learnRateIncreaseFactor; + double m_learnRateDecreaseFactor; + bool m_autoAdjustMinibatch; + size_t m_minibatchSearchCriterionErrorMargin; + size_t m_minibatchSizeTuningFrequency; + size_t m_minibatchSizeTuningMax; + + floatargvector m_dropoutRates; + size_t m_maxTempMemSizeInSamplesForCNN; + + int m_traceLevel; + + size_t m_numPrevLearnRates; + + double m_minLearnRate; + + GradientUpdateInfo m_gradType; + RMSPropInfo m_rpi; + + int m_numMBsToShowResult; + int m_numMBsToCUDAProfile; + + bool m_doGradientCheck; + double m_gradientCheckSigDigit; + + bool m_doUnitTest; + + bool m_useAllDataForPreComputedNode; + + // Parallel training + ParallelizationMethod m_parallelizationMethod; + bool m_enableDistributedMBReading; + int m_parallelizationStartEpochNum; + + // decide if/how often we measure and show sync performance stats (seconds spend on sync, seconds since last sync etc.) ? + // 0: No sync perfomance stats + // 1: Show stats on every sync + // n > 1: Show stats after every n sync + int m_syncStatsTrace; + + // Data parallel SGD training parameters + int m_numGradientBits; + bool m_bufferedAsyncGradientAggregation; + bool m_zeroThresholdFor1Bit; + + // Parallel training related with MA + size_t m_nFramesBetweenMASync; + + bool m_needAveMultiplier; + double m_L2RegWeight; + double m_L1RegWeight; + + //sequence training + double m_hSmoothingWeight; + double m_frameDropThresh; + bool m_doReferenceAlign; + double m_seqGammarCalcAMF; + double m_seqGammarCalcLMF; + double m_seqGammarCalcWP; + double m_seqGammarCalcbMMIFactor; + bool m_seqGammarCalcUsesMBR; +}; + +template class IDistGradAggregator; + +// ----------------------------------------------------------------------- +// class SGD +// ----------------------------------------------------------------------- + +// TODO: make this independent of ElemType. Then these repeated dynamic_pointer_casts will go away +// TODO: why is this a class, and not just a procedure? Then we wouldn't have to include the massive header +template +class SGD : public SGDParams +{ +protected: + typedef shared_ptr> ComputationNodePtr; + typedef ClassBasedCrossEntropyWithSoftmaxNode* ClassBasedCrossEntropyWithSoftmaxNodePtr; + +public: + // constructor from old CNTK config. This is a function template that is also used to get the config from Scripting. + template + SGD(const ConfigRecordType & configSGD) : + SGDParams(configSGD, sizeof(ElemType)), + // TODO: The next few do not belong into SGD any more than the network or reader we operate on. Either move network and reader in here, or move these out. + m_modelPath((const wstring &)configSGD(L"modelPath")), + m_keepCheckPointFiles(configSGD(L"keepCheckPointFiles", false)), + //m_validateAfterModelReloading(configSGD(L"validateAfterModelReloading", true)), + m_trainCriterionNodeName((const wstring &)configSGD(L"trainCriterionNodeName", L"")), + m_evalCriterionNodeName((const wstring &)configSGD(L"evalCriterionNodeName", L"")), + m_prevChosenMinibatchSize(0), + m_lastFinishedEpochTrainLoss(0.0), + m_distGradAgg(nullptr), + m_gradHeader(nullptr) + { + msra::files::make_intermediate_dirs(m_modelPath); + } + // note: This must be in the header, as we cannot properly specialize this constructor in the CPP to make sure all versions are generated. + + // constructor from Scripting + SGD(const ScriptableObjects::IConfigRecordPtr configp) : + SGD(*configp) + { } + + void Train(function createNetworkFn, DEVICEID_TYPE deviceId, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader, + const bool makeMode = true); + void Adapt(wstring origModelFileName, wstring refNodeName, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader, + const DEVICEID_TYPE deviceID, const bool makeMode = true); +#if 0 + // TODO: per discussion with Dong Yu, Guoguo Chen, and Yu Zhang, this function can be removed. + void SequenceTrain(IComputationNetBuilder* netBuilder, wstring origModelFileName, + IDataReader* trainSetDataReader, IDataReader* validationSetDataReader, + const DEVICEID_TYPE deviceID, const bool makeMode = true); +#endif + +protected: + std::vector & GetTrainCriterionNodes(ComputationNetworkPtr net); + std::vector & GetEvalCriterionNodes(ComputationNetworkPtr net); + + void TrainOrAdaptModel(int startEpoch, ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + ComputationNodeBasePtr refNode, + IDataReader* trainSetDataReader, + IDataReader* validationSetDataReader); + +protected: + // return true if precomputation is executed. + bool PreCompute(ComputationNetworkPtr net, + IDataReader* trainSetDataReader, + std::vector & featureNodes, + std::vector & labelNodes, + std::map*>* inputMatrices); + + // return a reasonable initial learning rate based on the initial mbsize + double SearchForBestLearnRate(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, const int epochNumber, + const double curLearnRate, + IDataReader* trainSetDataReader, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const bool learnRateInitialized, + const double largestPrevLearnRatePerSample); + + void TrainOneMiniEpochAndReloadModel(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, const int epochNumber, + const size_t epochSize, IDataReader* trainSetDataReader, + const double learnRatePerSample, + const size_t minibatchSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + /*out*/ double& epochCriterion, + /*out*/ std::vector& epochEvalErrors, + /*out*/ size_t& totalSamplesSeen, + std::string prefixMsg = ""); + + size_t AdaptiveMinibatchSizing(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t numFramesToUseInSearch, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + const size_t initialMinibatchSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const double learningRateAdjustmentFactor); + + // uses a small percentage of training data of minibatch to + // speculatively train with various MB sizes; then picks the best + size_t SearchForBestMinibatchSize(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t numFramesToUseInSearch, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + const size_t minMinibatchSize, const size_t maxMinibatchSize); + + // Attemps to compute the error signal for the whole utterance, which will + // be fed to the neural network as features. Currently it is a workaround + // for the two-forward-pass sequence and ctc training, which allows + // processing more utterances at the same time. Only used in Kaldi2Reader. + // TODO: move the two-forward-pass support out of the reader. + void AttemptUtteranceDerivativeFeatures(ComputationNetworkPtr net, + IDataReader* trainSetDataReader, + const std::vector & featureNodes, + std::map*>* inputMatrices); + + size_t TrainOneEpoch(ComputationNetworkPtr net, + ComputationNetworkPtr refNet, + const ComputationNodeBasePtr& refNode, + const int epochNumber, + const size_t epochSize, + IDataReader* trainSetDataReader, + const double learnRatePerSample, + size_t tunedMBSize, + const std::vector & featureNodes, + const std::vector & labelNodes, + const std::vector & criterionNodes, + const std::vector & evaluationNodes, + std::map*>* inputMatrices, + const std::list & learnableNodes, + std::list>& smoothedGradients, + /*out*/ double& epochCriterion, + /*out*/ std::vector& epochEvalErrors, + /*out*/ size_t& totalSamplesSeen, + std::string prefixMsg = ""); + + void InitDistGradAgg(int numEvalNodes, int traceLevel); + + bool ModelAveragingProcessing(size_t nSamplesSinceLastSync, const std::list& learnableNodes, size_t& nProcessedFrames, + float& SecondsSinceLastSyncFinished, float& SecondsSpentOnSync); + + size_t ModelAveragingSync(int nSamplesSinceLastSync, const std::list& learnableNodes); + +public: + // UpdateWeightsS - static version of UpdateWeights() + static void UpdateWeightsS(const SGD* sgd, Matrix& functionValues, + Matrix& gradientValues, + Matrix& smoothedGradient, + const double learnRatePerSample, + const double momentumPerSample, + size_t actualMBSize, + const double L2RegWeight, + const double L1RegWeight, + const bool needAveMultiplier, + const bool useNesterovMomentum + ); + +protected: + // UpdateWeights - update the weights in + void UpdateWeights(const ComputationNodeBasePtr& node, + Matrix& smoothedGradient, + const double learnRatePerSample, + const double momentumPerSample, + const size_t actualMBSize, + const double L2RegWeight, const double L1RegWeight, + const bool needAveMultiplier, + const bool useNesterovMomentum) const; + + void ClipGradient(Matrix& gradient, const size_t actualMBSize) const; + + void SaveCheckPointInfo(const size_t epoch, const size_t totalSamplesSeen, + const double learnRatePerSample, + const std::list>& smoothedGradients, + const double prevCriterion, + const size_t minibatchSize); + + bool LoadCheckPointInfo(const size_t epochNumber, + /*out*/ size_t& totalSamplesSeen, + /*out*/ double& learnRatePerSample, + std::list>& smoothedGradients, + /*out*/ double& prevCriterion, + /*out*/ size_t& minibatchSize); + + wstring GetCheckPointFileNameForEpoch(const int epoch); + wstring GetModelNameForEpoch(const int epoch, bool bLastModel = false); + + // return -1 if nothing exists + int DetermineStartEpoch(const bool makeMode); + + GradientsUpdateType GradUpdateType() const { return m_gradType.mType; } + double GradientUpdateNoiseStd() const { return m_gradType.mGaussianNoiseInjectStd; } + +public: + +#define EPSILON 1e-5 + + bool GradientCheck(ComputationNetworkPtr net, + const std::vector & criterionNodes, + const std::list & learnableNodes, + int npos); + +protected: + wstring m_modelPath; + bool m_keepCheckPointFiles; + //bool m_validateAfterModelReloading; // TODO: remove this. Why would one not validate a model? + + wstring m_trainCriterionNodeName; + wstring m_evalCriterionNodeName; + + size_t m_prevChosenMinibatchSize; + double m_lastFinishedEpochTrainLoss; + + IDistGradAggregator* m_distGradAgg; + struct DistGradHeader* m_gradHeader; + +private: + int SGDTrace(FILE *__restrict __stream, const char *__restrict __format, ...); +}; + +}}} diff --git a/Source/SGDLib/SGDLib.vcxproj b/Source/SGDLib/SGDLib.vcxproj index 04efe8783edd..e3e31cf095b4 100644 --- a/Source/SGDLib/SGDLib.vcxproj +++ b/Source/SGDLib/SGDLib.vcxproj @@ -1,218 +1,218 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} - - - - - - - - - Win32Proj - CNTK - SGDLib - - - - StaticLibrary - true - v120 - Unicode - - - StaticLibrary - false - v120 - true - Unicode - - - - - - - - - - - - true - ..\SequenceTrainingLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) - ..\1BitSGD;$(IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - false - ..\SequenceTrainingLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) - ..\1BitSGD;$(IncludePath) - C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) - Build - $(ExecutablePath) - $(Platform)\$(Configuration)\$(ProjectName)\ - false - - - - - - Level4 - Disabled - _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - QUANTIZED_GRADIENT_AGGREGATION;%(PreprocessorDefinitions) - true - true - true - /bigobj %(AdditionalOptions) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - Math.dll; nvml.dll; cudart64_70.dll - 100000000 - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt - - - true - Copy content files to target directory - - - - - - - - - Level4 - - - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - QUANTIZED_GRADIENT_AGGREGATION;%(PreprocessorDefinitions) - true - Speed - /d2Zi+ %(AdditionalOptions) - true - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" - - - Console - true - true - true - ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - true - Math.dll; nvml.dll; cudart64_70.dll - "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" - - - if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) - Copying NVidia GDK extension DLL to target folder - - - - - - - - - - - true - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - NotUsing - NotUsing - - - - - NotUsing - - - NotUsing - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {DE3C54E5-D7D0-47AF-A783-DFDCE59E7937} + + + + + + + + + Win32Proj + CNTK + SGDLib + + + + StaticLibrary + true + v120 + Unicode + + + StaticLibrary + false + v120 + true + Unicode + + + + + + + + + + + + true + ..\SequenceTrainingLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) + ..\1BitSGD;$(IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + false + ..\SequenceTrainingLib;..\ComputationNetworkLib;..\Math;..\Common\Include;..\CNTK\BrainScript;..\..\DataReader\HTKMLFReader;C:\Program Files (x86)\Microsoft SDKs\MPI\Include;$(CUDA_PATH)\include;$(VCInstallDir)include;$(WindowsSDK_IncludePath) + ..\1BitSGD;$(IncludePath) + C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64;$(SolutionDir)$(Platform)\$(Configuration);$(SolutionDir)..\Common\lib;$(VCInstallDir)lib\amd64;$(WindowsSDK_LibraryPath_x64);$(CUDA_PATH)\lib\$(Platform) + Build + $(ExecutablePath) + $(Platform)\$(Configuration)\$(ProjectName)\ + false + + + + + + Level4 + Disabled + _SCL_SECURE_NO_WARNINGS;WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + QUANTIZED_GRADIENT_AGGREGATION;%(PreprocessorDefinitions) + true + true + true + /bigobj %(AdditionalOptions) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + Math.dll; nvml.dll; cudart64_70.dll + 100000000 + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + $(TargetDir)config.txt;$(TargetDir)labels.txt;$(TargetDir)network.txt;$(TargetDir)NdlScript.txt + + + true + Copy content files to target directory + + + + + + + + + Level4 + + + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + QUANTIZED_GRADIENT_AGGREGATION;%(PreprocessorDefinitions) + true + Speed + /d2Zi+ %(AdditionalOptions) + true + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\include" + + + Console + true + true + true + ComputationNetworkLib.lib; Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + true + Math.dll; nvml.dll; cudart64_70.dll + "c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib" + + + if exist "%ProgramW6432%\NVIDIA Corporation\NVSMI" xcopy /I /D /Y "%ProgramW6432%\NVIDIA Corporation\NVSMI\nvml*.dll" $(TargetDir) + Copying NVidia GDK extension DLL to target folder + + + + + + + + + + + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + NotUsing + NotUsing + + + + + NotUsing + + + NotUsing + + + + + + + + \ No newline at end of file diff --git a/Source/SGDLib/SGDLib.vcxproj.filters b/Source/SGDLib/SGDLib.vcxproj.filters index a48e89c19e0c..4710ad336f8c 100644 --- a/Source/SGDLib/SGDLib.vcxproj.filters +++ b/Source/SGDLib/SGDLib.vcxproj.filters @@ -1,192 +1,192 @@ - - - - - Common - - - Common - - - Common - - - Common - - - Misc - - - Common - - - GPU Interfacing - - - SGD - - - Common - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - from ComputationNetworkLib\Network - - - from ComputationNetworkLib\Nodes - - - Misc - - - Misc - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - from ComputationNetworkLib\Nodes - - - Common\Include - - - Parallelization - - - Parallelization - - - Common\Include - - - SGD - - - Eval - - - Common\Include - - - from Math - - - from Math - - - from Math - - - Eval - - - Data Reading - - - MultiNetworks - - - MultiNetworks - - - Common\Include - - - Common\Include - - - Common\Include - - - MultiNetworks - - - Parallelization - - - - - {b3d05c7b-7bcf-4b12-bcb5-dced86717202} - - - {85226dda-87ba-4da6-af04-563d0ce23b94} - - - {3c119a92-ffb2-4850-adae-01778324974d} - - - {8d99b2cc-5209-40e4-8b4b-a7616973ae3b} - - - {8531d7fb-a673-491a-988a-012c92fafbfd} - - - {5e22e394-50bb-4ce7-bfda-9b8d2d1a2741} - - - {c263e5cd-26a3-4277-bf2f-f3de466267a3} - - - {d5cc574b-5fd1-476b-b69e-0c6428a55262} - - - {498bb2e9-53de-4955-970e-813e3f21025b} - - - {0b366814-48b2-4619-bf92-85ee24e3cbc1} - - - {4c82e709-ff3e-43ab-b94c-763e300b637e} - - - {b866d513-7bd0-497c-98c2-f62dbcd4cde4} - - - {ae1eea3c-d77f-46ec-bf4f-1cd093a295e8} - - + + + + + Common + + + Common + + + Common + + + Common + + + Misc + + + Common + + + GPU Interfacing + + + SGD + + + Common + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + from ComputationNetworkLib\Network + + + from ComputationNetworkLib\Nodes + + + Misc + + + Misc + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + from ComputationNetworkLib\Nodes + + + Common\Include + + + Parallelization + + + Parallelization + + + Common\Include + + + SGD + + + Eval + + + Common\Include + + + from Math + + + from Math + + + from Math + + + Eval + + + Data Reading + + + MultiNetworks + + + MultiNetworks + + + Common\Include + + + Common\Include + + + Common\Include + + + MultiNetworks + + + Parallelization + + + + + {b3d05c7b-7bcf-4b12-bcb5-dced86717202} + + + {85226dda-87ba-4da6-af04-563d0ce23b94} + + + {3c119a92-ffb2-4850-adae-01778324974d} + + + {8d99b2cc-5209-40e4-8b4b-a7616973ae3b} + + + {8531d7fb-a673-491a-988a-012c92fafbfd} + + + {5e22e394-50bb-4ce7-bfda-9b8d2d1a2741} + + + {c263e5cd-26a3-4277-bf2f-f3de466267a3} + + + {d5cc574b-5fd1-476b-b69e-0c6428a55262} + + + {498bb2e9-53de-4955-970e-813e3f21025b} + + + {0b366814-48b2-4619-bf92-85ee24e3cbc1} + + + {4c82e709-ff3e-43ab-b94c-763e300b637e} + + + {b866d513-7bd0-497c-98c2-f62dbcd4cde4} + + + {ae1eea3c-d77f-46ec-bf4f-1cd093a295e8} + + \ No newline at end of file diff --git a/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj b/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj index b379735411ff..2bf2dca5717f 100644 --- a/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj +++ b/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj @@ -1,84 +1,84 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {EAD17188-072C-4726-B840-A769C36DAD1B} - Win32Proj - SequenceTrainingLib - - - - StaticLibrary - v120 - Unicode - - - true - - - false - true - - - - - - - - $(VC_IncludePath);$(WindowsSDK_IncludePath);..\Common\Include;..\Math - - - - Level4 - true - WIN32;_LIB;%(PreprocessorDefinitions) - true - - - Console - true - - - - - Disabled - _DEBUG;%(PreprocessorDefinitions) - - - - - MaxSpeed - true - true - NDEBUG;%(PreprocessorDefinitions) - - - true - true - - - - - - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {EAD17188-072C-4726-B840-A769C36DAD1B} + Win32Proj + SequenceTrainingLib + + + + StaticLibrary + v120 + Unicode + + + true + + + false + true + + + + + + + + $(VC_IncludePath);$(WindowsSDK_IncludePath);..\Common\Include;..\Math + + + + Level4 + true + WIN32;_LIB;%(PreprocessorDefinitions) + true + + + Console + true + + + + + Disabled + _DEBUG;%(PreprocessorDefinitions) + + + + + MaxSpeed + true + true + NDEBUG;%(PreprocessorDefinitions) + + + true + true + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj.filters b/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj.filters index 4ecc05909c15..7e03a4650c50 100644 --- a/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj.filters +++ b/Source/SequenceTrainingLib/SequenceTrainingLib.vcxproj.filters @@ -1,37 +1,37 @@ - - - - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - Common\Include - - - - - - - - - - {1a125fc4-c205-4fed-9042-28e5951deb5f} - - - {9e99ce54-08c8-423c-810e-f44cfbadb2c5} - - + + + + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + Common\Include + + + + + + + + + + {1a125fc4-c205-4fed-9042-28e5951deb5f} + + + {9e99ce54-08c8-423c-810e-f44cfbadb2c5} + + \ No newline at end of file diff --git a/Source/SequenceTrainingLib/gammacalculation.h b/Source/SequenceTrainingLib/gammacalculation.h index f63c5048793c..fc50894ed99e 100644 --- a/Source/SequenceTrainingLib/gammacalculation.h +++ b/Source/SequenceTrainingLib/gammacalculation.h @@ -1,378 +1,378 @@ -#pragma once - -#include -#include "simplesenonehmm.h" -#include "latticearchive.h" -#include "latticesource.h" -#include "ssematrix.h" -#include "Matrix.h" -#include "CUDAPageLockedMemAllocator.h" - -#pragma warning (disable: 4127) // conditional expression is constant - -namespace msra { namespace lattices { - - struct SeqGammarCalParam{ - double amf; - double lmf; - double wp; - double bMMIfactor; - bool sMBRmode; - SeqGammarCalParam() - { - amf = 14.0; - lmf = 14.0; - wp = 0.0; - bMMIfactor = 0.0; - sMBRmode = false; - } - }; - - template - class GammaCalculation - { - bool cpumode; - public: - GammaCalculation() : cpumode(false) - { - initialmark = false; - lmf = 7.0f; // Note that 9 was best for Fisher --these should best be configurable - wp = 0.0f; - amf = 7.0f; - boostmmifactor = 0.0f; - seqsMBRmode = false; - } - ~GammaCalculation() - { - - } - - //======================================== - // Sec. 1 init functions - //======================================== - void init(msra::asr::simplesenonehmm hset, int DeviceId) - { - m_deviceid = DeviceId; - if (!initialmark) - { - m_hset = hset; - m_maxframenum = 0; - - // prep for parallel implementation (CUDA) - parallellattice.setdevice(DeviceId); - - if (parallellattice.enabled()) // send hmm set to GPU if GPU computation enabled - parallellattice.entercomputation(m_hset, mbrclassdef); // cache senone2classmap if mpemode - initialmark = true; - } - } - - //======================================== - // Sec. 2 set functions - //======================================== - void SetGammarCalculationParams(const SeqGammarCalParam& gammarParam) - { - lmf = (float)gammarParam.lmf; - amf = (float)gammarParam.amf; - wp = (float)gammarParam.wp; - seqsMBRmode = gammarParam.sMBRmode; - boostmmifactor = (float)gammarParam.bMMIfactor; - } - - //======================================== - // Sec. 3 calculation functions - //======================================== - void calgammaformb( Microsoft::MSR::CNTK::Matrix& functionValues, - std::vector> &lattices, - const Microsoft::MSR::CNTK::Matrix& loglikelihood, - Microsoft::MSR::CNTK::Matrix& labels, - Microsoft::MSR::CNTK::Matrix& gammafromlattice, - std::vector &uids, std::vector &boundaries, - size_t samplesInRecurrentStep, /* numParallelUtterance ? */ - std::shared_ptr pMBLayout, - std::vector &extrauttmap, - bool doreferencealign) - { - //check total frame number to be added ? - //int deviceid = loglikelihood.GetDeviceId(); - size_t boundaryframenum; - std::vector validframes; // [s] cursor pointing to next utterance begin within a single parallel sequence [s] - validframes.assign(samplesInRecurrentStep, 0); - ElemType objectValue = 0.0; - //convert from Microsoft::MSR::CNTK::Matrix to msra::math::ssematrixbase - size_t numrows = loglikelihood.GetNumRows(); - size_t numcols = loglikelihood.GetNumCols(); - Microsoft::MSR::CNTK::Matrix tempmatrix(m_deviceid); - - //copy loglikelihood to pred - if (numcols > pred.cols()) - { - pred.resize(numrows, numcols); - dengammas.resize(numrows, numcols); - } - - if (doreferencealign) - labels.SetValue((ElemType)(0.0f)); - - size_t T = numcols / samplesInRecurrentStep; // number of time steps in minibatch - if (samplesInRecurrentStep > 1) - { - assert(extrauttmap.size() == lattices.size()); - assert(T == pMBLayout->GetNumTimeSteps()); - } - - size_t mapi = 0; // parallel-sequence index for utterance [i] - // cal gamma for each utterance - size_t ts = 0; - for (size_t i = 0; i < lattices.size(); i++) - { - const size_t numframes = lattices[i]->getnumframes(); - - msra::dbn::matrixstripe predstripe(pred, ts, numframes); // logLLs for this utterance - msra::dbn::matrixstripe dengammasstripe(dengammas, ts, numframes); // denominator gammas - - if (samplesInRecurrentStep == 1) // no sequence parallelism - { - tempmatrix = loglikelihood.ColumnSlice(ts, numframes); - //if (m_deviceid == CPUDEVICE) - { - CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe); - } - - if (m_deviceid != CPUDEVICE) - parallellattice.setloglls(tempmatrix); - } - else // multiple parallel sequences - { - // get number of frames for the utterance - mapi = extrauttmap[i]; // parallel-sequence index; in case of >1 utterance within this parallel sequence, this is in order of concatenation - - // scan MBLayout for end of utterance - size_t mapframenum = SIZE_MAX; // duration of utterance [i] as determined from MBLayout - for (size_t t = validframes[mapi]; t < T; t++) - { - // TODO: Adapt this to new MBLayout, m_sequences would be easier to work off. - if (pMBLayout->IsEnd(mapi,t)) - { - mapframenum = t - validframes[mapi] + 1; - break; - } - } - - // must match the explicit information we get from the reader - if (numframes != mapframenum) - LogicError("gammacalculation: IsEnd() not working, numframes (%d) vs. mapframenum (%d)", (int)numframes, (int)mapframenum); - assert(numframes == mapframenum); - - if (numframes > tempmatrix.GetNumCols()) - tempmatrix.Resize(numrows, numframes); - - Microsoft::MSR::CNTK::Matrix loglikelihoodForCurrentParallelUtterance = loglikelihood.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1); - tempmatrix.CopyColumnsStrided(loglikelihoodForCurrentParallelUtterance, numframes, samplesInRecurrentStep, 1); - - //if (doreferencealign || m_deviceid == CPUDEVICE) - { - CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe); - } - - if (m_deviceid != CPUDEVICE) - { - parallellattice.setloglls(tempmatrix); - } - } - - array_ref uidsstripe(&uids[ts], numframes); - - if (doreferencealign) - { - boundaryframenum = numframes; - } - else - boundaryframenum = 0; - array_ref boundariesstripe(&boundaries[ts], boundaryframenum); - - double numavlogp = 0; - foreach_column(t, dengammasstripe) // we do not allocate memory for numgamma now, should be the same as numgammasstripe - { - const size_t s = uidsstripe[t ]; - numavlogp += predstripe(s, t) / amf; - } - numavlogp /= numframes; - - //auto_timer dengammatimer; - double denavlogp = lattices[i]->second.forwardbackward(parallellattice, - (const msra::math::ssematrixbase &) predstripe, (const msra::asr::simplesenonehmm &) m_hset, - (msra::math::ssematrixbase &) dengammasstripe, (msra::math::ssematrixbase &) gammasbuffer/*empty, not used*/, - lmf, wp, amf, boostmmifactor, seqsMBRmode, uidsstripe, boundariesstripe); - objectValue += (ElemType)((numavlogp - denavlogp) * numframes); - - if (samplesInRecurrentStep == 1) - { - tempmatrix = gammafromlattice.ColumnSlice(ts, numframes); - } - - //copy gamma to tempmatrix - if (m_deviceid == CPUDEVICE) - { - CopyFromSSEMatrixToCNTKMatrix(dengammas, numrows, numframes, tempmatrix, gammafromlattice.GetDeviceId()); - } - else - parallellattice.getgamma(tempmatrix); - - // set gamma for multi channel - if (samplesInRecurrentStep > 1) - { - Microsoft::MSR::CNTK::Matrix gammaFromLatticeForCurrentParallelUtterance = gammafromlattice.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1); - gammaFromLatticeForCurrentParallelUtterance.CopyColumnsStrided(tempmatrix, numframes, 1, samplesInRecurrentStep); - } - - if (doreferencealign) - { - for (size_t nframe = 0; nframe < numframes; nframe++) - { - size_t uid = uidsstripe[nframe]; - if (samplesInRecurrentStep > 1) - labels(uid, (nframe + validframes[mapi])*samplesInRecurrentStep + mapi) = 1.0; - else - labels(uid, ts+nframe) = 1.0; - } - } - if (samplesInRecurrentStep > 1) - validframes[mapi] += numframes; // advance the cursor within the parallel sequence - fprintf(stderr, "dengamma value %f\n", denavlogp); - ts += numframes; - } - functionValues.SetValue(objectValue); - } - - private: - // Helper methods for copying between ssematrix objects and CNTK matrices - void CopyFromCNTKMatrixToSSEMatrix(const Microsoft::MSR::CNTK::Matrix& src, size_t numCols, msra::math::ssematrixbase& dest) - { - if (!std::is_same::value) - { - LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!"); - } - - size_t numRows = src.GetNumRows(); - const Microsoft::MSR::CNTK::Matrix srcSlice = src.ColumnSlice(0, numCols); - if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < srcSlice.GetNumElements())) - { - m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(srcSlice.GetDeviceId(), srcSlice.GetNumElements()); - m_intermediateCUDACopyBufferSize = srcSlice.GetNumElements(); - } - - ElemType* pBuf = m_intermediateCUDACopyBuffer.get(); - srcSlice.CopyToArray(pBuf, m_intermediateCUDACopyBufferSize); - if (pBuf != m_intermediateCUDACopyBuffer.get()) - { - LogicError("Unexpected re-allocation of destination CPU buffer in Matrix::CopyToArray!"); - } - - if ((dest.getcolstride() == dest.rows()) && (numRows == dest.rows())) - { - memcpy(&dest(0, 0), (float*)pBuf, sizeof(ElemType) * numRows * numCols); - } - else - { - // We need to copy columnwise - for (size_t i = 0; i < numCols; ++i) - { - memcpy(&dest(0, i), (float*)(pBuf + (i * numRows)), sizeof(ElemType) * numRows); - } - } - } - - void CopyFromSSEMatrixToCNTKMatrix(const msra::math::ssematrixbase& src, size_t numRows, size_t numCols, Microsoft::MSR::CNTK::Matrix& dest, int deviceId) - { - if (!std::is_same::value) - { - LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!"); - } - - size_t numElements = numRows * numCols; - if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < numElements)) - { - m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(deviceId, numElements); - m_intermediateCUDACopyBufferSize = numElements; - } - - if ((src.getcolstride() == src.rows()) && (numRows == src.rows())) - { - memcpy((float*)m_intermediateCUDACopyBuffer.get(), &src(0, 0), sizeof(float) * numRows * numCols); - } - else - { - // We need to copy columnwise - for (size_t i = 0; i < numCols; ++i) - { - memcpy((float*)(m_intermediateCUDACopyBuffer.get() + (i * numRows)), &src(0, i), sizeof(float) * numRows); - } - } - - dest.SetValue(numRows, numCols, deviceId, m_intermediateCUDACopyBuffer.get(), 0); - } - - // TODO: This function is duplicate of the one in HTLMLFReader. - // This should be moved to a common utils library and removed from here as well as HTLMLFReader - unique_ptr& GetCUDAAllocator(int deviceID) - { - if (m_cudaAllocator != nullptr) - { - if (m_cudaAllocator->GetDeviceId() != deviceID) - { - m_cudaAllocator.reset(nullptr); - } - } - - if (m_cudaAllocator == nullptr) - { - m_cudaAllocator.reset(new Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator(deviceID)); - } - - return m_cudaAllocator; - } - - // TODO: This function is duplicate of the one in HTLMLFReader. - // This should be moved to a common utils library and removed from here as well as HTLMLFReader - std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements) - { - if (deviceID >= 0) - { - // Use pinned memory for GPU devices for better copy performance - size_t totalSize = sizeof(ElemType) * numElements; - return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { - this->GetCUDAAllocator(deviceID)->Free((char*)p); - }); - } - else - { - return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { - delete[] p; - }); - } - } - - protected: - msra::asr::simplesenonehmm m_hset; - msra::lattices::lattice::parallelstate parallellattice; - msra::lattices::mbrclassdefinition mbrclassdef = msra::lattices::senone; // defines the unit for minimum bayesian risk - bool initialmark; - msra::dbn::matrix dengammas; - msra::dbn::matrix pred; - int m_deviceid; //-1: cpu - size_t m_maxframenum; - float lmf ; // Note that 9 was best for Fisher --these should best be configurable - float wp ; - float amf; - msra::dbn::matrix gammasbuffer; - vector boundary; - float boostmmifactor; - bool seqsMBRmode; - - private: - std::unique_ptr m_cudaAllocator; - std::shared_ptr m_intermediateCUDACopyBuffer; - size_t m_intermediateCUDACopyBufferSize; - }; -}} +#pragma once + +#include +#include "simplesenonehmm.h" +#include "latticearchive.h" +#include "latticesource.h" +#include "ssematrix.h" +#include "Matrix.h" +#include "CUDAPageLockedMemAllocator.h" + +#pragma warning (disable: 4127) // conditional expression is constant + +namespace msra { namespace lattices { + + struct SeqGammarCalParam{ + double amf; + double lmf; + double wp; + double bMMIfactor; + bool sMBRmode; + SeqGammarCalParam() + { + amf = 14.0; + lmf = 14.0; + wp = 0.0; + bMMIfactor = 0.0; + sMBRmode = false; + } + }; + + template + class GammaCalculation + { + bool cpumode; + public: + GammaCalculation() : cpumode(false) + { + initialmark = false; + lmf = 7.0f; // Note that 9 was best for Fisher --these should best be configurable + wp = 0.0f; + amf = 7.0f; + boostmmifactor = 0.0f; + seqsMBRmode = false; + } + ~GammaCalculation() + { + + } + + //======================================== + // Sec. 1 init functions + //======================================== + void init(msra::asr::simplesenonehmm hset, int DeviceId) + { + m_deviceid = DeviceId; + if (!initialmark) + { + m_hset = hset; + m_maxframenum = 0; + + // prep for parallel implementation (CUDA) + parallellattice.setdevice(DeviceId); + + if (parallellattice.enabled()) // send hmm set to GPU if GPU computation enabled + parallellattice.entercomputation(m_hset, mbrclassdef); // cache senone2classmap if mpemode + initialmark = true; + } + } + + //======================================== + // Sec. 2 set functions + //======================================== + void SetGammarCalculationParams(const SeqGammarCalParam& gammarParam) + { + lmf = (float)gammarParam.lmf; + amf = (float)gammarParam.amf; + wp = (float)gammarParam.wp; + seqsMBRmode = gammarParam.sMBRmode; + boostmmifactor = (float)gammarParam.bMMIfactor; + } + + //======================================== + // Sec. 3 calculation functions + //======================================== + void calgammaformb( Microsoft::MSR::CNTK::Matrix& functionValues, + std::vector> &lattices, + const Microsoft::MSR::CNTK::Matrix& loglikelihood, + Microsoft::MSR::CNTK::Matrix& labels, + Microsoft::MSR::CNTK::Matrix& gammafromlattice, + std::vector &uids, std::vector &boundaries, + size_t samplesInRecurrentStep, /* numParallelUtterance ? */ + std::shared_ptr pMBLayout, + std::vector &extrauttmap, + bool doreferencealign) + { + //check total frame number to be added ? + //int deviceid = loglikelihood.GetDeviceId(); + size_t boundaryframenum; + std::vector validframes; // [s] cursor pointing to next utterance begin within a single parallel sequence [s] + validframes.assign(samplesInRecurrentStep, 0); + ElemType objectValue = 0.0; + //convert from Microsoft::MSR::CNTK::Matrix to msra::math::ssematrixbase + size_t numrows = loglikelihood.GetNumRows(); + size_t numcols = loglikelihood.GetNumCols(); + Microsoft::MSR::CNTK::Matrix tempmatrix(m_deviceid); + + //copy loglikelihood to pred + if (numcols > pred.cols()) + { + pred.resize(numrows, numcols); + dengammas.resize(numrows, numcols); + } + + if (doreferencealign) + labels.SetValue((ElemType)(0.0f)); + + size_t T = numcols / samplesInRecurrentStep; // number of time steps in minibatch + if (samplesInRecurrentStep > 1) + { + assert(extrauttmap.size() == lattices.size()); + assert(T == pMBLayout->GetNumTimeSteps()); + } + + size_t mapi = 0; // parallel-sequence index for utterance [i] + // cal gamma for each utterance + size_t ts = 0; + for (size_t i = 0; i < lattices.size(); i++) + { + const size_t numframes = lattices[i]->getnumframes(); + + msra::dbn::matrixstripe predstripe(pred, ts, numframes); // logLLs for this utterance + msra::dbn::matrixstripe dengammasstripe(dengammas, ts, numframes); // denominator gammas + + if (samplesInRecurrentStep == 1) // no sequence parallelism + { + tempmatrix = loglikelihood.ColumnSlice(ts, numframes); + //if (m_deviceid == CPUDEVICE) + { + CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe); + } + + if (m_deviceid != CPUDEVICE) + parallellattice.setloglls(tempmatrix); + } + else // multiple parallel sequences + { + // get number of frames for the utterance + mapi = extrauttmap[i]; // parallel-sequence index; in case of >1 utterance within this parallel sequence, this is in order of concatenation + + // scan MBLayout for end of utterance + size_t mapframenum = SIZE_MAX; // duration of utterance [i] as determined from MBLayout + for (size_t t = validframes[mapi]; t < T; t++) + { + // TODO: Adapt this to new MBLayout, m_sequences would be easier to work off. + if (pMBLayout->IsEnd(mapi,t)) + { + mapframenum = t - validframes[mapi] + 1; + break; + } + } + + // must match the explicit information we get from the reader + if (numframes != mapframenum) + LogicError("gammacalculation: IsEnd() not working, numframes (%d) vs. mapframenum (%d)", (int)numframes, (int)mapframenum); + assert(numframes == mapframenum); + + if (numframes > tempmatrix.GetNumCols()) + tempmatrix.Resize(numrows, numframes); + + Microsoft::MSR::CNTK::Matrix loglikelihoodForCurrentParallelUtterance = loglikelihood.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1); + tempmatrix.CopyColumnsStrided(loglikelihoodForCurrentParallelUtterance, numframes, samplesInRecurrentStep, 1); + + //if (doreferencealign || m_deviceid == CPUDEVICE) + { + CopyFromCNTKMatrixToSSEMatrix(tempmatrix, numframes, predstripe); + } + + if (m_deviceid != CPUDEVICE) + { + parallellattice.setloglls(tempmatrix); + } + } + + array_ref uidsstripe(&uids[ts], numframes); + + if (doreferencealign) + { + boundaryframenum = numframes; + } + else + boundaryframenum = 0; + array_ref boundariesstripe(&boundaries[ts], boundaryframenum); + + double numavlogp = 0; + foreach_column(t, dengammasstripe) // we do not allocate memory for numgamma now, should be the same as numgammasstripe + { + const size_t s = uidsstripe[t ]; + numavlogp += predstripe(s, t) / amf; + } + numavlogp /= numframes; + + //auto_timer dengammatimer; + double denavlogp = lattices[i]->second.forwardbackward(parallellattice, + (const msra::math::ssematrixbase &) predstripe, (const msra::asr::simplesenonehmm &) m_hset, + (msra::math::ssematrixbase &) dengammasstripe, (msra::math::ssematrixbase &) gammasbuffer/*empty, not used*/, + lmf, wp, amf, boostmmifactor, seqsMBRmode, uidsstripe, boundariesstripe); + objectValue += (ElemType)((numavlogp - denavlogp) * numframes); + + if (samplesInRecurrentStep == 1) + { + tempmatrix = gammafromlattice.ColumnSlice(ts, numframes); + } + + //copy gamma to tempmatrix + if (m_deviceid == CPUDEVICE) + { + CopyFromSSEMatrixToCNTKMatrix(dengammas, numrows, numframes, tempmatrix, gammafromlattice.GetDeviceId()); + } + else + parallellattice.getgamma(tempmatrix); + + // set gamma for multi channel + if (samplesInRecurrentStep > 1) + { + Microsoft::MSR::CNTK::Matrix gammaFromLatticeForCurrentParallelUtterance = gammafromlattice.ColumnSlice(mapi + (validframes[mapi] * samplesInRecurrentStep), ((numframes - 1) * samplesInRecurrentStep) + 1); + gammaFromLatticeForCurrentParallelUtterance.CopyColumnsStrided(tempmatrix, numframes, 1, samplesInRecurrentStep); + } + + if (doreferencealign) + { + for (size_t nframe = 0; nframe < numframes; nframe++) + { + size_t uid = uidsstripe[nframe]; + if (samplesInRecurrentStep > 1) + labels(uid, (nframe + validframes[mapi])*samplesInRecurrentStep + mapi) = 1.0; + else + labels(uid, ts+nframe) = 1.0; + } + } + if (samplesInRecurrentStep > 1) + validframes[mapi] += numframes; // advance the cursor within the parallel sequence + fprintf(stderr, "dengamma value %f\n", denavlogp); + ts += numframes; + } + functionValues.SetValue(objectValue); + } + + private: + // Helper methods for copying between ssematrix objects and CNTK matrices + void CopyFromCNTKMatrixToSSEMatrix(const Microsoft::MSR::CNTK::Matrix& src, size_t numCols, msra::math::ssematrixbase& dest) + { + if (!std::is_same::value) + { + LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!"); + } + + size_t numRows = src.GetNumRows(); + const Microsoft::MSR::CNTK::Matrix srcSlice = src.ColumnSlice(0, numCols); + if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < srcSlice.GetNumElements())) + { + m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(srcSlice.GetDeviceId(), srcSlice.GetNumElements()); + m_intermediateCUDACopyBufferSize = srcSlice.GetNumElements(); + } + + ElemType* pBuf = m_intermediateCUDACopyBuffer.get(); + srcSlice.CopyToArray(pBuf, m_intermediateCUDACopyBufferSize); + if (pBuf != m_intermediateCUDACopyBuffer.get()) + { + LogicError("Unexpected re-allocation of destination CPU buffer in Matrix::CopyToArray!"); + } + + if ((dest.getcolstride() == dest.rows()) && (numRows == dest.rows())) + { + memcpy(&dest(0, 0), (float*)pBuf, sizeof(ElemType) * numRows * numCols); + } + else + { + // We need to copy columnwise + for (size_t i = 0; i < numCols; ++i) + { + memcpy(&dest(0, i), (float*)(pBuf + (i * numRows)), sizeof(ElemType) * numRows); + } + } + } + + void CopyFromSSEMatrixToCNTKMatrix(const msra::math::ssematrixbase& src, size_t numRows, size_t numCols, Microsoft::MSR::CNTK::Matrix& dest, int deviceId) + { + if (!std::is_same::value) + { + LogicError("Cannot copy between a SSE matrix and a non-float type CNTK Matrix object!"); + } + + size_t numElements = numRows * numCols; + if ((m_intermediateCUDACopyBuffer == nullptr) || (m_intermediateCUDACopyBufferSize < numElements)) + { + m_intermediateCUDACopyBuffer = AllocateIntermediateBuffer(deviceId, numElements); + m_intermediateCUDACopyBufferSize = numElements; + } + + if ((src.getcolstride() == src.rows()) && (numRows == src.rows())) + { + memcpy((float*)m_intermediateCUDACopyBuffer.get(), &src(0, 0), sizeof(float) * numRows * numCols); + } + else + { + // We need to copy columnwise + for (size_t i = 0; i < numCols; ++i) + { + memcpy((float*)(m_intermediateCUDACopyBuffer.get() + (i * numRows)), &src(0, i), sizeof(float) * numRows); + } + } + + dest.SetValue(numRows, numCols, deviceId, m_intermediateCUDACopyBuffer.get(), 0); + } + + // TODO: This function is duplicate of the one in HTLMLFReader. + // This should be moved to a common utils library and removed from here as well as HTLMLFReader + unique_ptr& GetCUDAAllocator(int deviceID) + { + if (m_cudaAllocator != nullptr) + { + if (m_cudaAllocator->GetDeviceId() != deviceID) + { + m_cudaAllocator.reset(nullptr); + } + } + + if (m_cudaAllocator == nullptr) + { + m_cudaAllocator.reset(new Microsoft::MSR::CNTK::CUDAPageLockedMemAllocator(deviceID)); + } + + return m_cudaAllocator; + } + + // TODO: This function is duplicate of the one in HTLMLFReader. + // This should be moved to a common utils library and removed from here as well as HTLMLFReader + std::shared_ptr AllocateIntermediateBuffer(int deviceID, size_t numElements) + { + if (deviceID >= 0) + { + // Use pinned memory for GPU devices for better copy performance + size_t totalSize = sizeof(ElemType) * numElements; + return std::shared_ptr((ElemType*)GetCUDAAllocator(deviceID)->Malloc(totalSize), [this, deviceID](ElemType* p) { + this->GetCUDAAllocator(deviceID)->Free((char*)p); + }); + } + else + { + return std::shared_ptr(new ElemType[numElements], [](ElemType* p) { + delete[] p; + }); + } + } + + protected: + msra::asr::simplesenonehmm m_hset; + msra::lattices::lattice::parallelstate parallellattice; + msra::lattices::mbrclassdefinition mbrclassdef = msra::lattices::senone; // defines the unit for minimum bayesian risk + bool initialmark; + msra::dbn::matrix dengammas; + msra::dbn::matrix pred; + int m_deviceid; //-1: cpu + size_t m_maxframenum; + float lmf ; // Note that 9 was best for Fisher --these should best be configurable + float wp ; + float amf; + msra::dbn::matrix gammasbuffer; + vector boundary; + float boostmmifactor; + bool seqsMBRmode; + + private: + std::unique_ptr m_cudaAllocator; + std::shared_ptr m_intermediateCUDACopyBuffer; + size_t m_intermediateCUDACopyBufferSize; + }; +}} diff --git a/Source/SequenceTrainingLib/latticeforwardbackward.cpp b/Source/SequenceTrainingLib/latticeforwardbackward.cpp index 4abb50d3ce30..ec0f6916687b 100644 --- a/Source/SequenceTrainingLib/latticeforwardbackward.cpp +++ b/Source/SequenceTrainingLib/latticeforwardbackward.cpp @@ -1,1428 +1,1428 @@ -// latticearchive.cpp -- managing lattice archives -// -// F. Seide, V-hansu - +// latticearchive.cpp -- managing lattice archives +// +// F. Seide, V-hansu + #ifndef _CRT_SECURE_NO_WARNINGS #define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings #endif - -#include "Basics.h" -#include "simple_checked_arrays.h" -#include "latticearchive.h" -#include "simplesenonehmm.h" // the model -#include "ssematrix.h" // the matrices -#include "latticestorage.h" -#include -#include -#include - -using namespace std; - -#define VIRGINLOGZERO (10 * LOGZERO) // used for printing statistics on unseen states -#undef CPU_VERIFICATION - -#ifdef _WIN32 -int msra::numa::node_override = -1; // for numahelpers.h -#endif - -namespace msra { namespace lattices { - -// --------------------------------------------------------------------------- -// helper class for allocation lots of small matrices, no free -// --------------------------------------------------------------------------- - -class littlematrixheap -{ - static const size_t CHUNKSIZE; - typedef msra::math::ssematrixfrombuffer matrixfrombuffer; - std::list> heap; - size_t allocatedinlast; // in last heap element - size_t totalallocated; - std::vector matrices; -public: - littlematrixheap (size_t estimatednumentries) : totalallocated (0), allocatedinlast (0) { matrices.reserve (estimatednumentries + 1); } - msra::math::ssematrixbase & newmatrix (size_t rows, size_t cols) - { - const size_t elementsneeded = matrixfrombuffer::elementsneeded (rows, cols); - if (heap.empty() || (heap.back().size() - allocatedinlast) < elementsneeded) - { - const size_t nelem = max (CHUNKSIZE, elementsneeded + 3/*+3 for SSE alignment*/); - heap.push_back (std::vector (nelem)); - allocatedinlast = 0; - // make sure starting element is SSE-aligned (the constructor demands that) - const size_t offelem = (((size_t)&heap.back()[allocatedinlast]) / sizeof (float)) % 4; - if (offelem != 0) - allocatedinlast += 4 - offelem; - } - auto & buffer = heap.back(); - if (elementsneeded > heap.back().size() - allocatedinlast) - LogicError("newmatrix: allocation logic screwed up"); - // get our buffer into a handy vector-like thingy - array_ref vecbuffer (&buffer[allocatedinlast], elementsneeded); - // allocate in the current heap location - matrices.resize (matrices.size() + 1); - if (matrices.size()+1 > matrices.capacity()) - LogicError("newmatrix: littlematrixheap cannot grow but was constructed with too small number of eements"); - auto & matrix = matrices.back(); - matrix = matrixfrombuffer (vecbuffer, rows, cols); - allocatedinlast += elementsneeded; - totalallocated += elementsneeded; - return matrix; - } -}; - -const size_t littlematrixheap::CHUNKSIZE = 256*1024; // 1 MB - -// --------------------------------------------------------------------------- -// helpers for log-domain addition -// --------------------------------------------------------------------------- - -#ifndef LOGZERO -#define LOGZERO -1e30f -#endif - -// logadd (loga, logb) -> a += b, or loga = log [ exp(loga) + exp(logb) ] -static void logaddratio (float & loga, float diff) -{ - if (diff < -17.0f) return; // log (2^-24), 23-bit mantissa -> cut of after 24th bit - loga += logf (1.0f + expf (diff)); -} -static void logaddratio (double & loga, double diff) -{ - if (diff < -37.0f) return; // log (2^-53), 52-bit mantissa -> cut of after 53th bit - loga += log (1.0 + exp (diff)); -} -// loga <- log (exp (loga) + exp (logb)) = log (exp (loga) * (1.0 + exp (logb - loga)) = loga + log (1.0 + exp (logb - loga)) -template static void logadd (FLOAT & loga, FLOAT logb) -{ - if (logb > loga) // we add smaller to bigger - ::swap (loga, logb); - if (loga <= LOGZERO) // both are 0 - return; - logaddratio (loga, logb - loga); -} -template static void logmax (FLOAT & loga, FLOAT logb) // for testing (max approx) -{ - if (logb > loga) - loga = logb; -} - -template static FLOAT expdiff (FLOAT a, FLOAT b) // for testing -{ - if (b > a) - return exp(b) * (exp(a-b) - 1); - else - return exp(a) * (1 - exp(b-a)); -} - -template static bool islogzero (FLOAT v) { return v < LOGZERO/2; } // is this number to be considered 0 - -// --------------------------------------------------------------------------- -// other helpers go here -// --------------------------------------------------------------------------- - -// helper to reconstruct the phonetic transcript -/*static*/ std::string lattice::gettranscript (const_array_ref units, const msra::asr::simplesenonehmm & hset) -{ - std::string trans; - foreach_index (k, units) // we exploit that units have fixed boundaries - { - if (k > 0) trans.push_back (' '); - trans.append (hset.gethmm (units[k].unit).getname()); - } - return trans; -} - -// --------------------------------------------------------------------------- -// forwardbackwardedge() -- perform state-level forward-backward on a single lattice edge -// -// Results: -// - gammas(j,t) for valid time ranges (remaining areas are not initialized) -// - return value is edge acoustic score -// Gammas matrix must have two extra columns as buffer. -// --------------------------------------------------------------------------- - -/*static*/ float lattice::forwardbackwardedge (const_array_ref units, const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, - msra::math::ssematrixbase & loggammas, size_t edgeindex) -{ - // alphas and betas are stored in-place inside the loggammas matrix shifted by one?two columns - assert (loggammas.cols() == logLLs.cols() + 2); - msra::math::ssematrixstriperef logalphas (loggammas, 1, logLLs.cols()); // shifted views into gammas(,) for alphas and betas - msra::math::ssematrixstriperef logbetas (loggammas, 2, logLLs.cols()); - - // alphas(j,t) store the sum of all paths up to including state j at time t, including logLL(j,t) - // betas(j,t) store the sum of all paths exiting from state j at time t, not including logLL(j,t) - // gammas(j,t) = alphas(j,t) * betas(j,t) / totalLL - - // backward pass --token passing - size_t te = logbetas.cols(); - size_t je = logbetas.rows(); - float bwscore = 0.0f; // backward score - for (size_t k = units.size() -1; k+1 > 0; k--) - { - const auto & hmm = hset.gethmm (units[k].unit); - const size_t n = hmm.getnumstates(); - const auto & transP = hmm.gettransP(); - const size_t ts = te - units[k].frames; // end time of current unit - const size_t js = je - n; // range of state indices - - // pass in the transition score - // t = ts: exit transition (last frame only or tee transition) - float exitscore = 1e30f; // (something impossible) - if (te == ts) // tee transition - { - exitscore = bwscore + transP(-1,n); - } - else // not tee: expand all last states - { - for (size_t from = 0/*no tee possible here*/; from < n; from++) - { - const size_t i = js + from; // origin trellis node - logbetas(i,te-1) = bwscore + transP(from,n); - } - } - - // expand from states j at time t (not yet including LL) to time t-1 - for (size_t t = te -1; t+1 > ts/*note: cannot test t >= ts because t < 0 possible*/; t--) - { - for (size_t to = 0; to < n; to++) - { - const size_t j = js + to; // source trellis node - const size_t s = hmm.getsenoneid(to); // senone id for state at position 'to' in the HMM - const float acLL = logLLs(s,t); - if (islogzero (acLL)) - fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d unit %d (%s) frames [%d,%d) ac score(%d,%d) is zero (%d st, %d fr: %s)\n", - (int)edgeindex, (int) k, hmm.getname(), (int) ts, (int) te, - (int) s, (int) t, - (int) logbetas.rows(), (int) logbetas.cols(), gettranscript (units, hset).c_str()); - const float betajt = logbetas(j,t); // sum over all all path exiting from (j,t) to end - const float betajtpll = betajt + acLL; // incorporate acoustic score - if (t > ts) for (size_t from = 0/*no transition from entry state*/; from < n; from++) - { - const size_t i = js + from; // target trellis node - const float pathscore = betajtpll + transP(from,to); - if (to == 0) - logbetas(i,t-1/*propagate into preceding frame*/) = pathscore; - else - logadd (logbetas(i,t-1/*propagate into preceding frame*/), pathscore); - } - else // transition to entry state - { - const float pathscore = betajtpll + transP(-1,to); - if (to == 0) - exitscore = pathscore; - else - logadd (exitscore, pathscore); // propagate into preceding unit - } - } - } - - bwscore = exitscore; - if (islogzero (bwscore)) - fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d unit %d (%s) frames [%d,%d) bw score is zero (%d st, %d fr: %s)\n", - (int)edgeindex, (int) k, hmm.getname(), (int) ts, (int) te, (int) logbetas.rows(), (int) logbetas.cols(), gettranscript (units, hset).c_str()); - - te = ts; - je = js; - } - assert (te == 0 && je == 0); - const float totalbwscore = bwscore; - - // forward pass --regular Viterbi - // This also computes the gammas right away. - size_t ts = 0; // start frame for unit 'k' - size_t js = 0; // first row index of unit ' k' - float fwscore = 0.0f; // score passed across phone boundaries - foreach_index (k, units) // we exploit that units have fixed boundaries - { - const auto & hmm = hset.gethmm (units[k].unit); - const size_t n = hmm.getnumstates(); - const auto & transP = hmm.gettransP(); - const size_t te = ts + units[k].frames; // end time of current unit - const size_t je = js + n; // range of state indices - - // expand from states j at time t (including LL) to time t+1 - for (size_t t = ts; t < te; t++) // note: loop not entered for 0-frame units (tees) - { - for (size_t to = 0; to < n; to++) - { - const size_t j = js + to; // target trellis node - const size_t s = hmm.getsenoneid(to); - const float acLL = logLLs(s,t); - float alphajtnoll = LOGZERO; - if (t == ts) // entering score - { - const float pathscore = fwscore + transP(-1,to); - alphajtnoll = pathscore; - } - else for (size_t from = 0/*no entering possible*/; from < n; from++) - { - const size_t i = js + from; // origin trellis node - const float alphaitm1 = logalphas(i,t-1/*previous frame*/); - const float pathscore = alphaitm1 + transP(from,to); - logadd (alphajtnoll, pathscore); - } - logalphas(j,t) = alphajtnoll + acLL; - } - // update the gammas --do it here because in next frame, betas get overwritten by alphas (they share memory) - for (size_t j = js; j < je; j++) - { - if (!islogzero (totalbwscore)) - loggammas(j,t) = logalphas(j,t) + logbetas(j,t) - totalbwscore; - else // 0/0 problem, can occur if an ac score is so bad that it is 0 after going through softmax - loggammas(j,t) = LOGZERO; - } - } - // t = te: exit transition (last frame only or tee transition) - float exitscore; - if (te == ts) // tee transition - { - exitscore = fwscore + transP(-1,n); - } - else // not tee: expand all last states - { - exitscore = LOGZERO; - for (size_t from = 0/*no tee possible here*/; from < n; from++) - { - const size_t i = js + from; // origin trellis node - const float alphaitm1 = logalphas(i,te-1); // newly computed path score, transiting to t=te - const float pathscore = alphaitm1 + transP(from,n); - logadd (exitscore, pathscore); - } - } - fwscore = exitscore; // score passed on to next unit - js = je; - ts = te; - } - assert (js == logalphas.rows() && ts == logalphas.cols()); - const float totalfwscore = fwscore; - - // in extreme cases, we may have 0 ac probs, which lead to 0 path scores and division by 0 (subtracting LOGZERO) - // These cases must be handled separately. If the whole path is 0 (0 prob is on the only path at some point) then skip the lattice. - if (islogzero (totalbwscore) ^ islogzero (totalfwscore)) - fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d fw and bw 0 score %.10f vs. %.10f (%d st, %d fr: %s)\n", - (int)edgeindex, (float) totalfwscore, (float) totalbwscore, (int) js, (int) ts, gettranscript (units, hset).c_str()); - if (islogzero (totalbwscore)) - { - fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d has zero ac. score (%d st, %d fr: %s)\n", - (int)edgeindex, (int) js, (int) ts, gettranscript (units, hset).c_str()); - return LOGZERO; - } - - if (fabsf (totalfwscore - totalbwscore) / ts > 1e-4f) - fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d fw and bw score %.10f vs. %.10f (%d st, %d fr: %s)\n", - (int)edgeindex, (float) totalfwscore, (float) totalbwscore, (int) js, (int) ts, gettranscript (units, hset).c_str()); - - // we return the full path score - return totalfwscore; - -} - -// --------------------------------------------------------------------------- -// alignedge() -- perform Viterbi alignment on a single edge -// -// This is an alternative to forwardbackwardedge() that just uses the best path. -// Results: -// - if not returnsenoneids -> 'binary gammas(j,t)' for valid time ranges (remaining areas are not initialized); MMI-compatible -// - if returnsenoneids -> loggammas(0,t) will contain the senone ids directly instead (for sMBR mode) -// - return value is edge acoustic score -// Gammas matrix must have two extra columns as buffer. -// --------------------------------------------------------------------------- - -/*static*/ float lattice::alignedge (const_array_ref units, const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, - msra::math::ssematrixbase & loggammas, size_t edgeindex/*for diagnostic messages*/, const bool returnsenoneids, - array_ref thisedgealignmentsj) -{ - // alphas and betas are stored in-place inside the loggammas matrix shifted by one?two columns - assert (loggammas.cols() == logLLs.cols() + 2); - msra::math::ssematrixstriperef backpointers (loggammas, 0, logLLs.cols()); - msra::math::ssematrixstriperef pathscores (loggammas, 2, logLLs.cols()); - - // pathscores(j,t) store the sum of all paths up to including state j at time t, including logLL(j,t) - // backpointers(j,t) are the relative states that it came from - // gammas(j,t) <- 1 if on best path, 0 otherwise - - const int invalidbp = -2; - - // Viterbi alignment - size_t ts = 0; // start frame for unit 'k' - size_t js = 0; // first row index of unit 'k' - float fwscore = 0.0f; // score passed across phone boundaries - int fwbackpointer = -1; // bp passed across phone boundaries, -1 means start of utterance - foreach_index (k, units) // we exploit that units have fixed boundaries - { - const auto & hmm = hset.gethmm (units[k].unit); - const size_t n = hmm.getnumstates(); - const auto & transP = hmm.gettransP(); - const size_t te = ts + units[k].frames; // end time of current unit - const size_t je = js + hmm.getnumstates(); // range of state indices - - // expand from states j at time t (including LL) to time t+1 - for (size_t t = ts; t < te; t++) // note: loop not entered for 0-frame units (tees) - { - for (size_t j = js; j < je; j++) - { - const size_t to = j - js; // relative state - const size_t s = hmm.getsenoneid(to); - pathscores(j,t) = LOGZERO; - backpointers(j,t) = invalidbp; - if (t == ts) // entering score - { - const float pathscore = fwscore + transP(-1,to); - pathscores(j,t) = pathscore; - backpointers(j,t) = (float) fwbackpointer; - } - else for (size_t i = js; i < je; i++) - { - const size_t from = i - js; - const float alphaitm1 = pathscores(i,t-1/*previous frame*/); - const float pathscore = alphaitm1 + transP(from,to); - if (pathscore > pathscores(j,t)) - { - pathscores(j,t) = pathscore; - backpointers(j,t) = (float) i; - } - } - const float acLL = logLLs(s,t); - pathscores(j,t) += acLL; - } - } - // t = te: exit transition (last frame only or tee transition) - float exitscore = LOGZERO; - int exitbackpointer = invalidbp; - if (te == ts) // tee transition - { - exitscore = fwscore + transP(-1,n); - exitbackpointer = fwbackpointer; - } - else // not tee: expand all last states - { - for (size_t i = js; i < je; i++) - { - const size_t from = i - js; - const float alphaitm1 = pathscores(i,te-1); // newly computed path score, transiting to t=te - const float pathscore = alphaitm1 + transP(from,n); - if (pathscore > exitscore) - { - exitscore = pathscore; - exitbackpointer = (int) i; - } - } - } - if (exitbackpointer == invalidbp) - LogicError("exitbackpointer came up empty"); - fwscore = exitscore; // score passed on to next unit - fwbackpointer = exitbackpointer; // and accompanying backpointer - js = je; - ts = te; - } - assert (js == pathscores.rows() && ts == pathscores.cols()); - - // in extreme cases, we may have 0 ac probs, which lead to 0 path scores and division by 0 (subtracting LOGZERO) - // These cases must be handled separately. If the whole path is 0 (0 prob is on the only path at some point) then skip the lattice. - if (islogzero (fwscore)) - { - fprintf (stderr, "alignedge: WARNING: edge J=%d has zero ac. score (%d st, %d fr: %s)\n", - (int)edgeindex, (int) js, (int) ts, gettranscript (units, hset).c_str()); - return LOGZERO; - } - - // traceback & gamma update - size_t te = backpointers.cols(); - size_t je = backpointers.rows(); - int j = fwbackpointer; - for (size_t k = units.size() -1; k+1 > 0; k--) // go in units because we also need to clear out the column - { - const auto & hmm = hset.gethmm (units[k].unit); - const size_t ts = te - units[k].frames; // end time of current unit - const size_t js = je - hmm.getnumstates(); // range of state indices - for (size_t t = te -1; t + 1 > ts; t--) - { - if (j < (int) js || j >= (int) je) - LogicError("invalid backpointer resulting in state index out of range"); - - int bp = (int) backpointers(j,t); // save the backpointer before overwriting it (gammas and backpointers are aliases of each other) - //thisedgealignmentsj[t] = (unsigned short)hmm.getsenoneid(j - js); - if (!returnsenoneids) // return binary gammas (for MMI; this mode is compatible with softalignmode) - for (size_t i = js; i < je; i++) - loggammas(i,t) = ((int) i == j) ? 0.0f : LOGZERO; - else // return senone id (for sMBR; note: NOT compatible with softalignmode; calling code must know this) - thisedgealignmentsj[t] = (unsigned short) hmm.getsenoneid(j-js); - - if (bp == invalidbp) - LogicError("deltabackpointer not initialized"); - j = bp; // trace back one step - } - - te = ts; - je = js; - } - if (j != -1) - LogicError("invalid backpointer resulting in not reaching start of utterance when tracing back"); - assert (je == 0 && te == 0); - - // we return the full path score - return fwscore; -} - -// --------------------------------------------------------------------------- -// forwardbackwardlattice() -- lattice-level forward/backward -// -// This computes word posteriors, and also returns the per-node alphas and betas. -// Per-edge acoustic scores are passed in via a lambda, as this function is -// intended for use at multiple places with different scores. -// (Specifically, we also use it to determine a pruning threshold, based on -// the original lattice's ac. scores, before even bothering to compute the -// new ac. scores.) -// --------------------------------------------------------------------------- - -double lattice::forwardbackwardlattice (const std::vector & edgeacscores, parallelstate & parallelstate, std::vector & logpps, - std::vector & logalphas, std::vector & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, const bool sMBRmode, - const_array_ref & uids, const edgealignments & thisedgealignments, - std::vector & logEframescorrect, std::vector & Eframescorrectbuf, double & logEframescorrecttotal) const -{ // ^^ TODO: remove this - // --- hand off to parallelized (CUDA) implementation if available - if (parallelstate.enabled()) - { - double totalfwscore = parallelforwardbackwardlattice (parallelstate, edgeacscores, thisedgealignments, lmf, wp, amf, boostingfactor, logpps, logalphas, logbetas, sMBRmode, uids, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); - - return totalfwscore; - } - // if we get here, we have no CUDA, and do it the good ol' way - - // allocate return values - logpps.resize (edges.size()); // this is our primary return value - - // TODO: these are return values as well, but really shouldn't anymore; only used in some older baseline code we some day may want to compare against - logalphas.assign (nodes.size(), LOGZERO); - logalphas.front() = 0.0f; - logbetas.assign (nodes.size(), LOGZERO); - logbetas.back() = 0.0f; - - // --- sMBR version - - if (sMBRmode) - { - logEframescorrect.resize (edges.size()); - Eframescorrectbuf.resize (edges.size()); - - std::vector logaccalphas (nodes.size(), LOGZERO); // [i] expected frames-correct count over all paths from start to node i - std::vector logaccbetas (nodes.size(), LOGZERO); // [i] likewise - std::vector logframescorrectedge (edges.size()); // raw counts of correct frames in each edge - - // forward pass - foreach_index (j, edges) - { - if (islogzero(edgeacscores[j])) // indicates that this edge is pruned - continue; - const auto & e = edges[j]; - const double inscore = logalphas[e.S]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - const double pathscore = inscore + edgescore; - logadd (logalphas[e.E], pathscore); - - size_t ts = nodes[e.S].t; - size_t te = nodes[e.E].t; - size_t framescorrect = 0; // count raw number of correct frames - for (size_t t = ts; t < te; t++) - framescorrect += (thisedgealignments[j][t-ts] == uids[t]); - logframescorrectedge[j] = (framescorrect > 0) ? log ((double) framescorrect) : LOGZERO; // remember for backward pass - double loginaccs = logaccalphas[e.S] - logalphas[e.S]; - logadd (loginaccs, logframescorrectedge[j]); - double logpathacc = loginaccs + logalphas[e.S] + edgescore; - logadd (logaccalphas[e.E], logpathacc); - } - foreach_index (j, logaccalphas) - logaccalphas[j] -= logalphas[j]; - - const double totalfwscore = logalphas.back(); - const double totalfwacc = logaccalphas.back(); - if (islogzero (totalfwscore)) - { - fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); - return LOGZERO; // failed, do not use resulting matrix - } - - // backward pass and computation of state-conditioned frames-correct count - for (size_t j = edges.size() -1; j+1 > 0; j--) - { - if (islogzero(edgeacscores[j])) // indicates that this edge is pruned - continue; - const auto & e = edges[j]; - const double inscore = logbetas[e.E]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - const double pathscore = inscore + edgescore; - logadd (logbetas[e.S], pathscore); - - double loginaccs = logaccbetas[e.E] - logbetas[e.E]; - logadd (loginaccs, logframescorrectedge[j]); - double logpathacc = loginaccs + logbetas[e.E] + edgescore; - logadd (logaccbetas[e.S], logpathacc); - - // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) - double logpp = logalphas[e.S] + edgescore + logbetas[e.E] - totalfwscore; - if (logpp > 1e-2) - fprintf (stderr, "forwardbackward: WARNING: edge J=%d log posterior %.10f > 0\n", (int) j, (float) logpp); - if (logpp > 0.0) - logpp = 0.0; - logpps[j] = logpp; - double tmplogeframecorrect = logframescorrectedge[j]; - logadd (tmplogeframecorrect, logaccalphas[e.S]); - logadd (tmplogeframecorrect, logaccbetas[e.E] - logbetas[e.E]); - Eframescorrectbuf[j] = exp(tmplogeframecorrect); - } - foreach_index (j, logaccbetas) - logaccbetas[j] -= logbetas[j]; - const double totalbwscore = logbetas.front(); - const double totalbwacc = logaccbetas.front(); - if (fabs (totalfwscore - totalbwscore) / info.numframes > 1e-4) - fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); - - if (fabs (totalfwacc - totalbwacc) / info.numframes > 1e-4) - fprintf (stderr, "forwardbackwardlatticesMBR: WARNING: lattice fw and bw accs %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); - - logEframescorrecttotal = totalbwacc; - return totalbwscore; - } - - // --- MMI version - - // forward pass - foreach_index (j, edges) - { - const auto & e = edges[j]; - const double inscore = logalphas[e.S]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned - const double pathscore = inscore + edgescore; - logadd (logalphas[e.E], pathscore); - } - const double totalfwscore = logalphas.back(); - if (islogzero (totalfwscore)) - { - fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); - return LOGZERO; // failed, do not use resulting matrix - } - - // backward pass - // this also computes the word posteriors on the fly, since we are at it - for (size_t j = edges.size() -1; j+1 > 0; j--) - { - const auto & e = edges[j]; - const double inscore = logbetas[e.E]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - const double pathscore = inscore + edgescore; - logadd (logbetas[e.S], pathscore); - - // compute lattice posteriors on the fly since we are at it - double logpp = logalphas[e.S] + edgescore + logbetas[e.E] - totalfwscore; - if (logpp > 1e-2) - fprintf (stderr, "forwardbackward: WARNING: edge J=%d log posterior %.10f > 0\n", (int) j, (float) logpp); - if (logpp > 0.0) - logpp = 0.0; - logpps[j] = logpp; - } - - const double totalbwscore = logbetas.front(); - if (fabs (totalfwscore - totalbwscore) / info.numframes > 1e-4) - fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); - - return totalfwscore; -} - -// --------------------------------------------------------------------------- -// forwardbackwardlatticesMBR() -- compute expected frame-accuracy counts, -// both the conditioned one (corresponding to c(q) in Dan Povey's thesis) -// and the global one (which is the sMBR criterion to optimize). -// -// Outputs: -// - Eframescorrect[j] == expected frames-correct count conditioned on a state of edge[j]. -// We currently assume a hard state alignment. With that, the value turns out -// to be identical for all states of an edge, so we only store it once per edge. -// - return value: expected frames-correct count for entire lattice -// -// Call forwardbackwardlattices() first to compute logalphas/betas. -// --------------------------------------------------------------------------- - -double lattice::forwardbackwardlatticesMBR (const std::vector & edgeacscores, const msra::asr::simplesenonehmm & hset, - const std::vector & logalphas, const std::vector & logbetas, - const float lmf, const float wp, const float amf, const_array_ref & uids, - const edgealignments & thisedgealignments, std::vector & Eframescorrect) const -{ - std::vector accalphas (nodes.size(), 0); // [i] expected frames-correct count over all paths from start to node i - std::vector accbetas (nodes.size(), 0); // [i] likewise - std::vector maxcorrect (nodes.size(), 0); // [i] max correct frames up to this node (oracle) - - std::vector framescorrectedge (edges.size()); // raw counts of correct frames in each edge - - std::vector backpointersformaxcorr(nodes.size(), -2); // keep track of backpointer for the max corr - backpointersformaxcorr.front() = -1; - - // forward pass - foreach_index (j, edges) - { - if (islogzero(edgeacscores[j])) // indicates that this edge is pruned - continue; - const auto & e = edges[j]; - const double inaccs = accalphas[e.S]; - size_t ts = nodes[e.S].t; - size_t te = nodes[e.E].t; - - size_t framescorrect = 0; // count raw number of correct frames - for (size_t t = ts; t < te; t++) - framescorrect += (thisedgealignments[j][t-ts] == uids[t]); - framescorrectedge[j] = (double) framescorrect; // remember for backward pass - - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - // contribution to end node's path acc = start node's plus edge's correct count, weighted by LL, and divided by sum over LLs - double pathacc = (inaccs + framescorrectedge[j]) * exp (logalphas[e.S] + edgescore - logalphas[e.E]); - accalphas[e.E] += pathacc; - // also keep track of max accuracy, so we can find out whether the lattice contains the correct path - size_t oracleframescorrect = maxcorrect[e.S] + framescorrect; // keep track of most correct path up to end of this edge - if (oracleframescorrect > maxcorrect[e.E]) - { - maxcorrect[e.E] = oracleframescorrect; - backpointersformaxcorr[size_t(e.E)] = j; - } - } - const double totalfwacc = accalphas.back(); - - hset; // just for reference - - // report on ground-truth path - // TODO: we will later have code that adds this path if needed - size_t oracleframeacc = maxcorrect.back(); - if (oracleframeacc != info.numframes) - fprintf (stderr, "forwardbackwardlatticesMBR: ground-truth path missing from lattice (most correct path: %d out of %d frames correct)\n", (unsigned int) oracleframeacc, (int) info.numframes); - - // backward pass and computation of state-conditioned frames-correct count - for (size_t j = edges.size() -1; j+1 > 0; j--) - { - if (islogzero(edgeacscores[j])) // indicates that this edge is pruned - continue; - const auto & e = edges[j]; - const double inaccs = accbetas[e.E]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; - double pathacc = (inaccs + framescorrectedge[j]) * exp (logbetas[e.E] + edgescore - logbetas[e.S]); - accbetas[e.S] += pathacc; - - // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) - Eframescorrect[j] = (float) (accalphas[e.S] + accbetas[e.E] + framescorrectedge[j]); - } - - const double totalbwacc = accbetas.front(); - - if (fabs (totalfwacc - totalbwacc) / info.numframes > 1e-4) - fprintf (stderr, "forwardbackwardlatticesMBR: WARNING: lattice fw and bw accs %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); - - return totalbwacc; -} - -// --------------------------------------------------------------------------- -// bestpathlattice() -- lattice-level "forward/backward" that only returns the -// best path, but in the form of word posteriors, which are 1 or 0, just like -// a real lattice-level forward/backward would do. -// We don't really use this; this was only for a contrast experiment. -// --------------------------------------------------------------------------- - -double lattice::bestpathlattice (const std::vector & edgeacscores, std::vector & logpps, - const float lmf, const float wp, const float amf) const -{ - // forward pass --sortnedness => regular Viterbi - std::vector logalphas (nodes.size(), LOGZERO); - std::vector backpointers(nodes.size(), -2); - logalphas.front() = 0.0f; - backpointers.front() = -1; - foreach_index (j, edges) - { - const auto & e = edges[j]; - const double inscore = logalphas[e.S]; - const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned - const double pathscore = inscore + edgescore; - if (pathscore > logalphas[e.E]) - { - logalphas[e.E] = pathscore; - backpointers[e.E] = j; - } - } - - const double totalfwscore = logalphas.back(); - if (islogzero (totalfwscore)) - { - fprintf (stderr, "bestpathlattice: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); - return LOGZERO; // failed, do not use resulting matrix - } - - // traceback - // We encode the result by storing log 1 in edges on the best path, and log 0 else; - // this makes it naturally compatible with softalign mode - logpps.resize (edges.size()); - foreach_index (j, edges) logpps[j] = LOGZERO; - - int backpos = backpointers[nodes.size()-1]; - while (backpos >= 0) - { - logpps[backpos] = 0.0f; // edge is on best path -> PP = 1.0 - backpos = backpointers[edges[backpos].S]; - } - assert (backpos == -1); - - return totalfwscore; -} - -// --------------------------------------------------------------------------- -// forwardbackwardalign() -- compute the statelevel gammas or viterbi alignments -// the first phase of lattice::forwardbackward -// -// Outputs: -// --------------------------------------------------------------------------- -void lattice::forwardbackwardalign (parallelstate & parallelstate, - const msra::asr::simplesenonehmm & hset, const bool softalignstates, - const double minlogpp, const std::vector & origlogpps, - std::vector & abcs, littlematrixheap & matrixheap, - const bool returnsenoneids, - std::vector & edgeacscores, const msra::math::ssematrixbase & logLLs, - edgealignments & thisedgealignments, backpointers & thisbackpointers, array_ref & uids, const_array_ref bounds ) const -{ // NOTE: this will be removed and replaced by a proper representation of alignments someday - // do forward-backward or alignment on a per-edge basis. This gives us: - // - per-edge gamma[j,t] = P(s(t)==s_j|edge) if forwardbackward, per-edge alignment thisedgealignments[j] if alignment - // - per-edge acoustic scores - const size_t silunitid = hset.gethmmid("sil"); // shall be the same as parallelstate.getsilunitid() - bool parallelsil = true; - bool cpuverification = false; - -#ifndef PARALLEL_SIL // we use a define to make this marked - parallelsil = false; -#endif -#ifdef CPU_VERIFICATION - cpuverification = true; -#endif - - // Phase 1: abcs allocate - if (!parallelstate.enabled() || !parallelsil || cpuverification) // allocate abcs when 1.parallelstate not enabled (cpu mode); 2. enabled but not PARALLEL_SIL (silence need to be allocate); 3. cpuverfication - { - abcs.resize (edges.size(), NULL); // [edge index] -> alpha/beta/gamma matrices for each edge - size_t countskip = 0; // if pruning: count how many edges are pruned - - foreach_index (j, edges) - { - // determine number of frames - // TODO: this is not efficient--we only use a block-diagonal-like structure, rest is empty (exploiting the fixed boundaries) - const size_t edgeframes = nodes[edges[j].E].t - nodes[edges[j].S].t; - if (edgeframes == 0) // dummy !NULL edge at end of lattice - { - if ((size_t) j != edges.size() -1) - RuntimeError("forwardbackwardalign: unxpected 0-frame edge (only allowed at very end)"); - // note: abcs[j] is already initialized to be NULL in this case, which protects us from accidentally using it - } - else - { - // determine the number of states in an edge - const auto & aligntokens = getaligninfo (j); // get alignment tokens - size_t edgestates = 0; - - bool edgehassil = false; - foreach_index (i, aligntokens) - if (aligntokens[i].unit == silunitid) - edgehassil = true; - - if (!cpuverification && !edgehassil && parallelstate.enabled()) // !cpuverification, parallel & is non sil, we do not allocate - { - abcs[j] = NULL; - continue; - } - - foreach_index (k, aligntokens) - edgestates += hset.gethmm (aligntokens[k].unit).getnumstates(); - - // allocate the matrix - if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) - countskip++; - else - abcs[j] = &matrixheap.newmatrix (edgestates, edgeframes + 2); // +2 to have one extra column for betas and one for gammas - } - } - if (minlogpp > LOGZERO) - fprintf(stderr, "forwardbackwardalign: %d of %d edges pruned\n", (int)countskip, (int)edges.size()); - } - - // Phase 2: alignment on CPU - if (parallelstate.enabled() && !parallelsil) // silence edge shall be process separately if not cuda and not PARALLEL_SIL - { - if (softalignstates) - LogicError("forwardbackwardalign: parallelized version currently only handles hard alignments"); - if (minlogpp > LOGZERO) - fprintf(stderr, "forwardbackwardalign: pruning not supported (we won't need it!) :)\n"); - edgeacscores.resize (edges.size()); - for (size_t j = 0; j < edges.size(); j++) - { - const auto & aligntokens = getaligninfo (j); // get alignment tokens - if (aligntokens.size() == 0) - continue; - bool edgehassil = false; - foreach_index (i, aligntokens) - { - if (aligntokens[i].unit == silunitid) - edgehassil = true; - } - if (!edgehassil) // only process sil - continue; - const edgeinfowithscores & e = edges[j]; - const size_t ts = nodes[e.S].t; - const size_t te = nodes[e.E].t; - const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); - edgeacscores[j] = alignedge (aligntokens, hset, edgeLLs, *abcs[j], j, true, thisedgealignments[j]); - } - } - - // Phase 3: alignment on GPU - if (parallelstate.enabled()) - parallelforwardbackwardalign (parallelstate, hset, logLLs, edgeacscores, thisedgealignments, thisbackpointers); - - //zhaorui align to reference mlf - if (bounds.size() > 0) - { - size_t framenum=bounds.size(); - - msra::math::ssematrixbase *refabcs; - size_t ts, te, t; - ts = te = 0; - - - aligninfo *refinfo; - unsigned short *refalign; - - refinfo = (aligninfo*) malloc(sizeof(aligninfo)*1); - refalign = (unsigned short *) malloc(sizeof(unsigned short ) * framenum); - - array_ref refunits(refinfo,1); - array_ref refedgealignmentsj (refalign , framenum); - - while (te < framenum) - { - // found one phone's boundary (ts, te) - t=ts+1; - while (t < framenum && bounds[t] == 0 ) - t++; - te=t; - - - //make one phone unit - size_t phoneid = bounds[ts]-1; - refunits[0].unit = phoneid; - refunits[0].frames = te-ts; - - size_t edgestates = hset.gethmm(phoneid).getnumstates(); - littlematrixheap refmatrixheap (1); // for abcs - refabcs = &refmatrixheap.newmatrix (edgestates, te-ts + 2); - const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); - //do alignment - alignedge ((const_array_ref) refunits, hset, edgeLLs, *refabcs, 0, true,refedgealignmentsj); - - for(t=ts; t< te; t++) - { - uids[t] = (size_t)refedgealignmentsj[t-ts]; - } - ts=te; - - } - - free(refinfo); - free(refalign); - } - - // Phase 4: alignment or forwardbackward on CPU for non parallel mode or verification - - if (!parallelstate.enabled() || cpuverification) //non parallel mode or verification - { - edgeacscores.resize (edges.size()); - std::vector edgeacscoresgpu; - edgealignments thisedgealignmentsgpu (thisedgealignments); - if (cpuverification) - { - parallelstate.getedgeacscores (edgeacscoresgpu); - parallelstate.copyalignments (thisedgealignmentsgpu); - } - foreach_index (j, edges) - { - const edgeinfowithscores & e = edges[j]; - const size_t ts = nodes[e.S].t; - const size_t te = nodes[e.E].t; - if (ts == te) // dummy !NULL edge at end - edgeacscores[j] = 0.0f; - else - { - const auto & aligntokens = getaligninfo (j); // get alignment tokens - const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); - if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) - edgeacscores[j] = LOGZERO; // will kill word level forwardbackward hypothesis - else if (softalignstates) - edgeacscores[j] = forwardbackwardedge (aligntokens, hset, edgeLLs, *abcs[j], j); - else - edgeacscores[j] = alignedge (aligntokens, hset, edgeLLs, *abcs[j], j, returnsenoneids, thisedgealignments[j]); - } - if (cpuverification) - { - const auto & aligntokens = getaligninfo (j); // get alignment tokens - bool edgehassil = false; - foreach_index (i, aligntokens) - { - if (aligntokens[i].unit == silunitid) - edgehassil = true; - } - if (fabs(edgeacscores[j] - edgeacscoresgpu[j]) > 1e-3) - { - fprintf (stderr, "edge %d, sil ? %d, edgeacscores / edgeacscoresgpu MISMATCH %f v.s. %f, diff %e\n", - j, edgehassil ? 1 : 0, (float)edgeacscores[j], (float)edgeacscoresgpu[j], - (float)(edgeacscores[j] - edgeacscoresgpu[j])); - fprintf (stderr, "aligntokens: "); - foreach_index (i, aligntokens) - fprintf (stderr, "%d %d; ", i, aligntokens[i].unit); - fprintf (stderr, "\n"); - } - for (size_t t = ts; t < te; t++) - { - if (thisedgealignments[j][t-ts] != thisedgealignmentsgpu[j][t-ts]) - fprintf (stderr, "edge %d, sil ? %d, time %d, alignment / alignmentgpu MISMATCH %d v.s. %d\n", j, edgehassil ? 1 : 0, (int)(t-ts), thisedgealignments[j][t-ts], thisedgealignmentsgpu[j][t-ts]); - } - } - } - } -} - -// compute the error signal for sMBR mode -void lattice::sMBRerrorsignal (parallelstate & parallelstate, - msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg, // output - const std::vector & logpps, const float amf, - double minlogpp, const std::vector & origlogpps, const std::vector & logEframescorrect, - const double logEframescorrecttotal, const edgealignments & thisedgealignments) const -{ - if (parallelstate.enabled()) // parallel version - { - /* time measurement for parallel sMBRerrorsignal - errorsignalcompute: 19.871935 ms (cuda) v.s. 448.711444 ms (emu) */ - if (minlogpp > LOGZERO) - fprintf(stderr, "sMBRerrorsignal: pruning not supported (we won't need it!) :)\n"); - parallelsMBRerrorsignal (parallelstate, thisedgealignments, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); - return; - } - - // linear mode - foreach_coord (i, j, errorsignal) - errorsignal(i,j) = 0.0f; // Note: we don't actually put anything into the numgammas - foreach_index (j, edges) - { - const auto & e = edges[j]; - if (nodes[e.S].t == nodes[e.E].t) // this happens for dummy !NULL edge at end of file - continue; - if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) // this is pruned - continue; - - size_t ts = nodes[e.S].t; - size_t te = nodes[e.E].t; - - const double diff = logEframescorrect[j] - logEframescorrecttotal; - // Note: the contribution of the states of an edge to their senones is the same for all states - // so we compute it once and add it to all; this will not be the case without hard alignments. - const double pp = exp (logpps[j]); // edge posterior - const float edgecorrect = (float) (pp * diff) / amf; - for (size_t t = ts; t < te; t++) - { - const size_t s = thisedgealignments[j][t-ts]; - errorsignal(s,t) += edgecorrect; - } - } -} - -// compute the error signal for MMI mode -void lattice::mmierrorsignal (parallelstate & parallelstate, double minlogpp, const std::vector & origlogpps, - std::vector & abcs, const bool softalignstates, - const std::vector & logpps, const msra::asr::simplesenonehmm & hset, - const edgealignments & thisedgealignments, msra::math::ssematrixbase & errorsignal) const -{ - if (parallelstate.enabled()) - { - if (minlogpp > LOGZERO) - fprintf(stderr, "mmierrorsignal: pruning not supported (we won't need it!) :)\n"); - if (softalignstates) - LogicError("mmierrorsignal: parallel version for softalignstates mode is not supported yet"); - parallelmmierrorsignal (parallelstate, thisedgealignments, logpps, errorsignal); - return; - } - - for (size_t j = 0; j < (errorsignal).cols(); j++) - for (size_t i = 0; i < (errorsignal).rows(); i++) - errorsignal(i,j) = VIRGINLOGZERO; // set to zero --note: may be in-place with logLLs, which now get overwritten - - // size_t warnings = 0; // [v-hansu] check code for mmi; search this comment to see all related codes - foreach_index (j, edges) - { - const auto & e = edges[j]; - if (nodes[e.S].t == nodes[e.E].t) // this happens for dummy !NULL edge at end of file - continue; - if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) // this is pruned - continue; - - const auto & aligntokens = getaligninfo (j); // get alignment tokens - auto & loggammas = *abcs[j]; - - const float edgelogP = (float) logpps[j]; - //if (islogzero (edgelogP)) // we had a 0 prob - // continue; - - // accumulate this edge's gamma matrix into target posteriors - const size_t tedge = nodes[e.S].t; - size_t ts = 0; // time index into gamma matrix - size_t js = 0; // state index into gamma matrix - foreach_index (k, aligntokens) // we exploit that units have fixed boundaries - { - const auto & unit = aligntokens[k]; - const size_t te = ts + unit.frames; - const auto & hmm = hset.gethmm (unit.unit); // TODO: inline these expressions - const size_t n = hmm.getnumstates(); - const size_t je = js + n; - // P(s) = P(s|e) * P(e) - for (size_t t = ts; t < te; t++) - { - const size_t tutt = t + tedge; // time index w.r.t. utterance - //double logsum = LOGZERO; // [v-hansu] check code for mmi; search this comment to see all related codes - for (size_t i = 0; i < n; i++) - { - const size_t j = js + i; // state index for this unit in matrix - const size_t s = hmm.getsenoneid (i); // state class index - const float gammajt = loggammas(j,t); - const float statelogP = edgelogP + gammajt; - logadd (errorsignal(s,tutt), statelogP); - } - } - ts = te; - js = je; - } - assert (ts + 2 == loggammas.cols() && js == loggammas.rows()); - } - - // check normalizedness (is that an actual English word?) - // also count non-zero probs - size_t nonzerostates = 0; - foreach_column (t, errorsignal) - { - double logsum = LOGZERO; - foreach_row (s, errorsignal) - { - if (islogzero (errorsignal(s,t))) - nonzerostates++; - else - logadd (logsum, (double) errorsignal(s,t)); - // TODO: count VIRGINLOGZERO, print per frame - } - if (fabs (logsum) / errorsignal.rows() > 1e-6) - fprintf (stderr, "forwardbackward: WARNING: overall posterior column(%d) sum = exp (%.10f) != 1\n", (int)t, logsum); - } - fprintf (stderr, "forwardbackward: %.3f%% non-zero state posteriors\n", 100.0f - nonzerostates * 100.0f / errorsignal.rows() / errorsignal.cols()); - - // convert to non-log posterior --that's what we return - foreach_coord (i, j, errorsignal) - errorsignal(i,j) = expf (errorsignal(i,j)); -} - -// compute ground truth's score -// It is critical to get all details consistent with the lattice, to avoid skewing the weights. -// ... TODO: we don't need this to be a class member, actually; try to just make it a 'static' function. -/*static*/ double lattice::scoregroundtruth (const_array_ref uids, const_array_ref transcript, const std::vector & transcriptunigrams, - const msra::math::ssematrixbase & logLLs, const msra::asr::simplesenonehmm & hset, const float lmf, const float wp, const float amf) -{ - if (transcript[0].firstframe != 0) // TODO: should we store the #frames instead? Then we can validate the total duration - LogicError("scoregroundtruth: first transcript[] token does not start at frame 0"); - - // get the silence models, since they are treated specially - const size_t numframes = logLLs.cols(); - const auto & sil = hset.gethmm (hset.gethmmid ("sil")); - const auto & sp = hset.gethmm (hset.gethmmid ("sp")); - if (sp.numstates != 1 || sil.numstates != 3) - RuntimeError("scoregroundtruth: only supports 1-state /sp/ and 3-state /sil/ tied to /sp/"); - const size_t silst = sp.senoneids[0]; - - // loop over words - double pathscore = 0.0; - foreach_index (i, transcript) - { - size_t ts = transcript[i].firstframe; - size_t te = ((size_t) i+1 < transcript.size()) ? transcript[i+1].firstframe : numframes; - if (ts >= te) - LogicError("scoregroundtruth: transcript[] tokens out of order"); - // acoustic score: loop over frames - const msra::asr::simplesenonehmm::transP * prevtransP = NULL; // previous transP - int prevs = -1; // previous state index - for (size_t t = ts; t < te; t++) - { - size_t senoneid = uids[t]; - // recover the transP and state index - int s; - const msra::asr::simplesenonehmm::transP * transP; - if (senoneid == silst) - { - if (prevtransP == &sil.gettransP()) // "silst" may be tied to /sp/, and thus the /sil/ center state may be ambiguous - { - transP = prevtransP; // remain in /sil/ - s = 1; // note that this will fail if /sp/ can follow /sil/ and if /sil/ may end with "silst" (both not allowed currently) - } - else // "silst" -> we are in /sp/ - { - transP = &sp.gettransP(); - s = 0; - } - } - else // all others must be non-ambiguous - { - int transPindex = hset.senonetransP (senoneid); - int sindex = hset.senonestate (senoneid); - if (transPindex == -1 || sindex == -1) - RuntimeError("scoregroundtruth: failed to resolve ambiguous senone %s", hset.getsenonename (senoneid)); - transP = &hset.transPs[transPindex]; - s = sindex; - } - // if changing phoneme then add necessary enter/exit score - if (transP != prevtransP) - { - if (prevtransP) // previous exit transition - pathscore += (*prevtransP)(prevs,prevtransP->getnumstates()); - prevs = -1; // enter transition - } - // add inter-state transP - pathscore += (*transP)(prevs,s); - // acoustic score - pathscore += logLLs (senoneid,t); - // remember state for next frame - prevtransP = transP; - prevs = s; - } - // add the last exit transition - if (prevtransP) // previous exit transition - pathscore += (*prevtransP)(prevs,prevtransP->getnumstates()); - // need to add a /sp/ tee transition if we don't end in sp, since our transcript dictionary includes a /sp/ at the end of each entry - if (uids[te-1] != sp.senoneids[0]) - pathscore += sp.gettransP()(-1,sp.numstates); - // lm - pathscore += transcriptunigrams[i] * lmf + wp; - } - if (islogzero (pathscore)) - fprintf (stderr, "scoregroundtruth: ground-truth path has zero probability; some model inconsistency, maybe?\n"); - // account for amf - pathscore /= amf; - fprintf (stderr, "scoregroundtruth: ground-truth score %.6f (%d frames)\n", pathscore, (int) numframes); - return pathscore; -} - -// --------------------------------------------------------------------------- -// sMBRdiagnostics() -- helper to print some diagnostics for analyzing sMBR results -// --------------------------------------------------------------------------- - -// static // with 'static', compiler will complain if the function is not used (we only compile it in sometimes for diagnostics) -void sMBRdiagnostics (const msra::math::ssematrixbase & errorsignal, const_array_ref uids, - const_array_ref bestpath, const vector & refseen, const msra::asr::simplesenonehmm & hset) -{ - // TODO: - // - print best positive runner-up - // - WARN tag if neg > pos - // - check the sum and warn if not 0 - // - indicate whether best path state is correct or not - size_t numcor = 0; - size_t numnegbetter = 0; // # frames the neg competitor is better - size_t numposbetter = 0; // # frames the pos competitor is better - foreach_column (t, errorsignal) - { - const size_t sref = uids[t]; - const char * srefname = hset.getsenonename (sref); - const size_t sbest = bestpath[t]; - const char * sbestname = hset.getsenonename (sbest); - if (sref == sbest) - numcor++; - // for each frame, print error signal for ground truth and runner up (second largest abs value) - size_t sneg = SIZE_MAX; // competitor - float eneg = 0.0f; - size_t spos = SIZE_MAX; // best postive competitor - float epos = 0.0f; - foreach_row (s, errorsignal) - { - if (s == sref) - continue; - if (errorsignal(s,t) < eneg) - { - sneg = s; - eneg = errorsignal(s,t); - } - if (errorsignal(s,t) > epos) - { - spos = s; - epos = errorsignal(s,t); - } - } - if (fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < -eneg) - numnegbetter++; - if (fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < epos) - numposbetter++; - const char * snegname = sneg == SIZE_MAX ? "-" : hset.getsenonename (sneg); - const char * sposname = spos == SIZE_MAX ? "-" : hset.getsenonename (spos); - fprintf (stderr, "e(%d): ref %s: %.6f / %s: %.6f / %s: %.6f / top %s: %.6f%s%s%s%s%s\n", - (int) t, srefname, errorsignal (sref, t), snegname, eneg, sposname, epos, sbestname, errorsignal (sbest, t), - sbest == sref ? "" : " ERR", - fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < 0 ? " INV!!" : "", - fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < -eneg ? " WEAK" : "", - fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < epos ? " 2ND" : "", - refseen[t] ? "" : " NOREF"); - } - // print this to validate our bestpath computation - fprintf (stderr, "sMBRdiagnostics: %d frames correct out of %d, %.2f%%, neg better in %d, pos in %d\n", - (int) numcor, (int)errorsignal.cols(), 100.0f * numcor / errorsignal.cols(), - (int) numnegbetter, (int) numposbetter); -} - -// static // with 'static', compiler will complain if the function is not used (we only compile it in sometimes for diagnostics) -void sMBRsuppressweirdstuff (msra::math::ssematrixbase & errorsignal, const_array_ref uids) -{ - size_t numweird = 0; - foreach_column (t, errorsignal) - { - const size_t sref = uids[t]; - // for each frame, print error signal for ground truth and runner up (second largest abs value) - const float eref = errorsignal(sref,t); - bool isweird = eref < 0.0f; // negative for reference!? - for (size_t s = 0; s < errorsignal.rows() && !isweird; s++) - { - if (s == sref) - continue; - if (fabs (errorsignal(s,t)) > eref) - isweird = true; - } - if (isweird) - { - foreach_row (s, errorsignal) - errorsignal(s,t) = 0.0f; - numweird++; - } - } - // print this to validate our bestpath computation - fprintf (stderr, "sMBRsuppressweirdstuff: %d weird frames out of %d, %.2f%% were flattened\n", - (int) numweird, (int) errorsignal.cols(), 100.0f * numweird / errorsignal.cols()); -} - - -// --------------------------------------------------------------------------- -// forwardbackward() -- main function for MMI/sMBR -// -// This computes the lattice state-level statistics for sequence training using MMI or sMBR. -// -// Outputs, MMI mode: -// - result = dengammas = denominator gammas (non-log form) -// - returns log of sum over all paths' likelihoods (the denominator of the MMI objective) -// - note: numgammas is not used/touched in MMI mode -// -// Outputs, sMBR mode: -// TODO: fix this comment -// - result = errorsignal = (abs value of) negative contributions to error signal -// - errorsignalbuf = for temporarily use to get errorsignal -// - returns expected frames-correct count (the sMBR objective) -// --------------------------------------------------------------------------- - -double lattice::forwardbackward (parallelstate & parallelstate, const msra::math::ssematrixbase & logLLs, const msra::asr::simplesenonehmm & hset, - msra::math::ssematrixbase & result, msra::math::ssematrixbase & errorsignalbuf, - const float lmf, const float wp, const float amf, const float boostingfactor, - const bool sMBRmode, array_ref uids, const_array_ref bounds, - const_array_ref transcript, const std::vector & transcriptunigrams) const -{ - bool softalign = true; - bool softalignstates = false; // true if soft alignment within edges, currently we only support soft within edge in cpu mode - bool softalignlattice = softalign; // w.r.t. whole lattice - - edgealignments thisedgealignments (*this); // alignments memory allocate for this lattice - backpointers thisbackpointers (*this, hset); // memory for forwardbackward - - if (info.numframes != logLLs.cols()) - LogicError("forwardbackward: #frames mismatch between lattice (%d) and LLs (%d)", (int) info.numframes, (int) logLLs.cols()); - // TODO: the following checks should throw, but I don't dare in case this will crash a critical job... if we never see this warning, then - if (info.numframes != uids.size()) - fprintf (stderr, "forwardbackward: #frames mismatch between lattice (%d) and uids (%d)\n", (int) info.numframes, (int) uids.size()); - if (info.numframes != result.cols()) - fprintf (stderr, "forwardbackward: #frames mismatch between lattice (%d) and result (%d)\n", (int) info.numframes, (int) result.cols()); - - littlematrixheap matrixheap (info.numedges); // for abcs - - // PHASE 0: fake word level forward backwards --only used when pruning enabled - const double minlogpp = LOGZERO; // pruning threshold --LOGZERO means disabled - std::vector origlogpps; // word posterior from original lattice, for pruning decision - - // PHASE 1: per-edge forward backwards (="time alignments") - - // score the ground truth --only if a transcript is provided, which happens if the user provides a language model - // TODO: no longer used, remove this. 'transcript' parameter is no longer used in this function. - transcript; transcriptunigrams; - - // allocate alpha/beta/gamma matrices (all are sharing the same memory in-place) - std::vector abcs; - std::vector edgeacscores; // [edge index] acoustic scores - //funcation call for forwardbackward on edge level - forwardbackwardalign (parallelstate, hset, softalignstates, minlogpp, origlogpps, abcs, matrixheap, sMBRmode/*returnsenoneids*/, edgeacscores, logLLs, thisedgealignments, thisbackpointers, uids,bounds); - - // PHASE 2: lattice-level forward backward - - // we exploit that the lattice is sorted by (end node, start node) for in-place processing - - // checklattice(); // comment out by v-hansu to save time -#ifdef PRINT_TIME_MEASUREMENT - auto_timer latlevelfwbw; -#endif - std::vector logpps; - std::vector Eframescorrectbuf; // this is used for compute the Eframescorrectdiff - std::vector logEframescorrect; // this is the final output of PHASE 2 - std::vector logalphas; - std::vector logbetas; - double totalfwscore = 0; // TODO: name no longer precise in sMBRmode - double logEframescorrecttotal = LOGZERO; - - bool returnEframescorrect = sMBRmode; - if (softalignlattice) - { - totalfwscore = forwardbackwardlattice (edgeacscores, parallelstate, logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, (const_array_ref &)uids, thisedgealignments, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); - if (sMBRmode && !returnEframescorrect) - logEframescorrecttotal = forwardbackwardlatticesMBR (edgeacscores, hset, logalphas, logbetas, lmf, wp, amf, (const_array_ref &)uids, thisedgealignments, Eframescorrectbuf); - //^^ BUGBUG not tested - } - else - totalfwscore = bestpathlattice (edgeacscores, logpps, lmf, wp, amf); -#ifdef PRINT_TIME_MEASUREMENT - latlevelfwbw.show("latlevelfwbw"); // 68.395682 ms -#endif - if (islogzero (totalfwscore)) - { - fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); - return LOGZERO; // failed, do not use resulting matrix - } - - // PHASE 3: compute final state-level posteriors (MMI mode) - - // compute expected frames correct in sMBRmode - - const size_t numframes = logLLs.cols(); - assert (numframes == info.numframes); - //fprintf (stderr, "forwardbackward: total forward score %.6f (%d frames)\n", totalfwscore, (int) numframes); // for now--while we are debugging the GPU port - - // MMI mode - if(!sMBRmode) - { - // we first take the sum in log domain to avoid numerical issues - auto & dengammas = result; // result is denominator gammas - mmierrorsignal (parallelstate, minlogpp, origlogpps, abcs, softalignstates, logpps, hset, thisedgealignments, dengammas); - return totalfwscore / numframes; // return value is av. posterior - } - // sMBR mode - else - { - auto & errorsignal = result; - sMBRerrorsignal (parallelstate, errorsignal, errorsignalbuf, logpps, amf, minlogpp, origlogpps, logEframescorrect, logEframescorrecttotal, thisedgealignments); - - static bool dummyvariable = (fprintf(stderr, "note: new version with kappa adjustment, kappa = %.2f\n", 1/amf ), true); // we only print once - return exp (logEframescorrecttotal) / numframes; // return value is av. expected frame-correct count - } -} - -};}; + +#include "Basics.h" +#include "simple_checked_arrays.h" +#include "latticearchive.h" +#include "simplesenonehmm.h" // the model +#include "ssematrix.h" // the matrices +#include "latticestorage.h" +#include +#include +#include + +using namespace std; + +#define VIRGINLOGZERO (10 * LOGZERO) // used for printing statistics on unseen states +#undef CPU_VERIFICATION + +#ifdef _WIN32 +int msra::numa::node_override = -1; // for numahelpers.h +#endif + +namespace msra { namespace lattices { + +// --------------------------------------------------------------------------- +// helper class for allocation lots of small matrices, no free +// --------------------------------------------------------------------------- + +class littlematrixheap +{ + static const size_t CHUNKSIZE; + typedef msra::math::ssematrixfrombuffer matrixfrombuffer; + std::list> heap; + size_t allocatedinlast; // in last heap element + size_t totalallocated; + std::vector matrices; +public: + littlematrixheap (size_t estimatednumentries) : totalallocated (0), allocatedinlast (0) { matrices.reserve (estimatednumentries + 1); } + msra::math::ssematrixbase & newmatrix (size_t rows, size_t cols) + { + const size_t elementsneeded = matrixfrombuffer::elementsneeded (rows, cols); + if (heap.empty() || (heap.back().size() - allocatedinlast) < elementsneeded) + { + const size_t nelem = max (CHUNKSIZE, elementsneeded + 3/*+3 for SSE alignment*/); + heap.push_back (std::vector (nelem)); + allocatedinlast = 0; + // make sure starting element is SSE-aligned (the constructor demands that) + const size_t offelem = (((size_t)&heap.back()[allocatedinlast]) / sizeof (float)) % 4; + if (offelem != 0) + allocatedinlast += 4 - offelem; + } + auto & buffer = heap.back(); + if (elementsneeded > heap.back().size() - allocatedinlast) + LogicError("newmatrix: allocation logic screwed up"); + // get our buffer into a handy vector-like thingy + array_ref vecbuffer (&buffer[allocatedinlast], elementsneeded); + // allocate in the current heap location + matrices.resize (matrices.size() + 1); + if (matrices.size()+1 > matrices.capacity()) + LogicError("newmatrix: littlematrixheap cannot grow but was constructed with too small number of eements"); + auto & matrix = matrices.back(); + matrix = matrixfrombuffer (vecbuffer, rows, cols); + allocatedinlast += elementsneeded; + totalallocated += elementsneeded; + return matrix; + } +}; + +const size_t littlematrixheap::CHUNKSIZE = 256*1024; // 1 MB + +// --------------------------------------------------------------------------- +// helpers for log-domain addition +// --------------------------------------------------------------------------- + +#ifndef LOGZERO +#define LOGZERO -1e30f +#endif + +// logadd (loga, logb) -> a += b, or loga = log [ exp(loga) + exp(logb) ] +static void logaddratio (float & loga, float diff) +{ + if (diff < -17.0f) return; // log (2^-24), 23-bit mantissa -> cut of after 24th bit + loga += logf (1.0f + expf (diff)); +} +static void logaddratio (double & loga, double diff) +{ + if (diff < -37.0f) return; // log (2^-53), 52-bit mantissa -> cut of after 53th bit + loga += log (1.0 + exp (diff)); +} +// loga <- log (exp (loga) + exp (logb)) = log (exp (loga) * (1.0 + exp (logb - loga)) = loga + log (1.0 + exp (logb - loga)) +template static void logadd (FLOAT & loga, FLOAT logb) +{ + if (logb > loga) // we add smaller to bigger + ::swap (loga, logb); + if (loga <= LOGZERO) // both are 0 + return; + logaddratio (loga, logb - loga); +} +template static void logmax (FLOAT & loga, FLOAT logb) // for testing (max approx) +{ + if (logb > loga) + loga = logb; +} + +template static FLOAT expdiff (FLOAT a, FLOAT b) // for testing +{ + if (b > a) + return exp(b) * (exp(a-b) - 1); + else + return exp(a) * (1 - exp(b-a)); +} + +template static bool islogzero (FLOAT v) { return v < LOGZERO/2; } // is this number to be considered 0 + +// --------------------------------------------------------------------------- +// other helpers go here +// --------------------------------------------------------------------------- + +// helper to reconstruct the phonetic transcript +/*static*/ std::string lattice::gettranscript (const_array_ref units, const msra::asr::simplesenonehmm & hset) +{ + std::string trans; + foreach_index (k, units) // we exploit that units have fixed boundaries + { + if (k > 0) trans.push_back (' '); + trans.append (hset.gethmm (units[k].unit).getname()); + } + return trans; +} + +// --------------------------------------------------------------------------- +// forwardbackwardedge() -- perform state-level forward-backward on a single lattice edge +// +// Results: +// - gammas(j,t) for valid time ranges (remaining areas are not initialized) +// - return value is edge acoustic score +// Gammas matrix must have two extra columns as buffer. +// --------------------------------------------------------------------------- + +/*static*/ float lattice::forwardbackwardedge (const_array_ref units, const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, + msra::math::ssematrixbase & loggammas, size_t edgeindex) +{ + // alphas and betas are stored in-place inside the loggammas matrix shifted by one?two columns + assert (loggammas.cols() == logLLs.cols() + 2); + msra::math::ssematrixstriperef logalphas (loggammas, 1, logLLs.cols()); // shifted views into gammas(,) for alphas and betas + msra::math::ssematrixstriperef logbetas (loggammas, 2, logLLs.cols()); + + // alphas(j,t) store the sum of all paths up to including state j at time t, including logLL(j,t) + // betas(j,t) store the sum of all paths exiting from state j at time t, not including logLL(j,t) + // gammas(j,t) = alphas(j,t) * betas(j,t) / totalLL + + // backward pass --token passing + size_t te = logbetas.cols(); + size_t je = logbetas.rows(); + float bwscore = 0.0f; // backward score + for (size_t k = units.size() -1; k+1 > 0; k--) + { + const auto & hmm = hset.gethmm (units[k].unit); + const size_t n = hmm.getnumstates(); + const auto & transP = hmm.gettransP(); + const size_t ts = te - units[k].frames; // end time of current unit + const size_t js = je - n; // range of state indices + + // pass in the transition score + // t = ts: exit transition (last frame only or tee transition) + float exitscore = 1e30f; // (something impossible) + if (te == ts) // tee transition + { + exitscore = bwscore + transP(-1,n); + } + else // not tee: expand all last states + { + for (size_t from = 0/*no tee possible here*/; from < n; from++) + { + const size_t i = js + from; // origin trellis node + logbetas(i,te-1) = bwscore + transP(from,n); + } + } + + // expand from states j at time t (not yet including LL) to time t-1 + for (size_t t = te -1; t+1 > ts/*note: cannot test t >= ts because t < 0 possible*/; t--) + { + for (size_t to = 0; to < n; to++) + { + const size_t j = js + to; // source trellis node + const size_t s = hmm.getsenoneid(to); // senone id for state at position 'to' in the HMM + const float acLL = logLLs(s,t); + if (islogzero (acLL)) + fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d unit %d (%s) frames [%d,%d) ac score(%d,%d) is zero (%d st, %d fr: %s)\n", + (int)edgeindex, (int) k, hmm.getname(), (int) ts, (int) te, + (int) s, (int) t, + (int) logbetas.rows(), (int) logbetas.cols(), gettranscript (units, hset).c_str()); + const float betajt = logbetas(j,t); // sum over all all path exiting from (j,t) to end + const float betajtpll = betajt + acLL; // incorporate acoustic score + if (t > ts) for (size_t from = 0/*no transition from entry state*/; from < n; from++) + { + const size_t i = js + from; // target trellis node + const float pathscore = betajtpll + transP(from,to); + if (to == 0) + logbetas(i,t-1/*propagate into preceding frame*/) = pathscore; + else + logadd (logbetas(i,t-1/*propagate into preceding frame*/), pathscore); + } + else // transition to entry state + { + const float pathscore = betajtpll + transP(-1,to); + if (to == 0) + exitscore = pathscore; + else + logadd (exitscore, pathscore); // propagate into preceding unit + } + } + } + + bwscore = exitscore; + if (islogzero (bwscore)) + fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d unit %d (%s) frames [%d,%d) bw score is zero (%d st, %d fr: %s)\n", + (int)edgeindex, (int) k, hmm.getname(), (int) ts, (int) te, (int) logbetas.rows(), (int) logbetas.cols(), gettranscript (units, hset).c_str()); + + te = ts; + je = js; + } + assert (te == 0 && je == 0); + const float totalbwscore = bwscore; + + // forward pass --regular Viterbi + // This also computes the gammas right away. + size_t ts = 0; // start frame for unit 'k' + size_t js = 0; // first row index of unit ' k' + float fwscore = 0.0f; // score passed across phone boundaries + foreach_index (k, units) // we exploit that units have fixed boundaries + { + const auto & hmm = hset.gethmm (units[k].unit); + const size_t n = hmm.getnumstates(); + const auto & transP = hmm.gettransP(); + const size_t te = ts + units[k].frames; // end time of current unit + const size_t je = js + n; // range of state indices + + // expand from states j at time t (including LL) to time t+1 + for (size_t t = ts; t < te; t++) // note: loop not entered for 0-frame units (tees) + { + for (size_t to = 0; to < n; to++) + { + const size_t j = js + to; // target trellis node + const size_t s = hmm.getsenoneid(to); + const float acLL = logLLs(s,t); + float alphajtnoll = LOGZERO; + if (t == ts) // entering score + { + const float pathscore = fwscore + transP(-1,to); + alphajtnoll = pathscore; + } + else for (size_t from = 0/*no entering possible*/; from < n; from++) + { + const size_t i = js + from; // origin trellis node + const float alphaitm1 = logalphas(i,t-1/*previous frame*/); + const float pathscore = alphaitm1 + transP(from,to); + logadd (alphajtnoll, pathscore); + } + logalphas(j,t) = alphajtnoll + acLL; + } + // update the gammas --do it here because in next frame, betas get overwritten by alphas (they share memory) + for (size_t j = js; j < je; j++) + { + if (!islogzero (totalbwscore)) + loggammas(j,t) = logalphas(j,t) + logbetas(j,t) - totalbwscore; + else // 0/0 problem, can occur if an ac score is so bad that it is 0 after going through softmax + loggammas(j,t) = LOGZERO; + } + } + // t = te: exit transition (last frame only or tee transition) + float exitscore; + if (te == ts) // tee transition + { + exitscore = fwscore + transP(-1,n); + } + else // not tee: expand all last states + { + exitscore = LOGZERO; + for (size_t from = 0/*no tee possible here*/; from < n; from++) + { + const size_t i = js + from; // origin trellis node + const float alphaitm1 = logalphas(i,te-1); // newly computed path score, transiting to t=te + const float pathscore = alphaitm1 + transP(from,n); + logadd (exitscore, pathscore); + } + } + fwscore = exitscore; // score passed on to next unit + js = je; + ts = te; + } + assert (js == logalphas.rows() && ts == logalphas.cols()); + const float totalfwscore = fwscore; + + // in extreme cases, we may have 0 ac probs, which lead to 0 path scores and division by 0 (subtracting LOGZERO) + // These cases must be handled separately. If the whole path is 0 (0 prob is on the only path at some point) then skip the lattice. + if (islogzero (totalbwscore) ^ islogzero (totalfwscore)) + fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d fw and bw 0 score %.10f vs. %.10f (%d st, %d fr: %s)\n", + (int)edgeindex, (float) totalfwscore, (float) totalbwscore, (int) js, (int) ts, gettranscript (units, hset).c_str()); + if (islogzero (totalbwscore)) + { + fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d has zero ac. score (%d st, %d fr: %s)\n", + (int)edgeindex, (int) js, (int) ts, gettranscript (units, hset).c_str()); + return LOGZERO; + } + + if (fabsf (totalfwscore - totalbwscore) / ts > 1e-4f) + fprintf (stderr, "forwardbackwardedge: WARNING: edge J=%d fw and bw score %.10f vs. %.10f (%d st, %d fr: %s)\n", + (int)edgeindex, (float) totalfwscore, (float) totalbwscore, (int) js, (int) ts, gettranscript (units, hset).c_str()); + + // we return the full path score + return totalfwscore; + +} + +// --------------------------------------------------------------------------- +// alignedge() -- perform Viterbi alignment on a single edge +// +// This is an alternative to forwardbackwardedge() that just uses the best path. +// Results: +// - if not returnsenoneids -> 'binary gammas(j,t)' for valid time ranges (remaining areas are not initialized); MMI-compatible +// - if returnsenoneids -> loggammas(0,t) will contain the senone ids directly instead (for sMBR mode) +// - return value is edge acoustic score +// Gammas matrix must have two extra columns as buffer. +// --------------------------------------------------------------------------- + +/*static*/ float lattice::alignedge (const_array_ref units, const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, + msra::math::ssematrixbase & loggammas, size_t edgeindex/*for diagnostic messages*/, const bool returnsenoneids, + array_ref thisedgealignmentsj) +{ + // alphas and betas are stored in-place inside the loggammas matrix shifted by one?two columns + assert (loggammas.cols() == logLLs.cols() + 2); + msra::math::ssematrixstriperef backpointers (loggammas, 0, logLLs.cols()); + msra::math::ssematrixstriperef pathscores (loggammas, 2, logLLs.cols()); + + // pathscores(j,t) store the sum of all paths up to including state j at time t, including logLL(j,t) + // backpointers(j,t) are the relative states that it came from + // gammas(j,t) <- 1 if on best path, 0 otherwise + + const int invalidbp = -2; + + // Viterbi alignment + size_t ts = 0; // start frame for unit 'k' + size_t js = 0; // first row index of unit 'k' + float fwscore = 0.0f; // score passed across phone boundaries + int fwbackpointer = -1; // bp passed across phone boundaries, -1 means start of utterance + foreach_index (k, units) // we exploit that units have fixed boundaries + { + const auto & hmm = hset.gethmm (units[k].unit); + const size_t n = hmm.getnumstates(); + const auto & transP = hmm.gettransP(); + const size_t te = ts + units[k].frames; // end time of current unit + const size_t je = js + hmm.getnumstates(); // range of state indices + + // expand from states j at time t (including LL) to time t+1 + for (size_t t = ts; t < te; t++) // note: loop not entered for 0-frame units (tees) + { + for (size_t j = js; j < je; j++) + { + const size_t to = j - js; // relative state + const size_t s = hmm.getsenoneid(to); + pathscores(j,t) = LOGZERO; + backpointers(j,t) = invalidbp; + if (t == ts) // entering score + { + const float pathscore = fwscore + transP(-1,to); + pathscores(j,t) = pathscore; + backpointers(j,t) = (float) fwbackpointer; + } + else for (size_t i = js; i < je; i++) + { + const size_t from = i - js; + const float alphaitm1 = pathscores(i,t-1/*previous frame*/); + const float pathscore = alphaitm1 + transP(from,to); + if (pathscore > pathscores(j,t)) + { + pathscores(j,t) = pathscore; + backpointers(j,t) = (float) i; + } + } + const float acLL = logLLs(s,t); + pathscores(j,t) += acLL; + } + } + // t = te: exit transition (last frame only or tee transition) + float exitscore = LOGZERO; + int exitbackpointer = invalidbp; + if (te == ts) // tee transition + { + exitscore = fwscore + transP(-1,n); + exitbackpointer = fwbackpointer; + } + else // not tee: expand all last states + { + for (size_t i = js; i < je; i++) + { + const size_t from = i - js; + const float alphaitm1 = pathscores(i,te-1); // newly computed path score, transiting to t=te + const float pathscore = alphaitm1 + transP(from,n); + if (pathscore > exitscore) + { + exitscore = pathscore; + exitbackpointer = (int) i; + } + } + } + if (exitbackpointer == invalidbp) + LogicError("exitbackpointer came up empty"); + fwscore = exitscore; // score passed on to next unit + fwbackpointer = exitbackpointer; // and accompanying backpointer + js = je; + ts = te; + } + assert (js == pathscores.rows() && ts == pathscores.cols()); + + // in extreme cases, we may have 0 ac probs, which lead to 0 path scores and division by 0 (subtracting LOGZERO) + // These cases must be handled separately. If the whole path is 0 (0 prob is on the only path at some point) then skip the lattice. + if (islogzero (fwscore)) + { + fprintf (stderr, "alignedge: WARNING: edge J=%d has zero ac. score (%d st, %d fr: %s)\n", + (int)edgeindex, (int) js, (int) ts, gettranscript (units, hset).c_str()); + return LOGZERO; + } + + // traceback & gamma update + size_t te = backpointers.cols(); + size_t je = backpointers.rows(); + int j = fwbackpointer; + for (size_t k = units.size() -1; k+1 > 0; k--) // go in units because we also need to clear out the column + { + const auto & hmm = hset.gethmm (units[k].unit); + const size_t ts = te - units[k].frames; // end time of current unit + const size_t js = je - hmm.getnumstates(); // range of state indices + for (size_t t = te -1; t + 1 > ts; t--) + { + if (j < (int) js || j >= (int) je) + LogicError("invalid backpointer resulting in state index out of range"); + + int bp = (int) backpointers(j,t); // save the backpointer before overwriting it (gammas and backpointers are aliases of each other) + //thisedgealignmentsj[t] = (unsigned short)hmm.getsenoneid(j - js); + if (!returnsenoneids) // return binary gammas (for MMI; this mode is compatible with softalignmode) + for (size_t i = js; i < je; i++) + loggammas(i,t) = ((int) i == j) ? 0.0f : LOGZERO; + else // return senone id (for sMBR; note: NOT compatible with softalignmode; calling code must know this) + thisedgealignmentsj[t] = (unsigned short) hmm.getsenoneid(j-js); + + if (bp == invalidbp) + LogicError("deltabackpointer not initialized"); + j = bp; // trace back one step + } + + te = ts; + je = js; + } + if (j != -1) + LogicError("invalid backpointer resulting in not reaching start of utterance when tracing back"); + assert (je == 0 && te == 0); + + // we return the full path score + return fwscore; +} + +// --------------------------------------------------------------------------- +// forwardbackwardlattice() -- lattice-level forward/backward +// +// This computes word posteriors, and also returns the per-node alphas and betas. +// Per-edge acoustic scores are passed in via a lambda, as this function is +// intended for use at multiple places with different scores. +// (Specifically, we also use it to determine a pruning threshold, based on +// the original lattice's ac. scores, before even bothering to compute the +// new ac. scores.) +// --------------------------------------------------------------------------- + +double lattice::forwardbackwardlattice (const std::vector & edgeacscores, parallelstate & parallelstate, std::vector & logpps, + std::vector & logalphas, std::vector & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, const bool sMBRmode, + const_array_ref & uids, const edgealignments & thisedgealignments, + std::vector & logEframescorrect, std::vector & Eframescorrectbuf, double & logEframescorrecttotal) const +{ // ^^ TODO: remove this + // --- hand off to parallelized (CUDA) implementation if available + if (parallelstate.enabled()) + { + double totalfwscore = parallelforwardbackwardlattice (parallelstate, edgeacscores, thisedgealignments, lmf, wp, amf, boostingfactor, logpps, logalphas, logbetas, sMBRmode, uids, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); + + return totalfwscore; + } + // if we get here, we have no CUDA, and do it the good ol' way + + // allocate return values + logpps.resize (edges.size()); // this is our primary return value + + // TODO: these are return values as well, but really shouldn't anymore; only used in some older baseline code we some day may want to compare against + logalphas.assign (nodes.size(), LOGZERO); + logalphas.front() = 0.0f; + logbetas.assign (nodes.size(), LOGZERO); + logbetas.back() = 0.0f; + + // --- sMBR version + + if (sMBRmode) + { + logEframescorrect.resize (edges.size()); + Eframescorrectbuf.resize (edges.size()); + + std::vector logaccalphas (nodes.size(), LOGZERO); // [i] expected frames-correct count over all paths from start to node i + std::vector logaccbetas (nodes.size(), LOGZERO); // [i] likewise + std::vector logframescorrectedge (edges.size()); // raw counts of correct frames in each edge + + // forward pass + foreach_index (j, edges) + { + if (islogzero(edgeacscores[j])) // indicates that this edge is pruned + continue; + const auto & e = edges[j]; + const double inscore = logalphas[e.S]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + const double pathscore = inscore + edgescore; + logadd (logalphas[e.E], pathscore); + + size_t ts = nodes[e.S].t; + size_t te = nodes[e.E].t; + size_t framescorrect = 0; // count raw number of correct frames + for (size_t t = ts; t < te; t++) + framescorrect += (thisedgealignments[j][t-ts] == uids[t]); + logframescorrectedge[j] = (framescorrect > 0) ? log ((double) framescorrect) : LOGZERO; // remember for backward pass + double loginaccs = logaccalphas[e.S] - logalphas[e.S]; + logadd (loginaccs, logframescorrectedge[j]); + double logpathacc = loginaccs + logalphas[e.S] + edgescore; + logadd (logaccalphas[e.E], logpathacc); + } + foreach_index (j, logaccalphas) + logaccalphas[j] -= logalphas[j]; + + const double totalfwscore = logalphas.back(); + const double totalfwacc = logaccalphas.back(); + if (islogzero (totalfwscore)) + { + fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); + return LOGZERO; // failed, do not use resulting matrix + } + + // backward pass and computation of state-conditioned frames-correct count + for (size_t j = edges.size() -1; j+1 > 0; j--) + { + if (islogzero(edgeacscores[j])) // indicates that this edge is pruned + continue; + const auto & e = edges[j]; + const double inscore = logbetas[e.E]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + const double pathscore = inscore + edgescore; + logadd (logbetas[e.S], pathscore); + + double loginaccs = logaccbetas[e.E] - logbetas[e.E]; + logadd (loginaccs, logframescorrectedge[j]); + double logpathacc = loginaccs + logbetas[e.E] + edgescore; + logadd (logaccbetas[e.S], logpathacc); + + // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) + double logpp = logalphas[e.S] + edgescore + logbetas[e.E] - totalfwscore; + if (logpp > 1e-2) + fprintf (stderr, "forwardbackward: WARNING: edge J=%d log posterior %.10f > 0\n", (int) j, (float) logpp); + if (logpp > 0.0) + logpp = 0.0; + logpps[j] = logpp; + double tmplogeframecorrect = logframescorrectedge[j]; + logadd (tmplogeframecorrect, logaccalphas[e.S]); + logadd (tmplogeframecorrect, logaccbetas[e.E] - logbetas[e.E]); + Eframescorrectbuf[j] = exp(tmplogeframecorrect); + } + foreach_index (j, logaccbetas) + logaccbetas[j] -= logbetas[j]; + const double totalbwscore = logbetas.front(); + const double totalbwacc = logaccbetas.front(); + if (fabs (totalfwscore - totalbwscore) / info.numframes > 1e-4) + fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); + + if (fabs (totalfwacc - totalbwacc) / info.numframes > 1e-4) + fprintf (stderr, "forwardbackwardlatticesMBR: WARNING: lattice fw and bw accs %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); + + logEframescorrecttotal = totalbwacc; + return totalbwscore; + } + + // --- MMI version + + // forward pass + foreach_index (j, edges) + { + const auto & e = edges[j]; + const double inscore = logalphas[e.S]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned + const double pathscore = inscore + edgescore; + logadd (logalphas[e.E], pathscore); + } + const double totalfwscore = logalphas.back(); + if (islogzero (totalfwscore)) + { + fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); + return LOGZERO; // failed, do not use resulting matrix + } + + // backward pass + // this also computes the word posteriors on the fly, since we are at it + for (size_t j = edges.size() -1; j+1 > 0; j--) + { + const auto & e = edges[j]; + const double inscore = logbetas[e.E]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + const double pathscore = inscore + edgescore; + logadd (logbetas[e.S], pathscore); + + // compute lattice posteriors on the fly since we are at it + double logpp = logalphas[e.S] + edgescore + logbetas[e.E] - totalfwscore; + if (logpp > 1e-2) + fprintf (stderr, "forwardbackward: WARNING: edge J=%d log posterior %.10f > 0\n", (int) j, (float) logpp); + if (logpp > 0.0) + logpp = 0.0; + logpps[j] = logpp; + } + + const double totalbwscore = logbetas.front(); + if (fabs (totalfwscore - totalbwscore) / info.numframes > 1e-4) + fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); + + return totalfwscore; +} + +// --------------------------------------------------------------------------- +// forwardbackwardlatticesMBR() -- compute expected frame-accuracy counts, +// both the conditioned one (corresponding to c(q) in Dan Povey's thesis) +// and the global one (which is the sMBR criterion to optimize). +// +// Outputs: +// - Eframescorrect[j] == expected frames-correct count conditioned on a state of edge[j]. +// We currently assume a hard state alignment. With that, the value turns out +// to be identical for all states of an edge, so we only store it once per edge. +// - return value: expected frames-correct count for entire lattice +// +// Call forwardbackwardlattices() first to compute logalphas/betas. +// --------------------------------------------------------------------------- + +double lattice::forwardbackwardlatticesMBR (const std::vector & edgeacscores, const msra::asr::simplesenonehmm & hset, + const std::vector & logalphas, const std::vector & logbetas, + const float lmf, const float wp, const float amf, const_array_ref & uids, + const edgealignments & thisedgealignments, std::vector & Eframescorrect) const +{ + std::vector accalphas (nodes.size(), 0); // [i] expected frames-correct count over all paths from start to node i + std::vector accbetas (nodes.size(), 0); // [i] likewise + std::vector maxcorrect (nodes.size(), 0); // [i] max correct frames up to this node (oracle) + + std::vector framescorrectedge (edges.size()); // raw counts of correct frames in each edge + + std::vector backpointersformaxcorr(nodes.size(), -2); // keep track of backpointer for the max corr + backpointersformaxcorr.front() = -1; + + // forward pass + foreach_index (j, edges) + { + if (islogzero(edgeacscores[j])) // indicates that this edge is pruned + continue; + const auto & e = edges[j]; + const double inaccs = accalphas[e.S]; + size_t ts = nodes[e.S].t; + size_t te = nodes[e.E].t; + + size_t framescorrect = 0; // count raw number of correct frames + for (size_t t = ts; t < te; t++) + framescorrect += (thisedgealignments[j][t-ts] == uids[t]); + framescorrectedge[j] = (double) framescorrect; // remember for backward pass + + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + // contribution to end node's path acc = start node's plus edge's correct count, weighted by LL, and divided by sum over LLs + double pathacc = (inaccs + framescorrectedge[j]) * exp (logalphas[e.S] + edgescore - logalphas[e.E]); + accalphas[e.E] += pathacc; + // also keep track of max accuracy, so we can find out whether the lattice contains the correct path + size_t oracleframescorrect = maxcorrect[e.S] + framescorrect; // keep track of most correct path up to end of this edge + if (oracleframescorrect > maxcorrect[e.E]) + { + maxcorrect[e.E] = oracleframescorrect; + backpointersformaxcorr[size_t(e.E)] = j; + } + } + const double totalfwacc = accalphas.back(); + + hset; // just for reference + + // report on ground-truth path + // TODO: we will later have code that adds this path if needed + size_t oracleframeacc = maxcorrect.back(); + if (oracleframeacc != info.numframes) + fprintf (stderr, "forwardbackwardlatticesMBR: ground-truth path missing from lattice (most correct path: %d out of %d frames correct)\n", (unsigned int) oracleframeacc, (int) info.numframes); + + // backward pass and computation of state-conditioned frames-correct count + for (size_t j = edges.size() -1; j+1 > 0; j--) + { + if (islogzero(edgeacscores[j])) // indicates that this edge is pruned + continue; + const auto & e = edges[j]; + const double inaccs = accbetas[e.E]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; + double pathacc = (inaccs + framescorrectedge[j]) * exp (logbetas[e.E] + edgescore - logbetas[e.S]); + accbetas[e.S] += pathacc; + + // sum up to get final expected frames-correct count per state == per edge (since we assume hard state alignment) + Eframescorrect[j] = (float) (accalphas[e.S] + accbetas[e.E] + framescorrectedge[j]); + } + + const double totalbwacc = accbetas.front(); + + if (fabs (totalfwacc - totalbwacc) / info.numframes > 1e-4) + fprintf (stderr, "forwardbackwardlatticesMBR: WARNING: lattice fw and bw accs %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwacc, (float) totalbwacc, (int) nodes.size(), (int) edges.size()); + + return totalbwacc; +} + +// --------------------------------------------------------------------------- +// bestpathlattice() -- lattice-level "forward/backward" that only returns the +// best path, but in the form of word posteriors, which are 1 or 0, just like +// a real lattice-level forward/backward would do. +// We don't really use this; this was only for a contrast experiment. +// --------------------------------------------------------------------------- + +double lattice::bestpathlattice (const std::vector & edgeacscores, std::vector & logpps, + const float lmf, const float wp, const float amf) const +{ + // forward pass --sortnedness => regular Viterbi + std::vector logalphas (nodes.size(), LOGZERO); + std::vector backpointers(nodes.size(), -2); + logalphas.front() = 0.0f; + backpointers.front() = -1; + foreach_index (j, edges) + { + const auto & e = edges[j]; + const double inscore = logalphas[e.S]; + const double edgescore = (e.l * lmf + wp + edgeacscores[j]) / amf; // note: edgeacscores[j] == LOGZERO if edge was pruned + const double pathscore = inscore + edgescore; + if (pathscore > logalphas[e.E]) + { + logalphas[e.E] = pathscore; + backpointers[e.E] = j; + } + } + + const double totalfwscore = logalphas.back(); + if (islogzero (totalfwscore)) + { + fprintf (stderr, "bestpathlattice: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); + return LOGZERO; // failed, do not use resulting matrix + } + + // traceback + // We encode the result by storing log 1 in edges on the best path, and log 0 else; + // this makes it naturally compatible with softalign mode + logpps.resize (edges.size()); + foreach_index (j, edges) logpps[j] = LOGZERO; + + int backpos = backpointers[nodes.size()-1]; + while (backpos >= 0) + { + logpps[backpos] = 0.0f; // edge is on best path -> PP = 1.0 + backpos = backpointers[edges[backpos].S]; + } + assert (backpos == -1); + + return totalfwscore; +} + +// --------------------------------------------------------------------------- +// forwardbackwardalign() -- compute the statelevel gammas or viterbi alignments +// the first phase of lattice::forwardbackward +// +// Outputs: +// --------------------------------------------------------------------------- +void lattice::forwardbackwardalign (parallelstate & parallelstate, + const msra::asr::simplesenonehmm & hset, const bool softalignstates, + const double minlogpp, const std::vector & origlogpps, + std::vector & abcs, littlematrixheap & matrixheap, + const bool returnsenoneids, + std::vector & edgeacscores, const msra::math::ssematrixbase & logLLs, + edgealignments & thisedgealignments, backpointers & thisbackpointers, array_ref & uids, const_array_ref bounds ) const +{ // NOTE: this will be removed and replaced by a proper representation of alignments someday + // do forward-backward or alignment on a per-edge basis. This gives us: + // - per-edge gamma[j,t] = P(s(t)==s_j|edge) if forwardbackward, per-edge alignment thisedgealignments[j] if alignment + // - per-edge acoustic scores + const size_t silunitid = hset.gethmmid("sil"); // shall be the same as parallelstate.getsilunitid() + bool parallelsil = true; + bool cpuverification = false; + +#ifndef PARALLEL_SIL // we use a define to make this marked + parallelsil = false; +#endif +#ifdef CPU_VERIFICATION + cpuverification = true; +#endif + + // Phase 1: abcs allocate + if (!parallelstate.enabled() || !parallelsil || cpuverification) // allocate abcs when 1.parallelstate not enabled (cpu mode); 2. enabled but not PARALLEL_SIL (silence need to be allocate); 3. cpuverfication + { + abcs.resize (edges.size(), NULL); // [edge index] -> alpha/beta/gamma matrices for each edge + size_t countskip = 0; // if pruning: count how many edges are pruned + + foreach_index (j, edges) + { + // determine number of frames + // TODO: this is not efficient--we only use a block-diagonal-like structure, rest is empty (exploiting the fixed boundaries) + const size_t edgeframes = nodes[edges[j].E].t - nodes[edges[j].S].t; + if (edgeframes == 0) // dummy !NULL edge at end of lattice + { + if ((size_t) j != edges.size() -1) + RuntimeError("forwardbackwardalign: unxpected 0-frame edge (only allowed at very end)"); + // note: abcs[j] is already initialized to be NULL in this case, which protects us from accidentally using it + } + else + { + // determine the number of states in an edge + const auto & aligntokens = getaligninfo (j); // get alignment tokens + size_t edgestates = 0; + + bool edgehassil = false; + foreach_index (i, aligntokens) + if (aligntokens[i].unit == silunitid) + edgehassil = true; + + if (!cpuverification && !edgehassil && parallelstate.enabled()) // !cpuverification, parallel & is non sil, we do not allocate + { + abcs[j] = NULL; + continue; + } + + foreach_index (k, aligntokens) + edgestates += hset.gethmm (aligntokens[k].unit).getnumstates(); + + // allocate the matrix + if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) + countskip++; + else + abcs[j] = &matrixheap.newmatrix (edgestates, edgeframes + 2); // +2 to have one extra column for betas and one for gammas + } + } + if (minlogpp > LOGZERO) + fprintf(stderr, "forwardbackwardalign: %d of %d edges pruned\n", (int)countskip, (int)edges.size()); + } + + // Phase 2: alignment on CPU + if (parallelstate.enabled() && !parallelsil) // silence edge shall be process separately if not cuda and not PARALLEL_SIL + { + if (softalignstates) + LogicError("forwardbackwardalign: parallelized version currently only handles hard alignments"); + if (minlogpp > LOGZERO) + fprintf(stderr, "forwardbackwardalign: pruning not supported (we won't need it!) :)\n"); + edgeacscores.resize (edges.size()); + for (size_t j = 0; j < edges.size(); j++) + { + const auto & aligntokens = getaligninfo (j); // get alignment tokens + if (aligntokens.size() == 0) + continue; + bool edgehassil = false; + foreach_index (i, aligntokens) + { + if (aligntokens[i].unit == silunitid) + edgehassil = true; + } + if (!edgehassil) // only process sil + continue; + const edgeinfowithscores & e = edges[j]; + const size_t ts = nodes[e.S].t; + const size_t te = nodes[e.E].t; + const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); + edgeacscores[j] = alignedge (aligntokens, hset, edgeLLs, *abcs[j], j, true, thisedgealignments[j]); + } + } + + // Phase 3: alignment on GPU + if (parallelstate.enabled()) + parallelforwardbackwardalign (parallelstate, hset, logLLs, edgeacscores, thisedgealignments, thisbackpointers); + + //zhaorui align to reference mlf + if (bounds.size() > 0) + { + size_t framenum=bounds.size(); + + msra::math::ssematrixbase *refabcs; + size_t ts, te, t; + ts = te = 0; + + + aligninfo *refinfo; + unsigned short *refalign; + + refinfo = (aligninfo*) malloc(sizeof(aligninfo)*1); + refalign = (unsigned short *) malloc(sizeof(unsigned short ) * framenum); + + array_ref refunits(refinfo,1); + array_ref refedgealignmentsj (refalign , framenum); + + while (te < framenum) + { + // found one phone's boundary (ts, te) + t=ts+1; + while (t < framenum && bounds[t] == 0 ) + t++; + te=t; + + + //make one phone unit + size_t phoneid = bounds[ts]-1; + refunits[0].unit = phoneid; + refunits[0].frames = te-ts; + + size_t edgestates = hset.gethmm(phoneid).getnumstates(); + littlematrixheap refmatrixheap (1); // for abcs + refabcs = &refmatrixheap.newmatrix (edgestates, te-ts + 2); + const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); + //do alignment + alignedge ((const_array_ref) refunits, hset, edgeLLs, *refabcs, 0, true,refedgealignmentsj); + + for(t=ts; t< te; t++) + { + uids[t] = (size_t)refedgealignmentsj[t-ts]; + } + ts=te; + + } + + free(refinfo); + free(refalign); + } + + // Phase 4: alignment or forwardbackward on CPU for non parallel mode or verification + + if (!parallelstate.enabled() || cpuverification) //non parallel mode or verification + { + edgeacscores.resize (edges.size()); + std::vector edgeacscoresgpu; + edgealignments thisedgealignmentsgpu (thisedgealignments); + if (cpuverification) + { + parallelstate.getedgeacscores (edgeacscoresgpu); + parallelstate.copyalignments (thisedgealignmentsgpu); + } + foreach_index (j, edges) + { + const edgeinfowithscores & e = edges[j]; + const size_t ts = nodes[e.S].t; + const size_t te = nodes[e.E].t; + if (ts == te) // dummy !NULL edge at end + edgeacscores[j] = 0.0f; + else + { + const auto & aligntokens = getaligninfo (j); // get alignment tokens + const auto edgeLLs = msra::math::ssematrixstriperef (const_cast (logLLs), ts, te - ts); + if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) + edgeacscores[j] = LOGZERO; // will kill word level forwardbackward hypothesis + else if (softalignstates) + edgeacscores[j] = forwardbackwardedge (aligntokens, hset, edgeLLs, *abcs[j], j); + else + edgeacscores[j] = alignedge (aligntokens, hset, edgeLLs, *abcs[j], j, returnsenoneids, thisedgealignments[j]); + } + if (cpuverification) + { + const auto & aligntokens = getaligninfo (j); // get alignment tokens + bool edgehassil = false; + foreach_index (i, aligntokens) + { + if (aligntokens[i].unit == silunitid) + edgehassil = true; + } + if (fabs(edgeacscores[j] - edgeacscoresgpu[j]) > 1e-3) + { + fprintf (stderr, "edge %d, sil ? %d, edgeacscores / edgeacscoresgpu MISMATCH %f v.s. %f, diff %e\n", + j, edgehassil ? 1 : 0, (float)edgeacscores[j], (float)edgeacscoresgpu[j], + (float)(edgeacscores[j] - edgeacscoresgpu[j])); + fprintf (stderr, "aligntokens: "); + foreach_index (i, aligntokens) + fprintf (stderr, "%d %d; ", i, aligntokens[i].unit); + fprintf (stderr, "\n"); + } + for (size_t t = ts; t < te; t++) + { + if (thisedgealignments[j][t-ts] != thisedgealignmentsgpu[j][t-ts]) + fprintf (stderr, "edge %d, sil ? %d, time %d, alignment / alignmentgpu MISMATCH %d v.s. %d\n", j, edgehassil ? 1 : 0, (int)(t-ts), thisedgealignments[j][t-ts], thisedgealignmentsgpu[j][t-ts]); + } + } + } + } +} + +// compute the error signal for sMBR mode +void lattice::sMBRerrorsignal (parallelstate & parallelstate, + msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg, // output + const std::vector & logpps, const float amf, + double minlogpp, const std::vector & origlogpps, const std::vector & logEframescorrect, + const double logEframescorrecttotal, const edgealignments & thisedgealignments) const +{ + if (parallelstate.enabled()) // parallel version + { + /* time measurement for parallel sMBRerrorsignal + errorsignalcompute: 19.871935 ms (cuda) v.s. 448.711444 ms (emu) */ + if (minlogpp > LOGZERO) + fprintf(stderr, "sMBRerrorsignal: pruning not supported (we won't need it!) :)\n"); + parallelsMBRerrorsignal (parallelstate, thisedgealignments, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); + return; + } + + // linear mode + foreach_coord (i, j, errorsignal) + errorsignal(i,j) = 0.0f; // Note: we don't actually put anything into the numgammas + foreach_index (j, edges) + { + const auto & e = edges[j]; + if (nodes[e.S].t == nodes[e.E].t) // this happens for dummy !NULL edge at end of file + continue; + if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) // this is pruned + continue; + + size_t ts = nodes[e.S].t; + size_t te = nodes[e.E].t; + + const double diff = logEframescorrect[j] - logEframescorrecttotal; + // Note: the contribution of the states of an edge to their senones is the same for all states + // so we compute it once and add it to all; this will not be the case without hard alignments. + const double pp = exp (logpps[j]); // edge posterior + const float edgecorrect = (float) (pp * diff) / amf; + for (size_t t = ts; t < te; t++) + { + const size_t s = thisedgealignments[j][t-ts]; + errorsignal(s,t) += edgecorrect; + } + } +} + +// compute the error signal for MMI mode +void lattice::mmierrorsignal (parallelstate & parallelstate, double minlogpp, const std::vector & origlogpps, + std::vector & abcs, const bool softalignstates, + const std::vector & logpps, const msra::asr::simplesenonehmm & hset, + const edgealignments & thisedgealignments, msra::math::ssematrixbase & errorsignal) const +{ + if (parallelstate.enabled()) + { + if (minlogpp > LOGZERO) + fprintf(stderr, "mmierrorsignal: pruning not supported (we won't need it!) :)\n"); + if (softalignstates) + LogicError("mmierrorsignal: parallel version for softalignstates mode is not supported yet"); + parallelmmierrorsignal (parallelstate, thisedgealignments, logpps, errorsignal); + return; + } + + for (size_t j = 0; j < (errorsignal).cols(); j++) + for (size_t i = 0; i < (errorsignal).rows(); i++) + errorsignal(i,j) = VIRGINLOGZERO; // set to zero --note: may be in-place with logLLs, which now get overwritten + + // size_t warnings = 0; // [v-hansu] check code for mmi; search this comment to see all related codes + foreach_index (j, edges) + { + const auto & e = edges[j]; + if (nodes[e.S].t == nodes[e.E].t) // this happens for dummy !NULL edge at end of file + continue; + if (minlogpp > LOGZERO && origlogpps[j] < minlogpp) // this is pruned + continue; + + const auto & aligntokens = getaligninfo (j); // get alignment tokens + auto & loggammas = *abcs[j]; + + const float edgelogP = (float) logpps[j]; + //if (islogzero (edgelogP)) // we had a 0 prob + // continue; + + // accumulate this edge's gamma matrix into target posteriors + const size_t tedge = nodes[e.S].t; + size_t ts = 0; // time index into gamma matrix + size_t js = 0; // state index into gamma matrix + foreach_index (k, aligntokens) // we exploit that units have fixed boundaries + { + const auto & unit = aligntokens[k]; + const size_t te = ts + unit.frames; + const auto & hmm = hset.gethmm (unit.unit); // TODO: inline these expressions + const size_t n = hmm.getnumstates(); + const size_t je = js + n; + // P(s) = P(s|e) * P(e) + for (size_t t = ts; t < te; t++) + { + const size_t tutt = t + tedge; // time index w.r.t. utterance + //double logsum = LOGZERO; // [v-hansu] check code for mmi; search this comment to see all related codes + for (size_t i = 0; i < n; i++) + { + const size_t j = js + i; // state index for this unit in matrix + const size_t s = hmm.getsenoneid (i); // state class index + const float gammajt = loggammas(j,t); + const float statelogP = edgelogP + gammajt; + logadd (errorsignal(s,tutt), statelogP); + } + } + ts = te; + js = je; + } + assert (ts + 2 == loggammas.cols() && js == loggammas.rows()); + } + + // check normalizedness (is that an actual English word?) + // also count non-zero probs + size_t nonzerostates = 0; + foreach_column (t, errorsignal) + { + double logsum = LOGZERO; + foreach_row (s, errorsignal) + { + if (islogzero (errorsignal(s,t))) + nonzerostates++; + else + logadd (logsum, (double) errorsignal(s,t)); + // TODO: count VIRGINLOGZERO, print per frame + } + if (fabs (logsum) / errorsignal.rows() > 1e-6) + fprintf (stderr, "forwardbackward: WARNING: overall posterior column(%d) sum = exp (%.10f) != 1\n", (int)t, logsum); + } + fprintf (stderr, "forwardbackward: %.3f%% non-zero state posteriors\n", 100.0f - nonzerostates * 100.0f / errorsignal.rows() / errorsignal.cols()); + + // convert to non-log posterior --that's what we return + foreach_coord (i, j, errorsignal) + errorsignal(i,j) = expf (errorsignal(i,j)); +} + +// compute ground truth's score +// It is critical to get all details consistent with the lattice, to avoid skewing the weights. +// ... TODO: we don't need this to be a class member, actually; try to just make it a 'static' function. +/*static*/ double lattice::scoregroundtruth (const_array_ref uids, const_array_ref transcript, const std::vector & transcriptunigrams, + const msra::math::ssematrixbase & logLLs, const msra::asr::simplesenonehmm & hset, const float lmf, const float wp, const float amf) +{ + if (transcript[0].firstframe != 0) // TODO: should we store the #frames instead? Then we can validate the total duration + LogicError("scoregroundtruth: first transcript[] token does not start at frame 0"); + + // get the silence models, since they are treated specially + const size_t numframes = logLLs.cols(); + const auto & sil = hset.gethmm (hset.gethmmid ("sil")); + const auto & sp = hset.gethmm (hset.gethmmid ("sp")); + if (sp.numstates != 1 || sil.numstates != 3) + RuntimeError("scoregroundtruth: only supports 1-state /sp/ and 3-state /sil/ tied to /sp/"); + const size_t silst = sp.senoneids[0]; + + // loop over words + double pathscore = 0.0; + foreach_index (i, transcript) + { + size_t ts = transcript[i].firstframe; + size_t te = ((size_t) i+1 < transcript.size()) ? transcript[i+1].firstframe : numframes; + if (ts >= te) + LogicError("scoregroundtruth: transcript[] tokens out of order"); + // acoustic score: loop over frames + const msra::asr::simplesenonehmm::transP * prevtransP = NULL; // previous transP + int prevs = -1; // previous state index + for (size_t t = ts; t < te; t++) + { + size_t senoneid = uids[t]; + // recover the transP and state index + int s; + const msra::asr::simplesenonehmm::transP * transP; + if (senoneid == silst) + { + if (prevtransP == &sil.gettransP()) // "silst" may be tied to /sp/, and thus the /sil/ center state may be ambiguous + { + transP = prevtransP; // remain in /sil/ + s = 1; // note that this will fail if /sp/ can follow /sil/ and if /sil/ may end with "silst" (both not allowed currently) + } + else // "silst" -> we are in /sp/ + { + transP = &sp.gettransP(); + s = 0; + } + } + else // all others must be non-ambiguous + { + int transPindex = hset.senonetransP (senoneid); + int sindex = hset.senonestate (senoneid); + if (transPindex == -1 || sindex == -1) + RuntimeError("scoregroundtruth: failed to resolve ambiguous senone %s", hset.getsenonename (senoneid)); + transP = &hset.transPs[transPindex]; + s = sindex; + } + // if changing phoneme then add necessary enter/exit score + if (transP != prevtransP) + { + if (prevtransP) // previous exit transition + pathscore += (*prevtransP)(prevs,prevtransP->getnumstates()); + prevs = -1; // enter transition + } + // add inter-state transP + pathscore += (*transP)(prevs,s); + // acoustic score + pathscore += logLLs (senoneid,t); + // remember state for next frame + prevtransP = transP; + prevs = s; + } + // add the last exit transition + if (prevtransP) // previous exit transition + pathscore += (*prevtransP)(prevs,prevtransP->getnumstates()); + // need to add a /sp/ tee transition if we don't end in sp, since our transcript dictionary includes a /sp/ at the end of each entry + if (uids[te-1] != sp.senoneids[0]) + pathscore += sp.gettransP()(-1,sp.numstates); + // lm + pathscore += transcriptunigrams[i] * lmf + wp; + } + if (islogzero (pathscore)) + fprintf (stderr, "scoregroundtruth: ground-truth path has zero probability; some model inconsistency, maybe?\n"); + // account for amf + pathscore /= amf; + fprintf (stderr, "scoregroundtruth: ground-truth score %.6f (%d frames)\n", pathscore, (int) numframes); + return pathscore; +} + +// --------------------------------------------------------------------------- +// sMBRdiagnostics() -- helper to print some diagnostics for analyzing sMBR results +// --------------------------------------------------------------------------- + +// static // with 'static', compiler will complain if the function is not used (we only compile it in sometimes for diagnostics) +void sMBRdiagnostics (const msra::math::ssematrixbase & errorsignal, const_array_ref uids, + const_array_ref bestpath, const vector & refseen, const msra::asr::simplesenonehmm & hset) +{ + // TODO: + // - print best positive runner-up + // - WARN tag if neg > pos + // - check the sum and warn if not 0 + // - indicate whether best path state is correct or not + size_t numcor = 0; + size_t numnegbetter = 0; // # frames the neg competitor is better + size_t numposbetter = 0; // # frames the pos competitor is better + foreach_column (t, errorsignal) + { + const size_t sref = uids[t]; + const char * srefname = hset.getsenonename (sref); + const size_t sbest = bestpath[t]; + const char * sbestname = hset.getsenonename (sbest); + if (sref == sbest) + numcor++; + // for each frame, print error signal for ground truth and runner up (second largest abs value) + size_t sneg = SIZE_MAX; // competitor + float eneg = 0.0f; + size_t spos = SIZE_MAX; // best postive competitor + float epos = 0.0f; + foreach_row (s, errorsignal) + { + if (s == sref) + continue; + if (errorsignal(s,t) < eneg) + { + sneg = s; + eneg = errorsignal(s,t); + } + if (errorsignal(s,t) > epos) + { + spos = s; + epos = errorsignal(s,t); + } + } + if (fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < -eneg) + numnegbetter++; + if (fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < epos) + numposbetter++; + const char * snegname = sneg == SIZE_MAX ? "-" : hset.getsenonename (sneg); + const char * sposname = spos == SIZE_MAX ? "-" : hset.getsenonename (spos); + fprintf (stderr, "e(%d): ref %s: %.6f / %s: %.6f / %s: %.6f / top %s: %.6f%s%s%s%s%s\n", + (int) t, srefname, errorsignal (sref, t), snegname, eneg, sposname, epos, sbestname, errorsignal (sbest, t), + sbest == sref ? "" : " ERR", + fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < 0 ? " INV!!" : "", + fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < -eneg ? " WEAK" : "", + fabs (errorsignal (sref, t)) > 0.0001f && errorsignal (sref, t) < epos ? " 2ND" : "", + refseen[t] ? "" : " NOREF"); + } + // print this to validate our bestpath computation + fprintf (stderr, "sMBRdiagnostics: %d frames correct out of %d, %.2f%%, neg better in %d, pos in %d\n", + (int) numcor, (int)errorsignal.cols(), 100.0f * numcor / errorsignal.cols(), + (int) numnegbetter, (int) numposbetter); +} + +// static // with 'static', compiler will complain if the function is not used (we only compile it in sometimes for diagnostics) +void sMBRsuppressweirdstuff (msra::math::ssematrixbase & errorsignal, const_array_ref uids) +{ + size_t numweird = 0; + foreach_column (t, errorsignal) + { + const size_t sref = uids[t]; + // for each frame, print error signal for ground truth and runner up (second largest abs value) + const float eref = errorsignal(sref,t); + bool isweird = eref < 0.0f; // negative for reference!? + for (size_t s = 0; s < errorsignal.rows() && !isweird; s++) + { + if (s == sref) + continue; + if (fabs (errorsignal(s,t)) > eref) + isweird = true; + } + if (isweird) + { + foreach_row (s, errorsignal) + errorsignal(s,t) = 0.0f; + numweird++; + } + } + // print this to validate our bestpath computation + fprintf (stderr, "sMBRsuppressweirdstuff: %d weird frames out of %d, %.2f%% were flattened\n", + (int) numweird, (int) errorsignal.cols(), 100.0f * numweird / errorsignal.cols()); +} + + +// --------------------------------------------------------------------------- +// forwardbackward() -- main function for MMI/sMBR +// +// This computes the lattice state-level statistics for sequence training using MMI or sMBR. +// +// Outputs, MMI mode: +// - result = dengammas = denominator gammas (non-log form) +// - returns log of sum over all paths' likelihoods (the denominator of the MMI objective) +// - note: numgammas is not used/touched in MMI mode +// +// Outputs, sMBR mode: +// TODO: fix this comment +// - result = errorsignal = (abs value of) negative contributions to error signal +// - errorsignalbuf = for temporarily use to get errorsignal +// - returns expected frames-correct count (the sMBR objective) +// --------------------------------------------------------------------------- + +double lattice::forwardbackward (parallelstate & parallelstate, const msra::math::ssematrixbase & logLLs, const msra::asr::simplesenonehmm & hset, + msra::math::ssematrixbase & result, msra::math::ssematrixbase & errorsignalbuf, + const float lmf, const float wp, const float amf, const float boostingfactor, + const bool sMBRmode, array_ref uids, const_array_ref bounds, + const_array_ref transcript, const std::vector & transcriptunigrams) const +{ + bool softalign = true; + bool softalignstates = false; // true if soft alignment within edges, currently we only support soft within edge in cpu mode + bool softalignlattice = softalign; // w.r.t. whole lattice + + edgealignments thisedgealignments (*this); // alignments memory allocate for this lattice + backpointers thisbackpointers (*this, hset); // memory for forwardbackward + + if (info.numframes != logLLs.cols()) + LogicError("forwardbackward: #frames mismatch between lattice (%d) and LLs (%d)", (int) info.numframes, (int) logLLs.cols()); + // TODO: the following checks should throw, but I don't dare in case this will crash a critical job... if we never see this warning, then + if (info.numframes != uids.size()) + fprintf (stderr, "forwardbackward: #frames mismatch between lattice (%d) and uids (%d)\n", (int) info.numframes, (int) uids.size()); + if (info.numframes != result.cols()) + fprintf (stderr, "forwardbackward: #frames mismatch between lattice (%d) and result (%d)\n", (int) info.numframes, (int) result.cols()); + + littlematrixheap matrixheap (info.numedges); // for abcs + + // PHASE 0: fake word level forward backwards --only used when pruning enabled + const double minlogpp = LOGZERO; // pruning threshold --LOGZERO means disabled + std::vector origlogpps; // word posterior from original lattice, for pruning decision + + // PHASE 1: per-edge forward backwards (="time alignments") + + // score the ground truth --only if a transcript is provided, which happens if the user provides a language model + // TODO: no longer used, remove this. 'transcript' parameter is no longer used in this function. + transcript; transcriptunigrams; + + // allocate alpha/beta/gamma matrices (all are sharing the same memory in-place) + std::vector abcs; + std::vector edgeacscores; // [edge index] acoustic scores + //funcation call for forwardbackward on edge level + forwardbackwardalign (parallelstate, hset, softalignstates, minlogpp, origlogpps, abcs, matrixheap, sMBRmode/*returnsenoneids*/, edgeacscores, logLLs, thisedgealignments, thisbackpointers, uids,bounds); + + // PHASE 2: lattice-level forward backward + + // we exploit that the lattice is sorted by (end node, start node) for in-place processing + + // checklattice(); // comment out by v-hansu to save time +#ifdef PRINT_TIME_MEASUREMENT + auto_timer latlevelfwbw; +#endif + std::vector logpps; + std::vector Eframescorrectbuf; // this is used for compute the Eframescorrectdiff + std::vector logEframescorrect; // this is the final output of PHASE 2 + std::vector logalphas; + std::vector logbetas; + double totalfwscore = 0; // TODO: name no longer precise in sMBRmode + double logEframescorrecttotal = LOGZERO; + + bool returnEframescorrect = sMBRmode; + if (softalignlattice) + { + totalfwscore = forwardbackwardlattice (edgeacscores, parallelstate, logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, (const_array_ref &)uids, thisedgealignments, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); + if (sMBRmode && !returnEframescorrect) + logEframescorrecttotal = forwardbackwardlatticesMBR (edgeacscores, hset, logalphas, logbetas, lmf, wp, amf, (const_array_ref &)uids, thisedgealignments, Eframescorrectbuf); + //^^ BUGBUG not tested + } + else + totalfwscore = bestpathlattice (edgeacscores, logpps, lmf, wp, amf); +#ifdef PRINT_TIME_MEASUREMENT + latlevelfwbw.show("latlevelfwbw"); // 68.395682 ms +#endif + if (islogzero (totalfwscore)) + { + fprintf (stderr, "forwardbackward: WARNING: no path found in lattice (%d nodes/%d edges)\n", (int) nodes.size(), (int) edges.size()); + return LOGZERO; // failed, do not use resulting matrix + } + + // PHASE 3: compute final state-level posteriors (MMI mode) + + // compute expected frames correct in sMBRmode + + const size_t numframes = logLLs.cols(); + assert (numframes == info.numframes); + //fprintf (stderr, "forwardbackward: total forward score %.6f (%d frames)\n", totalfwscore, (int) numframes); // for now--while we are debugging the GPU port + + // MMI mode + if(!sMBRmode) + { + // we first take the sum in log domain to avoid numerical issues + auto & dengammas = result; // result is denominator gammas + mmierrorsignal (parallelstate, minlogpp, origlogpps, abcs, softalignstates, logpps, hset, thisedgealignments, dengammas); + return totalfwscore / numframes; // return value is av. posterior + } + // sMBR mode + else + { + auto & errorsignal = result; + sMBRerrorsignal (parallelstate, errorsignal, errorsignalbuf, logpps, amf, minlogpp, origlogpps, logEframescorrect, logEframescorrecttotal, thisedgealignments); + + static bool dummyvariable = (fprintf(stderr, "note: new version with kappa adjustment, kappa = %.2f\n", 1/amf ), true); // we only print once + return exp (logEframescorrecttotal) / numframes; // return value is av. expected frame-correct count + } +} + +};}; diff --git a/Source/SequenceTrainingLib/parallelforwardbackward.cpp b/Source/SequenceTrainingLib/parallelforwardbackward.cpp index bc4baaad9d8f..a47858bf1df9 100644 --- a/Source/SequenceTrainingLib/parallelforwardbackward.cpp +++ b/Source/SequenceTrainingLib/parallelforwardbackward.cpp @@ -1,866 +1,866 @@ -// parallelforwardbackward.cpp -- parallelized implementation(s) of lattice forward/backward implemented --currently through CUDA -// -// F. Seide, V-hansu - -#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings - -#include "BestGpu.h" // for CPUONLY -#include "latticearchive.h" // we implement parts of class lattice -#include "simple_checked_arrays.h" -#include "simplesenonehmm.h" // the model -#include "ssematrix.h" // the matrices -#include "cudalattice.h" -#include "latticefunctionskernels.h" // for emulation -#include "cudalatticeops.h" -#include // for debug -#include "cudalib.h" - -#define TWO_CHANNEL // [v-hansu] -using namespace msra::cuda; - -#ifndef CPUONLY -#pragma comment (lib, "MathCUDA.lib") // built by CNTKMathCUDA project -#endif - -namespace msra { namespace lattices { - - // emulation support - struct dim3 - { - size_t x, y, z; - dim3(size_t x = 1, size_t y = 1, size_t z = 1) : x(x), y(y), z(z){} - }; - - static dim3 blockIdx, gridDim, threadIdx, blockDim; - - template - __forceinline void emulatecuda (const dim3 & b, const dim3 & t, FUNC f) - { - gridDim = b; - blockDim = t; - for (blockIdx.x = 0; blockIdx.x < gridDim.x; blockIdx.x++) - for (blockIdx.y = 0; blockIdx.y < gridDim.y; blockIdx.y++) - for (blockIdx.z = 0; blockIdx.z < gridDim.z; blockIdx.z++) - for (threadIdx.z = 0; threadIdx.z < blockDim.z; threadIdx.z++) - for (threadIdx.y = 0; threadIdx.y < blockDim.y; threadIdx.y++) - for (threadIdx.x = 0; threadIdx.x < blockDim.x; threadIdx.x++) - f(); - } - - void setvaluej (std::vector & thisvector, double value, size_t nelem) - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < nelem) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::setvaluej (j, thisvector, value); - } - } - - void expdiffj (std::vector & Eframescorrectbuf, double & logEframescorrecttotal, size_t nelem, std::vector & logEframescorrect) - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < nelem) // note: will cause issues if we ever use __synctreads() - { - logEframescorrect[j] = (float)(Eframescorrectbuf[j] - logEframescorrecttotal); - } - } - // this must be identical to an actual CUDA kernel (except for the input data types: vectorref -> std::vector) - void edgealignmentj (const std::vector & hmms, const std::vector & transPs, const size_t spalignunitid, const size_t silalignunitid, - const std::vector & nodes, const std::vector & edges, - const std::vector & aligns, - const msra::math::ssematrixbase & logLLs, const std::vector & alignoffsets, - std::vector & backptrstorage, const std::vector & backptroffsets, - std::vector & alignresult, std::vector & edgeacscores) - { - // this function identifies which element in the grid we are, and then passes to the actual function that does stuff - // compute j; but check if it is in range; return if not - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::edgealignmentj (j, hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, - alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); - } - } - - void forwardlatticej (const size_t batchsize, const size_t startindex, const std::vector & edgeacscores, - const size_t spalignunitid, const size_t silalignunitid, - const std::vector & edges, const std::vector & nodes, - const std::vector & aligns, - const std::vector & alignments, const std::vector & alignmentoffsets, - std::vector & logalphas, float lmf, float wp, float amf, const float boostingfactor, - const std::vector & uids, const std::vector & senone2classmap, const bool returnEframescorrect, - std::vector & logframescorrectedge, std::vector & logaccalphas) - { - const size_t shufflemode = 1; - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < batchsize) // note: will cause issues if we ever use __synctreads() in forwardlatticej - { - msra::lattices::latticefunctionskernels::forwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignmentoffsets, - logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas); - } - } - - void backwardlatticej (const size_t batchsize, const size_t startindex, const std::vector & edgeacscores, - const size_t spalignunitid, const size_t silalignunitid, - const std::vector & edges, - const std::vector & nodes, - const std::vector & aligns, const double totalfwscore, - std::vector & logpps, std::vector & logalphas, std::vector & logbetas, - float lmf, float wp, float amf, const float boostingfactor, const bool returnEframescorrect, std::vector & logframescorrectedge, - std::vector & logaccalphas, std::vector & Eframescorrectbuf, std::vector & logaccbetas) - { - const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block - const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; - const size_t j = jinblock + blockIdx.x * tpb; - if (j < batchsize) // note: will cause issues if we ever use __synctreads() in backwardlatticej - { - msra::lattices::latticefunctionskernels::backwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, - edges, nodes, aligns, totalfwscore, logpps, logalphas, - logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, logframescorrectedge, - logaccalphas, Eframescorrectbuf, logaccbetas); - } - } - - void sMBRerrorsignalj (const std::vector & alignstateids, const std::vector & alignoffsets, - const std::vector & edges, const std::vector & nodes, - const std::vector & logpps, const float amf, const std::vector & logEframescorrect, - const double logEframescorrecttotal, msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) - { - const size_t shufflemode = 3; - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::sMBRerrorsignalj (j, alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, - errorsignal, errorsignalneg); - } - } - - void stateposteriorsj (const std::vector & alignstateids, const std::vector & alignoffsets, - const std::vector & edges, const std::vector & nodes, - const std::vector & logqs, msra::math::ssematrixbase & logacc) - { - const size_t shufflemode = 3; - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < edges.size()) // note: will cause issues if we ever use __synctreads() - { - msra::lattices::latticefunctionskernels::stateposteriorsj (j, alignstateids, alignoffsets, edges, nodes, logqs, logacc); - } - } - - void checkshuffle (std::vector & checkvector1, std::vector & checkvector2, size_t shufflemode) - { - const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); - if (j < checkvector1.size()) - { - checkvector1[j] = -1; - } - if (j < checkvector2.size()) - { - checkvector2[j] -= 1; - } - } - - void errorcomputationi (msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg, float amf) - { - const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); - if (i >= errorsignal.rows()) - return; - // clear all columns - const size_t m = errorsignal.cols(); - for (size_t j = 0; j < m; j++) - errorsignal(i,j) = (expf(errorsignal(i,j)) - expf(errorsignalneg(i,j)))/amf; - } - - void weightedcomponentexpproducts (const msra::math::ssematrixbase & loggammas, const msra::math::ssematrixbase & logEframescorrect, - const double logEframecorrecttotal, const float kappa, msra::math::ssematrixbase & errorsignal) - { - const size_t s = threadIdx.x + (blockIdx.x * blockDim.x); - if (s < errorsignal.rows()) - { - msra::lattices::latticefunctionskernels::computesMBRerrorsignals (s, loggammas, logEframescorrect, logEframecorrecttotal, kappa, errorsignal); - } - } - - // this function behaves as its CUDA counterpart, except that it takes CPU-side std::vectors for everything - // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) - static void emulateedgealignment (const std::vector & hmms, const std::vector & transPs, const size_t spalignunitid, const size_t silalignunitid, - const std::vector & nodes, const std::vector & edges, - const std::vector & aligns, - const msra::math::ssematrixbase & logLLs, const std::vector & alignoffsets, - std::vector & backptrstorage, const std::vector & backptroffsets, - std::vector & alignresult, std::vector & edgeacscores) - { - // TODO: This function is about determining the parallelization layout - const size_t numedges = edges.size(); - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { edgealignmentj (hmms, transPs, spalignunitid, silalignunitid, nodes, edges, aligns, logLLs, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); }); - } - - static double emulateforwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, - const size_t numlaunchforward, const size_t numlaunchbackward, - const size_t spalignunitid, const size_t silalignunitid, - const std::vector & edgeacscores, - const std::vector & edges, const std::vector & nodes, - const std::vector & aligns, - const std::vector & alignments, const std::vector & alignoffsets, - std::vector & logpps, std::vector & logalphas, std::vector & logbetas, - const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, - const std::vector & uids, const std::vector & senone2classmap, - std::vector & logaccalphas, std::vector & logaccbetas, std::vector & logframescorrectedge, - std::vector & logEframescorrect, std::vector & Eframescorrectbuf, double & logEframescorrecttotal) - { - Eframescorrectbuf; // TODO: remove this [v-hansu] - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((logalphas.size() + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { setvaluej (logalphas, LOGZERO, logalphas.size());}); - emulatecuda (b, t, [&]() { setvaluej (logbetas, LOGZERO, logbetas.size());}); - emulatecuda (b, t, [&]() { setvaluej (logaccalphas, LOGZERO, logaccalphas.size());}); - emulatecuda (b, t, [&]() { setvaluej (logaccbetas, LOGZERO, logaccbetas.size());}); - - logalphas.front() = 0; - logbetas[nodes.size() -1] = 0; - - // forward pass - - size_t startindex = 0; - for (size_t i = 0; i < numlaunchforward; i++) - { - dim3 b ((unsigned int) ((batchsizeforward[i] + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { forwardlatticej (batchsizeforward[i], startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignoffsets, - logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas);} ); - startindex += batchsizeforward[i]; - } - double totalfwscore = logalphas[nodes.size() - 1]; - double totalfwacc; - if (returnEframescorrect) - { - totalfwacc = logaccalphas[nodes.size() - 1]; - totalfwacc -= totalfwscore; - } - - // backward pass - startindex = edges.size(); - for (size_t i = 0; i < numlaunchbackward; i++) - { - dim3 b ((unsigned int) ((batchsizebackward[i] + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { backwardlatticej (batchsizebackward[i], startindex - batchsizebackward[i], edgeacscores, - spalignunitid, silalignunitid, edges, nodes, aligns, - totalfwscore, logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, - returnEframescorrect, logframescorrectedge, logaccalphas, logEframescorrect, logaccbetas); }); - startindex -= batchsizebackward[i]; - } - double totalbwscore = logbetas.front(); - if (returnEframescorrect) - logEframescorrecttotal = logaccbetas.front() - totalbwscore; - -#if 1 //check for matching - double difffwbw = totalfwscore - totalbwscore; - double absdifffwbw = difffwbw > 0 ? difffwbw : 0 - difffwbw; - - if (absdifffwbw / nodes.size() > 1e-4) - fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); -#endif - return totalfwscore; - } - // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything - // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) - static void emulatesMBRerrorsignal (const std::vector & alignstateids, const std::vector & alignoffsets, - const std::vector & edges, const std::vector & nodes, - const std::vector & logpps, const float amf, - const std::vector & logEframescorrect, const double logEframescorrecttotal, - msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) - { - - const size_t numedges = edges.size(); - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - foreach_coord (i, j, errorsignal) - errorsignal(i,j) = errorsignalneg(i,j) = LOGZERO; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { sMBRerrorsignalj (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); }); - dim3 b1 ((((unsigned int) errorsignal.rows())+31)/32); - emulatecuda (b1, 32, [&]() { errorcomputationi (errorsignal, errorsignalneg, amf); }); - } - - // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything - // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) - static void emulatemmierrorsignal (const std::vector & alignstateids, const std::vector & alignoffsets, - const std::vector & edges, const std::vector & nodes, - const std::vector & logpps, msra::math::ssematrixbase & errorsignal) - { - const size_t numedges = edges.size(); - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - foreach_coord (i, j, errorsignal) - errorsignal(i,j) = LOGZERO; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { stateposteriorsj (alignstateids, alignoffsets, edges, nodes, logpps, errorsignal);}); - foreach_coord (i, j, errorsignal) - errorsignal(i,j) = expf(errorsignal(i,j)); - } - - // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything - // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) - /*static*/ void emulatestateposteriors (const std::vector & alignstateids, const std::vector & alignoffsets, - const std::vector & edges, const std::vector & nodes, - const std::vector & logqs, msra::math::ssematrixbase & logacc) - { - foreach_coord (i, j, logacc) - logacc(i,j) = LOGZERO; - const size_t numedges = edges.size(); - dim3 t (32, 8); - const size_t tpb = t.x * t.y; - dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); - emulatecuda (b, t, [&]() { stateposteriorsj (alignstateids, alignoffsets, edges, nodes, logqs, logacc); }); - } - - // ----------------------------------------------------------------------- - // parallelstate (-impl) --holds variables for CUDA access - // ----------------------------------------------------------------------- - - struct parallelstateimpl - { - bool emulation; - size_t deviceid; - parallelstateimpl(size_t deviceid) : deviceid(deviceid), emulation(false), // change this to true to switch to emulation - // models - lr3transPgpu(msra::cuda::newlr3transPvector(deviceid)), hmmsgpu(msra::cuda::newlrhmmdefvector(deviceid)), spalignunitid(SIZE_MAX), silalignunitid(SIZE_MAX), - // current lattice, logLLs, and return values - edgesgpu(msra::cuda::newedgeinfovector(deviceid)), nodesgpu(msra::cuda::newnodeinfovector(deviceid)), aligngpu(msra::cuda::newaligninfovector(deviceid)), - alignresult(msra::cuda::newushortvector(deviceid)), alignoffsetsgpu(msra::cuda::newuintvector(deviceid)), - edgeacscoresgpu(msra::cuda::newfloatvector(deviceid)), cudalogLLs(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), logppsgpu(msra::cuda::newdoublevector(deviceid)), - logalphasgpu(msra::cuda::newdoublevector(deviceid)), logbetasgpu(msra::cuda::newdoublevector(deviceid)), logaccalphasgpu(msra::cuda::newdoublevector(deviceid)), - logaccbetasgpu(msra::cuda::newdoublevector(deviceid)), logframescorrectedgegpu(msra::cuda::newdoublevector(deviceid)), Eframescorrectbufgpu(msra::cuda::newdoublevector(deviceid)), - logEframescorrectgpu(msra::cuda::newdoublevector(deviceid)), uidsgpu(msra::cuda::newushortvector(deviceid)), senone2classmapgpu(msra::cuda::newushortvector(deviceid)), - errorsignalgpu(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), errorsignalneggpu(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), errorsignalgpustorage(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), - errorsignalneggpustorage(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), backptrstoragegpu(msra::cuda::newushortvector(deviceid)), backptroffsetsgpu(msra::cuda::newsizetvector(deviceid)) - { - } - - size_t getdevice() - { - return deviceid; - } - - // models - - std::unique_ptr lr3transPgpu; - std::unique_ptr hmmsgpu; - - /*lr3transPvector* lr3transPgpu; - lrhmmdefvector* hmmsgpu;*/ - size_t spalignunitid; - size_t silalignunitid; - // models for use with emulation - std::vector hmmscpuforgpu; - std::vector lr3transPcpuforgpu; - std::vector senone2classmapcpuforgpu; - - // this caches the models over - // static_assert (sizeof (lr3transP) == 32, "unexpected size of lr3transP"); - // TOOD: give this function a proper name - static_assert (sizeof (lrhmmdef) == 8, "unexpected size of lrhmmdef"); - void cachehset (const msra::asr::simplesenonehmm & hset, mbrclassdefinition mbrclassdef) - { - // only copy once - // TODO: this can only be cached once --but there is no check whether a different model is passed - if (lr3transPgpu->size() > 0) - LogicError("cachehset: cannot bind to multiple model sets"); - - // transPs - lr3transPcpuforgpu.resize (hset.transPs.size()); - const std::vector & transPs = hset.transPs; - spalignunitid = hset.gethmmid ("sp"); - silalignunitid = hset.gethmmid ("sil"); - - foreach_index (i, transPs) - { - const size_t numstates = transPs[i].getnumstates(); - - lr3transPcpuforgpu[i].numstates = numstates; - for(int m = 0; m < numstates+1; m++) - { - - for(int n = 0; n < numstates+1; n++) - { - lr3transPcpuforgpu[i].loga[m][n] = transPs[i](m-1, n); - } - } - } - - lr3transPgpu->assign (lr3transPcpuforgpu, false); - - if (mbrclassdef == monophone) - { - const auto & sphmm = hset.gethmm (spalignunitid); - const auto & silhmm = hset.gethmm (silalignunitid); - const size_t numsenones = hset.getnumsenone(); - senone2classmapcpuforgpu.resize(numsenones); - for (size_t i = 0; i < numsenones; i++) - { - if (hset.statebelongstohmm (i, sphmm) || hset.statebelongstohmm (i, silhmm)) - senone2classmapcpuforgpu[i] = silhmm.transPindex; - else - senone2classmapcpuforgpu[i] = (unsigned short) hset.senonetransP(i); - } - senone2classmapgpu->assign (senone2classmapcpuforgpu, false); - } - - // else // mbrclassdefinition:: senones has no mapping - - // hmm defs - hmmscpuforgpu.resize (hset.hmms.size()); - const std::vector & hmms = hset.hmms; - foreach_index (i, hmmscpuforgpu) - { - hmmscpuforgpu[i].numstates = (unsigned char) hmms[i].getnumstates(); - if (hmmscpuforgpu[i].numstates != hmms[i].getnumstates()) - LogicError("parallelforwardbackwardalign : hmms.numstates is out of range of unsigned char"); - - for (size_t m = 0; m < hmmscpuforgpu[i].numstates; m++) - { - hmmscpuforgpu[i].senoneids[m] = (unsigned short) hmms[i].getsenoneid(m); - if (hmmscpuforgpu[i].senoneids[m] != hmms[i].getsenoneid(m)) - LogicError("parallelforwardbackwardalign : hmms.numstates is out of range of unsigned short"); - } - - hmmscpuforgpu[i].transPindex = (unsigned short) hmms[i].gettransPindex(); - if (hmmscpuforgpu[i].transPindex != hmms[i].gettransPindex()) - LogicError("parallelforwardbackwardalign : hmms.transPindex is out of range of unsigned short"); - } - hmmsgpu->assign (hmmscpuforgpu, true/*sync*/); // need to sync if we free the memory right after (and we won't buy much from async) - - // if we are not emulating then we will delete our CPU-side copy to save memory - if (!emulation) - { - lr3transPcpuforgpu.clear(); - senone2classmapcpuforgpu.clear(); - hmmscpuforgpu.clear(); - } -#ifdef FORBID_INVALID_SIL_PATH - fprintf (stderr, "forbid invalid sil path\n"); // [v-hansu] might be inappropriate to print here, but it is convenient. -#endif - } - // check that we got a model and the right one - void validatehset (const msra::asr::simplesenonehmm & hset) - { - if (hmmsgpu->size() != hset.hmms.size() || lr3transPgpu->size() != hset.transPs.size()) - LogicError("validatehset: not bound to hset or to wrong hset"); - } - - // current lattice - std::unique_ptr edgesgpu; - std::unique_ptr nodesgpu; - std::unique_ptr aligngpu; - std::unique_ptr alignresult; // concatenated alignments; edges[j]'s alignment starts at offset alignoffsets[j] - std::unique_ptr alignoffsetsgpu; - std::unique_ptr edgeacscoresgpu; - std::unique_ptr> cudalogLLs; - - std::unique_ptr logppsgpu; - std::unique_ptr logalphasgpu; - std::unique_ptr logbetasgpu; - std::unique_ptr logaccalphasgpu; - std::unique_ptr logaccbetasgpu; - - std::unique_ptr logframescorrectedgegpu; - std::unique_ptr Eframescorrectbufgpu; - std::unique_ptr logEframescorrectgpu; - - std::unique_ptr backptrstoragegpu; - std::unique_ptr backptroffsetsgpu; - - std::unique_ptr uidsgpu; - std::unique_ptr senone2classmapgpu; - - std::unique_ptr> errorsignalgpustorage; - std::unique_ptr> errorsignalneggpustorage; - std::unique_ptr> errorsignalgpu; - std::unique_ptr> errorsignalneggpu; - - // cache current lattice - // This is a weird mix of const/non-const and private lattice data... :( - template - void setutterancedata (const edgestype & edges, const nodestype & nodes, const aligntype & align, - const msra::math::ssematrixbase & /*logLLs*/, std::vector & edgeacscores, - edgealignments & edgeAlignments, backpointers & backPointers) - { - // lattice data - edgesgpu->assign (edges, false); - nodesgpu->assign (nodes, false); - aligngpu->assign (align, false); - alignoffsetsgpu->assign (edgeAlignments.getalignoffsets(), false); - backptrstoragegpu->allocate (backPointers.getbackptrstoragesize()); - backptroffsetsgpu->assign (backPointers.getbackptroffsets(), false); - -#ifndef PARALLEL_SIL - alignresult->assign (edgeAlignments.getalignmentsbuffer(), false); - edgeacscoresgpu->assign (edgeacscores, false); -#else - alignresult->allocate (edgeAlignments.getalignbuffersize()); - edgeacscoresgpu->allocate (edges.size()); - edgeacscores; // reference to make compilor happy -#endif - - // LLs - //zhaorui - /*cudalogLLs->allocate (logLLs.rows(), logLLs.cols()); - cudalogLLs->assign(0, logLLs.rows(), 0, logLLs.cols(), &logLLs(0,0), logLLs.getcolstride(), true); // doing this last with 'true' so we can measure time better; maybe remove later*/ - - } - //template - void setloglls(const Microsoft::MSR::CNTK::Matrix& loglls) - { - *cudalogLLs = loglls; - } - void getgamma(Microsoft::MSR::CNTK::Matrix& loglls) - { - loglls = *errorsignalgpu; - } - template - void copyalignments (edgealignments & edgeAlignments) - { - alignresult->fetch(edgeAlignments.getalignmentsbuffer(), true); - } - - // [v-hansu] allocate memory for vectors relating to forward-backward - // allocateaccvectors implies return Eframecorrect - template - void allocfwbwvectors (const edgestype & edges, const nodestype & nodes, const std::vector & uids, - const bool allocateframescorrect, const bool copyuids, const bool allocateaccvectors) - { - logppsgpu->allocate (edges.size()); -#ifndef TWO_CHANNEL - const size_t alphabetanoderatio = 1; -#else - const size_t alphabetanoderatio = 2; -#endif - logalphasgpu->allocate (alphabetanoderatio * nodes.size()); - logbetasgpu->allocate (alphabetanoderatio * nodes.size()); - - if (allocateframescorrect) - logframescorrectedgegpu->allocate (edges.size()); - - if (copyuids) - uidsgpu->assign (uids, true); - - if (allocateaccvectors) - { - logaccalphasgpu->allocate (alphabetanoderatio * nodes.size()); - logaccbetasgpu->allocate (alphabetanoderatio * nodes.size()); - - Eframescorrectbufgpu->allocate (edges.size()); - logEframescorrectgpu->allocate (edges.size()); - - } - } - - // check if gpumatrixstorage supports size of cpumatrix, if not allocate. set gpumatrix to part of gpumatrixstorage - void cacheerrorsignal (const msra::math::ssematrixbase & errorsignal, const bool cacheerrsignalneg) - { - if (errorsignalgpustorage->GetNumRows() != 0 && errorsignalgpustorage->GetNumRows() != errorsignal.rows()) - throw::logic_error ("gpumatrixstorage->rows() shall be fixed once allocated"); - if (errorsignalgpustorage->GetNumCols() < errorsignal.cols()) - errorsignalgpustorage->Resize(errorsignal.rows(), errorsignal.cols()); - *errorsignalgpu = errorsignalgpustorage->ColumnSlice(0, errorsignal.cols()); - - if (cacheerrsignalneg) - { - if (errorsignalneggpustorage->GetNumRows() != 0 && errorsignalneggpustorage->GetNumRows() != errorsignal.rows()) - throw::logic_error ("gpumatrixstorage->rows() shall be fixed once allocated"); - if (errorsignalneggpustorage->GetNumCols() < errorsignal.cols()) - errorsignalneggpustorage->Resize(errorsignal.rows(), errorsignal.cols()); - *errorsignalneggpu = errorsignalneggpustorage->ColumnSlice(0, errorsignal.cols()); - } - } - - void getedgeacscores (std::vector & edgeacscores) - { - edgeacscores.resize (edgeacscoresgpu->size()); - edgeacscoresgpu->fetch (edgeacscores, true); - } - - void getedgealignments (std::vector & edgealignments) - { - edgealignments.resize (alignresult->size()); - alignresult->fetch (edgealignments, true); - } - }; - - void lattice::parallelstate::setdevice(size_t deviceid) - { - bool pcpumode = (deviceid == CPUDEVICE); - if (!pcpumode) - { - pimpl = new parallelstateimpl(deviceid); - } - else - { - delete pimpl; - pimpl = NULL; - } - // else we leave it at NULL - } - - size_t lattice::parallelstate::getdevice() - { - return pimpl->getdevice(); - } - - void lattice::parallelstate::release(bool pcpumode) - { - if (!pcpumode) - { - /* if (pimpl != NULL) - { - delete pimpl; - pimpl = NULL; - }*/ - } - } - lattice::parallelstate::parallelstate() { pimpl = nullptr; } - lattice::parallelstate::~parallelstate() { delete pimpl; } - void lattice::parallelstate::entercomputation (const msra::asr::simplesenonehmm & hset, const mbrclassdefinition mbrclassdef) { pimpl->cachehset (hset, mbrclassdef); } // pass models in (to GPU) //TODO: rethink the naming of this function - void lattice::parallelstate::copyalignments (edgealignments & edgealignments) { pimpl->copyalignments (edgealignments); } - const size_t lattice::parallelstate::getsilunitid () { return pimpl->silalignunitid; } - void lattice::parallelstate::getedgeacscores (std::vector & edgeacscores) { pimpl->getedgeacscores (edgeacscores); } - void lattice::parallelstate::getedgealignments (std::vector & edgealignments) { pimpl->getedgealignments (edgealignments); } - //template - void lattice::parallelstate::setloglls(const Microsoft::MSR::CNTK::Matrix& loglls) { pimpl->setloglls(loglls); } - - // TODO: Overload to enable compilation for DoublePrecision though its currently unsupported - void lattice::parallelstate::setloglls(const Microsoft::MSR::CNTK::Matrix& /*loglls*/) - { - throw::logic_error("Double precision not supported for sequence training"); - } - - void lattice::parallelstate::getgamma(Microsoft::MSR::CNTK::Matrix& loglls) { pimpl->getgamma(loglls); } - - // TODO: Overload to enable compilation for DoublePrecision though its currently unsupported - void lattice::parallelstate::getgamma(Microsoft::MSR::CNTK::Matrix& /*loglls*/) - { - throw::logic_error("Double precision not supported for sequence training"); - } - - // ----------------------------------------------------------------------- - // parallel implementations of key processing steps - // ----------------------------------------------------------------------- - - // parallelforwardbackwardalign() -- compute the statelevel gammas or viterbi alignments - void lattice::parallelforwardbackwardalign (parallelstate & parallelstate, - const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, - std::vector & edgeacscores, edgealignments & edgealignments, backpointers & backpointers) const - { - parallelstate->validatehset (hset); // ensure the models have been correctly cached on the GPU already - - if (!parallelstate->emulation) - { - // move lattice to GPU - parallelstate->setutterancedata (edges, nodes, align, logLLs, // inputs - edgeacscores, edgealignments, backpointers); // inouts - - // launch the kernel - std::unique_ptr latticefunctions (msra::cuda::newlatticefunctions(parallelstate.getdevice())); - latticefunctions->edgealignment(*parallelstate->hmmsgpu.get(), *parallelstate->lr3transPgpu.get(), - parallelstate->spalignunitid, parallelstate->silalignunitid, - *parallelstate->cudalogLLs.get(), *parallelstate->nodesgpu.get(), - *parallelstate->edgesgpu.get(), *parallelstate->aligngpu.get(), - *parallelstate->alignoffsetsgpu.get(), - *parallelstate->backptrstoragegpu.get(), *parallelstate->backptroffsetsgpu.get(), - *parallelstate->alignresult.get(), *parallelstate->edgeacscoresgpu.get()); - } - else - { - if (parallelstate->hmmscpuforgpu.size() == 0) - throw::logic_error("we no longer support emulation for edgealign, please copy hmmscpuforgpu and lr3transPcpuforgpu if you want"); - edgeacscores.resize (edges.size()); - emulateedgealignment (parallelstate->hmmscpuforgpu, parallelstate->lr3transPcpuforgpu, parallelstate->spalignunitid, - parallelstate->silalignunitid, - nodes, edges, align, logLLs, edgealignments.getalignoffsets(), - backpointers.getbackptrbuffer(), backpointers.getbackptroffsets(), - edgealignments.getalignmentsbuffer(), edgeacscores); - // emulate the GPU version, save result back to GPU - parallelstate->alignresult->assign (edgealignments.getalignmentsbuffer(), false); - parallelstate->edgeacscoresgpu->assign (edgeacscores, true); - } - } - - // parallelforwardbackwardlattice() -- compute the latticelevel logpps using forwardbackward - double lattice::parallelforwardbackwardlattice (parallelstate & parallelstate, const std::vector & edgeacscores, - const edgealignments & thisedgealignments, const float lmf, const float wp, - const float amf, const float boostingfactor, std::vector & logpps, - std::vector & logalphas, std::vector & logbetas, const bool returnEframescorrect, - const_array_ref & uids, std::vector & logEframescorrect, - std::vector & Eframescorrectbuf, double & logEframescorrecttotal) const - { //^^ TODO: remove this - vector batchsizeforward; // record the batch size that exclude the data dependency for forward - vector batchsizebackward; // record the batch size that exclude the data dependency for backward - - size_t endindexforward = edges[0].E; - size_t countbatchforward = 0; - - size_t endindexbackward = edges.back().S; - size_t countbatchbackward = 0; - foreach_index (j, edges) // compute the batch size info for kernel launches - { - if (edges[j].S < endindexforward) - countbatchforward++; // note: we don't check forward because the order of end node is assured. - else - { - batchsizeforward.push_back (countbatchforward); - countbatchforward = 1; - endindexforward = edges[j].E; - } - const size_t backj = edges.size() -1 - j; - if (edges[backj].E > endindexbackward) - { - countbatchbackward++; - if (endindexbackward < edges[backj].S) - endindexbackward = edges[backj].S; - } - else - { - batchsizebackward.push_back (countbatchbackward); - countbatchbackward = 1; - endindexbackward = edges[backj].S; - } - } - batchsizeforward.push_back (countbatchforward); - batchsizebackward.push_back (countbatchbackward); - - std::vector uidsuint(uids.size()); // actually we shall not do this, but as it will not take much time, let us just leave it here now. - foreach_index(i, uidsuint) uidsuint[i] = (unsigned short)uids[i]; - - double totalfwscore = 0.0f; - if (!parallelstate->emulation) - { - if (verbosity>=2) - fprintf(stderr, "parallelforwardbackwardlattice: %d launches for forward, %d launches for backward\n", (int)batchsizeforward.size(), (int)batchsizebackward.size()); - - const bool allocateframescorrect = (returnEframescorrect || boostingfactor != 0.0f); - const bool copyuids = (returnEframescorrect || boostingfactor != 0.0f); - const bool allocateaccvectors = returnEframescorrect; - parallelstate->allocfwbwvectors (edges, nodes, uidsuint, allocateframescorrect, copyuids, allocateaccvectors); - - std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); // final CUDA call - latticefunctions->forwardbackwardlattice (&batchsizeforward[0], &batchsizebackward[0], batchsizeforward.size(), batchsizebackward.size(), - parallelstate->spalignunitid, parallelstate->silalignunitid, - *parallelstate->edgeacscoresgpu.get(), *parallelstate->edgesgpu.get(), - *parallelstate->nodesgpu.get(), *parallelstate->aligngpu.get(), - *parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), - *parallelstate->logppsgpu.get(), *parallelstate->logalphasgpu.get(), - *parallelstate->logbetasgpu.get(), lmf, wp, amf, boostingfactor, - returnEframescorrect, *parallelstate->uidsgpu.get(), *parallelstate->senone2classmapgpu.get(), - *parallelstate->logaccalphasgpu.get(), *parallelstate->logaccbetasgpu.get(), - *parallelstate->logframescorrectedgegpu.get(), *parallelstate->logEframescorrectgpu.get(), - *parallelstate->Eframescorrectbufgpu.get(), logEframescorrecttotal, totalfwscore); - } - else // emulation - { -#ifndef TWO_CHANNEL - fprintf (stderr, "forbid invalid sil path\n"); - const size_t alphabetanoderatio = 1; -#else - const size_t alphabetanoderatio = 2; -#endif - logpps.resize (edges.size()); - logalphas.resize (alphabetanoderatio * nodes.size()); - logbetas.resize (alphabetanoderatio * nodes.size()); - - std::vector logaccalphas; - std::vector logaccbetas; - std::vector logframescorrectedge; - logframescorrectedge.resize (edges.size()); - - if (returnEframescorrect) - { - logaccalphas.resize (alphabetanoderatio * nodes.size()); - logaccbetas.resize (alphabetanoderatio * nodes.size()); - - logEframescorrect.resize (edges.size()); - Eframescorrectbuf.resize (edges.size()); - } - - totalfwscore = emulateforwardbackwardlattice (&batchsizeforward[0], &batchsizebackward[0], - batchsizeforward.size(), batchsizebackward.size(), - parallelstate->spalignunitid, parallelstate->silalignunitid, - edgeacscores, edges, nodes, align, - thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), - logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, uidsuint, parallelstate->senone2classmapcpuforgpu, - logaccalphas, logaccbetas, logframescorrectedge, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); - - } - return totalfwscore; - } - - // ------------------------------------------------------------------------ - // parallel implementations of sMBR error updating step - // ------------------------------------------------------------------------ - void lattice::parallelsMBRerrorsignal (parallelstate & parallelstate, const edgealignments & thisedgealignments, - const std::vector & logpps, const float amf, const std::vector & logEframescorrect, - const double logEframescorrecttotal, msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) const - { - /* time measurement for CUDA - cudaerrorcopyto: 3.058592 ms - cudaerrorsignalsync: 0.356998 ms - cudaerrorcopyback: 8.983703 ms */ - - /* time measurement for emulation - emuresettozeros: 102.979899 ms - emuerrorsignal: 336.407459 ms */ - - if (!parallelstate->emulation) - { - // We allocate a pos and a neg buffer. - const bool cacheerrorsignalneg = true; - parallelstate->cacheerrorsignal (errorsignal, cacheerrorsignalneg); - - std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); - latticefunctions->sMBRerrorsignal (*parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), *parallelstate->edgesgpu.get(), - *parallelstate->nodesgpu.get(), *parallelstate->logppsgpu.get(),amf, *parallelstate->logEframescorrectgpu.get(), - logEframescorrecttotal, - *parallelstate->errorsignalgpu.get(), *parallelstate->errorsignalneggpu.get()); - - if (errorsignal.rows() > 0 && errorsignal.cols() > 0) - { - parallelstate->errorsignalgpu->CopySection(errorsignal.rows(), errorsignal.cols(), &errorsignal(0, 0), errorsignal.getcolstride()); - } - } - else - { - emulatesMBRerrorsignal (thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); - } - } - - // ------------------------------------------------------------------------ - // parallel implementations of MMI error updating step - // ------------------------------------------------------------------------ - void lattice::parallelmmierrorsignal (parallelstate & parallelstate, const edgealignments & thisedgealignments, - const std::vector & logpps, msra::math::ssematrixbase & errorsignal) const - { - if (!parallelstate->emulation) - { - const bool cacheerrorsignalneg = false; // we do not need it in mmi mode - parallelstate->cacheerrorsignal (errorsignal, cacheerrorsignalneg); - - std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); - latticefunctions->mmierrorsignal (*parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), *parallelstate->edgesgpu.get(), - *parallelstate->nodesgpu.get(), *parallelstate->logppsgpu.get(), *parallelstate->errorsignalgpu.get()); - - //parallelstate->errorsignalgpu->fetch (0, errorsignal.rows(), 0, errorsignal.cols(), &errorsignal(0, 0), errorsignal.getcolstride(), true); - } - else - { - emulatemmierrorsignal (thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), edges, nodes, logpps, errorsignal); - } - } -};}; +// parallelforwardbackward.cpp -- parallelized implementation(s) of lattice forward/backward implemented --currently through CUDA +// +// F. Seide, V-hansu + +#define _CRT_SECURE_NO_WARNINGS // "secure" CRT not available on all platforms --add this at the top of all CPP files that give "function or variable may be unsafe" warnings + +#include "BestGpu.h" // for CPUONLY +#include "latticearchive.h" // we implement parts of class lattice +#include "simple_checked_arrays.h" +#include "simplesenonehmm.h" // the model +#include "ssematrix.h" // the matrices +#include "cudalattice.h" +#include "latticefunctionskernels.h" // for emulation +#include "cudalatticeops.h" +#include // for debug +#include "cudalib.h" + +#define TWO_CHANNEL // [v-hansu] +using namespace msra::cuda; + +#ifndef CPUONLY +#pragma comment (lib, "MathCUDA.lib") // built by CNTKMathCUDA project +#endif + +namespace msra { namespace lattices { + + // emulation support + struct dim3 + { + size_t x, y, z; + dim3(size_t x = 1, size_t y = 1, size_t z = 1) : x(x), y(y), z(z){} + }; + + static dim3 blockIdx, gridDim, threadIdx, blockDim; + + template + __forceinline void emulatecuda (const dim3 & b, const dim3 & t, FUNC f) + { + gridDim = b; + blockDim = t; + for (blockIdx.x = 0; blockIdx.x < gridDim.x; blockIdx.x++) + for (blockIdx.y = 0; blockIdx.y < gridDim.y; blockIdx.y++) + for (blockIdx.z = 0; blockIdx.z < gridDim.z; blockIdx.z++) + for (threadIdx.z = 0; threadIdx.z < blockDim.z; threadIdx.z++) + for (threadIdx.y = 0; threadIdx.y < blockDim.y; threadIdx.y++) + for (threadIdx.x = 0; threadIdx.x < blockDim.x; threadIdx.x++) + f(); + } + + void setvaluej (std::vector & thisvector, double value, size_t nelem) + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < nelem) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::setvaluej (j, thisvector, value); + } + } + + void expdiffj (std::vector & Eframescorrectbuf, double & logEframescorrecttotal, size_t nelem, std::vector & logEframescorrect) + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < nelem) // note: will cause issues if we ever use __synctreads() + { + logEframescorrect[j] = (float)(Eframescorrectbuf[j] - logEframescorrecttotal); + } + } + // this must be identical to an actual CUDA kernel (except for the input data types: vectorref -> std::vector) + void edgealignmentj (const std::vector & hmms, const std::vector & transPs, const size_t spalignunitid, const size_t silalignunitid, + const std::vector & nodes, const std::vector & edges, + const std::vector & aligns, + const msra::math::ssematrixbase & logLLs, const std::vector & alignoffsets, + std::vector & backptrstorage, const std::vector & backptroffsets, + std::vector & alignresult, std::vector & edgeacscores) + { + // this function identifies which element in the grid we are, and then passes to the actual function that does stuff + // compute j; but check if it is in range; return if not + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::edgealignmentj (j, hmms, transPs, spalignunitid, silalignunitid, logLLs, nodes, edges, aligns, + alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); + } + } + + void forwardlatticej (const size_t batchsize, const size_t startindex, const std::vector & edgeacscores, + const size_t spalignunitid, const size_t silalignunitid, + const std::vector & edges, const std::vector & nodes, + const std::vector & aligns, + const std::vector & alignments, const std::vector & alignmentoffsets, + std::vector & logalphas, float lmf, float wp, float amf, const float boostingfactor, + const std::vector & uids, const std::vector & senone2classmap, const bool returnEframescorrect, + std::vector & logframescorrectedge, std::vector & logaccalphas) + { + const size_t shufflemode = 1; + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < batchsize) // note: will cause issues if we ever use __synctreads() in forwardlatticej + { + msra::lattices::latticefunctionskernels::forwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignmentoffsets, + logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas); + } + } + + void backwardlatticej (const size_t batchsize, const size_t startindex, const std::vector & edgeacscores, + const size_t spalignunitid, const size_t silalignunitid, + const std::vector & edges, + const std::vector & nodes, + const std::vector & aligns, const double totalfwscore, + std::vector & logpps, std::vector & logalphas, std::vector & logbetas, + float lmf, float wp, float amf, const float boostingfactor, const bool returnEframescorrect, std::vector & logframescorrectedge, + std::vector & logaccalphas, std::vector & Eframescorrectbuf, std::vector & logaccbetas) + { + const size_t tpb = blockDim.x * blockDim.y; // total #threads in a block + const size_t jinblock = threadIdx.x + threadIdx.y * blockDim.x; + const size_t j = jinblock + blockIdx.x * tpb; + if (j < batchsize) // note: will cause issues if we ever use __synctreads() in backwardlatticej + { + msra::lattices::latticefunctionskernels::backwardlatticej (j + startindex, edgeacscores, spalignunitid, silalignunitid, + edges, nodes, aligns, totalfwscore, logpps, logalphas, + logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, logframescorrectedge, + logaccalphas, Eframescorrectbuf, logaccbetas); + } + } + + void sMBRerrorsignalj (const std::vector & alignstateids, const std::vector & alignoffsets, + const std::vector & edges, const std::vector & nodes, + const std::vector & logpps, const float amf, const std::vector & logEframescorrect, + const double logEframescorrecttotal, msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) + { + const size_t shufflemode = 3; + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::sMBRerrorsignalj (j, alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, + errorsignal, errorsignalneg); + } + } + + void stateposteriorsj (const std::vector & alignstateids, const std::vector & alignoffsets, + const std::vector & edges, const std::vector & nodes, + const std::vector & logqs, msra::math::ssematrixbase & logacc) + { + const size_t shufflemode = 3; + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < edges.size()) // note: will cause issues if we ever use __synctreads() + { + msra::lattices::latticefunctionskernels::stateposteriorsj (j, alignstateids, alignoffsets, edges, nodes, logqs, logacc); + } + } + + void checkshuffle (std::vector & checkvector1, std::vector & checkvector2, size_t shufflemode) + { + const size_t j = msra::lattices::latticefunctionskernels::shuffle (threadIdx.x, blockDim.x, threadIdx.y, blockDim.y, blockIdx.x, gridDim.x, shufflemode); + if (j < checkvector1.size()) + { + checkvector1[j] = -1; + } + if (j < checkvector2.size()) + { + checkvector2[j] -= 1; + } + } + + void errorcomputationi (msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg, float amf) + { + const size_t i = threadIdx.x + (blockIdx.x * blockDim.x); + if (i >= errorsignal.rows()) + return; + // clear all columns + const size_t m = errorsignal.cols(); + for (size_t j = 0; j < m; j++) + errorsignal(i,j) = (expf(errorsignal(i,j)) - expf(errorsignalneg(i,j)))/amf; + } + + void weightedcomponentexpproducts (const msra::math::ssematrixbase & loggammas, const msra::math::ssematrixbase & logEframescorrect, + const double logEframecorrecttotal, const float kappa, msra::math::ssematrixbase & errorsignal) + { + const size_t s = threadIdx.x + (blockIdx.x * blockDim.x); + if (s < errorsignal.rows()) + { + msra::lattices::latticefunctionskernels::computesMBRerrorsignals (s, loggammas, logEframescorrect, logEframecorrecttotal, kappa, errorsignal); + } + } + + // this function behaves as its CUDA counterpart, except that it takes CPU-side std::vectors for everything + // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) + static void emulateedgealignment (const std::vector & hmms, const std::vector & transPs, const size_t spalignunitid, const size_t silalignunitid, + const std::vector & nodes, const std::vector & edges, + const std::vector & aligns, + const msra::math::ssematrixbase & logLLs, const std::vector & alignoffsets, + std::vector & backptrstorage, const std::vector & backptroffsets, + std::vector & alignresult, std::vector & edgeacscores) + { + // TODO: This function is about determining the parallelization layout + const size_t numedges = edges.size(); + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { edgealignmentj (hmms, transPs, spalignunitid, silalignunitid, nodes, edges, aligns, logLLs, alignoffsets, backptrstorage, backptroffsets, alignresult, edgeacscores); }); + } + + static double emulateforwardbackwardlattice (const size_t * batchsizeforward, const size_t * batchsizebackward, + const size_t numlaunchforward, const size_t numlaunchbackward, + const size_t spalignunitid, const size_t silalignunitid, + const std::vector & edgeacscores, + const std::vector & edges, const std::vector & nodes, + const std::vector & aligns, + const std::vector & alignments, const std::vector & alignoffsets, + std::vector & logpps, std::vector & logalphas, std::vector & logbetas, + const float lmf, const float wp, const float amf, const float boostingfactor, const bool returnEframescorrect, + const std::vector & uids, const std::vector & senone2classmap, + std::vector & logaccalphas, std::vector & logaccbetas, std::vector & logframescorrectedge, + std::vector & logEframescorrect, std::vector & Eframescorrectbuf, double & logEframescorrecttotal) + { + Eframescorrectbuf; // TODO: remove this [v-hansu] + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((logalphas.size() + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { setvaluej (logalphas, LOGZERO, logalphas.size());}); + emulatecuda (b, t, [&]() { setvaluej (logbetas, LOGZERO, logbetas.size());}); + emulatecuda (b, t, [&]() { setvaluej (logaccalphas, LOGZERO, logaccalphas.size());}); + emulatecuda (b, t, [&]() { setvaluej (logaccbetas, LOGZERO, logaccbetas.size());}); + + logalphas.front() = 0; + logbetas[nodes.size() -1] = 0; + + // forward pass + + size_t startindex = 0; + for (size_t i = 0; i < numlaunchforward; i++) + { + dim3 b ((unsigned int) ((batchsizeforward[i] + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { forwardlatticej (batchsizeforward[i], startindex, edgeacscores, spalignunitid, silalignunitid, edges, nodes, aligns, alignments, alignoffsets, + logalphas, lmf, wp, amf, boostingfactor, uids, senone2classmap, returnEframescorrect, logframescorrectedge, logaccalphas);} ); + startindex += batchsizeforward[i]; + } + double totalfwscore = logalphas[nodes.size() - 1]; + double totalfwacc; + if (returnEframescorrect) + { + totalfwacc = logaccalphas[nodes.size() - 1]; + totalfwacc -= totalfwscore; + } + + // backward pass + startindex = edges.size(); + for (size_t i = 0; i < numlaunchbackward; i++) + { + dim3 b ((unsigned int) ((batchsizebackward[i] + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { backwardlatticej (batchsizebackward[i], startindex - batchsizebackward[i], edgeacscores, + spalignunitid, silalignunitid, edges, nodes, aligns, + totalfwscore, logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, + returnEframescorrect, logframescorrectedge, logaccalphas, logEframescorrect, logaccbetas); }); + startindex -= batchsizebackward[i]; + } + double totalbwscore = logbetas.front(); + if (returnEframescorrect) + logEframescorrecttotal = logaccbetas.front() - totalbwscore; + +#if 1 //check for matching + double difffwbw = totalfwscore - totalbwscore; + double absdifffwbw = difffwbw > 0 ? difffwbw : 0 - difffwbw; + + if (absdifffwbw / nodes.size() > 1e-4) + fprintf (stderr, "forwardbackward: WARNING: lattice fw and bw scores %.10f vs. %.10f (%d nodes/%d edges)\n", (float) totalfwscore, (float) totalbwscore, (int) nodes.size(), (int) edges.size()); +#endif + return totalfwscore; + } + // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything + // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) + static void emulatesMBRerrorsignal (const std::vector & alignstateids, const std::vector & alignoffsets, + const std::vector & edges, const std::vector & nodes, + const std::vector & logpps, const float amf, + const std::vector & logEframescorrect, const double logEframescorrecttotal, + msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) + { + + const size_t numedges = edges.size(); + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + foreach_coord (i, j, errorsignal) + errorsignal(i,j) = errorsignalneg(i,j) = LOGZERO; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { sMBRerrorsignalj (alignstateids, alignoffsets, edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); }); + dim3 b1 ((((unsigned int) errorsignal.rows())+31)/32); + emulatecuda (b1, 32, [&]() { errorcomputationi (errorsignal, errorsignalneg, amf); }); + } + + // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything + // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) + static void emulatemmierrorsignal (const std::vector & alignstateids, const std::vector & alignoffsets, + const std::vector & edges, const std::vector & nodes, + const std::vector & logpps, msra::math::ssematrixbase & errorsignal) + { + const size_t numedges = edges.size(); + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + foreach_coord (i, j, errorsignal) + errorsignal(i,j) = LOGZERO; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { stateposteriorsj (alignstateids, alignoffsets, edges, nodes, logpps, errorsignal);}); + foreach_coord (i, j, errorsignal) + errorsignal(i,j) = expf(errorsignal(i,j)); + } + + // this function behaves as its CUDA conterparts, except that it takes CPU-side std::vectors for everything + // this must be identical to CUDA kernel-launch function in -ops class (except for the input data types: vectorref -> std::vector) + /*static*/ void emulatestateposteriors (const std::vector & alignstateids, const std::vector & alignoffsets, + const std::vector & edges, const std::vector & nodes, + const std::vector & logqs, msra::math::ssematrixbase & logacc) + { + foreach_coord (i, j, logacc) + logacc(i,j) = LOGZERO; + const size_t numedges = edges.size(); + dim3 t (32, 8); + const size_t tpb = t.x * t.y; + dim3 b ((unsigned int) ((numedges + tpb - 1) / tpb)); + emulatecuda (b, t, [&]() { stateposteriorsj (alignstateids, alignoffsets, edges, nodes, logqs, logacc); }); + } + + // ----------------------------------------------------------------------- + // parallelstate (-impl) --holds variables for CUDA access + // ----------------------------------------------------------------------- + + struct parallelstateimpl + { + bool emulation; + size_t deviceid; + parallelstateimpl(size_t deviceid) : deviceid(deviceid), emulation(false), // change this to true to switch to emulation + // models + lr3transPgpu(msra::cuda::newlr3transPvector(deviceid)), hmmsgpu(msra::cuda::newlrhmmdefvector(deviceid)), spalignunitid(SIZE_MAX), silalignunitid(SIZE_MAX), + // current lattice, logLLs, and return values + edgesgpu(msra::cuda::newedgeinfovector(deviceid)), nodesgpu(msra::cuda::newnodeinfovector(deviceid)), aligngpu(msra::cuda::newaligninfovector(deviceid)), + alignresult(msra::cuda::newushortvector(deviceid)), alignoffsetsgpu(msra::cuda::newuintvector(deviceid)), + edgeacscoresgpu(msra::cuda::newfloatvector(deviceid)), cudalogLLs(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), logppsgpu(msra::cuda::newdoublevector(deviceid)), + logalphasgpu(msra::cuda::newdoublevector(deviceid)), logbetasgpu(msra::cuda::newdoublevector(deviceid)), logaccalphasgpu(msra::cuda::newdoublevector(deviceid)), + logaccbetasgpu(msra::cuda::newdoublevector(deviceid)), logframescorrectedgegpu(msra::cuda::newdoublevector(deviceid)), Eframescorrectbufgpu(msra::cuda::newdoublevector(deviceid)), + logEframescorrectgpu(msra::cuda::newdoublevector(deviceid)), uidsgpu(msra::cuda::newushortvector(deviceid)), senone2classmapgpu(msra::cuda::newushortvector(deviceid)), + errorsignalgpu(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), errorsignalneggpu(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), errorsignalgpustorage(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), + errorsignalneggpustorage(new Microsoft::MSR::CNTK::Matrix((int)deviceid)), backptrstoragegpu(msra::cuda::newushortvector(deviceid)), backptroffsetsgpu(msra::cuda::newsizetvector(deviceid)) + { + } + + size_t getdevice() + { + return deviceid; + } + + // models + + std::unique_ptr lr3transPgpu; + std::unique_ptr hmmsgpu; + + /*lr3transPvector* lr3transPgpu; + lrhmmdefvector* hmmsgpu;*/ + size_t spalignunitid; + size_t silalignunitid; + // models for use with emulation + std::vector hmmscpuforgpu; + std::vector lr3transPcpuforgpu; + std::vector senone2classmapcpuforgpu; + + // this caches the models over + // static_assert (sizeof (lr3transP) == 32, "unexpected size of lr3transP"); + // TOOD: give this function a proper name + static_assert (sizeof (lrhmmdef) == 8, "unexpected size of lrhmmdef"); + void cachehset (const msra::asr::simplesenonehmm & hset, mbrclassdefinition mbrclassdef) + { + // only copy once + // TODO: this can only be cached once --but there is no check whether a different model is passed + if (lr3transPgpu->size() > 0) + LogicError("cachehset: cannot bind to multiple model sets"); + + // transPs + lr3transPcpuforgpu.resize (hset.transPs.size()); + const std::vector & transPs = hset.transPs; + spalignunitid = hset.gethmmid ("sp"); + silalignunitid = hset.gethmmid ("sil"); + + foreach_index (i, transPs) + { + const size_t numstates = transPs[i].getnumstates(); + + lr3transPcpuforgpu[i].numstates = numstates; + for(int m = 0; m < numstates+1; m++) + { + + for(int n = 0; n < numstates+1; n++) + { + lr3transPcpuforgpu[i].loga[m][n] = transPs[i](m-1, n); + } + } + } + + lr3transPgpu->assign (lr3transPcpuforgpu, false); + + if (mbrclassdef == monophone) + { + const auto & sphmm = hset.gethmm (spalignunitid); + const auto & silhmm = hset.gethmm (silalignunitid); + const size_t numsenones = hset.getnumsenone(); + senone2classmapcpuforgpu.resize(numsenones); + for (size_t i = 0; i < numsenones; i++) + { + if (hset.statebelongstohmm (i, sphmm) || hset.statebelongstohmm (i, silhmm)) + senone2classmapcpuforgpu[i] = silhmm.transPindex; + else + senone2classmapcpuforgpu[i] = (unsigned short) hset.senonetransP(i); + } + senone2classmapgpu->assign (senone2classmapcpuforgpu, false); + } + + // else // mbrclassdefinition:: senones has no mapping + + // hmm defs + hmmscpuforgpu.resize (hset.hmms.size()); + const std::vector & hmms = hset.hmms; + foreach_index (i, hmmscpuforgpu) + { + hmmscpuforgpu[i].numstates = (unsigned char) hmms[i].getnumstates(); + if (hmmscpuforgpu[i].numstates != hmms[i].getnumstates()) + LogicError("parallelforwardbackwardalign : hmms.numstates is out of range of unsigned char"); + + for (size_t m = 0; m < hmmscpuforgpu[i].numstates; m++) + { + hmmscpuforgpu[i].senoneids[m] = (unsigned short) hmms[i].getsenoneid(m); + if (hmmscpuforgpu[i].senoneids[m] != hmms[i].getsenoneid(m)) + LogicError("parallelforwardbackwardalign : hmms.numstates is out of range of unsigned short"); + } + + hmmscpuforgpu[i].transPindex = (unsigned short) hmms[i].gettransPindex(); + if (hmmscpuforgpu[i].transPindex != hmms[i].gettransPindex()) + LogicError("parallelforwardbackwardalign : hmms.transPindex is out of range of unsigned short"); + } + hmmsgpu->assign (hmmscpuforgpu, true/*sync*/); // need to sync if we free the memory right after (and we won't buy much from async) + + // if we are not emulating then we will delete our CPU-side copy to save memory + if (!emulation) + { + lr3transPcpuforgpu.clear(); + senone2classmapcpuforgpu.clear(); + hmmscpuforgpu.clear(); + } +#ifdef FORBID_INVALID_SIL_PATH + fprintf (stderr, "forbid invalid sil path\n"); // [v-hansu] might be inappropriate to print here, but it is convenient. +#endif + } + // check that we got a model and the right one + void validatehset (const msra::asr::simplesenonehmm & hset) + { + if (hmmsgpu->size() != hset.hmms.size() || lr3transPgpu->size() != hset.transPs.size()) + LogicError("validatehset: not bound to hset or to wrong hset"); + } + + // current lattice + std::unique_ptr edgesgpu; + std::unique_ptr nodesgpu; + std::unique_ptr aligngpu; + std::unique_ptr alignresult; // concatenated alignments; edges[j]'s alignment starts at offset alignoffsets[j] + std::unique_ptr alignoffsetsgpu; + std::unique_ptr edgeacscoresgpu; + std::unique_ptr> cudalogLLs; + + std::unique_ptr logppsgpu; + std::unique_ptr logalphasgpu; + std::unique_ptr logbetasgpu; + std::unique_ptr logaccalphasgpu; + std::unique_ptr logaccbetasgpu; + + std::unique_ptr logframescorrectedgegpu; + std::unique_ptr Eframescorrectbufgpu; + std::unique_ptr logEframescorrectgpu; + + std::unique_ptr backptrstoragegpu; + std::unique_ptr backptroffsetsgpu; + + std::unique_ptr uidsgpu; + std::unique_ptr senone2classmapgpu; + + std::unique_ptr> errorsignalgpustorage; + std::unique_ptr> errorsignalneggpustorage; + std::unique_ptr> errorsignalgpu; + std::unique_ptr> errorsignalneggpu; + + // cache current lattice + // This is a weird mix of const/non-const and private lattice data... :( + template + void setutterancedata (const edgestype & edges, const nodestype & nodes, const aligntype & align, + const msra::math::ssematrixbase & /*logLLs*/, std::vector & edgeacscores, + edgealignments & edgeAlignments, backpointers & backPointers) + { + // lattice data + edgesgpu->assign (edges, false); + nodesgpu->assign (nodes, false); + aligngpu->assign (align, false); + alignoffsetsgpu->assign (edgeAlignments.getalignoffsets(), false); + backptrstoragegpu->allocate (backPointers.getbackptrstoragesize()); + backptroffsetsgpu->assign (backPointers.getbackptroffsets(), false); + +#ifndef PARALLEL_SIL + alignresult->assign (edgeAlignments.getalignmentsbuffer(), false); + edgeacscoresgpu->assign (edgeacscores, false); +#else + alignresult->allocate (edgeAlignments.getalignbuffersize()); + edgeacscoresgpu->allocate (edges.size()); + edgeacscores; // reference to make compilor happy +#endif + + // LLs + //zhaorui + /*cudalogLLs->allocate (logLLs.rows(), logLLs.cols()); + cudalogLLs->assign(0, logLLs.rows(), 0, logLLs.cols(), &logLLs(0,0), logLLs.getcolstride(), true); // doing this last with 'true' so we can measure time better; maybe remove later*/ + + } + //template + void setloglls(const Microsoft::MSR::CNTK::Matrix& loglls) + { + *cudalogLLs = loglls; + } + void getgamma(Microsoft::MSR::CNTK::Matrix& loglls) + { + loglls = *errorsignalgpu; + } + template + void copyalignments (edgealignments & edgeAlignments) + { + alignresult->fetch(edgeAlignments.getalignmentsbuffer(), true); + } + + // [v-hansu] allocate memory for vectors relating to forward-backward + // allocateaccvectors implies return Eframecorrect + template + void allocfwbwvectors (const edgestype & edges, const nodestype & nodes, const std::vector & uids, + const bool allocateframescorrect, const bool copyuids, const bool allocateaccvectors) + { + logppsgpu->allocate (edges.size()); +#ifndef TWO_CHANNEL + const size_t alphabetanoderatio = 1; +#else + const size_t alphabetanoderatio = 2; +#endif + logalphasgpu->allocate (alphabetanoderatio * nodes.size()); + logbetasgpu->allocate (alphabetanoderatio * nodes.size()); + + if (allocateframescorrect) + logframescorrectedgegpu->allocate (edges.size()); + + if (copyuids) + uidsgpu->assign (uids, true); + + if (allocateaccvectors) + { + logaccalphasgpu->allocate (alphabetanoderatio * nodes.size()); + logaccbetasgpu->allocate (alphabetanoderatio * nodes.size()); + + Eframescorrectbufgpu->allocate (edges.size()); + logEframescorrectgpu->allocate (edges.size()); + + } + } + + // check if gpumatrixstorage supports size of cpumatrix, if not allocate. set gpumatrix to part of gpumatrixstorage + void cacheerrorsignal (const msra::math::ssematrixbase & errorsignal, const bool cacheerrsignalneg) + { + if (errorsignalgpustorage->GetNumRows() != 0 && errorsignalgpustorage->GetNumRows() != errorsignal.rows()) + throw::logic_error ("gpumatrixstorage->rows() shall be fixed once allocated"); + if (errorsignalgpustorage->GetNumCols() < errorsignal.cols()) + errorsignalgpustorage->Resize(errorsignal.rows(), errorsignal.cols()); + *errorsignalgpu = errorsignalgpustorage->ColumnSlice(0, errorsignal.cols()); + + if (cacheerrsignalneg) + { + if (errorsignalneggpustorage->GetNumRows() != 0 && errorsignalneggpustorage->GetNumRows() != errorsignal.rows()) + throw::logic_error ("gpumatrixstorage->rows() shall be fixed once allocated"); + if (errorsignalneggpustorage->GetNumCols() < errorsignal.cols()) + errorsignalneggpustorage->Resize(errorsignal.rows(), errorsignal.cols()); + *errorsignalneggpu = errorsignalneggpustorage->ColumnSlice(0, errorsignal.cols()); + } + } + + void getedgeacscores (std::vector & edgeacscores) + { + edgeacscores.resize (edgeacscoresgpu->size()); + edgeacscoresgpu->fetch (edgeacscores, true); + } + + void getedgealignments (std::vector & edgealignments) + { + edgealignments.resize (alignresult->size()); + alignresult->fetch (edgealignments, true); + } + }; + + void lattice::parallelstate::setdevice(size_t deviceid) + { + bool pcpumode = (deviceid == CPUDEVICE); + if (!pcpumode) + { + pimpl = new parallelstateimpl(deviceid); + } + else + { + delete pimpl; + pimpl = NULL; + } + // else we leave it at NULL + } + + size_t lattice::parallelstate::getdevice() + { + return pimpl->getdevice(); + } + + void lattice::parallelstate::release(bool pcpumode) + { + if (!pcpumode) + { + /* if (pimpl != NULL) + { + delete pimpl; + pimpl = NULL; + }*/ + } + } + lattice::parallelstate::parallelstate() { pimpl = nullptr; } + lattice::parallelstate::~parallelstate() { delete pimpl; } + void lattice::parallelstate::entercomputation (const msra::asr::simplesenonehmm & hset, const mbrclassdefinition mbrclassdef) { pimpl->cachehset (hset, mbrclassdef); } // pass models in (to GPU) //TODO: rethink the naming of this function + void lattice::parallelstate::copyalignments (edgealignments & edgealignments) { pimpl->copyalignments (edgealignments); } + const size_t lattice::parallelstate::getsilunitid () { return pimpl->silalignunitid; } + void lattice::parallelstate::getedgeacscores (std::vector & edgeacscores) { pimpl->getedgeacscores (edgeacscores); } + void lattice::parallelstate::getedgealignments (std::vector & edgealignments) { pimpl->getedgealignments (edgealignments); } + //template + void lattice::parallelstate::setloglls(const Microsoft::MSR::CNTK::Matrix& loglls) { pimpl->setloglls(loglls); } + + // TODO: Overload to enable compilation for DoublePrecision though its currently unsupported + void lattice::parallelstate::setloglls(const Microsoft::MSR::CNTK::Matrix& /*loglls*/) + { + throw::logic_error("Double precision not supported for sequence training"); + } + + void lattice::parallelstate::getgamma(Microsoft::MSR::CNTK::Matrix& loglls) { pimpl->getgamma(loglls); } + + // TODO: Overload to enable compilation for DoublePrecision though its currently unsupported + void lattice::parallelstate::getgamma(Microsoft::MSR::CNTK::Matrix& /*loglls*/) + { + throw::logic_error("Double precision not supported for sequence training"); + } + + // ----------------------------------------------------------------------- + // parallel implementations of key processing steps + // ----------------------------------------------------------------------- + + // parallelforwardbackwardalign() -- compute the statelevel gammas or viterbi alignments + void lattice::parallelforwardbackwardalign (parallelstate & parallelstate, + const msra::asr::simplesenonehmm & hset, const msra::math::ssematrixbase & logLLs, + std::vector & edgeacscores, edgealignments & edgealignments, backpointers & backpointers) const + { + parallelstate->validatehset (hset); // ensure the models have been correctly cached on the GPU already + + if (!parallelstate->emulation) + { + // move lattice to GPU + parallelstate->setutterancedata (edges, nodes, align, logLLs, // inputs + edgeacscores, edgealignments, backpointers); // inouts + + // launch the kernel + std::unique_ptr latticefunctions (msra::cuda::newlatticefunctions(parallelstate.getdevice())); + latticefunctions->edgealignment(*parallelstate->hmmsgpu.get(), *parallelstate->lr3transPgpu.get(), + parallelstate->spalignunitid, parallelstate->silalignunitid, + *parallelstate->cudalogLLs.get(), *parallelstate->nodesgpu.get(), + *parallelstate->edgesgpu.get(), *parallelstate->aligngpu.get(), + *parallelstate->alignoffsetsgpu.get(), + *parallelstate->backptrstoragegpu.get(), *parallelstate->backptroffsetsgpu.get(), + *parallelstate->alignresult.get(), *parallelstate->edgeacscoresgpu.get()); + } + else + { + if (parallelstate->hmmscpuforgpu.size() == 0) + throw::logic_error("we no longer support emulation for edgealign, please copy hmmscpuforgpu and lr3transPcpuforgpu if you want"); + edgeacscores.resize (edges.size()); + emulateedgealignment (parallelstate->hmmscpuforgpu, parallelstate->lr3transPcpuforgpu, parallelstate->spalignunitid, + parallelstate->silalignunitid, + nodes, edges, align, logLLs, edgealignments.getalignoffsets(), + backpointers.getbackptrbuffer(), backpointers.getbackptroffsets(), + edgealignments.getalignmentsbuffer(), edgeacscores); + // emulate the GPU version, save result back to GPU + parallelstate->alignresult->assign (edgealignments.getalignmentsbuffer(), false); + parallelstate->edgeacscoresgpu->assign (edgeacscores, true); + } + } + + // parallelforwardbackwardlattice() -- compute the latticelevel logpps using forwardbackward + double lattice::parallelforwardbackwardlattice (parallelstate & parallelstate, const std::vector & edgeacscores, + const edgealignments & thisedgealignments, const float lmf, const float wp, + const float amf, const float boostingfactor, std::vector & logpps, + std::vector & logalphas, std::vector & logbetas, const bool returnEframescorrect, + const_array_ref & uids, std::vector & logEframescorrect, + std::vector & Eframescorrectbuf, double & logEframescorrecttotal) const + { //^^ TODO: remove this + vector batchsizeforward; // record the batch size that exclude the data dependency for forward + vector batchsizebackward; // record the batch size that exclude the data dependency for backward + + size_t endindexforward = edges[0].E; + size_t countbatchforward = 0; + + size_t endindexbackward = edges.back().S; + size_t countbatchbackward = 0; + foreach_index (j, edges) // compute the batch size info for kernel launches + { + if (edges[j].S < endindexforward) + countbatchforward++; // note: we don't check forward because the order of end node is assured. + else + { + batchsizeforward.push_back (countbatchforward); + countbatchforward = 1; + endindexforward = edges[j].E; + } + const size_t backj = edges.size() -1 - j; + if (edges[backj].E > endindexbackward) + { + countbatchbackward++; + if (endindexbackward < edges[backj].S) + endindexbackward = edges[backj].S; + } + else + { + batchsizebackward.push_back (countbatchbackward); + countbatchbackward = 1; + endindexbackward = edges[backj].S; + } + } + batchsizeforward.push_back (countbatchforward); + batchsizebackward.push_back (countbatchbackward); + + std::vector uidsuint(uids.size()); // actually we shall not do this, but as it will not take much time, let us just leave it here now. + foreach_index(i, uidsuint) uidsuint[i] = (unsigned short)uids[i]; + + double totalfwscore = 0.0f; + if (!parallelstate->emulation) + { + if (verbosity>=2) + fprintf(stderr, "parallelforwardbackwardlattice: %d launches for forward, %d launches for backward\n", (int)batchsizeforward.size(), (int)batchsizebackward.size()); + + const bool allocateframescorrect = (returnEframescorrect || boostingfactor != 0.0f); + const bool copyuids = (returnEframescorrect || boostingfactor != 0.0f); + const bool allocateaccvectors = returnEframescorrect; + parallelstate->allocfwbwvectors (edges, nodes, uidsuint, allocateframescorrect, copyuids, allocateaccvectors); + + std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); // final CUDA call + latticefunctions->forwardbackwardlattice (&batchsizeforward[0], &batchsizebackward[0], batchsizeforward.size(), batchsizebackward.size(), + parallelstate->spalignunitid, parallelstate->silalignunitid, + *parallelstate->edgeacscoresgpu.get(), *parallelstate->edgesgpu.get(), + *parallelstate->nodesgpu.get(), *parallelstate->aligngpu.get(), + *parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), + *parallelstate->logppsgpu.get(), *parallelstate->logalphasgpu.get(), + *parallelstate->logbetasgpu.get(), lmf, wp, amf, boostingfactor, + returnEframescorrect, *parallelstate->uidsgpu.get(), *parallelstate->senone2classmapgpu.get(), + *parallelstate->logaccalphasgpu.get(), *parallelstate->logaccbetasgpu.get(), + *parallelstate->logframescorrectedgegpu.get(), *parallelstate->logEframescorrectgpu.get(), + *parallelstate->Eframescorrectbufgpu.get(), logEframescorrecttotal, totalfwscore); + } + else // emulation + { +#ifndef TWO_CHANNEL + fprintf (stderr, "forbid invalid sil path\n"); + const size_t alphabetanoderatio = 1; +#else + const size_t alphabetanoderatio = 2; +#endif + logpps.resize (edges.size()); + logalphas.resize (alphabetanoderatio * nodes.size()); + logbetas.resize (alphabetanoderatio * nodes.size()); + + std::vector logaccalphas; + std::vector logaccbetas; + std::vector logframescorrectedge; + logframescorrectedge.resize (edges.size()); + + if (returnEframescorrect) + { + logaccalphas.resize (alphabetanoderatio * nodes.size()); + logaccbetas.resize (alphabetanoderatio * nodes.size()); + + logEframescorrect.resize (edges.size()); + Eframescorrectbuf.resize (edges.size()); + } + + totalfwscore = emulateforwardbackwardlattice (&batchsizeforward[0], &batchsizebackward[0], + batchsizeforward.size(), batchsizebackward.size(), + parallelstate->spalignunitid, parallelstate->silalignunitid, + edgeacscores, edges, nodes, align, + thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), + logpps, logalphas, logbetas, lmf, wp, amf, boostingfactor, returnEframescorrect, uidsuint, parallelstate->senone2classmapcpuforgpu, + logaccalphas, logaccbetas, logframescorrectedge, logEframescorrect, Eframescorrectbuf, logEframescorrecttotal); + + } + return totalfwscore; + } + + // ------------------------------------------------------------------------ + // parallel implementations of sMBR error updating step + // ------------------------------------------------------------------------ + void lattice::parallelsMBRerrorsignal (parallelstate & parallelstate, const edgealignments & thisedgealignments, + const std::vector & logpps, const float amf, const std::vector & logEframescorrect, + const double logEframescorrecttotal, msra::math::ssematrixbase & errorsignal, msra::math::ssematrixbase & errorsignalneg) const + { + /* time measurement for CUDA + cudaerrorcopyto: 3.058592 ms + cudaerrorsignalsync: 0.356998 ms + cudaerrorcopyback: 8.983703 ms */ + + /* time measurement for emulation + emuresettozeros: 102.979899 ms + emuerrorsignal: 336.407459 ms */ + + if (!parallelstate->emulation) + { + // We allocate a pos and a neg buffer. + const bool cacheerrorsignalneg = true; + parallelstate->cacheerrorsignal (errorsignal, cacheerrorsignalneg); + + std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); + latticefunctions->sMBRerrorsignal (*parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), *parallelstate->edgesgpu.get(), + *parallelstate->nodesgpu.get(), *parallelstate->logppsgpu.get(),amf, *parallelstate->logEframescorrectgpu.get(), + logEframescorrecttotal, + *parallelstate->errorsignalgpu.get(), *parallelstate->errorsignalneggpu.get()); + + if (errorsignal.rows() > 0 && errorsignal.cols() > 0) + { + parallelstate->errorsignalgpu->CopySection(errorsignal.rows(), errorsignal.cols(), &errorsignal(0, 0), errorsignal.getcolstride()); + } + } + else + { + emulatesMBRerrorsignal (thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), edges, nodes, logpps, amf, logEframescorrect, logEframescorrecttotal, errorsignal, errorsignalneg); + } + } + + // ------------------------------------------------------------------------ + // parallel implementations of MMI error updating step + // ------------------------------------------------------------------------ + void lattice::parallelmmierrorsignal (parallelstate & parallelstate, const edgealignments & thisedgealignments, + const std::vector & logpps, msra::math::ssematrixbase & errorsignal) const + { + if (!parallelstate->emulation) + { + const bool cacheerrorsignalneg = false; // we do not need it in mmi mode + parallelstate->cacheerrorsignal (errorsignal, cacheerrorsignalneg); + + std::unique_ptr latticefunctions(msra::cuda::newlatticefunctions(parallelstate.getdevice())); + latticefunctions->mmierrorsignal (*parallelstate->alignresult.get(), *parallelstate->alignoffsetsgpu.get(), *parallelstate->edgesgpu.get(), + *parallelstate->nodesgpu.get(), *parallelstate->logppsgpu.get(), *parallelstate->errorsignalgpu.get()); + + //parallelstate->errorsignalgpu->fetch (0, errorsignal.rows(), 0, errorsignal.cols(), &errorsignal(0, 0), errorsignal.getcolstride(), true); + } + else + { + emulatemmierrorsignal (thisedgealignments.getalignmentsbuffer(), thisedgealignments.getalignoffsets(), edges, nodes, logpps, errorsignal); + } + } +};}; diff --git a/Tests/EndToEndTests/Image/Data/Test.txt b/Tests/EndToEndTests/Image/Data/Test.txt index 2f1eb576544c..5009ac9b0b88 100644 --- a/Tests/EndToEndTests/Image/Data/Test.txt +++ b/Tests/EndToEndTests/Image/Data/Test.txt @@ -1,100 +1,100 @@ -0 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 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+An4/34/34/cen7-fclc-b.mfc=Features/000000000.chunk[250382,250679] +An4/851/851/an97-mskh-b.mfc=Features/000000000.chunk[250680,250817] +An4/147/147/cen3-flrp-b.mfc=Features/000000000.chunk[250818,250975] +An4/550/550/cen6-mjdr-b.mfc=Features/000000000.chunk[250976,251143] +An4/407/407/cen5-mdmc-b.mfc=Features/000000000.chunk[251144,251521] +An4/587/587/cen5-mjhp-b.mfc=Features/000000000.chunk[251522,251799] +An4/22/22/cen8-fbbh-b.mfc=Features/000000000.chunk[251800,252077] +An4/138/138/cen8-flmm2-b.mfc=Features/000000000.chunk[252078,252655] +An4/361/361/an233-mcsc-b.mfc=Features/000000000.chunk[252656,252733] diff --git a/Tests/EndToEndTests/Speech/Data/state.list b/Tests/EndToEndTests/Speech/Data/state.list index a2cc83e9f294..485e37948de0 100644 --- a/Tests/EndToEndTests/Speech/Data/state.list +++ b/Tests/EndToEndTests/Speech/Data/state.list @@ -1,132 +1,132 @@ -_ah_[2] -_ah_[3] -_ah_[4] -_hmm_[2] -_hmm_[3] -_hmm_[4] -_noise_[2] -_noise_[3] -_noise_[4] -aa_s2_1 -aa_s3_1 -aa_s4_1 -ae_s2_1 -ae_s3_1 -ae_s4_1 -ah_s2_1 -ah_s3_1 -ah_s4_1 -ao_s2_1 -ao_s3_1 -ao_s4_1 -aw_s2_1 -aw_s3_1 -aw_s4_1 -ax_s2_1 -ax_s3_1 -ax_s4_1 -ay_s2_1 -ay_s3_1 -ay_s4_1 -b_s2_1 -b_s3_1 -b_s4_1 -ch_s2_1 -ch_s3_1 -ch_s4_1 -d_s2_1 -d_s3_1 -d_s4_1 -dh_s2_1 -dh_s3_1 -dh_s4_1 -eh_s2_1 -eh_s3_1 -eh_s4_1 -er_s2_1 -er_s3_1 -er_s4_1 -ey_s2_1 -ey_s3_1 -ey_s4_1 -f_s2_1 -f_s3_1 -f_s4_1 -g_s2_1 -g_s3_1 -g_s4_1 -hh_s2_1 -hh_s3_1 -hh_s4_1 -ih_s2_1 -ih_s3_1 -ih_s4_1 -iy_s2_1 -iy_s3_1 -iy_s4_1 -jh_s2_1 -jh_s3_1 -jh_s4_1 -k_s2_1 -k_s3_1 -k_s4_1 -l_s2_1 -l_s3_1 -l_s4_1 -m_s2_1 -m_s3_1 -m_s4_1 -n_s2_1 -n_s3_1 -n_s4_1 -ng_s2_1 -ng_s3_1 -ng_s4_1 -ow_s2_1 -ow_s3_1 -ow_s4_1 -oy_s2_1 -oy_s3_1 -oy_s4_1 -p_s2_1 -p_s3_1 -p_s4_1 -r_s2_1 -r_s3_1 -r_s4_1 -s_s2_1 -s_s3_1 -s_s4_1 -sh_s2_1 -sh_s3_1 -sh_s4_1 -sil[2] -sil[3] -sil[4] -t_s2_1 -t_s3_1 -t_s4_1 -th_s2_1 -th_s3_1 -th_s4_1 -uh_s2_1 -uh_s3_1 -uh_s4_1 -uw_s2_1 -uw_s3_1 -uw_s4_1 -v_s2_1 -v_s3_1 -v_s4_1 -w_s2_1 -w_s3_1 -w_s4_1 -y_s2_1 -y_s3_1 -y_s4_1 -z_s2_1 -z_s3_1 -z_s4_1 -zh_s2_1 -zh_s3_1 -zh_s4_1 +_ah_[2] +_ah_[3] +_ah_[4] +_hmm_[2] +_hmm_[3] +_hmm_[4] +_noise_[2] +_noise_[3] +_noise_[4] +aa_s2_1 +aa_s3_1 +aa_s4_1 +ae_s2_1 +ae_s3_1 +ae_s4_1 +ah_s2_1 +ah_s3_1 +ah_s4_1 +ao_s2_1 +ao_s3_1 +ao_s4_1 +aw_s2_1 +aw_s3_1 +aw_s4_1 +ax_s2_1 +ax_s3_1 +ax_s4_1 +ay_s2_1 +ay_s3_1 +ay_s4_1 +b_s2_1 +b_s3_1 +b_s4_1 +ch_s2_1 +ch_s3_1 +ch_s4_1 +d_s2_1 +d_s3_1 +d_s4_1 +dh_s2_1 +dh_s3_1 +dh_s4_1 +eh_s2_1 +eh_s3_1 +eh_s4_1 +er_s2_1 +er_s3_1 +er_s4_1 +ey_s2_1 +ey_s3_1 +ey_s4_1 +f_s2_1 +f_s3_1 +f_s4_1 +g_s2_1 +g_s3_1 +g_s4_1 +hh_s2_1 +hh_s3_1 +hh_s4_1 +ih_s2_1 +ih_s3_1 +ih_s4_1 +iy_s2_1 +iy_s3_1 +iy_s4_1 +jh_s2_1 +jh_s3_1 +jh_s4_1 +k_s2_1 +k_s3_1 +k_s4_1 +l_s2_1 +l_s3_1 +l_s4_1 +m_s2_1 +m_s3_1 +m_s4_1 +n_s2_1 +n_s3_1 +n_s4_1 +ng_s2_1 +ng_s3_1 +ng_s4_1 +ow_s2_1 +ow_s3_1 +ow_s4_1 +oy_s2_1 +oy_s3_1 +oy_s4_1 +p_s2_1 +p_s3_1 +p_s4_1 +r_s2_1 +r_s3_1 +r_s4_1 +s_s2_1 +s_s3_1 +s_s4_1 +sh_s2_1 +sh_s3_1 +sh_s4_1 +sil[2] +sil[3] +sil[4] +t_s2_1 +t_s3_1 +t_s4_1 +th_s2_1 +th_s3_1 +th_s4_1 +uh_s2_1 +uh_s3_1 +uh_s4_1 +uw_s2_1 +uw_s3_1 +uw_s4_1 +v_s2_1 +v_s3_1 +v_s4_1 +w_s2_1 +w_s3_1 +w_s4_1 +y_s2_1 +y_s3_1 +y_s4_1 +z_s2_1 +z_s3_1 +z_s4_1 +zh_s2_1 +zh_s3_1 +zh_s4_1 diff --git a/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.cpu.txt b/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.cpu.txt index f282beb99c8a..ff037b498733 100644 --- a/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.cpu.txt +++ b/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.cpu.txt @@ -1,731 +1,731 @@ === Running /cygdrive/e/NetScale/CNTK/git_repos/public_master/x64/release/cntk.exe configFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\QuickE2E\cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data DeviceId=-1 -------------------------------------------------------------------- -Build info: - - Built time: Aug 25 2015 16:45:49 - Last modified date: Mon Aug 24 16:38:42 2015 - Built by amitaga on Amitaga-Win-DT3 - Build Path: E:\NetScale\CNTK\git_repos\public_master\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 -------------------------------------------------------------------- -running on Amitaga-Win-DT3 at 2015/08/26 00:49:28 -command line options: -configFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\QuickE2E\cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data DeviceId=-1 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=$DeviceId$ -parallelTrain=false -speechTrain=[ - action=train - modelPath=$RunDir$/models/cntkSpeech.dnn - deviceId=$DeviceId$ - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=$DataDir$/glob_0000.mlf - labelMappingFile=$DataDir$/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=-1 -parallelTrain=false -speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn - deviceId=-1 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=speechTrain -configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -configparameters: cntk.config:deviceId=-1 -configparameters: cntk.config:parallelTrain=false -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -configparameters: cntk.config:speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn - deviceId=-1 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] - -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: speechTrain -precision = float -SimpleNetworkBuilder Using CPU -reading script file glob_0000.scp ... 948 entries -trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion -total 132 state names in state list E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list -htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf ... total 948 entries -...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances -label set 0: 129 classes -minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Validating node CrossEntropyWithSoftmax - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 3]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 3]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 3]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 3], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 3]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 3]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 3], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 3]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 3]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 3], B2[132, 1]) -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 3], HLast[132, 3]) - -Found 3 PreCompute nodes - NodeName: InvStdOfFeatures - NodeName: MeanOfFeatures - NodeName: Prior -minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses -requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms - - -Validating node InvStdOfFeatures - -Validating --> features = InputValue -Validating --> InvStdOfFeatures = InvStdDev(features[363, 64]) - - - -Validating node MeanOfFeatures - -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 64]) - - - -Validating node Prior - -Validating --> labels = InputValue -Validating --> Prior = Mean(labels[132, 64]) - -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 1: learning rate per sample = 0.015625 momentum = 0.900000 -minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses - -Starting minibatch loop, distributed reading is: DISABLED - - -Validating node EvalErrorPrediction - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 64]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 64]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 64], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 64]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 64], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 64]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 64]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 64], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 64]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 64]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 64], B2[132, 1]) -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 64], HLast[132, 64]) - - Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.46945000; EvalErr[0]PerSample = 0.90781248; TotalTime = 0.15951s; TotalTimePerSample = 0.24923ms; SamplesPerSecond = 4012 - Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22299910; EvalErr[0]PerSample = 0.90156251; TotalTime = 0.11267s; TotalTimePerSample = 0.17605ms; SamplesPerSecond = 5680 - Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.93971014; EvalErr[0]PerSample = 0.84687501; TotalTime = 0.08605s; TotalTimePerSample = 0.13445ms; SamplesPerSecond = 7437 - Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.92341161; EvalErr[0]PerSample = 0.90468752; TotalTime = 0.08652s; TotalTimePerSample = 0.13519ms; SamplesPerSecond = 7396 - Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.84073496; EvalErr[0]PerSample = 0.91093749; TotalTime = 0.08751s; TotalTimePerSample = 0.13673ms; SamplesPerSecond = 7313 - Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.71251225; EvalErr[0]PerSample = 0.88437498; TotalTime = 0.08594s; TotalTimePerSample = 0.13429ms; SamplesPerSecond = 7446 - Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51561427; EvalErr[0]PerSample = 0.82499999; TotalTime = 0.06897s; TotalTimePerSample = 0.10776ms; SamplesPerSecond = 9279 - Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.49347544; EvalErr[0]PerSample = 0.81093752; TotalTime = 0.07648s; TotalTimePerSample = 0.11950ms; SamplesPerSecond = 8367 - Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.34738159; EvalErr[0]PerSample = 0.76562500; TotalTime = 0.07461s; TotalTimePerSample = 0.11658ms; SamplesPerSecond = 8578 - Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51958919; EvalErr[0]PerSample = 0.79843748; TotalTime = 0.07481s; TotalTimePerSample = 0.11688ms; SamplesPerSecond = 8555 - Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.24653316; EvalErr[0]PerSample = 0.80312502; TotalTime = 0.07801s; TotalTimePerSample = 0.12189ms; SamplesPerSecond = 8203 - Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33394170; EvalErr[0]PerSample = 0.80000001; TotalTime = 0.06899s; TotalTimePerSample = 0.10780ms; SamplesPerSecond = 9276 - Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.17777109; EvalErr[0]PerSample = 0.77031249; TotalTime = 0.07871s; TotalTimePerSample = 0.12298ms; SamplesPerSecond = 8131 - Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.09841919; EvalErr[0]PerSample = 0.76875001; TotalTime = 0.07223s; TotalTimePerSample = 0.11287ms; SamplesPerSecond = 8860 - Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06454778; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.06287s; TotalTimePerSample = 0.09824ms; SamplesPerSecond = 10179 - Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91630864; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.07019s; TotalTimePerSample = 0.10967ms; SamplesPerSecond = 9117 - Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90603018; EvalErr[0]PerSample = 0.73281252; TotalTime = 0.07790s; TotalTimePerSample = 0.12172ms; SamplesPerSecond = 8215 - Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.74090576; EvalErr[0]PerSample = 0.65937501; TotalTime = 0.06086s; TotalTimePerSample = 0.09509ms; SamplesPerSecond = 10516 - Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67082524; EvalErr[0]PerSample = 0.67343748; TotalTime = 0.07217s; TotalTimePerSample = 0.11276ms; SamplesPerSecond = 8868 - Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67603755; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.06790s; TotalTimePerSample = 0.10610ms; SamplesPerSecond = 9425 - Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.54728389; EvalErr[0]PerSample = 0.62968749; TotalTime = 0.06230s; TotalTimePerSample = 0.09734ms; SamplesPerSecond = 10273 - Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.61919546; EvalErr[0]PerSample = 0.67343748; TotalTime = 0.06631s; TotalTimePerSample = 0.10361ms; SamplesPerSecond = 9651 - Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52385259; EvalErr[0]PerSample = 0.65781248; TotalTime = 0.06634s; TotalTimePerSample = 0.10366ms; SamplesPerSecond = 9646 - Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.47539663; EvalErr[0]PerSample = 0.63437498; TotalTime = 0.05956s; TotalTimePerSample = 0.09306ms; SamplesPerSecond = 10745 - Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.43261099; EvalErr[0]PerSample = 0.61406249; TotalTime = 0.07746s; TotalTimePerSample = 0.12103ms; SamplesPerSecond = 8262 - Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41724253; EvalErr[0]PerSample = 0.63125002; TotalTime = 0.06865s; TotalTimePerSample = 0.10726ms; SamplesPerSecond = 9322 - Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.17669678; EvalErr[0]PerSample = 0.57812500; TotalTime = 0.05732s; TotalTimePerSample = 0.08956ms; SamplesPerSecond = 11165 - Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.31016231; EvalErr[0]PerSample = 0.64062500; TotalTime = 0.07145s; TotalTimePerSample = 0.11164ms; SamplesPerSecond = 8957 - Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.26395869; EvalErr[0]PerSample = 0.61093748; TotalTime = 0.06920s; TotalTimePerSample = 0.10813ms; SamplesPerSecond = 9248 - Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15880728; EvalErr[0]PerSample = 0.58281249; TotalTime = 0.05704s; TotalTimePerSample = 0.08913ms; SamplesPerSecond = 11219 - Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.22708130; EvalErr[0]PerSample = 0.59218752; TotalTime = 0.06261s; TotalTimePerSample = 0.09782ms; SamplesPerSecond = 10222 - Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25599360; EvalErr[0]PerSample = 0.60624999; TotalTime = 0.06078s; TotalTimePerSample = 0.09496ms; SamplesPerSecond = 10530 -Finished Epoch[ 1 of 3]: [Training Set] TrainLossPerSample = 3.007015; EvalErrPerSample = 0.72827148; AvgLearningRatePerSample = 0.015625; EpochTime=2.990345 -Starting Epoch 2: learning rate per sample = 0.001953 momentum = 0.656119 -minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 1, with 1 datapasses - -Starting minibatch loop, distributed reading is: DISABLED - Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.10253263; EvalErr[0]PerSample = 0.56484377; TotalTime = 0.16070s; TotalTimePerSample = 0.06277ms; SamplesPerSecond = 15930 - Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.00544047; EvalErr[0]PerSample = 0.54843748; TotalTime = 0.16252s; TotalTimePerSample = 0.06349ms; SamplesPerSecond = 15751 - Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.00762367; EvalErr[0]PerSample = 0.54960936; TotalTime = 0.15862s; TotalTimePerSample = 0.06196ms; SamplesPerSecond = 16138 - Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.92045140; EvalErr[0]PerSample = 0.53281248; TotalTime = 0.16430s; TotalTimePerSample = 0.06418ms; SamplesPerSecond = 15581 - Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90173948; EvalErr[0]PerSample = 0.52265626; TotalTime = 0.16651s; TotalTimePerSample = 0.06504ms; SamplesPerSecond = 15374 - Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.91355443; EvalErr[0]PerSample = 0.53984374; TotalTime = 0.15059s; TotalTimePerSample = 0.05882ms; SamplesPerSecond = 17000 - Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.91760945; EvalErr[0]PerSample = 0.53125000; TotalTime = 0.13933s; TotalTimePerSample = 0.05443ms; SamplesPerSecond = 18373 - Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.87678528; EvalErr[0]PerSample = 0.52890623; TotalTime = 0.14171s; TotalTimePerSample = 0.05536ms; SamplesPerSecond = 18064 -Finished Epoch[ 2 of 3]: [Training Set] TrainLossPerSample = 1.9557171; EvalErrPerSample = 0.53979492; AvgLearningRatePerSample = 0.001953125; EpochTime=1.245717 -Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119 -minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses - -Starting minibatch loop, distributed reading is: DISABLED - Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.88589633; EvalErr[0]PerSample = 0.52529299; TotalTime = 0.59385s; TotalTimePerSample = 0.05799ms; SamplesPerSecond = 17243 - Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.89380074; EvalErr[0]PerSample = 0.51816404; TotalTime = 0.51284s; TotalTimePerSample = 0.05008ms; SamplesPerSecond = 19967 -Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8898486; EvalErrPerSample = 0.52172852; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=1.109687 -COMPLETED +------------------------------------------------------------------- +Build info: + + Built time: Aug 25 2015 16:45:49 + Last modified date: Mon Aug 24 16:38:42 2015 + Built by amitaga on Amitaga-Win-DT3 + Build Path: E:\NetScale\CNTK\git_repos\public_master\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 +------------------------------------------------------------------- +running on Amitaga-Win-DT3 at 2015/08/26 00:49:28 +command line options: +configFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\QuickE2E\cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data DeviceId=-1 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=$DeviceId$ +parallelTrain=false +speechTrain=[ + action=train + modelPath=$RunDir$/models/cntkSpeech.dnn + deviceId=$DeviceId$ + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=$DataDir$/glob_0000.mlf + labelMappingFile=$DataDir$/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=-1 +parallelTrain=false +speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn + deviceId=-1 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=speechTrain +configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +configparameters: cntk.config:deviceId=-1 +configparameters: cntk.config:parallelTrain=false +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +configparameters: cntk.config:speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn + deviceId=-1 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] + +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: speechTrain +precision = float +SimpleNetworkBuilder Using CPU +reading script file glob_0000.scp ... 948 entries +trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion +total 132 state names in state list E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list +htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf ... total 948 entries +...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances +label set 0: 129 classes +minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Validating node CrossEntropyWithSoftmax + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 3]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 3]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 3]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 3], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 3]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 3]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 3], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 3]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 3]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 3], B2[132, 1]) +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 3], HLast[132, 3]) + +Found 3 PreCompute nodes + NodeName: InvStdOfFeatures + NodeName: MeanOfFeatures + NodeName: Prior +minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses +requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms + + +Validating node InvStdOfFeatures + +Validating --> features = InputValue +Validating --> InvStdOfFeatures = InvStdDev(features[363, 64]) + + + +Validating node MeanOfFeatures + +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 64]) + + + +Validating node Prior + +Validating --> labels = InputValue +Validating --> Prior = Mean(labels[132, 64]) + +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 1: learning rate per sample = 0.015625 momentum = 0.900000 +minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses + +Starting minibatch loop, distributed reading is: DISABLED + + +Validating node EvalErrorPrediction + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 64]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 64]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 64], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 64]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 64], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 64]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 64]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 64], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 64]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 64]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 64], B2[132, 1]) +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 64], HLast[132, 64]) + + Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.46945000; EvalErr[0]PerSample = 0.90781248; TotalTime = 0.15951s; TotalTimePerSample = 0.24923ms; SamplesPerSecond = 4012 + Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22299910; EvalErr[0]PerSample = 0.90156251; TotalTime = 0.11267s; TotalTimePerSample = 0.17605ms; SamplesPerSecond = 5680 + Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.93971014; EvalErr[0]PerSample = 0.84687501; TotalTime = 0.08605s; TotalTimePerSample = 0.13445ms; SamplesPerSecond = 7437 + Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.92341161; EvalErr[0]PerSample = 0.90468752; TotalTime = 0.08652s; TotalTimePerSample = 0.13519ms; SamplesPerSecond = 7396 + Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.84073496; EvalErr[0]PerSample = 0.91093749; TotalTime = 0.08751s; TotalTimePerSample = 0.13673ms; SamplesPerSecond = 7313 + Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.71251225; EvalErr[0]PerSample = 0.88437498; TotalTime = 0.08594s; TotalTimePerSample = 0.13429ms; SamplesPerSecond = 7446 + Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51561427; EvalErr[0]PerSample = 0.82499999; TotalTime = 0.06897s; TotalTimePerSample = 0.10776ms; SamplesPerSecond = 9279 + Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.49347544; EvalErr[0]PerSample = 0.81093752; TotalTime = 0.07648s; TotalTimePerSample = 0.11950ms; SamplesPerSecond = 8367 + Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.34738159; EvalErr[0]PerSample = 0.76562500; TotalTime = 0.07461s; TotalTimePerSample = 0.11658ms; SamplesPerSecond = 8578 + Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51958919; EvalErr[0]PerSample = 0.79843748; TotalTime = 0.07481s; TotalTimePerSample = 0.11688ms; SamplesPerSecond = 8555 + Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.24653316; EvalErr[0]PerSample = 0.80312502; TotalTime = 0.07801s; TotalTimePerSample = 0.12189ms; SamplesPerSecond = 8203 + Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33394170; EvalErr[0]PerSample = 0.80000001; TotalTime = 0.06899s; TotalTimePerSample = 0.10780ms; SamplesPerSecond = 9276 + Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.17777109; EvalErr[0]PerSample = 0.77031249; TotalTime = 0.07871s; TotalTimePerSample = 0.12298ms; SamplesPerSecond = 8131 + Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.09841919; EvalErr[0]PerSample = 0.76875001; TotalTime = 0.07223s; TotalTimePerSample = 0.11287ms; SamplesPerSecond = 8860 + Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06454778; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.06287s; TotalTimePerSample = 0.09824ms; SamplesPerSecond = 10179 + Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91630864; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.07019s; TotalTimePerSample = 0.10967ms; SamplesPerSecond = 9117 + Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90603018; EvalErr[0]PerSample = 0.73281252; TotalTime = 0.07790s; TotalTimePerSample = 0.12172ms; SamplesPerSecond = 8215 + Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.74090576; EvalErr[0]PerSample = 0.65937501; TotalTime = 0.06086s; TotalTimePerSample = 0.09509ms; SamplesPerSecond = 10516 + Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67082524; EvalErr[0]PerSample = 0.67343748; TotalTime = 0.07217s; TotalTimePerSample = 0.11276ms; SamplesPerSecond = 8868 + Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67603755; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.06790s; TotalTimePerSample = 0.10610ms; SamplesPerSecond = 9425 + Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.54728389; EvalErr[0]PerSample = 0.62968749; TotalTime = 0.06230s; TotalTimePerSample = 0.09734ms; SamplesPerSecond = 10273 + Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.61919546; EvalErr[0]PerSample = 0.67343748; TotalTime = 0.06631s; TotalTimePerSample = 0.10361ms; SamplesPerSecond = 9651 + Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52385259; EvalErr[0]PerSample = 0.65781248; TotalTime = 0.06634s; TotalTimePerSample = 0.10366ms; SamplesPerSecond = 9646 + Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.47539663; EvalErr[0]PerSample = 0.63437498; TotalTime = 0.05956s; TotalTimePerSample = 0.09306ms; SamplesPerSecond = 10745 + Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.43261099; EvalErr[0]PerSample = 0.61406249; TotalTime = 0.07746s; TotalTimePerSample = 0.12103ms; SamplesPerSecond = 8262 + Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41724253; EvalErr[0]PerSample = 0.63125002; TotalTime = 0.06865s; TotalTimePerSample = 0.10726ms; SamplesPerSecond = 9322 + Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.17669678; EvalErr[0]PerSample = 0.57812500; TotalTime = 0.05732s; TotalTimePerSample = 0.08956ms; SamplesPerSecond = 11165 + Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.31016231; EvalErr[0]PerSample = 0.64062500; TotalTime = 0.07145s; TotalTimePerSample = 0.11164ms; SamplesPerSecond = 8957 + Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.26395869; EvalErr[0]PerSample = 0.61093748; TotalTime = 0.06920s; TotalTimePerSample = 0.10813ms; SamplesPerSecond = 9248 + Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15880728; EvalErr[0]PerSample = 0.58281249; TotalTime = 0.05704s; TotalTimePerSample = 0.08913ms; SamplesPerSecond = 11219 + Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.22708130; EvalErr[0]PerSample = 0.59218752; TotalTime = 0.06261s; TotalTimePerSample = 0.09782ms; SamplesPerSecond = 10222 + Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25599360; EvalErr[0]PerSample = 0.60624999; TotalTime = 0.06078s; TotalTimePerSample = 0.09496ms; SamplesPerSecond = 10530 +Finished Epoch[ 1 of 3]: [Training Set] TrainLossPerSample = 3.007015; EvalErrPerSample = 0.72827148; AvgLearningRatePerSample = 0.015625; EpochTime=2.990345 +Starting Epoch 2: learning rate per sample = 0.001953 momentum = 0.656119 +minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 1, with 1 datapasses + +Starting minibatch loop, distributed reading is: DISABLED + Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.10253263; EvalErr[0]PerSample = 0.56484377; TotalTime = 0.16070s; TotalTimePerSample = 0.06277ms; SamplesPerSecond = 15930 + Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.00544047; EvalErr[0]PerSample = 0.54843748; TotalTime = 0.16252s; TotalTimePerSample = 0.06349ms; SamplesPerSecond = 15751 + Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.00762367; EvalErr[0]PerSample = 0.54960936; TotalTime = 0.15862s; TotalTimePerSample = 0.06196ms; SamplesPerSecond = 16138 + Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.92045140; EvalErr[0]PerSample = 0.53281248; TotalTime = 0.16430s; TotalTimePerSample = 0.06418ms; SamplesPerSecond = 15581 + Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90173948; EvalErr[0]PerSample = 0.52265626; TotalTime = 0.16651s; TotalTimePerSample = 0.06504ms; SamplesPerSecond = 15374 + Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.91355443; EvalErr[0]PerSample = 0.53984374; TotalTime = 0.15059s; TotalTimePerSample = 0.05882ms; SamplesPerSecond = 17000 + Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.91760945; EvalErr[0]PerSample = 0.53125000; TotalTime = 0.13933s; TotalTimePerSample = 0.05443ms; SamplesPerSecond = 18373 + Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.87678528; EvalErr[0]PerSample = 0.52890623; TotalTime = 0.14171s; TotalTimePerSample = 0.05536ms; SamplesPerSecond = 18064 +Finished Epoch[ 2 of 3]: [Training Set] TrainLossPerSample = 1.9557171; EvalErrPerSample = 0.53979492; AvgLearningRatePerSample = 0.001953125; EpochTime=1.245717 +Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119 +minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses + +Starting minibatch loop, distributed reading is: DISABLED + Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.88589633; EvalErr[0]PerSample = 0.52529299; TotalTime = 0.59385s; TotalTimePerSample = 0.05799ms; SamplesPerSecond = 17243 + Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.89380074; EvalErr[0]PerSample = 0.51816404; TotalTime = 0.51284s; TotalTimePerSample = 0.05008ms; SamplesPerSecond = 19967 +Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8898486; EvalErrPerSample = 0.52172852; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=1.109687 +COMPLETED === Deleting last epoch data ==== Re-running from checkpoint -------------------------------------------------------------------- -Build info: - - Built time: Aug 25 2015 16:45:49 - Last modified date: Mon Aug 24 16:38:42 2015 - Built by amitaga on Amitaga-Win-DT3 - Build Path: E:\NetScale\CNTK\git_repos\public_master\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 -------------------------------------------------------------------- -running on Amitaga-Win-DT3 at 2015/08/26 00:49:37 -command line options: -configFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\QuickE2E\cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data DeviceId=-1 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=$DeviceId$ -parallelTrain=false -speechTrain=[ - action=train - modelPath=$RunDir$/models/cntkSpeech.dnn - deviceId=$DeviceId$ - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=$DataDir$/glob_0000.mlf - labelMappingFile=$DataDir$/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=-1 -parallelTrain=false -speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn - deviceId=-1 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=speechTrain -configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data -configparameters: cntk.config:deviceId=-1 -configparameters: cntk.config:parallelTrain=false -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu -configparameters: cntk.config:speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn - deviceId=-1 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] - -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: speechTrain -precision = float -SimpleNetworkBuilder Using CPU -reading script file glob_0000.scp ... 948 entries -trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion -total 132 state names in state list E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list -htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf ... total 948 entries -...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances -label set 0: 129 classes -minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames -Starting from checkpoint. Load Network From File C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn.2. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 256], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1]) -B2[132, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1]) -B1[512, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1]) -B0[512, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -InvStdOfFeatures[363, 1] = InvStdDev(features[363, 256]) -MeanOfFeatures[363, 1] = Mean(features[363, 256]) -features[363, 256] = InputValue -W0[512, 363] = LearnableParameter -W1[512, 512] = LearnableParameter -W2[132, 512] = LearnableParameter -labels[132, 256] = InputValue - -Validating node CrossEntropyWithSoftmax - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 256]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256]) - - - -Validating node ScaledLogLikelihood - -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 256]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) -Validating --> labels = InputValue -Validating --> Prior = Mean(labels[132, 256]) -Validating --> LogOfPrior = Log(Prior[132, 1]) -Validating --> ScaledLogLikelihood = Minus(HLast[132, 256], LogOfPrior[132, 1]) - - - -Validating node EvalErrorPrediction - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 256]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 256], HLast[132, 256]) - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Validating node CrossEntropyWithSoftmax - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 256]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256]) - -No PreCompute nodes found, skipping PreCompute step -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119 -minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses -requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms - -Starting minibatch loop, distributed reading is: DISABLED - - -Validating node EvalErrorPrediction - -Validating --> labels = InputValue -Validating --> W2 = LearnableParameter -Validating --> W1 = LearnableParameter -Validating --> W0 = LearnableParameter -Validating --> features = InputValue -Validating --> MeanOfFeatures = Mean(features[363, 1024]) -Validating --> InvStdOfFeatures = InvStdDev(features[363, 1024]) -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 1024], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 1024]) -Validating --> B0 = LearnableParameter -Validating --> W0*features+B0 = Plus(W0*features[512, 1024], B0[512, 1]) -Validating --> H1 = Sigmoid(W0*features+B0[512, 1024]) -Validating --> W1*H1 = Times(W1[512, 512], H1[512, 1024]) -Validating --> B1 = LearnableParameter -Validating --> W1*H1+B1 = Plus(W1*H1[512, 1024], B1[512, 1]) -Validating --> H2 = Sigmoid(W1*H1+B1[512, 1024]) -Validating --> W2*H1 = Times(W2[132, 512], H2[512, 1024]) -Validating --> B2 = LearnableParameter -Validating --> HLast = Plus(W2*H1[132, 1024], B2[132, 1]) -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 1024], HLast[132, 1024]) - - Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.88589633; EvalErr[0]PerSample = 0.52529299; TotalTime = 0.86012s; TotalTimePerSample = 0.08400ms; SamplesPerSecond = 11905 - Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.89380074; EvalErr[0]PerSample = 0.51816404; TotalTime = 0.60616s; TotalTimePerSample = 0.05919ms; SamplesPerSecond = 16893 -Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8898486; EvalErrPerSample = 0.52172852; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=2.711551 -COMPLETED +------------------------------------------------------------------- +Build info: + + Built time: Aug 25 2015 16:45:49 + Last modified date: Mon Aug 24 16:38:42 2015 + Built by amitaga on Amitaga-Win-DT3 + Build Path: E:\NetScale\CNTK\git_repos\public_master\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 +------------------------------------------------------------------- +running on Amitaga-Win-DT3 at 2015/08/26 00:49:37 +command line options: +configFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\QuickE2E\cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data DeviceId=-1 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=$DeviceId$ +parallelTrain=false +speechTrain=[ + action=train + modelPath=$RunDir$/models/cntkSpeech.dnn + deviceId=$DeviceId$ + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=$DataDir$/glob_0000.mlf + labelMappingFile=$DataDir$/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=-1 +parallelTrain=false +speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn + deviceId=-1 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=speechTrain +configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data +configparameters: cntk.config:deviceId=-1 +configparameters: cntk.config:parallelTrain=false +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu +configparameters: cntk.config:speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn + deviceId=-1 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] + +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: speechTrain +precision = float +SimpleNetworkBuilder Using CPU +reading script file glob_0000.scp ... 948 entries +trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion +total 132 state names in state list E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/state.list +htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\public_master\Tests\Speech\Data/glob_0000.mlf ... total 948 entries +...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances +label set 0: 129 classes +minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames +Starting from checkpoint. Load Network From File C:\cygwin64\tmp\cntk-test-20150825164805.73967\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn.2. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 256], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1]) +B2[132, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1]) +B1[512, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1]) +B0[512, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +InvStdOfFeatures[363, 1] = InvStdDev(features[363, 256]) +MeanOfFeatures[363, 1] = Mean(features[363, 256]) +features[363, 256] = InputValue +W0[512, 363] = LearnableParameter +W1[512, 512] = LearnableParameter +W2[132, 512] = LearnableParameter +labels[132, 256] = InputValue + +Validating node CrossEntropyWithSoftmax + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 256]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256]) + + + +Validating node ScaledLogLikelihood + +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 256]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) +Validating --> labels = InputValue +Validating --> Prior = Mean(labels[132, 256]) +Validating --> LogOfPrior = Log(Prior[132, 1]) +Validating --> ScaledLogLikelihood = Minus(HLast[132, 256], LogOfPrior[132, 1]) + + + +Validating node EvalErrorPrediction + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 256]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 256], HLast[132, 256]) + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Validating node CrossEntropyWithSoftmax + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 256]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 256]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 256]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 256]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1]) +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256]) + +No PreCompute nodes found, skipping PreCompute step +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119 +minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses +requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms + +Starting minibatch loop, distributed reading is: DISABLED + + +Validating node EvalErrorPrediction + +Validating --> labels = InputValue +Validating --> W2 = LearnableParameter +Validating --> W1 = LearnableParameter +Validating --> W0 = LearnableParameter +Validating --> features = InputValue +Validating --> MeanOfFeatures = Mean(features[363, 1024]) +Validating --> InvStdOfFeatures = InvStdDev(features[363, 1024]) +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 1024], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 1024]) +Validating --> B0 = LearnableParameter +Validating --> W0*features+B0 = Plus(W0*features[512, 1024], B0[512, 1]) +Validating --> H1 = Sigmoid(W0*features+B0[512, 1024]) +Validating --> W1*H1 = Times(W1[512, 512], H1[512, 1024]) +Validating --> B1 = LearnableParameter +Validating --> W1*H1+B1 = Plus(W1*H1[512, 1024], B1[512, 1]) +Validating --> H2 = Sigmoid(W1*H1+B1[512, 1024]) +Validating --> W2*H1 = Times(W2[132, 512], H2[512, 1024]) +Validating --> B2 = LearnableParameter +Validating --> HLast = Plus(W2*H1[132, 1024], B2[132, 1]) +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 1024], HLast[132, 1024]) + + Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.88589633; EvalErr[0]PerSample = 0.52529299; TotalTime = 0.86012s; TotalTimePerSample = 0.08400ms; SamplesPerSecond = 11905 + Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.89380074; EvalErr[0]PerSample = 0.51816404; TotalTime = 0.60616s; TotalTimePerSample = 0.05919ms; SamplesPerSecond = 16893 +Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8898486; EvalErrPerSample = 0.52172852; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=2.711551 +COMPLETED diff --git a/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.gpu.txt b/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.gpu.txt index 0a45aa98c81f..0e9622b78d41 100644 --- a/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.gpu.txt +++ b/Tests/EndToEndTests/Speech/QuickE2E/baseline.windows.gpu.txt @@ -1,1350 +1,1350 @@ === Running /cygdrive/e/NetScale/CNTK/git_repos/cplx_master2/x64/debug/cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 -------------------------------------------------------------------- -Build info: - - Built time: Oct 24 2015 13:33:25 - Last modified date: Thu Oct 22 16:00:27 2015 - Built by amitaga on Amitaga-Win-DT3 - Build Path: E:\NetScale\CNTK\git_repos\cplx_master2\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 -------------------------------------------------------------------- -running on Amitaga-Win-DT3 at 2015/10/24 22:07:22 -command line: -E:\NetScale\CNTK\git_repos\cplx_master2\x64\debug\cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=$DeviceId$ -parallelTrain=false -speechTrain=[ - action=train - modelPath=$RunDir$/models/cntkSpeech.dnn - deviceId=$DeviceId$ - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=$DataDir$/glob_0000.mlf - labelMappingFile=$DataDir$/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=0 -parallelTrain=false -speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn - deviceId=0 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=speechTrain -configparameters: cntk.config:ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -configparameters: cntk.config:deviceId=0 -configparameters: cntk.config:parallelTrain=false -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -configparameters: cntk.config:speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn - deviceId=0 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] - -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: speechTrain -precision = float -CNTKModelPath: C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn -CNTKCommandTrainInfo: speechTrain : 3 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3 -CNTKCommandTrainBegin: speechTrain -SimpleNetworkBuilder Using GPU 0 -reading script file glob_0000.scp ... 948 entries -trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion -total 132 state names in state list E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list -htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf ... total 948 entries -...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances -label set 0: 129 classes -minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames -SetUniformRandomValue (GPU): creating curand object with seed 1 -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - -Precomputing --> 3 PreCompute nodes found. - - NodeName: InvStdOfFeatures - NodeName: MeanOfFeatures - NodeName: Prior -minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses -requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms - - -Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] - -Validating for node InvStdOfFeatures, final verification. - -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node MeanOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] - -Validating for node MeanOfFeatures, final verification. - -Validating --> features = InputValue -> [363, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node Prior. 2 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] - -Validating for node Prior. 1 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] - -Validating for node Prior, final verification. - -Validating --> labels = InputValue -> [132, MBSize 3] -Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - -Precomputing --> Completed. - -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 -minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 62] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 62] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] - -Validating for node EvalErrorPrediction. 10 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 62] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 62] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [132, MBSize 62] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 62] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - -Starting minibatch loop. - Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.45645981; EvalErr[0]PerSample = 0.92500000; TotalTime = 0.15527s; TotalTimePerSample = 0.24261ms; SamplesPerSecond = 4121 - Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22315750; EvalErr[0]PerSample = 0.90156250; TotalTime = 0.17254s; TotalTimePerSample = 0.26960ms; SamplesPerSecond = 3709 - Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.95180664; EvalErr[0]PerSample = 0.84687500; TotalTime = 0.16283s; TotalTimePerSample = 0.25443ms; SamplesPerSecond = 3930 - Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.94158020; EvalErr[0]PerSample = 0.89843750; TotalTime = 0.15770s; TotalTimePerSample = 0.24641ms; SamplesPerSecond = 4058 - Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.85668945; EvalErr[0]PerSample = 0.91093750; TotalTime = 0.17209s; TotalTimePerSample = 0.26889ms; SamplesPerSecond = 3719 - Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.72866364; EvalErr[0]PerSample = 0.89531250; TotalTime = 0.16186s; TotalTimePerSample = 0.25291ms; SamplesPerSecond = 3954 - Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51809235; EvalErr[0]PerSample = 0.82968750; TotalTime = 0.15901s; TotalTimePerSample = 0.24846ms; SamplesPerSecond = 4024 - Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.48455200; EvalErr[0]PerSample = 0.80781250; TotalTime = 0.15480s; TotalTimePerSample = 0.24188ms; SamplesPerSecond = 4134 - Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33829346; EvalErr[0]PerSample = 0.76875000; TotalTime = 0.15737s; TotalTimePerSample = 0.24588ms; SamplesPerSecond = 4066 - Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.50167236; EvalErr[0]PerSample = 0.79843750; TotalTime = 0.15904s; TotalTimePerSample = 0.24849ms; SamplesPerSecond = 4024 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.22861633; EvalErr[0]PerSample = 0.80000000; TotalTime = 0.16485s; TotalTimePerSample = 0.25757ms; SamplesPerSecond = 3882 - Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.32616882; EvalErr[0]PerSample = 0.79062500; TotalTime = 0.15116s; TotalTimePerSample = 0.23618ms; SamplesPerSecond = 4234 - Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.16897583; EvalErr[0]PerSample = 0.77968750; TotalTime = 0.15167s; TotalTimePerSample = 0.23699ms; SamplesPerSecond = 4219 - Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.08891907; EvalErr[0]PerSample = 0.77656250; TotalTime = 0.16170s; TotalTimePerSample = 0.25265ms; SamplesPerSecond = 3958 - Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06005249; EvalErr[0]PerSample = 0.72968750; TotalTime = 0.15522s; TotalTimePerSample = 0.24254ms; SamplesPerSecond = 4123 - Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91128540; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.15756s; TotalTimePerSample = 0.24618ms; SamplesPerSecond = 4062 - Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90172119; EvalErr[0]PerSample = 0.72968750; TotalTime = 0.15992s; TotalTimePerSample = 0.24987ms; SamplesPerSecond = 4002 - Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.73261719; EvalErr[0]PerSample = 0.65312500; TotalTime = 0.16060s; TotalTimePerSample = 0.25093ms; SamplesPerSecond = 3985 - Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.66515503; EvalErr[0]PerSample = 0.68437500; TotalTime = 0.15478s; TotalTimePerSample = 0.24184ms; SamplesPerSecond = 4134 - Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67383423; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.14563s; TotalTimePerSample = 0.22755ms; SamplesPerSecond = 4394 - Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52869263; EvalErr[0]PerSample = 0.63593750; TotalTime = 0.15331s; TotalTimePerSample = 0.23955ms; SamplesPerSecond = 4174 - Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.60032349; EvalErr[0]PerSample = 0.66718750; TotalTime = 0.15816s; TotalTimePerSample = 0.24713ms; SamplesPerSecond = 4046 - Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.51134033; EvalErr[0]PerSample = 0.64843750; TotalTime = 0.16587s; TotalTimePerSample = 0.25917ms; SamplesPerSecond = 3858 - Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.45362549; EvalErr[0]PerSample = 0.63750000; TotalTime = 0.15854s; TotalTimePerSample = 0.24772ms; SamplesPerSecond = 4036 - Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41640015; EvalErr[0]PerSample = 0.61562500; TotalTime = 0.15948s; TotalTimePerSample = 0.24919ms; SamplesPerSecond = 4013 - Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.39745483; EvalErr[0]PerSample = 0.62812500; TotalTime = 0.16179s; TotalTimePerSample = 0.25280ms; SamplesPerSecond = 3955 - Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.16415405; EvalErr[0]PerSample = 0.56718750; TotalTime = 0.16235s; TotalTimePerSample = 0.25367ms; SamplesPerSecond = 3942 - Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.30347290; EvalErr[0]PerSample = 0.63593750; TotalTime = 0.15271s; TotalTimePerSample = 0.23861ms; SamplesPerSecond = 4190 - Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.24398804; EvalErr[0]PerSample = 0.60937500; TotalTime = 0.16522s; TotalTimePerSample = 0.25815ms; SamplesPerSecond = 3873 - Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15322266; EvalErr[0]PerSample = 0.57968750; TotalTime = 0.15988s; TotalTimePerSample = 0.24982ms; SamplesPerSecond = 4002 - Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.21664429; EvalErr[0]PerSample = 0.59531250; TotalTime = 0.14906s; TotalTimePerSample = 0.23290ms; SamplesPerSecond = 4293 - Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25246582; EvalErr[0]PerSample = 0.60156250; TotalTime = 0.14161s; TotalTimePerSample = 0.22126ms; SamplesPerSecond = 4519 -Finished Epoch[ 1 of 3]: [Training Set] TrainLossPerSample = 3.0000031; EvalErrPerSample = 0.72836918; AvgLearningRatePerSample = 0.015625; EpochTime=5.105428 -Starting Epoch 2: learning rate per sample = 0.001953 effective momentum = 0.656119 -minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 1, with 1 datapasses - -Starting minibatch loop. - Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.08151951; EvalErr[0]PerSample = 0.55859375; TotalTime = 0.35121s; TotalTimePerSample = 0.13719ms; SamplesPerSecond = 7289 - Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98395710; EvalErr[0]PerSample = 0.54257813; TotalTime = 0.31404s; TotalTimePerSample = 0.12267ms; SamplesPerSecond = 8151 - Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98575516; EvalErr[0]PerSample = 0.54492188; TotalTime = 0.27053s; TotalTimePerSample = 0.10567ms; SamplesPerSecond = 9463 - Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90485039; EvalErr[0]PerSample = 0.53164062; TotalTime = 0.24565s; TotalTimePerSample = 0.09596ms; SamplesPerSecond = 10421 - Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.88324280; EvalErr[0]PerSample = 0.52539063; TotalTime = 0.22956s; TotalTimePerSample = 0.08967ms; SamplesPerSecond = 11151 - Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89109344; EvalErr[0]PerSample = 0.53359375; TotalTime = 0.22156s; TotalTimePerSample = 0.08655ms; SamplesPerSecond = 11554 - Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89496155; EvalErr[0]PerSample = 0.52890625; TotalTime = 0.21987s; TotalTimePerSample = 0.08589ms; SamplesPerSecond = 11643 - Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.85944366; EvalErr[0]PerSample = 0.52265625; TotalTime = 0.19881s; TotalTimePerSample = 0.07766ms; SamplesPerSecond = 12876 -Finished Epoch[ 2 of 3]: [Training Set] TrainLossPerSample = 1.935603; EvalErrPerSample = 0.53603518; AvgLearningRatePerSample = 0.001953125; EpochTime=2.098193 -Starting Epoch 3: learning rate per sample = 0.000098 effective momentum = 0.656119 -minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses - -Starting minibatch loop. - Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752853; EvalErr[0]PerSample = 0.52177734; TotalTime = 0.71783s; TotalTimePerSample = 0.07010ms; SamplesPerSecond = 14265 - Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358818; EvalErr[0]PerSample = 0.51542969; TotalTime = 0.60551s; TotalTimePerSample = 0.05913ms; SamplesPerSecond = 16911 -Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8705584; EvalErrPerSample = 0.5186035; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=1.428405 -CNTKCommandTrainEnd: speechTrain -COMPLETED +------------------------------------------------------------------- +Build info: + + Built time: Oct 24 2015 13:33:25 + Last modified date: Thu Oct 22 16:00:27 2015 + Built by amitaga on Amitaga-Win-DT3 + Build Path: E:\NetScale\CNTK\git_repos\cplx_master2\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 +------------------------------------------------------------------- +running on Amitaga-Win-DT3 at 2015/10/24 22:07:22 +command line: +E:\NetScale\CNTK\git_repos\cplx_master2\x64\debug\cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=$DeviceId$ +parallelTrain=false +speechTrain=[ + action=train + modelPath=$RunDir$/models/cntkSpeech.dnn + deviceId=$DeviceId$ + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=$DataDir$/glob_0000.mlf + labelMappingFile=$DataDir$/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=0 +parallelTrain=false +speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn + deviceId=0 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=speechTrain +configparameters: cntk.config:ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +configparameters: cntk.config:deviceId=0 +configparameters: cntk.config:parallelTrain=false +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +configparameters: cntk.config:speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn + deviceId=0 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] + +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: speechTrain +precision = float +CNTKModelPath: C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn +CNTKCommandTrainInfo: speechTrain : 3 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3 +CNTKCommandTrainBegin: speechTrain +SimpleNetworkBuilder Using GPU 0 +reading script file glob_0000.scp ... 948 entries +trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion +total 132 state names in state list E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list +htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf ... total 948 entries +...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances +label set 0: 129 classes +minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames +SetUniformRandomValue (GPU): creating curand object with seed 1 +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 3] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 3]) -> [512, MBSize 3] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 3], B0[512, 1]) -> [512, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 3], B1[512, 1]) -> [512, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 3]) -> [512, MBSize 3] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 3]) -> [132, MBSize 3] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 3], B2[132, 1]) -> [132, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 3], HLast[132, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + +Precomputing --> 3 PreCompute nodes found. + + NodeName: InvStdOfFeatures + NodeName: MeanOfFeatures + NodeName: Prior +minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses +requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms + + +Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] + +Validating for node InvStdOfFeatures, final verification. + +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 3]) -> [363, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node MeanOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] + +Validating for node MeanOfFeatures, final verification. + +Validating --> features = InputValue -> [363, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 3]) -> [363, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node Prior. 2 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] + +Validating for node Prior. 1 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] + +Validating for node Prior, final verification. + +Validating --> labels = InputValue -> [132, MBSize 3] +Validating --> Prior = Mean(labels[132, MBSize 3]) -> [132, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + +Precomputing --> Completed. + +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 +minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 62] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 62] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] + +Validating for node EvalErrorPrediction. 10 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 62] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 62] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [132, MBSize 62] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 62] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 62]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 62]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 62], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 62] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 62]) -> [512, MBSize 62] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 62], B0[512, 1]) -> [512, MBSize 62] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 62], B1[512, 1]) -> [512, MBSize 62] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 62]) -> [512, MBSize 62] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 62]) -> [132, MBSize 62] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 62], B2[132, 1]) -> [132, MBSize 62] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 62], HLast[132, MBSize 62]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + +Starting minibatch loop. + Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.45645981; EvalErr[0]PerSample = 0.92500000; TotalTime = 0.15527s; TotalTimePerSample = 0.24261ms; SamplesPerSecond = 4121 + Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22315750; EvalErr[0]PerSample = 0.90156250; TotalTime = 0.17254s; TotalTimePerSample = 0.26960ms; SamplesPerSecond = 3709 + Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.95180664; EvalErr[0]PerSample = 0.84687500; TotalTime = 0.16283s; TotalTimePerSample = 0.25443ms; SamplesPerSecond = 3930 + Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.94158020; EvalErr[0]PerSample = 0.89843750; TotalTime = 0.15770s; TotalTimePerSample = 0.24641ms; SamplesPerSecond = 4058 + Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.85668945; EvalErr[0]PerSample = 0.91093750; TotalTime = 0.17209s; TotalTimePerSample = 0.26889ms; SamplesPerSecond = 3719 + Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.72866364; EvalErr[0]PerSample = 0.89531250; TotalTime = 0.16186s; TotalTimePerSample = 0.25291ms; SamplesPerSecond = 3954 + Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51809235; EvalErr[0]PerSample = 0.82968750; TotalTime = 0.15901s; TotalTimePerSample = 0.24846ms; SamplesPerSecond = 4024 + Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.48455200; EvalErr[0]PerSample = 0.80781250; TotalTime = 0.15480s; TotalTimePerSample = 0.24188ms; SamplesPerSecond = 4134 + Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33829346; EvalErr[0]PerSample = 0.76875000; TotalTime = 0.15737s; TotalTimePerSample = 0.24588ms; SamplesPerSecond = 4066 + Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.50167236; EvalErr[0]PerSample = 0.79843750; TotalTime = 0.15904s; TotalTimePerSample = 0.24849ms; SamplesPerSecond = 4024 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.22861633; EvalErr[0]PerSample = 0.80000000; TotalTime = 0.16485s; TotalTimePerSample = 0.25757ms; SamplesPerSecond = 3882 + Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.32616882; EvalErr[0]PerSample = 0.79062500; TotalTime = 0.15116s; TotalTimePerSample = 0.23618ms; SamplesPerSecond = 4234 + Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.16897583; EvalErr[0]PerSample = 0.77968750; TotalTime = 0.15167s; TotalTimePerSample = 0.23699ms; SamplesPerSecond = 4219 + Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.08891907; EvalErr[0]PerSample = 0.77656250; TotalTime = 0.16170s; TotalTimePerSample = 0.25265ms; SamplesPerSecond = 3958 + Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06005249; EvalErr[0]PerSample = 0.72968750; TotalTime = 0.15522s; TotalTimePerSample = 0.24254ms; SamplesPerSecond = 4123 + Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91128540; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.15756s; TotalTimePerSample = 0.24618ms; SamplesPerSecond = 4062 + Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90172119; EvalErr[0]PerSample = 0.72968750; TotalTime = 0.15992s; TotalTimePerSample = 0.24987ms; SamplesPerSecond = 4002 + Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.73261719; EvalErr[0]PerSample = 0.65312500; TotalTime = 0.16060s; TotalTimePerSample = 0.25093ms; SamplesPerSecond = 3985 + Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.66515503; EvalErr[0]PerSample = 0.68437500; TotalTime = 0.15478s; TotalTimePerSample = 0.24184ms; SamplesPerSecond = 4134 + Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67383423; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.14563s; TotalTimePerSample = 0.22755ms; SamplesPerSecond = 4394 + Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52869263; EvalErr[0]PerSample = 0.63593750; TotalTime = 0.15331s; TotalTimePerSample = 0.23955ms; SamplesPerSecond = 4174 + Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.60032349; EvalErr[0]PerSample = 0.66718750; TotalTime = 0.15816s; TotalTimePerSample = 0.24713ms; SamplesPerSecond = 4046 + Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.51134033; EvalErr[0]PerSample = 0.64843750; TotalTime = 0.16587s; TotalTimePerSample = 0.25917ms; SamplesPerSecond = 3858 + Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.45362549; EvalErr[0]PerSample = 0.63750000; TotalTime = 0.15854s; TotalTimePerSample = 0.24772ms; SamplesPerSecond = 4036 + Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41640015; EvalErr[0]PerSample = 0.61562500; TotalTime = 0.15948s; TotalTimePerSample = 0.24919ms; SamplesPerSecond = 4013 + Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.39745483; EvalErr[0]PerSample = 0.62812500; TotalTime = 0.16179s; TotalTimePerSample = 0.25280ms; SamplesPerSecond = 3955 + Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.16415405; EvalErr[0]PerSample = 0.56718750; TotalTime = 0.16235s; TotalTimePerSample = 0.25367ms; SamplesPerSecond = 3942 + Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.30347290; EvalErr[0]PerSample = 0.63593750; TotalTime = 0.15271s; TotalTimePerSample = 0.23861ms; SamplesPerSecond = 4190 + Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.24398804; EvalErr[0]PerSample = 0.60937500; TotalTime = 0.16522s; TotalTimePerSample = 0.25815ms; SamplesPerSecond = 3873 + Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15322266; EvalErr[0]PerSample = 0.57968750; TotalTime = 0.15988s; TotalTimePerSample = 0.24982ms; SamplesPerSecond = 4002 + Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.21664429; EvalErr[0]PerSample = 0.59531250; TotalTime = 0.14906s; TotalTimePerSample = 0.23290ms; SamplesPerSecond = 4293 + Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25246582; EvalErr[0]PerSample = 0.60156250; TotalTime = 0.14161s; TotalTimePerSample = 0.22126ms; SamplesPerSecond = 4519 +Finished Epoch[ 1 of 3]: [Training Set] TrainLossPerSample = 3.0000031; EvalErrPerSample = 0.72836918; AvgLearningRatePerSample = 0.015625; EpochTime=5.105428 +Starting Epoch 2: learning rate per sample = 0.001953 effective momentum = 0.656119 +minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 1, with 1 datapasses + +Starting minibatch loop. + Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.08151951; EvalErr[0]PerSample = 0.55859375; TotalTime = 0.35121s; TotalTimePerSample = 0.13719ms; SamplesPerSecond = 7289 + Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98395710; EvalErr[0]PerSample = 0.54257813; TotalTime = 0.31404s; TotalTimePerSample = 0.12267ms; SamplesPerSecond = 8151 + Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98575516; EvalErr[0]PerSample = 0.54492188; TotalTime = 0.27053s; TotalTimePerSample = 0.10567ms; SamplesPerSecond = 9463 + Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90485039; EvalErr[0]PerSample = 0.53164062; TotalTime = 0.24565s; TotalTimePerSample = 0.09596ms; SamplesPerSecond = 10421 + Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.88324280; EvalErr[0]PerSample = 0.52539063; TotalTime = 0.22956s; TotalTimePerSample = 0.08967ms; SamplesPerSecond = 11151 + Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89109344; EvalErr[0]PerSample = 0.53359375; TotalTime = 0.22156s; TotalTimePerSample = 0.08655ms; SamplesPerSecond = 11554 + Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89496155; EvalErr[0]PerSample = 0.52890625; TotalTime = 0.21987s; TotalTimePerSample = 0.08589ms; SamplesPerSecond = 11643 + Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.85944366; EvalErr[0]PerSample = 0.52265625; TotalTime = 0.19881s; TotalTimePerSample = 0.07766ms; SamplesPerSecond = 12876 +Finished Epoch[ 2 of 3]: [Training Set] TrainLossPerSample = 1.935603; EvalErrPerSample = 0.53603518; AvgLearningRatePerSample = 0.001953125; EpochTime=2.098193 +Starting Epoch 3: learning rate per sample = 0.000098 effective momentum = 0.656119 +minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses + +Starting minibatch loop. + Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752853; EvalErr[0]PerSample = 0.52177734; TotalTime = 0.71783s; TotalTimePerSample = 0.07010ms; SamplesPerSecond = 14265 + Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358818; EvalErr[0]PerSample = 0.51542969; TotalTime = 0.60551s; TotalTimePerSample = 0.05913ms; SamplesPerSecond = 16911 +Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8705584; EvalErrPerSample = 0.5186035; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=1.428405 +CNTKCommandTrainEnd: speechTrain +COMPLETED === Deleting last epoch data ==== Re-running from checkpoint === Running /cygdrive/e/NetScale/CNTK/git_repos/cplx_master2/x64/debug/cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 -------------------------------------------------------------------- -Build info: - - Built time: Oct 24 2015 13:33:25 - Last modified date: Thu Oct 22 16:00:27 2015 - Built by amitaga on Amitaga-Win-DT3 - Build Path: E:\NetScale\CNTK\git_repos\cplx_master2\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 -------------------------------------------------------------------- -running on Amitaga-Win-DT3 at 2015/10/24 22:08:20 -command line: -E:\NetScale\CNTK\git_repos\cplx_master2\x64\debug\cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=$DeviceId$ -parallelTrain=false -speechTrain=[ - action=train - modelPath=$RunDir$/models/cntkSpeech.dnn - deviceId=$DeviceId$ - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=$DataDir$/glob_0000.mlf - labelMappingFile=$DataDir$/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -precision=float -command=speechTrain -deviceId=0 -parallelTrain=false -speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn - deviceId=0 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] -RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=speechTrain -configparameters: cntk.config:ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E -configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data -configparameters: cntk.config:deviceId=0 -configparameters: cntk.config:parallelTrain=false -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu -configparameters: cntk.config:speechTrain=[ - action=train - modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn - deviceId=0 - traceLevel=1 - SimpleNetworkBuilder=[ - layerSizes=363:512:512:132 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - ExperimentalNetworkBuilder=[ // the same as above but with BS - layerSizes=363:512:512:132 - trainingCriterion='CE' - evalCriterion='Err' - applyMeanVarNorm=true - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = if trainingCriterion == 'CE' - then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') - else Fail('unknown trainingCriterion ' + trainingCriterion) - Err = if evalCriterion == 'Err' then - ErrorPrediction(labels, outZ, tag='eval') - else Fail('unknown evalCriterion ' + evalCriterion) - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - SGD=[ - epochSize=20480 - minibatchSize=64:256:1024: - learningRatesPerMB=1.0:0.5:0.1 - numMBsToShowResult=10 - momentumPerMB=0.9:0.656119 - dropoutRate=0.0 - maxEpochs=3 - keepCheckPointFiles=true - AutoAdjust=[ - reduceLearnRateIfImproveLessThan=0 - loadBestModel=true - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.5 - learnRateIncreaseFactor=1.382 - autoAdjustLR=AdjustAfterEpoch - ] - clippingThresholdPerSample=1#INF - ] - reader=[ - readerType=HTKMLFReader - readMethod=blockRandomize - miniBatchMode=Partial - randomize=Auto - verbosity=0 - features=[ - dim=363 - type=Real - scpFile=glob_0000.scp - ] - labels=[ - mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf - labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list - labelDim=132 - labelType=Category - ] - ] -] - -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: speechTrain -precision = float -CNTKModelPath: C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn -CNTKCommandTrainInfo: speechTrain : 3 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3 -CNTKCommandTrainBegin: speechTrain -SimpleNetworkBuilder Using GPU 0 -reading script file glob_0000.scp ... 948 entries -trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion -total 132 state names in state list E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list -htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf ... total 948 entries -...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances -label set 0: 129 classes -minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames -Starting from checkpoint. Load Network From File C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn.2. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 0], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1]) -B2[132, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1]) -B1[512, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1]) -B0[512, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -InvStdOfFeatures[363, 1] = InvStdDev(features[363, 0]) -MeanOfFeatures[363, 1] = Mean(features[363, 0]) -features[363, 0] = InputValue -W0[512, 363] = LearnableParameter -W1[512, 512] = LearnableParameter -W2[132, 512] = LearnableParameter -labels[132, 0] = InputValue - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] -Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [132, MBSize 0] -Validating --> W2 = LearnableParameter -> [132, 512] -Validating --> W1 = LearnableParameter -> [512, 512] -Validating --> W0 = LearnableParameter -> [512, 363] -Validating --> features = InputValue -> [363, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] -Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] -Validating --> B0 = LearnableParameter -> [512, 1] -Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> B1 = LearnableParameter -> [512, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] -Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] -Validating --> B2 = LearnableParameter -> [132, 1] -Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... -No PreCompute nodes found, skipping PreCompute step -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 3: learning rate per sample = 0.000098 effective momentum = 0.656119 -minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses -requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms - -Starting minibatch loop. - Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752853; EvalErr[0]PerSample = 0.52177734; TotalTime = 1.50756s; TotalTimePerSample = 0.14722ms; SamplesPerSecond = 6792 - Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358818; EvalErr[0]PerSample = 0.51542969; TotalTime = 0.86938s; TotalTimePerSample = 0.08490ms; SamplesPerSecond = 11778 -Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8705584; EvalErrPerSample = 0.5186035; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=6.283729 -CNTKCommandTrainEnd: speechTrain -COMPLETED +------------------------------------------------------------------- +Build info: + + Built time: Oct 24 2015 13:33:25 + Last modified date: Thu Oct 22 16:00:27 2015 + Built by amitaga on Amitaga-Win-DT3 + Build Path: E:\NetScale\CNTK\git_repos\cplx_master2\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 +------------------------------------------------------------------- +running on Amitaga-Win-DT3 at 2015/10/24 22:08:20 +command line: +E:\NetScale\CNTK\git_repos\cplx_master2\x64\debug\cntk.exe configFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E/cntk.config RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E DeviceId=0 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=$DeviceId$ +parallelTrain=false +speechTrain=[ + action=train + modelPath=$RunDir$/models/cntkSpeech.dnn + deviceId=$DeviceId$ + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=$DataDir$/glob_0000.mlf + labelMappingFile=$DataDir$/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +precision=float +command=speechTrain +deviceId=0 +parallelTrain=false +speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn + deviceId=0 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] +RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=speechTrain +configparameters: cntk.config:ConfigDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\QuickE2E +configparameters: cntk.config:DataDir=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data +configparameters: cntk.config:deviceId=0 +configparameters: cntk.config:parallelTrain=false +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu +configparameters: cntk.config:speechTrain=[ + action=train + modelPath=C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn + deviceId=0 + traceLevel=1 + SimpleNetworkBuilder=[ + layerSizes=363:512:512:132 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + ExperimentalNetworkBuilder=[ // the same as above but with BS + layerSizes=363:512:512:132 + trainingCriterion='CE' + evalCriterion='Err' + applyMeanVarNorm=true + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = if trainingCriterion == 'CE' + then CrossEntropyWithSoftmax(labels, outZ, tag='criterion') + else Fail('unknown trainingCriterion ' + trainingCriterion) + Err = if evalCriterion == 'Err' then + ErrorPrediction(labels, outZ, tag='eval') + else Fail('unknown evalCriterion ' + evalCriterion) + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + SGD=[ + epochSize=20480 + minibatchSize=64:256:1024: + learningRatesPerMB=1.0:0.5:0.1 + numMBsToShowResult=10 + momentumPerMB=0.9:0.656119 + dropoutRate=0.0 + maxEpochs=3 + keepCheckPointFiles=true + AutoAdjust=[ + reduceLearnRateIfImproveLessThan=0 + loadBestModel=true + increaseLearnRateIfImproveMoreThan=1000000000 + learnRateDecreaseFactor=0.5 + learnRateIncreaseFactor=1.382 + autoAdjustLR=AdjustAfterEpoch + ] + clippingThresholdPerSample=1#INF + ] + reader=[ + readerType=HTKMLFReader + readMethod=blockRandomize + miniBatchMode=Partial + randomize=Auto + verbosity=0 + features=[ + dim=363 + type=Real + scpFile=glob_0000.scp + ] + labels=[ + mlfFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf + labelMappingFile=E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list + labelDim=132 + labelType=Category + ] + ] +] + +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: speechTrain +precision = float +CNTKModelPath: C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn +CNTKCommandTrainInfo: speechTrain : 3 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3 +CNTKCommandTrainBegin: speechTrain +SimpleNetworkBuilder Using GPU 0 +reading script file glob_0000.scp ... 948 entries +trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion +total 132 state names in state list E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/state.list +htkmlfreader: reading MLF file E:\NetScale\CNTK\git_repos\cplx_master2\Tests\Speech\Data/glob_0000.mlf ... total 948 entries +...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances +label set 0: 129 classes +minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames +Starting from checkpoint. Load Network From File C:\cygwin64\tmp\cntk-test-20151024140721.927553\Speech_QuickE2E@debug_gpu/models/cntkSpeech.dnn.2. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 0], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1]) +B2[132, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1]) +B1[512, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1]) +B0[512, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) +InvStdOfFeatures[363, 1] = InvStdDev(features[363, 0]) +MeanOfFeatures[363, 1] = Mean(features[363, 0]) +features[363, 0] = InputValue +W0[512, 363] = LearnableParameter +W1[512, 512] = LearnableParameter +W2[132, 512] = LearnableParameter +labels[132, 0] = InputValue + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> Prior = Mean(labels[132, MBSize 0]) -> [132, 1] +Validating --> LogOfPrior = Log(Prior[132, 1]) -> [132, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[132, MBSize 0], LogOfPrior[132, 1]) -> [132, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [132, MBSize 0] +Validating --> W2 = LearnableParameter -> [132, 512] +Validating --> W1 = LearnableParameter -> [512, 512] +Validating --> W0 = LearnableParameter -> [512, 363] +Validating --> features = InputValue -> [363, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[363, MBSize 0]) -> [363, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[363, MBSize 0]) -> [363, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, MBSize 0], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1]) -> [363, MBSize 0] +Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, MBSize 0]) -> [512, MBSize 0] +Validating --> B0 = LearnableParameter -> [512, 1] +Validating --> W0*features+B0 = Plus(W0*features[512, MBSize 0], B0[512, 1]) -> [512, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W1*H1 = Times(W1[512, 512], H1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> B1 = LearnableParameter -> [512, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[512, MBSize 0], B1[512, 1]) -> [512, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[512, MBSize 0]) -> [512, MBSize 0] +Validating --> W2*H1 = Times(W2[132, 512], H2[512, MBSize 0]) -> [132, MBSize 0] +Validating --> B2 = LearnableParameter -> [132, 1] +Validating --> HLast = Plus(W2*H1[132, MBSize 0], B2[132, 1]) -> [132, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, MBSize 0], HLast[132, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... +No PreCompute nodes found, skipping PreCompute step +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 3: learning rate per sample = 0.000098 effective momentum = 0.656119 +minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses +requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms + +Starting minibatch loop. + Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752853; EvalErr[0]PerSample = 0.52177734; TotalTime = 1.50756s; TotalTimePerSample = 0.14722ms; SamplesPerSecond = 6792 + Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358818; EvalErr[0]PerSample = 0.51542969; TotalTime = 0.86938s; TotalTimePerSample = 0.08490ms; SamplesPerSecond = 11778 +Finished Epoch[ 3 of 3]: [Training Set] TrainLossPerSample = 1.8705584; EvalErrPerSample = 0.5186035; AvgLearningRatePerSample = 9.765625146e-005; EpochTime=6.283729 +CNTKCommandTrainEnd: speechTrain +COMPLETED diff --git a/Tests/EndToEndTests/Speech/QuickE2E/cntk.config b/Tests/EndToEndTests/Speech/QuickE2E/cntk.config index cad6e2c3c4a8..c46af4acb0b6 100644 --- a/Tests/EndToEndTests/Speech/QuickE2E/cntk.config +++ b/Tests/EndToEndTests/Speech/QuickE2E/cntk.config @@ -1,94 +1,94 @@ -precision = "float" -command = speechTrain -deviceId = $DeviceId$ - -parallelTrain = false -makeMode = false - -speechTrain = [ - action = "train" - modelPath = "$RunDir$/models/cntkSpeech.dnn" - deviceId = $DeviceId$ - traceLevel = 1 - SimpleNetworkBuilder = [ - layerSizes = 363:512:512:132 - trainingCriterion = "CrossEntropyWithSoftmax" - evalCriterion = "ErrorPrediction" - layerTypes = "Sigmoid" - applyMeanVarNorm = true - initValueScale = 1.0 - uniformInit = true - needPrior = true - ] - - ExperimentalNetworkBuilder = [ // the same as above but with BS. Currently not used. Enable by removing the SimpleNetworkBuilder above. - // note: this does not produce identical results because of different initialization order of random-initialized LearnableParameters - layerSizes=363:512:512:132 // [0..] - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes[i:1..Length(layerSizes)-2]=Sigmoid - applyMeanVarNorm=true - initValueScale=1.0 - uniformInit=true - - BFF(in, rows, cols) = [ B = Parameter(rows, 1, init = 'fixedValue', value = 0) ; W = Parameter(rows, cols, init = if uniformInit then 'uniform' else 'gaussian'/*, initValueScale from outer scope*/) ; z = W*in+B ] - GBFF(f, in, rows, cols) = [ Eh = rows,f(BFF(in, rows, cols).z) ] - - L = Length(layerSizes)-1 // number of model layers - features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') - featNorm = if applyMeanVarNorm - then MeanVarNorm(features) - else features - layers[layer:1..L-1] = if layer > 1 - then GBFF(layerTypes[layer], layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) - else GBFF(layerTypes[layer], featNorm, layerSizes[layer], layerSizes[layer-1]) - outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) - outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) - CE = trainingCriterion(labels, outZ, tag='criterion') - Err = evalCriterion(labels, outZ, tag='eval') - logPrior = LogPrior(labels) - // TODO: how to add a tag to an infix operation? - ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') - ] - - SGD = [ - epochSize = 20480 - minibatchSize = 64:256:1024: - learningRatesPerMB = 1.0:0.5:0.1 - numMBsToShowResult = 10 - momentumPerMB = 0.9:0.656119 - dropoutRate = 0.0 - maxEpochs = 3 - keepCheckPointFiles = true - - AutoAdjust = [ - reduceLearnRateIfImproveLessThan = 0 - loadBestModel = true - increaseLearnRateIfImproveMoreThan = 1000000000 - learnRateDecreaseFactor = 0.5 - learnRateIncreaseFactor = 1.382 - autoAdjustLR = "adjustAfterEpoch" - ] - clippingThresholdPerSample = 1#INF - ] - reader = [ - readerType = "HTKMLFReader" - readMethod = "blockRandomize" - miniBatchMode = "partial" - randomize = "auto" - verbosity = 0 - features = [ - dim = 363 - type = "real" - scpFile = "glob_0000.scp" - ] - - labels = [ - mlfFile = "$DataDir$/glob_0000.mlf" - labelMappingFile = "$DataDir$/state.list" - - labelDim = 132 - labelType = "category" - ] - ] -] +precision = "float" +command = speechTrain +deviceId = $DeviceId$ + +parallelTrain = false +makeMode = false + +speechTrain = [ + action = "train" + modelPath = "$RunDir$/models/cntkSpeech.dnn" + deviceId = $DeviceId$ + traceLevel = 1 + SimpleNetworkBuilder = [ + layerSizes = 363:512:512:132 + trainingCriterion = "CrossEntropyWithSoftmax" + evalCriterion = "ErrorPrediction" + layerTypes = "Sigmoid" + applyMeanVarNorm = true + initValueScale = 1.0 + uniformInit = true + needPrior = true + ] + + ExperimentalNetworkBuilder = [ // the same as above but with BS. Currently not used. Enable by removing the SimpleNetworkBuilder above. + // note: this does not produce identical results because of different initialization order of random-initialized LearnableParameters + layerSizes=363:512:512:132 // [0..] + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes[i:1..Length(layerSizes)-2]=Sigmoid + applyMeanVarNorm=true + initValueScale=1.0 + uniformInit=true + + BFF(in, rows, cols) = [ B = Parameter(rows, 1, init = 'fixedValue', value = 0) ; W = Parameter(rows, cols, init = if uniformInit then 'uniform' else 'gaussian'/*, initValueScale from outer scope*/) ; z = W*in+B ] + GBFF(f, in, rows, cols) = [ Eh = rows,f(BFF(in, rows, cols).z) ] + + L = Length(layerSizes)-1 // number of model layers + features = Input(layerSizes[0], 1, tag='feature') ; labels = Input(layerSizes[Length(layerSizes)-1], 1, tag='label') + featNorm = if applyMeanVarNorm + then MeanVarNorm(features) + else features + layers[layer:1..L-1] = if layer > 1 + then GBFF(layerTypes[layer], layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1]) + else GBFF(layerTypes[layer], featNorm, layerSizes[layer], layerSizes[layer-1]) + outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1]) + outZ = outLayer.z // + PastValue(layerSizes[L], 1, outLayer.z) + CE = trainingCriterion(labels, outZ, tag='criterion') + Err = evalCriterion(labels, outZ, tag='eval') + logPrior = LogPrior(labels) + // TODO: how to add a tag to an infix operation? + ScaledLogLikelihood = Minus (outZ, logPrior, tag='output') + ] + + SGD = [ + epochSize = 20480 + minibatchSize = 64:256:1024: + learningRatesPerMB = 1.0:0.5:0.1 + numMBsToShowResult = 10 + momentumPerMB = 0.9:0.656119 + dropoutRate = 0.0 + maxEpochs = 3 + keepCheckPointFiles = true + + AutoAdjust = [ + reduceLearnRateIfImproveLessThan = 0 + loadBestModel = true + increaseLearnRateIfImproveMoreThan = 1000000000 + learnRateDecreaseFactor = 0.5 + learnRateIncreaseFactor = 1.382 + autoAdjustLR = "adjustAfterEpoch" + ] + clippingThresholdPerSample = 1#INF + ] + reader = [ + readerType = "HTKMLFReader" + readMethod = "blockRandomize" + miniBatchMode = "partial" + randomize = "auto" + verbosity = 0 + features = [ + dim = 363 + type = "real" + scpFile = "glob_0000.scp" + ] + + labels = [ + mlfFile = "$DataDir$/glob_0000.mlf" + labelMappingFile = "$DataDir$/state.list" + + labelDim = 132 + labelType = "category" + ] + ] +] diff --git a/Tests/EndToEndTests/Speech/Simple/baseline.windows.cpu.txt b/Tests/EndToEndTests/Speech/Simple/baseline.windows.cpu.txt index 2a20c84a90f3..6c931cea2b55 100644 --- a/Tests/EndToEndTests/Speech/Simple/baseline.windows.cpu.txt +++ b/Tests/EndToEndTests/Speech/Simple/baseline.windows.cpu.txt @@ -1,4833 +1,4833 @@ -=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 -------------------------------------------------------------------- -Build info: - - Built time: Nov 19 2015 14:09:57 - Last modified date: Thu Nov 19 08:15:28 2015 - Built by svcphil on dphaim-26-new - Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 - Build Branch: HEAD - Build SHA1: a342184dcad3179b0e33596ac330ce724e9cee4b -------------------------------------------------------------------- -running on liana-08-w at 2015/11/19 14:21:28 -command line: -C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=$RunDir$/models/simple.dnn -deviceId=$DeviceNumber$ -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -outputPath=$RunDir$/SimpleOutput -] -RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -deviceId=-1 -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput -] -RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=-1 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output -configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -configparameters: cntk.config:deviceId=-1 -configparameters: cntk.config:DeviceNumber=-1 -configparameters: cntk.config:modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -configparameters: cntk.config:Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] - -configparameters: cntk.config:Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput -] - -configparameters: cntk.config:traceLevel=1 -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: Simple_Demo Simple_Demo_Output -precision = float -CNTKModelPath: C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -CNTKCommandTrainInfo: Simple_Demo : 50 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 -CNTKCommandTrainBegin: Simple_Demo -SimpleNetworkBuilder Using CPU -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - - -Allocating matrices for forward propagation. - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Allocating matrices for gradient computing - -Precomputing --> 3 PreCompute nodes found. - - NodeName: InvStdOfFeatures - NodeName: MeanOfFeatures - NodeName: Prior -starting at epoch 0 counting lines to determine record count - - 10000 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch - - -Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] - -Validating for node InvStdOfFeatures, final verification. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node MeanOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] - -Validating for node MeanOfFeatures, final verification. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node Prior. 2 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] - -Validating for node Prior, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - -randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 0: 2334, 3830, ... - -Precomputing --> Completed. - -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 -starting epoch 0 at record count 0, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 0: 2334, 3830, ... - Epoch[ 1 of 50]-Minibatch[ 1- 10 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70002374; EvalErr[0]PerSample = 0.50400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65547 - Epoch[ 1 of 50]-Minibatch[ 11- 20 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72432672; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.01197s; TotalTimePerSample = 0.04788ms; SamplesPerSecond = 20885 - Epoch[ 1 of 50]-Minibatch[ 21- 30 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69646301; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 1 of 50]-Minibatch[ 31- 40 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71841040; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 - Epoch[ 1 of 50]-Minibatch[ 41- 50 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.78310376; EvalErr[0]PerSample = 0.52800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 - Epoch[ 1 of 50]-Minibatch[ 51- 60 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73678174; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82754 - Epoch[ 1 of 50]-Minibatch[ 61- 70 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74273096; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 - Epoch[ 1 of 50]-Minibatch[ 71- 80 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69696533; EvalErr[0]PerSample = 0.44000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 - Epoch[ 1 of 50]-Minibatch[ 81- 90 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72015430; EvalErr[0]PerSample = 0.42800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 - Epoch[ 1 of 50]-Minibatch[ 91- 100 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74531885; EvalErr[0]PerSample = 0.53600000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 - Epoch[ 1 of 50]-Minibatch[ 101- 110 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73130273; EvalErr[0]PerSample = 0.56000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 - Epoch[ 1 of 50]-Minibatch[ 111- 120 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70340771; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 - Epoch[ 1 of 50]-Minibatch[ 121- 130 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70279102; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 - Epoch[ 1 of 50]-Minibatch[ 131- 140 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70393750; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 - Epoch[ 1 of 50]-Minibatch[ 141- 150 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72239941; EvalErr[0]PerSample = 0.56800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01254ms; SamplesPerSecond = 79719 - Epoch[ 1 of 50]-Minibatch[ 151- 160 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70179590; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 - Epoch[ 1 of 50]-Minibatch[ 161- 170 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71486914; EvalErr[0]PerSample = 0.54400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 - Epoch[ 1 of 50]-Minibatch[ 171- 180 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70239160; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 - Epoch[ 1 of 50]-Minibatch[ 181- 190 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71378516; EvalErr[0]PerSample = 0.47200000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 - Epoch[ 1 of 50]-Minibatch[ 191- 200 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70370605; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 - Epoch[ 1 of 50]-Minibatch[ 201- 210 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69720996; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82481 - Epoch[ 1 of 50]-Minibatch[ 211- 220 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69622168; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 - Epoch[ 1 of 50]-Minibatch[ 221- 230 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72116895; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82617 - Epoch[ 1 of 50]-Minibatch[ 231- 240 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69737695; EvalErr[0]PerSample = 0.44800000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 - Epoch[ 1 of 50]-Minibatch[ 241- 250 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70615625; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[ 1 of 50]-Minibatch[ 251- 260 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69251172; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 1 of 50]-Minibatch[ 261- 270 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70424609; EvalErr[0]PerSample = 0.46000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 - Epoch[ 1 of 50]-Minibatch[ 271- 280 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69197461; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 - Epoch[ 1 of 50]-Minibatch[ 281- 290 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69758984; EvalErr[0]PerSample = 0.50400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 - Epoch[ 1 of 50]-Minibatch[ 291- 300 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68911914; EvalErr[0]PerSample = 0.40400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 - Epoch[ 1 of 50]-Minibatch[ 301- 310 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69688281; EvalErr[0]PerSample = 0.42400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01209ms; SamplesPerSecond = 82699 - Epoch[ 1 of 50]-Minibatch[ 311- 320 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68536328; EvalErr[0]PerSample = 0.39200000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82754 - Epoch[ 1 of 50]-Minibatch[ 321- 330 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68434766; EvalErr[0]PerSample = 0.44800000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 - Epoch[ 1 of 50]-Minibatch[ 331- 340 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69032812; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82918 - Epoch[ 1 of 50]-Minibatch[ 341- 350 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68711328; EvalErr[0]PerSample = 0.46400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82836 - Epoch[ 1 of 50]-Minibatch[ 351- 360 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.66240039; EvalErr[0]PerSample = 0.40000000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82808 - Epoch[ 1 of 50]-Minibatch[ 361- 370 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.63556445; EvalErr[0]PerSample = 0.36000000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 - Epoch[ 1 of 50]-Minibatch[ 371- 380 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.60319531; EvalErr[0]PerSample = 0.17600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 - Epoch[ 1 of 50]-Minibatch[ 381- 390 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.55960937; EvalErr[0]PerSample = 0.14800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 - Epoch[ 1 of 50]-Minibatch[ 391- 400 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.46704102; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82453 -Finished Epoch[ 1 of 50]: [Training Set] TrainLossPerSample = 0.69325215; EvalErrPerSample = 0.4531; AvgLearningRatePerSample = 0.01999999955; EpochTime=0.134564 -Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 1 at record count 10000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1901 retries for 10000 elements (19.0%) to ensure window condition -randomordering: recached sequence for seed 1: 1914, 1172, ... - Epoch[ 2 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.40249194; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75803 - Epoch[ 2 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.35273859; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00481s; TotalTimePerSample = 0.01923ms; SamplesPerSecond = 51996 - Epoch[ 2 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.28230634; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 2 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.27482300; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 - Epoch[ 2 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22246130; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 - Epoch[ 2 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619019; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82535 - Epoch[ 2 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22821680; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 - Epoch[ 2 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19877063; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82481 - Epoch[ 2 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21215894; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 2 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17010425; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 - Epoch[ 2 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20611206; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80360 - Epoch[ 2 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20604297; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 2 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17485327; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 2 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15899878; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 - Epoch[ 2 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572290; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82453 - Epoch[ 2 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15648706; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 - Epoch[ 2 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16314795; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 - Epoch[ 2 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18076172; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 - Epoch[ 2 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14921509; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 - Epoch[ 2 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15605103; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 - Epoch[ 2 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15768115; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 - Epoch[ 2 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14727295; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 2 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177051; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 2 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17959912; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 - Epoch[ 2 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15195068; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 - Epoch[ 2 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14948584; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 - Epoch[ 2 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18874512; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00316s; TotalTimePerSample = 0.01265ms; SamplesPerSecond = 79038 - Epoch[ 2 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14938379; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 2 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15367627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 - Epoch[ 2 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19336426; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 - Epoch[ 2 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15837061; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 - Epoch[ 2 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789453; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 2 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14981152; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 2 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15530029; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 2 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17563477; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 - Epoch[ 2 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12291064; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 2 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12840234; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 - Epoch[ 2 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16160254; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 - Epoch[ 2 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19109912; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 - Epoch[ 2 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20358594; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 -Finished Epoch[ 2 of 50]: [Training Set] TrainLossPerSample = 0.18712991; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125498 -Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 2 at record count 20000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1912 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 2: 3447, 110, ... - Epoch[ 3 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17238383; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 - Epoch[ 3 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18818459; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 - Epoch[ 3 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15802277; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 3 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18184222; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 3 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17347736; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 3 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16783289; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 - Epoch[ 3 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11263281; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 - Epoch[ 3 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16322668; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 3 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20259961; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 3 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17802710; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 3 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13942529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 - Epoch[ 3 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15911414; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00343s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72971 - Epoch[ 3 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21205322; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[ 3 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20571143; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 3 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12194995; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 - Epoch[ 3 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10843677; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 3 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16375562; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 3 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14576929; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 3 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12491528; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 3 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17839331; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 - Epoch[ 3 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19846533; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 - Epoch[ 3 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716040; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 - Epoch[ 3 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14919946; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 3 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795898; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 3 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09909546; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 - Epoch[ 3 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14711084; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 3 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17405420; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 3 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949805; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 3 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18007227; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00301s; TotalTimePerSample = 0.01203ms; SamplesPerSecond = 83139 - Epoch[ 3 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12333936; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 - Epoch[ 3 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17010840; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 3 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13394141; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 - Epoch[ 3 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12574951; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[ 3 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14083252; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 3 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19624414; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 3 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15029590; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 3 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12545410; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 - Epoch[ 3 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17764307; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[ 3 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17303564; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 - Epoch[ 3 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17385254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 -Finished Epoch[ 3 of 50]: [Training Set] TrainLossPerSample = 0.15902165; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125763 -Starting Epoch 4: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 3 at record count 30000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1941 retries for 10000 elements (19.4%) to ensure window condition -randomordering: recached sequence for seed 3: 1741, 4693, ... - Epoch[ 4 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18574167; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74074 - Epoch[ 4 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19552863; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00665s; TotalTimePerSample = 0.02660ms; SamplesPerSecond = 37599 - Epoch[ 4 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13308151; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 4 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20122906; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47321 - Epoch[ 4 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19024731; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 4 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22164557; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 4 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17202637; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 4 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15516443; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 4 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13772925; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 - Epoch[ 4 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14771838; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 4 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15806226; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51271 - Epoch[ 4 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11111121; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 4 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17290845; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 4 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10793408; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 4 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15473315; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 4 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19119116; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 4 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12782153; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 4 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11329663; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 - Epoch[ 4 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18824927; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 4 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17514160; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 - Epoch[ 4 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18781836; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 4 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13335425; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 4 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15953931; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 4 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16786304; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 4 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16218237; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 4 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17439844; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 4 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12411182; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[ 4 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18026221; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 4 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11037988; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 4 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20034375; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 4 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14125391; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 4 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11299268; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[ 4 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14577051; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81433 - Epoch[ 4 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15335791; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 4 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15446289; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 4 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15231689; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 - Epoch[ 4 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16738574; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 4 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15105371; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00312s; TotalTimePerSample = 0.01249ms; SamplesPerSecond = 80076 - Epoch[ 4 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17064062; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 - Epoch[ 4 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15144287; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 -Finished Epoch[ 4 of 50]: [Training Set] TrainLossPerSample = 0.15853731; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131853 -Starting Epoch 5: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 4 at record count 40000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1967 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 4: 4062, 4375, ... - Epoch[ 5 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18211707; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68212 - Epoch[ 5 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270682; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00499s; TotalTimePerSample = 0.01995ms; SamplesPerSecond = 50130 - Epoch[ 5 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14021991; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 - Epoch[ 5 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13036838; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 - Epoch[ 5 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16885815; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 - Epoch[ 5 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16560083; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 - Epoch[ 5 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521637; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[ 5 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16385156; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[ 5 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15566504; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 - Epoch[ 5 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14036731; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 - Epoch[ 5 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15665149; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 - Epoch[ 5 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21973108; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00313s; TotalTimePerSample = 0.01253ms; SamplesPerSecond = 79795 - Epoch[ 5 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10951392; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 5 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17261426; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 - Epoch[ 5 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14421240; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[ 5 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18687842; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 5 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13771851; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 5 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18702417; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 5 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19803906; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 5 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16439648; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 5 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13458398; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 5 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14413330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 - Epoch[ 5 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16508472; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 5 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18465308; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 - Epoch[ 5 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034399; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 - Epoch[ 5 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14785254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 - Epoch[ 5 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12280029; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 - Epoch[ 5 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14299121; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 5 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14473291; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82535 - Epoch[ 5 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13322998; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 5 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16276221; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[ 5 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17622998; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 5 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15482129; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 5 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16986865; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 - Epoch[ 5 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12328027; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[ 5 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19244189; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 5 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16235889; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 5 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16860645; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 - Epoch[ 5 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14493848; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 5 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14430078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 -Finished Epoch[ 5 of 50]: [Training Set] TrainLossPerSample = 0.15854415; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.126562 -Starting Epoch 6: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 5 at record count 50000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1962 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 5: 2260, 139, ... - Epoch[ 6 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20848543; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76312 - Epoch[ 6 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18413786; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51610 - Epoch[ 6 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12884314; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 6 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17770209; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 6 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16133618; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[ 6 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17693451; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 6 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17611108; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 6 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16652490; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 6 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15625903; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 6 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13772961; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 6 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12265063; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 - Epoch[ 6 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15450598; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 - Epoch[ 6 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18095142; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 6 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13513940; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 6 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16936719; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[ 6 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14850415; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 - Epoch[ 6 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10062085; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 6 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18162427; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 6 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17896509; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 6 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14618848; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 6 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15001221; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 - Epoch[ 6 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13238550; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 6 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745386; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 6 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18064160; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00525s; TotalTimePerSample = 0.02099ms; SamplesPerSecond = 47646 - Epoch[ 6 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14728540; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[ 6 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14484814; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 - Epoch[ 6 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15216748; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[ 6 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17140381; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 - Epoch[ 6 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16164160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00317s; TotalTimePerSample = 0.01268ms; SamplesPerSecond = 78864 - Epoch[ 6 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15148389; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 6 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15548877; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 - Epoch[ 6 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17652832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 6 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12786475; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01209ms; SamplesPerSecond = 82699 - Epoch[ 6 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13548047; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[ 6 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13521143; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 6 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11820703; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[ 6 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17178711; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 6 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232471; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 6 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20254590; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 6 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262207; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 -Finished Epoch[ 6 of 50]: [Training Set] TrainLossPerSample = 0.15674913; EvalErrPerSample = 0.072899997; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127935 -Starting Epoch 7: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 6 at record count 60000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1971 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 6: 3864, 19, ... - Epoch[ 7 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17245078; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75941 - Epoch[ 7 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19818744; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51535 - Epoch[ 7 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468723; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 7 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19880463; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 7 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194360; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 7 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16685468; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 7 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13934058; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[ 7 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18776672; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 7 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15224536; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 7 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15151465; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 - Epoch[ 7 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009729; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 7 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14528259; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81063 - Epoch[ 7 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14854077; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 7 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16354980; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 - Epoch[ 7 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15437085; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[ 7 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713916; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[ 7 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14201392; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 7 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13360254; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 7 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034888; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 - Epoch[ 7 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11665967; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 - Epoch[ 7 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11198755; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 7 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14900244; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 - Epoch[ 7 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15741577; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 7 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14853564; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 - Epoch[ 7 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11856958; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 - Epoch[ 7 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631104; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[ 7 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16389893; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 7 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12061914; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 - Epoch[ 7 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17542578; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[ 7 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16121924; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 - Epoch[ 7 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15175781; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 7 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14326563; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[ 7 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15750830; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 - Epoch[ 7 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21582031; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 7 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15399707; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 7 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739600; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 7 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13247070; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 - Epoch[ 7 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16683496; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 - Epoch[ 7 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13261475; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 7 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16716357; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 -Finished Epoch[ 7 of 50]: [Training Set] TrainLossPerSample = 0.15718038; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125899 -Starting Epoch 8: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 7 at record count 70000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1996 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 7: 1209, 4212, ... - Epoch[ 8 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19877904; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00331s; TotalTimePerSample = 0.01325ms; SamplesPerSecond = 75460 - Epoch[ 8 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18074571; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51313 - Epoch[ 8 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12930881; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 8 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13702704; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 8 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14323309; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[ 8 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16479724; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 - Epoch[ 8 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11388129; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 - Epoch[ 8 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18741321; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 - Epoch[ 8 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15643237; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 8 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16937451; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 8 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635852; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 - Epoch[ 8 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10614185; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 - Epoch[ 8 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17952051; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 - Epoch[ 8 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20380176; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 - Epoch[ 8 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14754346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 - Epoch[ 8 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15922070; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[ 8 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13510278; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81116 - Epoch[ 8 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12481543; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[ 8 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15976733; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 - Epoch[ 8 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13234937; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 - Epoch[ 8 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19277612; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 - Epoch[ 8 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317163; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 8 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12374902; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 - Epoch[ 8 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13515283; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 - Epoch[ 8 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19020288; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 8 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17203125; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 - Epoch[ 8 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18403027; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 - Epoch[ 8 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17481885; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 - Epoch[ 8 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16984912; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 - Epoch[ 8 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20073486; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 8 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15632080; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 - Epoch[ 8 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22257129; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[ 8 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11293311; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 - Epoch[ 8 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11900879; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 8 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16342529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 - Epoch[ 8 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16408691; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 - Epoch[ 8 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17154541; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 - Epoch[ 8 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16180078; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 - Epoch[ 8 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15100049; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 - Epoch[ 8 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14595703; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 -Finished Epoch[ 8 of 50]: [Training Set] TrainLossPerSample = 0.15926951; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.126201 -Starting Epoch 9: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 8 at record count 80000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1981 retries for 10000 elements (19.8%) to ensure window condition -randomordering: recached sequence for seed 8: 258, 4597, ... - Epoch[ 9 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13674069; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01309ms; SamplesPerSecond = 76405 - Epoch[ 9 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11702635; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 - Epoch[ 9 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842564; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 - Epoch[ 9 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499652; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02115ms; SamplesPerSecond = 47276 - Epoch[ 9 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11475745; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 - Epoch[ 9 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15994904; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[ 9 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19827142; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 - Epoch[ 9 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658093; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 - Epoch[ 9 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12600732; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 9 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13172473; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 - Epoch[ 9 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17483435; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[ 9 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15718530; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 - Epoch[ 9 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11679797; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 - Epoch[ 9 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15759924; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 - Epoch[ 9 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14018481; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 - Epoch[ 9 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17445630; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 - Epoch[ 9 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18871265; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 - Epoch[ 9 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15529590; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79239 - Epoch[ 9 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322241; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 9 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20066455; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[ 9 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18700708; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 9 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14747046; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 - Epoch[ 9 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14981421; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[ 9 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15509644; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[ 9 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13184204; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[ 9 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16714648; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 - Epoch[ 9 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353101; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 9 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16314990; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[ 9 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12779297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[ 9 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18806982; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 - Epoch[ 9 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16694678; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01365ms; SamplesPerSecond = 73249 - Epoch[ 9 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15696777; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[ 9 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13640088; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 - Epoch[ 9 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19312012; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[ 9 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17352100; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 - Epoch[ 9 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18453564; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[ 9 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13698291; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 - Epoch[ 9 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18448828; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81433 - Epoch[ 9 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11695068; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 - Epoch[ 9 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17289453; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 -Finished Epoch[ 9 of 50]: [Training Set] TrainLossPerSample = 0.15717906; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131099 -Starting Epoch 10: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 9 at record count 90000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1914 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 9: 1957, 3535, ... - Epoch[10 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15815988; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76149 - Epoch[10 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588196; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00507s; TotalTimePerSample = 0.02029ms; SamplesPerSecond = 49290 - Epoch[10 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19559683; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[10 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15650873; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 - Epoch[10 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16335553; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[10 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17638708; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 - Epoch[10 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16771643; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[10 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11975793; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 - Epoch[10 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15952380; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 - Epoch[10 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15673218; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[10 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17476709; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 - Epoch[10 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18008984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[10 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17350000; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[10 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12354517; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[10 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16949609; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[10 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16343677; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[10 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16678516; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 - Epoch[10 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11746558; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 - Epoch[10 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15827710; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[10 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17702612; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 - Epoch[10 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20688770; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[10 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17176245; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[10 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15878784; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[10 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20615088; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 - Epoch[10 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200415; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[10 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12214331; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 - Epoch[10 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15946631; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 - Epoch[10 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16630420; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 - Epoch[10 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10444971; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[10 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10899414; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 - Epoch[10 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15568896; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[10 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19664648; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[10 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16486670; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 - Epoch[10 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11217139; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[10 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12276953; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[10 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14118750; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 - Epoch[10 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16956396; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00322s; TotalTimePerSample = 0.01286ms; SamplesPerSecond = 77736 - Epoch[10 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18732910; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 - Epoch[10 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16965137; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 - Epoch[10 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15103662; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 -Finished Epoch[10 of 50]: [Training Set] TrainLossPerSample = 0.15804678; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127015 -Starting Epoch 11: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 10 at record count 100000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1865 retries for 10000 elements (18.6%) to ensure window condition -randomordering: recached sequence for seed 10: 3356, 877, ... - Epoch[11 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19904596; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01309ms; SamplesPerSecond = 76405 - Epoch[11 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12819327; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 - Epoch[11 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16058502; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 - Epoch[11 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14434543; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 - Epoch[11 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14051538; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[11 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16834058; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00313s; TotalTimePerSample = 0.01253ms; SamplesPerSecond = 79821 - Epoch[11 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200586; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 - Epoch[11 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10635474; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80775 - Epoch[11 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13951978; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[11 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15526868; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80984 - Epoch[11 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15423694; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 80025 - Epoch[11 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15439380; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 - Epoch[11 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13974609; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 - Epoch[11 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10850623; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14422498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16235693; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 - Epoch[11 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18480713; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13377002; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14019043; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80932 - Epoch[11 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232812; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15934985; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16396509; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 - Epoch[11 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13943994; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 - Epoch[11 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15856152; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[11 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20442163; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46720 - Epoch[11 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18395508; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 - Epoch[11 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17780688; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 - Epoch[11 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17736963; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 - Epoch[11 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22826904; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[11 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17714990; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 - Epoch[11 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14284717; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 - Epoch[11 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 - Epoch[11 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17360303; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 - Epoch[11 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11425732; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[11 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15241650; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 - Epoch[11 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15622266; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 - Epoch[11 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17193799; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 - Epoch[11 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20015576; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80932 - Epoch[11 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15971143; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 - Epoch[11 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12481396; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 -Finished Epoch[11 of 50]: [Training Set] TrainLossPerSample = 0.15740953; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131325 -Starting Epoch 12: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 11 at record count 110000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1961 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 11: 2406, 1536, ... - Epoch[12 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15112077; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76057 - Epoch[12 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13805214; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00493s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50668 - Epoch[12 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16700012; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 - Epoch[12 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11470972; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[12 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456842; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 - Epoch[12 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17456824; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 - Epoch[12 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18122705; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80256 - Epoch[12 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636694; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[12 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14702148; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 - Epoch[12 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13681128; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[12 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20395264; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 - Epoch[12 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15242615; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 - Epoch[12 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15183557; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80334 - Epoch[12 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13492712; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[12 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17645776; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 - Epoch[12 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13103125; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 - Epoch[12 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21384326; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[12 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15316968; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 - Epoch[12 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13777979; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00298s; TotalTimePerSample = 0.01193ms; SamplesPerSecond = 83836 - Epoch[12 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13050586; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 - Epoch[12 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15227588; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 - Epoch[12 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20449072; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 - Epoch[12 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19038770; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 - Epoch[12 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14301831; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 - Epoch[12 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17311987; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 - Epoch[12 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12977148; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 - Epoch[12 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16124243; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 - Epoch[12 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15764600; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[12 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12035010; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 - Epoch[12 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12236279; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[12 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19375684; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 - Epoch[12 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13069238; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 - Epoch[12 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15182031; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 - Epoch[12 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16397412; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 - Epoch[12 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 - Epoch[12 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20146240; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[12 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12055029; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 - Epoch[12 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16200439; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 - Epoch[12 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20963672; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[12 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15343359; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 -Finished Epoch[12 of 50]: [Training Set] TrainLossPerSample = 0.15737277; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127231 -Starting Epoch 13: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 12 at record count 120000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2004 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 12: 1583, 2526, ... - Epoch[13 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12830981; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 - Epoch[13 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20211029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50231 - Epoch[13 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17840430; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79415 - Epoch[13 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15879236; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 - Epoch[13 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10898322; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00402s; TotalTimePerSample = 0.01606ms; SamplesPerSecond = 62266 - Epoch[13 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17485632; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 - Epoch[13 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16192310; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 - Epoch[13 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312061; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 - Epoch[13 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13282751; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 - Epoch[13 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14269019; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80334 - Epoch[13 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14420691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 - Epoch[13 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540918; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 - Epoch[13 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20124243; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[13 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15036206; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 - Epoch[13 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10167212; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 - Epoch[13 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17007324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01334ms; SamplesPerSecond = 74985 - Epoch[13 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12729102; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00331s; TotalTimePerSample = 0.01325ms; SamplesPerSecond = 75460 - Epoch[13 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19446069; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01326ms; SamplesPerSecond = 75392 - Epoch[13 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15536450; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74360 - Epoch[13 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17620239; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01330ms; SamplesPerSecond = 75187 - Epoch[13 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15503101; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75075 - Epoch[13 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19150610; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74693 - Epoch[13 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14232446; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 - Epoch[13 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14458325; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01343ms; SamplesPerSecond = 74449 - Epoch[13 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18954443; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74626 - Epoch[13 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17082227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 - Epoch[13 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17740283; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01327ms; SamplesPerSecond = 75369 - Epoch[13 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16458936; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00322s; TotalTimePerSample = 0.01287ms; SamplesPerSecond = 77712 - Epoch[13 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17239062; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79415 - Epoch[13 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14639941; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74382 - Epoch[13 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15091553; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00334s; TotalTimePerSample = 0.01334ms; SamplesPerSecond = 74962 - Epoch[13 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18959277; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75075 - Epoch[13 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15226514; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75052 - Epoch[13 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15816553; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00332s; TotalTimePerSample = 0.01328ms; SamplesPerSecond = 75278 - Epoch[13 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13357324; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00346s; TotalTimePerSample = 0.01382ms; SamplesPerSecond = 72358 - Epoch[13 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10534766; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 - Epoch[13 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20473242; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74338 - Epoch[13 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793311; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74537 - Epoch[13 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14926563; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74426 - Epoch[13 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14114600; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74404 -Finished Epoch[13 of 50]: [Training Set] TrainLossPerSample = 0.15789582; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.134401 -Starting Epoch 14: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 13 at record count 130000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 13: 503, 1097, ... - Epoch[14 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14947922; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76242 - Epoch[14 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13988226; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48704 - Epoch[14 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14457452; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00321s; TotalTimePerSample = 0.01285ms; SamplesPerSecond = 77808 - Epoch[14 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18849722; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75052 - Epoch[14 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15743707; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00551s; TotalTimePerSample = 0.02206ms; SamplesPerSecond = 45330 - Epoch[14 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17268475; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73942 - Epoch[14 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12649329; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 - Epoch[14 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15325610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74626 - Epoch[14 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16319580; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74716 - Epoch[14 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15290247; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 - Epoch[14 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14664307; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00333s; TotalTimePerSample = 0.01330ms; SamplesPerSecond = 75165 - Epoch[14 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15380029; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 - Epoch[14 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13412048; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74360 - Epoch[14 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126929; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 - Epoch[14 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16534766; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 - Epoch[14 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11969702; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73399 - Epoch[14 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11787769; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74604 - Epoch[14 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12611548; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00317s; TotalTimePerSample = 0.01267ms; SamplesPerSecond = 78939 - Epoch[14 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20692041; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74493 - Epoch[14 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18696948; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 - Epoch[14 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15918701; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74738 - Epoch[14 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672168; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00334s; TotalTimePerSample = 0.01336ms; SamplesPerSecond = 74827 - Epoch[14 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16776294; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74604 - Epoch[14 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17649243; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 - Epoch[14 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17918311; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00332s; TotalTimePerSample = 0.01327ms; SamplesPerSecond = 75346 - Epoch[14 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18229102; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 - Epoch[14 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15990381; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 - Epoch[14 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13827246; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74738 - Epoch[14 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17928223; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 - Epoch[14 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12728760; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 - Epoch[14 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15062500; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00344s; TotalTimePerSample = 0.01377ms; SamplesPerSecond = 72632 - Epoch[14 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13134277; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00322s; TotalTimePerSample = 0.01288ms; SamplesPerSecond = 77663 - Epoch[14 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15669336; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74426 - Epoch[14 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14840771; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00318s; TotalTimePerSample = 0.01272ms; SamplesPerSecond = 78591 - Epoch[14 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14254639; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00337s; TotalTimePerSample = 0.01349ms; SamplesPerSecond = 74117 - Epoch[14 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16551855; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74693 - Epoch[14 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19324121; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74493 - Epoch[14 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17837939; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74671 - Epoch[14 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17495312; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01337ms; SamplesPerSecond = 74783 - Epoch[14 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13701074; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00331s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75620 -Finished Epoch[14 of 50]: [Training Set] TrainLossPerSample = 0.15705664; EvalErrPerSample = 0.074299999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.138958 -Starting Epoch 15: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 14 at record count 140000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 14: 989, 678, ... - Epoch[15 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20959497; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[15 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14418518; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00495s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50464 - Epoch[15 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18801270; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 - Epoch[15 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12507526; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 - Epoch[15 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13645209; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80256 - Epoch[15 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17137933; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 - Epoch[15 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20525153; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[15 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13449194; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 - Epoch[15 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12386584; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 - Epoch[15 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17504480; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00322s; TotalTimePerSample = 0.01286ms; SamplesPerSecond = 77736 - Epoch[15 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18549670; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01251ms; SamplesPerSecond = 79948 - Epoch[15 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14575098; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 - Epoch[15 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17600708; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79999 - Epoch[15 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718848; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80360 - Epoch[15 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16285669; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 - Epoch[15 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12723975; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[15 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11262451; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 - Epoch[15 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11530811; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[15 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14287646; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01251ms; SamplesPerSecond = 79923 - Epoch[15 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19053613; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 - Epoch[15 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17408228; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 - Epoch[15 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15081714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 80000 - Epoch[15 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17837671; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80102 - Epoch[15 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13743726; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80231 - Epoch[15 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15989746; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 - Epoch[15 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15915088; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 - Epoch[15 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12169214; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01249ms; SamplesPerSecond = 80076 - Epoch[15 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19548730; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79974 - Epoch[15 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14926123; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79693 - Epoch[15 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521191; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79974 - Epoch[15 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15001807; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 - Epoch[15 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14514063; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70641 - Epoch[15 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15551074; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00352s; TotalTimePerSample = 0.01407ms; SamplesPerSecond = 71083 - Epoch[15 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18218506; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00352s; TotalTimePerSample = 0.01408ms; SamplesPerSecond = 71042 - Epoch[15 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131006; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00362s; TotalTimePerSample = 0.01447ms; SamplesPerSecond = 69098 - Epoch[15 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15272461; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00355s; TotalTimePerSample = 0.01420ms; SamplesPerSecond = 70422 - Epoch[15 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17988477; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00347s; TotalTimePerSample = 0.01387ms; SamplesPerSecond = 72108 - Epoch[15 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15021924; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70641 - Epoch[15 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18285986; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00347s; TotalTimePerSample = 0.01389ms; SamplesPerSecond = 71983 - Epoch[15 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17852979; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 -Finished Epoch[15 of 50]: [Training Set] TrainLossPerSample = 0.15797588; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.13213 -Starting Epoch 16: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 15 at record count 150000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 15: 834, 4617, ... - Epoch[16 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18087309; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 - Epoch[16 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12064146; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48216 - Epoch[16 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13509888; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73942 - Epoch[16 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16519626; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73724 - Epoch[16 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14746753; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 - Epoch[16 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16059631; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00350s; TotalTimePerSample = 0.01401ms; SamplesPerSecond = 71367 - Epoch[16 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14891614; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 - Epoch[16 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009229; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73898 - Epoch[16 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14677124; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73768 - Epoch[16 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14683655; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74052 - Epoch[16 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14287915; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00324s; TotalTimePerSample = 0.01296ms; SamplesPerSecond = 77160 - Epoch[16 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19432043; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73986 - Epoch[16 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15663904; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 - Epoch[16 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11754077; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00322s; TotalTimePerSample = 0.01288ms; SamplesPerSecond = 77663 - Epoch[16 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14312231; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00350s; TotalTimePerSample = 0.01400ms; SamplesPerSecond = 71428 - Epoch[16 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16778052; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74074 - Epoch[16 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14940283; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00337s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74096 - Epoch[16 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21570117; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 - Epoch[16 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11603052; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 - Epoch[16 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456787; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01355ms; SamplesPerSecond = 73811 - Epoch[16 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050806; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01357ms; SamplesPerSecond = 73702 - Epoch[16 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14824438; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00343s; TotalTimePerSample = 0.01373ms; SamplesPerSecond = 72822 - Epoch[16 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10748706; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 - Epoch[16 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16115039; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73443 - Epoch[16 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19790210; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01354ms; SamplesPerSecond = 73877 - Epoch[16 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15511743; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00682s; TotalTimePerSample = 0.02728ms; SamplesPerSecond = 36656 - Epoch[16 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14571045; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01446ms; SamplesPerSecond = 69156 - Epoch[16 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14506445; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 73014 - Epoch[16 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16244141; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 - Epoch[16 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17346436; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73898 - Epoch[16 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16367285; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73768 - Epoch[16 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09780713; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 - Epoch[16 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13693506; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01357ms; SamplesPerSecond = 73702 - Epoch[16 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16609375; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73637 - Epoch[16 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17097021; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 - Epoch[16 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13705225; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73724 - Epoch[16 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17503369; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01360ms; SamplesPerSecond = 73507 - Epoch[16 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18202393; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73163 - Epoch[16 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387305; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00350s; TotalTimePerSample = 0.01400ms; SamplesPerSecond = 71428 - Epoch[16 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16323730; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00343s; TotalTimePerSample = 0.01372ms; SamplesPerSecond = 72907 -Finished Epoch[16 of 50]: [Training Set] TrainLossPerSample = 0.15635659; EvalErrPerSample = 0.071899995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.143279 -Starting Epoch 17: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 16 at record count 160000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2037 retries for 10000 elements (20.4%) to ensure window condition -randomordering: recached sequence for seed 16: 4884, 4383, ... - Epoch[17 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13506857; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 - Epoch[17 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892380; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02122ms; SamplesPerSecond = 47134 - Epoch[17 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15982581; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73206 - Epoch[17 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16914691; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73464 - Epoch[17 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17463800; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01365ms; SamplesPerSecond = 73270 - Epoch[17 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337073; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00339s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73637 - Epoch[17 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11288971; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 - Epoch[17 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14479626; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 - Epoch[17 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14517712; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72950 - Epoch[17 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14121558; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00359s; TotalTimePerSample = 0.01437ms; SamplesPerSecond = 69599 - Epoch[17 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12230225; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00346s; TotalTimePerSample = 0.01386ms; SamplesPerSecond = 72170 - Epoch[17 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17032593; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72928 - Epoch[17 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16533850; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00354s; TotalTimePerSample = 0.01417ms; SamplesPerSecond = 70561 - Epoch[17 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19219836; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01360ms; SamplesPerSecond = 73529 - Epoch[17 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14920825; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00341s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73399 - Epoch[17 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16778174; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 - Epoch[17 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14410010; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73421 - Epoch[17 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096069; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73464 - Epoch[17 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15516626; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73227 - Epoch[17 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15461401; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73292 - Epoch[17 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17591846; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 - Epoch[17 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777539; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72992 - Epoch[17 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14908203; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 - Epoch[17 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18884204; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73206 - Epoch[17 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13255542; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00353s; TotalTimePerSample = 0.01411ms; SamplesPerSecond = 70881 - Epoch[17 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16748437; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02084ms; SamplesPerSecond = 47993 - Epoch[17 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12382349; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 - Epoch[17 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15887451; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 - Epoch[17 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16871777; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00343s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72992 - Epoch[17 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16700195; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01368ms; SamplesPerSecond = 73078 - Epoch[17 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19813770; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73313 - Epoch[17 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13726709; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 - Epoch[17 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14840186; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 - Epoch[17 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12672119; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48123 - Epoch[17 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15882520; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00344s; TotalTimePerSample = 0.01376ms; SamplesPerSecond = 72695 - Epoch[17 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16427832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70521 - Epoch[17 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18704590; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00348s; TotalTimePerSample = 0.01392ms; SamplesPerSecond = 71818 - Epoch[17 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11677686; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00335s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74537 - Epoch[17 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16292725; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00344s; TotalTimePerSample = 0.01376ms; SamplesPerSecond = 72674 - Epoch[17 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19076270; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00343s; TotalTimePerSample = 0.01372ms; SamplesPerSecond = 72907 -Finished Epoch[17 of 50]: [Training Set] TrainLossPerSample = 0.1562067; EvalErrPerSample = 0.075300001; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.144045 -Starting Epoch 18: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 17 at record count 170000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1944 retries for 10000 elements (19.4%) to ensure window condition -randomordering: recached sequence for seed 17: 1700, 4307, ... - Epoch[18 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17602905; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76335 - Epoch[18 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12962930; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 - Epoch[18 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15791135; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 - Epoch[18 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16390161; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79592 - Epoch[18 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14452905; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01260ms; SamplesPerSecond = 79365 - Epoch[18 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12564063; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 - Epoch[18 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16398291; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79617 - Epoch[18 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15125537; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 - Epoch[18 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10915930; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00316s; TotalTimePerSample = 0.01264ms; SamplesPerSecond = 79113 - Epoch[18 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14124622; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79239 - Epoch[18 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20968835; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 - Epoch[18 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13917358; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79643 - Epoch[18 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16085022; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 - Epoch[18 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549524; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79668 - Epoch[18 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13878174; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79617 - Epoch[18 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14446997; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 - Epoch[18 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17112207; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00318s; TotalTimePerSample = 0.01272ms; SamplesPerSecond = 78616 - Epoch[18 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21744116; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79491 - Epoch[18 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12943896; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79592 - Epoch[18 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13674170; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79465 - Epoch[18 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15600317; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79264 - Epoch[18 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17383594; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79465 - Epoch[18 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13479980; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 - Epoch[18 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11709106; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79440 - Epoch[18 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13944995; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00397s; TotalTimePerSample = 0.01588ms; SamplesPerSecond = 62956 - Epoch[18 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22621606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00317s; TotalTimePerSample = 0.01267ms; SamplesPerSecond = 78914 - Epoch[18 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13783594; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79567 - Epoch[18 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12238086; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 - Epoch[18 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20077979; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79643 - Epoch[18 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18251855; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 - Epoch[18 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16045117; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79491 - Epoch[18 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16636084; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01260ms; SamplesPerSecond = 79390 - Epoch[18 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17318896; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 - Epoch[18 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13682520; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79567 - Epoch[18 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22038428; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00316s; TotalTimePerSample = 0.01265ms; SamplesPerSecond = 79063 - Epoch[18 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13850977; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79440 - Epoch[18 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14276904; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 - Epoch[18 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18119238; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 - Epoch[18 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16981934; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 - Epoch[18 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15691846; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 -Finished Epoch[18 of 50]: [Training Set] TrainLossPerSample = 0.15709546; EvalErrPerSample = 0.074299999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.130074 -Starting Epoch 19: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 18 at record count 180000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2073 retries for 10000 elements (20.7%) to ensure window condition -randomordering: recached sequence for seed 18: 3974, 797, ... - Epoch[19 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13168202; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76103 - Epoch[19 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13988628; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46957 - Epoch[19 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15568793; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00364s; TotalTimePerSample = 0.01455ms; SamplesPerSecond = 68719 - Epoch[19 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14448511; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00355s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70323 - Epoch[19 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13734735; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00599s; TotalTimePerSample = 0.02396ms; SamplesPerSecond = 41736 - Epoch[19 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15639966; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00357s; TotalTimePerSample = 0.01429ms; SamplesPerSecond = 69969 - Epoch[19 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15040472; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 - Epoch[19 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15769873; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72928 - Epoch[19 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16571130; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68418 - Epoch[19 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15984302; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00361s; TotalTimePerSample = 0.01446ms; SamplesPerSecond = 69156 - Epoch[19 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12869775; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70601 - Epoch[19 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007764; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01450ms; SamplesPerSecond = 68984 - Epoch[19 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13434424; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70323 - Epoch[19 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19713831; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 - Epoch[19 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16539819; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01432ms; SamplesPerSecond = 69832 - Epoch[19 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15775098; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00375s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66595 - Epoch[19 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17449683; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73659 - Epoch[19 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18054639; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00356s; TotalTimePerSample = 0.01424ms; SamplesPerSecond = 70224 - Epoch[19 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19580371; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00364s; TotalTimePerSample = 0.01458ms; SamplesPerSecond = 68587 - Epoch[19 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17742773; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00358s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 - Epoch[19 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20143457; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00364s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68756 - Epoch[19 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12416846; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69871 - Epoch[19 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14046631; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 - Epoch[19 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20061450; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70303 - Epoch[19 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16971094; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01450ms; SamplesPerSecond = 68965 - Epoch[19 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15172412; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00355s; TotalTimePerSample = 0.01419ms; SamplesPerSecond = 70482 - Epoch[19 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13443018; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00363s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68794 - Epoch[19 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12323096; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00355s; TotalTimePerSample = 0.01421ms; SamplesPerSecond = 70363 - Epoch[19 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20002246; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01467ms; SamplesPerSecond = 68157 - Epoch[19 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14361279; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01469ms; SamplesPerSecond = 68064 - Epoch[19 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11884912; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00364s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68756 - Epoch[19 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17547803; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00359s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69715 - Epoch[19 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18262158; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68436 - Epoch[19 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11165186; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70303 - Epoch[19 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13564307; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 - Epoch[19 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15268945; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01470ms; SamplesPerSecond = 68045 - Epoch[19 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16245166; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 - Epoch[19 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12254980; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 - Epoch[19 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14969043; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01426ms; SamplesPerSecond = 70145 - Epoch[19 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16413672; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73486 -Finished Epoch[19 of 50]: [Training Set] TrainLossPerSample = 0.15590012; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.149183 -Starting Epoch 20: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 19 at record count 190000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1918 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 19: 549, 1755, ... - Epoch[20 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18188667; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 - Epoch[20 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519978; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 - Epoch[20 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15642200; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72950 - Epoch[20 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12080978; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01514ms; SamplesPerSecond = 66032 - Epoch[20 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13863098; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01436ms; SamplesPerSecond = 69657 - Epoch[20 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15650378; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67934 - Epoch[20 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18442590; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69890 - Epoch[20 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13180127; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66155 - Epoch[20 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13930054; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 - Epoch[20 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16425256; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68380 - Epoch[20 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19111047; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00357s; TotalTimePerSample = 0.01427ms; SamplesPerSecond = 70067 - Epoch[20 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12297009; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 - Epoch[20 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15583582; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01437ms; SamplesPerSecond = 69579 - Epoch[20 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16438818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 - Epoch[20 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15821484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00353s; TotalTimePerSample = 0.01412ms; SamplesPerSecond = 70841 - Epoch[20 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317896; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69696 - Epoch[20 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15155737; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68418 - Epoch[20 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13631714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69676 - Epoch[20 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17534424; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 - Epoch[20 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549976; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00358s; TotalTimePerSample = 0.01433ms; SamplesPerSecond = 69773 - Epoch[20 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14183154; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 - Epoch[20 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16146045; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65893 - Epoch[20 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21492310; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00357s; TotalTimePerSample = 0.01429ms; SamplesPerSecond = 69988 - Epoch[20 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13325562; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 - Epoch[20 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456714; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00357s; TotalTimePerSample = 0.01427ms; SamplesPerSecond = 70086 - Epoch[20 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15659009; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68399 - Epoch[20 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16902222; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01438ms; SamplesPerSecond = 69560 - Epoch[20 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15981104; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00368s; TotalTimePerSample = 0.01473ms; SamplesPerSecond = 67879 - Epoch[20 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16168359; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67078 - Epoch[20 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14797168; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68399 - Epoch[20 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16445166; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00358s; TotalTimePerSample = 0.01433ms; SamplesPerSecond = 69793 - Epoch[20 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12721533; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01463ms; SamplesPerSecond = 68343 - Epoch[20 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579053; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00358s; TotalTimePerSample = 0.01430ms; SamplesPerSecond = 69910 - Epoch[20 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18053223; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67953 - Epoch[20 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540820; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00359s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 - Epoch[20 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18781592; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00382s; TotalTimePerSample = 0.01529ms; SamplesPerSecond = 65393 - Epoch[20 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19209521; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 - Epoch[20 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14946777; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 - Epoch[20 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14759277; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69890 - Epoch[20 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13829053; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00369s; TotalTimePerSample = 0.01476ms; SamplesPerSecond = 67769 -Finished Epoch[20 of 50]: [Training Set] TrainLossPerSample = 0.15708567; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.148148 -Starting Epoch 21: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 20 at record count 200000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 20: 1080, 44, ... - Epoch[21 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12482390; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 - Epoch[21 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17546846; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00557s; TotalTimePerSample = 0.02227ms; SamplesPerSecond = 44899 - Epoch[21 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19240039; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 - Epoch[21 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12887006; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67494 - Epoch[21 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13593225; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 - Epoch[21 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17156799; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 - Epoch[21 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11016656; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00360s; TotalTimePerSample = 0.01441ms; SamplesPerSecond = 69405 - Epoch[21 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14242212; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01531ms; SamplesPerSecond = 65308 - Epoch[21 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11643091; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 - Epoch[21 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579993; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67567 - Epoch[21 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16201392; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01449ms; SamplesPerSecond = 69003 - Epoch[21 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12312732; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67658 - Epoch[21 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19885461; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00360s; TotalTimePerSample = 0.01440ms; SamplesPerSecond = 69463 - Epoch[21 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15726025; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 - Epoch[21 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12881958; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00360s; TotalTimePerSample = 0.01438ms; SamplesPerSecond = 69521 - Epoch[21 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19554468; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 - Epoch[21 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16273682; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00363s; TotalTimePerSample = 0.01451ms; SamplesPerSecond = 68927 - Epoch[21 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16055859; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01487ms; SamplesPerSecond = 67258 - Epoch[21 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18578052; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 - Epoch[21 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14378711; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00392s; TotalTimePerSample = 0.01568ms; SamplesPerSecond = 63791 - Epoch[21 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12746191; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00363s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68794 - Epoch[21 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12992017; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 - Epoch[21 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16369800; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 - Epoch[21 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19116650; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67403 - Epoch[21 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17885107; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 - Epoch[21 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15923657; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00619s; TotalTimePerSample = 0.02474ms; SamplesPerSecond = 40413 - Epoch[21 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16519727; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01455ms; SamplesPerSecond = 68719 - Epoch[21 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13316406; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01489ms; SamplesPerSecond = 67150 - Epoch[21 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19117871; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 - Epoch[21 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17145117; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67276 - Epoch[21 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20479297; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68851 - Epoch[21 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15686377; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66312 - Epoch[21 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18002539; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67060 - Epoch[21 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15793604; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67585 - Epoch[21 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999805; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69252 - Epoch[21 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12539844; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00371s; TotalTimePerSample = 0.01485ms; SamplesPerSecond = 67330 - Epoch[21 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14626563; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69041 - Epoch[21 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21309863; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01495ms; SamplesPerSecond = 66898 - Epoch[21 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13187744; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66471 - Epoch[21 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13131494; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 -Finished Epoch[21 of 50]: [Training Set] TrainLossPerSample = 0.15653156; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.152793 -Starting Epoch 22: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 21 at record count 210000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2012 retries for 10000 elements (20.1%) to ensure window condition -randomordering: recached sequence for seed 21: 549, 612, ... - Epoch[22 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17214368; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01320ms; SamplesPerSecond = 75757 - Epoch[22 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13493817; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44587 - Epoch[22 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23933429; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68889 - Epoch[22 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14054822; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 - Epoch[22 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13355316; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01456ms; SamplesPerSecond = 68700 - Epoch[22 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12845679; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 - Epoch[22 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13766351; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01834ms; SamplesPerSecond = 54513 - Epoch[22 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18023999; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00385s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65002 - Epoch[22 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14117981; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01460ms; SamplesPerSecond = 68474 - Epoch[22 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12569360; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67114 - Epoch[22 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22072864; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68324 - Epoch[22 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17385925; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66934 - Epoch[22 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16276953; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67458 - Epoch[22 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14364160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 - Epoch[22 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12422998; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65945 - Epoch[22 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12879321; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00385s; TotalTimePerSample = 0.01541ms; SamplesPerSecond = 64884 - Epoch[22 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17239917; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68851 - Epoch[22 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15272241; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 - Epoch[22 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549023; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00433s; TotalTimePerSample = 0.01732ms; SamplesPerSecond = 57736 - Epoch[22 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18117432; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01670ms; SamplesPerSecond = 59865 - Epoch[22 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13781250; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00419s; TotalTimePerSample = 0.01677ms; SamplesPerSecond = 59637 - Epoch[22 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17124634; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00433s; TotalTimePerSample = 0.01730ms; SamplesPerSecond = 57790 - Epoch[22 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14588330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01671ms; SamplesPerSecond = 59851 - Epoch[22 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15950610; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00418s; TotalTimePerSample = 0.01671ms; SamplesPerSecond = 59851 - Epoch[22 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797070; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00461s; TotalTimePerSample = 0.01845ms; SamplesPerSecond = 54194 - Epoch[22 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13840186; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00418s; TotalTimePerSample = 0.01674ms; SamplesPerSecond = 59751 - Epoch[22 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18444922; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59808 - Epoch[22 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14643213; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00432s; TotalTimePerSample = 0.01727ms; SamplesPerSecond = 57897 - Epoch[22 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11097461; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00416s; TotalTimePerSample = 0.01665ms; SamplesPerSecond = 60067 - Epoch[22 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18351416; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00422s; TotalTimePerSample = 0.01688ms; SamplesPerSecond = 59241 - Epoch[22 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17264697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00430s; TotalTimePerSample = 0.01721ms; SamplesPerSecond = 58112 - Epoch[22 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14929102; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00416s; TotalTimePerSample = 0.01666ms; SamplesPerSecond = 60038 - Epoch[22 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13344678; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59822 - Epoch[22 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13797266; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00432s; TotalTimePerSample = 0.01729ms; SamplesPerSecond = 57830 - Epoch[22 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16307861; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00417s; TotalTimePerSample = 0.01670ms; SamplesPerSecond = 59880 - Epoch[22 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19140332; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00421s; TotalTimePerSample = 0.01682ms; SamplesPerSecond = 59438 - Epoch[22 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12631348; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00433s; TotalTimePerSample = 0.01734ms; SamplesPerSecond = 57683 - Epoch[22 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13973828; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00417s; TotalTimePerSample = 0.01669ms; SamplesPerSecond = 59908 - Epoch[22 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08310840; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59822 - Epoch[22 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21134424; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00433s; TotalTimePerSample = 0.01734ms; SamplesPerSecond = 57683 -Finished Epoch[22 of 50]: [Training Set] TrainLossPerSample = 0.15510236; EvalErrPerSample = 0.073299997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.164444 -Starting Epoch 23: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 22 at record count 220000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1950 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 22: 43, 802, ... - Epoch[23 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13851323; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 - Epoch[23 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15266893; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00556s; TotalTimePerSample = 0.02224ms; SamplesPerSecond = 44955 - Epoch[23 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15029691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 - Epoch[23 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13997247; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 - Epoch[23 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15457629; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66471 - Epoch[23 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13599609; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68306 - Epoch[23 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17177649; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 - Epoch[23 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15163049; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01468ms; SamplesPerSecond = 68119 - Epoch[23 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14814685; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00375s; TotalTimePerSample = 0.01500ms; SamplesPerSecond = 66648 - Epoch[23 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15877173; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 - Epoch[23 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19294788; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63922 - Epoch[23 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18038538; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00367s; TotalTimePerSample = 0.01467ms; SamplesPerSecond = 68175 - Epoch[23 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11728796; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00349s; TotalTimePerSample = 0.01398ms; SamplesPerSecond = 71551 - Epoch[23 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08960864; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69676 - Epoch[23 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17279297; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01506ms; SamplesPerSecond = 66401 - Epoch[23 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18872900; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68324 - Epoch[23 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15471533; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 - Epoch[23 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15328271; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 - Epoch[23 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12188086; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 - Epoch[23 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14665259; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67916 - Epoch[23 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20669824; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66952 - Epoch[23 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17103247; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 - Epoch[23 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15786304; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44851 - Epoch[23 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745117; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00370s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67640 - Epoch[23 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12732666; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00391s; TotalTimePerSample = 0.01566ms; SamplesPerSecond = 63873 - Epoch[23 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14623828; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68436 - Epoch[23 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15708105; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01509ms; SamplesPerSecond = 66277 - Epoch[23 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13106592; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01468ms; SamplesPerSecond = 68138 - Epoch[23 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19996533; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00375s; TotalTimePerSample = 0.01501ms; SamplesPerSecond = 66631 - Epoch[23 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16580322; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68231 - Epoch[23 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17845557; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 - Epoch[23 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20788770; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68380 - Epoch[23 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15010986; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00376s; TotalTimePerSample = 0.01506ms; SamplesPerSecond = 66418 - Epoch[23 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15772803; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67549 - Epoch[23 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13234326; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66295 - Epoch[23 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16466455; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 - Epoch[23 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11601953; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00393s; TotalTimePerSample = 0.01572ms; SamplesPerSecond = 63597 - Epoch[23 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14522217; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00367s; TotalTimePerSample = 0.01469ms; SamplesPerSecond = 68082 - Epoch[23 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12782617; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00348s; TotalTimePerSample = 0.01392ms; SamplesPerSecond = 71859 - Epoch[23 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14913232; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 -Finished Epoch[23 of 50]: [Training Set] TrainLossPerSample = 0.15451369; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.1553 -Starting Epoch 24: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 23 at record count 230000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1952 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 23: 3227, 3145, ... - Epoch[24 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14052142; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 - Epoch[24 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11839349; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00566s; TotalTimePerSample = 0.02266ms; SamplesPerSecond = 44138 - Epoch[24 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13736136; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67677 - Epoch[24 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17809995; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00606s; TotalTimePerSample = 0.02423ms; SamplesPerSecond = 41267 - Epoch[24 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15297235; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67403 - Epoch[24 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17571277; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00382s; TotalTimePerSample = 0.01528ms; SamplesPerSecond = 65427 - Epoch[24 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19117938; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00361s; TotalTimePerSample = 0.01442ms; SamplesPerSecond = 69328 - Epoch[24 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477551; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 - Epoch[24 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17912805; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66365 - Epoch[24 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13628906; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67458 - Epoch[24 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14371887; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00352s; TotalTimePerSample = 0.01407ms; SamplesPerSecond = 71083 - Epoch[24 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14074475; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01517ms; SamplesPerSecond = 65910 - Epoch[24 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15530225; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00385s; TotalTimePerSample = 0.01539ms; SamplesPerSecond = 64968 - Epoch[24 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19925366; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01511ms; SamplesPerSecond = 66190 - Epoch[24 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14178516; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00369s; TotalTimePerSample = 0.01477ms; SamplesPerSecond = 67713 - Epoch[24 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18568652; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65858 - Epoch[24 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14750757; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01836ms; SamplesPerSecond = 54478 - Epoch[24 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116553; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00395s; TotalTimePerSample = 0.01580ms; SamplesPerSecond = 63307 - Epoch[24 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13254932; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67060 - Epoch[24 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16391455; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00378s; TotalTimePerSample = 0.01513ms; SamplesPerSecond = 66085 - Epoch[24 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12146313; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00369s; TotalTimePerSample = 0.01476ms; SamplesPerSecond = 67732 - Epoch[24 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16737085; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65530 - Epoch[24 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14815918; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 - Epoch[24 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15551050; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63500 - Epoch[24 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15667847; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67476 - Epoch[24 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13440381; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65963 - Epoch[24 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17122607; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 - Epoch[24 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14292285; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65685 - Epoch[24 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688672; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 - Epoch[24 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13779199; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00392s; TotalTimePerSample = 0.01568ms; SamplesPerSecond = 63791 - Epoch[24 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12947656; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67567 - Epoch[24 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18507080; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00380s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65720 - Epoch[24 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11831396; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67677 - Epoch[24 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19608057; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00351s; TotalTimePerSample = 0.01404ms; SamplesPerSecond = 71204 - Epoch[24 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13332031; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63484 - Epoch[24 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18062402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00370s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67494 - Epoch[24 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16128076; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00380s; TotalTimePerSample = 0.01519ms; SamplesPerSecond = 65841 - Epoch[24 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18098535; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00369s; TotalTimePerSample = 0.01477ms; SamplesPerSecond = 67695 - Epoch[24 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13035645; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65634 - Epoch[24 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14853516; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01481ms; SamplesPerSecond = 67531 -Finished Epoch[24 of 50]: [Training Set] TrainLossPerSample = 0.15456297; EvalErrPerSample = 0.073899999; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.156134 -Starting Epoch 25: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 24 at record count 240000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1932 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 24: 3407, 2083, ... - Epoch[25 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10413789; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 - Epoch[25 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15427972; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02245ms; SamplesPerSecond = 44547 - Epoch[25 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14306274; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00384s; TotalTimePerSample = 0.01536ms; SamplesPerSecond = 65087 - Epoch[25 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16880078; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00375s; TotalTimePerSample = 0.01498ms; SamplesPerSecond = 66737 - Epoch[25 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17541754; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00384s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65172 - Epoch[25 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11391406; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67222 - Epoch[25 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14680377; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00397s; TotalTimePerSample = 0.01588ms; SamplesPerSecond = 62988 - Epoch[25 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17015393; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00352s; TotalTimePerSample = 0.01406ms; SamplesPerSecond = 71103 - Epoch[25 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16514246; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00352s; TotalTimePerSample = 0.01410ms; SamplesPerSecond = 70921 - Epoch[25 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18206763; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00352s; TotalTimePerSample = 0.01408ms; SamplesPerSecond = 71002 - Epoch[25 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16409290; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01481ms; SamplesPerSecond = 67512 - Epoch[25 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10293591; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00384s; TotalTimePerSample = 0.01535ms; SamplesPerSecond = 65155 - Epoch[25 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12705823; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00388s; TotalTimePerSample = 0.01553ms; SamplesPerSecond = 64383 - Epoch[25 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12850781; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00383s; TotalTimePerSample = 0.01530ms; SamplesPerSecond = 65342 - Epoch[25 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15889661; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67367 - Epoch[25 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15900195; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00384s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65172 - Epoch[25 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10437720; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00372s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67114 - Epoch[25 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15870410; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00399s; TotalTimePerSample = 0.01594ms; SamplesPerSecond = 62719 - Epoch[25 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15275415; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00373s; TotalTimePerSample = 0.01493ms; SamplesPerSecond = 66988 - Epoch[25 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18503589; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00383s; TotalTimePerSample = 0.01533ms; SamplesPerSecond = 65240 - Epoch[25 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19125000; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67222 - Epoch[25 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17112036; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65257 - Epoch[25 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16705054; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67096 - Epoch[25 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16799976; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00398s; TotalTimePerSample = 0.01592ms; SamplesPerSecond = 62798 - Epoch[25 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19638110; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67186 - Epoch[25 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18267236; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65189 - Epoch[25 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14591772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00382s; TotalTimePerSample = 0.01529ms; SamplesPerSecond = 65410 - Epoch[25 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13971826; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01545ms; SamplesPerSecond = 64733 - Epoch[25 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15680713; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64750 - Epoch[25 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572021; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65599 - Epoch[25 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16551855; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66934 - Epoch[25 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15907275; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00351s; TotalTimePerSample = 0.01406ms; SamplesPerSecond = 71143 - Epoch[25 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15407373; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00384s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65036 - Epoch[25 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14376416; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 - Epoch[25 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00398s; TotalTimePerSample = 0.01590ms; SamplesPerSecond = 62877 - Epoch[25 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232422; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00374s; TotalTimePerSample = 0.01498ms; SamplesPerSecond = 66755 - Epoch[25 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14375244; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 - Epoch[25 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17384912; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67024 - Epoch[25 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12632861; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00400s; TotalTimePerSample = 0.01601ms; SamplesPerSecond = 62468 - Epoch[25 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16370215; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00384s; TotalTimePerSample = 0.01536ms; SamplesPerSecond = 65121 -Finished Epoch[25 of 50]: [Training Set] TrainLossPerSample = 0.1546814; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.15433 -Starting Epoch 26: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 25 at record count 250000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1924 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 25: 1325, 1664, ... - Epoch[26 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14296657; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76242 - Epoch[26 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21987227; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45703 - Epoch[26 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13585349; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66560 - Epoch[26 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16297119; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64783 - Epoch[26 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13785205; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67024 - Epoch[26 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18902814; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 - Epoch[26 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11547314; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00389s; TotalTimePerSample = 0.01556ms; SamplesPerSecond = 64283 - Epoch[26 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14476709; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00384s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65036 - Epoch[26 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18713660; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65651 - Epoch[26 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13506458; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01543ms; SamplesPerSecond = 64817 - Epoch[26 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508728; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01496ms; SamplesPerSecond = 66827 - Epoch[26 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12936316; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00400s; TotalTimePerSample = 0.01599ms; SamplesPerSecond = 62531 - Epoch[26 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12021448; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67186 - Epoch[26 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14798718; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00384s; TotalTimePerSample = 0.01535ms; SamplesPerSecond = 65138 - Epoch[26 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15024536; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00375s; TotalTimePerSample = 0.01499ms; SamplesPerSecond = 66720 - Epoch[26 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11538232; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00387s; TotalTimePerSample = 0.01547ms; SamplesPerSecond = 64649 - Epoch[26 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10707275; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01546ms; SamplesPerSecond = 64699 - Epoch[26 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11743384; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 - Epoch[26 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17169653; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66916 - Epoch[26 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17983130; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00385s; TotalTimePerSample = 0.01540ms; SamplesPerSecond = 64918 - Epoch[26 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13164038; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70621 - Epoch[26 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16592725; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66952 - Epoch[26 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10557300; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00400s; TotalTimePerSample = 0.01600ms; SamplesPerSecond = 62515 - Epoch[26 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13424634; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00374s; TotalTimePerSample = 0.01495ms; SamplesPerSecond = 66880 - Epoch[26 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16414233; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00623s; TotalTimePerSample = 0.02490ms; SamplesPerSecond = 40160 - Epoch[26 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18921021; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66120 - Epoch[26 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14548486; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70541 - Epoch[26 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17447925; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00401s; TotalTimePerSample = 0.01606ms; SamplesPerSecond = 62282 - Epoch[26 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19369873; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66524 - Epoch[26 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11958496; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 - Epoch[26 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12995361; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 - Epoch[26 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17100439; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63922 - Epoch[26 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16168213; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00390s; TotalTimePerSample = 0.01559ms; SamplesPerSecond = 64151 - Epoch[26 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20277344; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 - Epoch[26 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15511426; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66560 - Epoch[26 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22491211; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64783 - Epoch[26 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20030127; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 - Epoch[26 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17554395; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00404s; TotalTimePerSample = 0.01616ms; SamplesPerSecond = 61865 - Epoch[26 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12255664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01496ms; SamplesPerSecond = 66862 - Epoch[26 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12970947; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 -Finished Epoch[26 of 50]: [Training Set] TrainLossPerSample = 0.15407094; EvalErrPerSample = 0.072499998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.157749 -Starting Epoch 27: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 26 at record count 260000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1927 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 26: 375, 3629, ... - Epoch[27 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12591264; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76149 - Epoch[27 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14306953; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00577s; TotalTimePerSample = 0.02308ms; SamplesPerSecond = 43335 - Epoch[27 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14663956; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66137 - Epoch[27 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12528238; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00392s; TotalTimePerSample = 0.01569ms; SamplesPerSecond = 63742 - Epoch[27 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13291266; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 - Epoch[27 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14019751; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00403s; TotalTimePerSample = 0.01611ms; SamplesPerSecond = 62080 - Epoch[27 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10290942; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00355s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70501 - Epoch[27 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13283911; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00375s; TotalTimePerSample = 0.01499ms; SamplesPerSecond = 66720 - Epoch[27 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18021509; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00402s; TotalTimePerSample = 0.01607ms; SamplesPerSecond = 62235 - Epoch[27 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16835486; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 - Epoch[27 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21902234; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00406s; TotalTimePerSample = 0.01622ms; SamplesPerSecond = 61637 - Epoch[27 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18301453; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66507 - Epoch[27 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17154932; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00388s; TotalTimePerSample = 0.01553ms; SamplesPerSecond = 64399 - Epoch[27 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14856836; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66489 - Epoch[27 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14754175; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00388s; TotalTimePerSample = 0.01552ms; SamplesPerSecond = 64449 - Epoch[27 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17677759; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 - Epoch[27 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14752319; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00400s; TotalTimePerSample = 0.01600ms; SamplesPerSecond = 62484 - Epoch[27 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12513721; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66330 - Epoch[27 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13795337; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64069 - Epoch[27 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14083496; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00378s; TotalTimePerSample = 0.01510ms; SamplesPerSecond = 66225 - Epoch[27 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13369287; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00404s; TotalTimePerSample = 0.01615ms; SamplesPerSecond = 61911 - Epoch[27 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20411377; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 - Epoch[27 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18830322; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00388s; TotalTimePerSample = 0.01554ms; SamplesPerSecond = 64366 - Epoch[27 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23027222; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01509ms; SamplesPerSecond = 66260 - Epoch[27 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13498975; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01560ms; SamplesPerSecond = 64102 - Epoch[27 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20234009; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00393s; TotalTimePerSample = 0.01572ms; SamplesPerSecond = 63597 - Epoch[27 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621289; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00387s; TotalTimePerSample = 0.01550ms; SamplesPerSecond = 64532 - Epoch[27 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10671240; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01510ms; SamplesPerSecond = 66225 - Epoch[27 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19180225; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00389s; TotalTimePerSample = 0.01557ms; SamplesPerSecond = 64234 - Epoch[27 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13920410; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01517ms; SamplesPerSecond = 65928 - Epoch[27 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12604297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01456ms; SamplesPerSecond = 68700 - Epoch[27 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999072; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63500 - Epoch[27 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22732324; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 - Epoch[27 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10179541; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64053 - Epoch[27 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12488281; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 - Epoch[27 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13114551; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00403s; TotalTimePerSample = 0.01613ms; SamplesPerSecond = 61988 - Epoch[27 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18670459; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66489 - Epoch[27 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14991797; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00389s; TotalTimePerSample = 0.01555ms; SamplesPerSecond = 64316 - Epoch[27 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17380664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00378s; TotalTimePerSample = 0.01514ms; SamplesPerSecond = 66067 - Epoch[27 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15199316; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00389s; TotalTimePerSample = 0.01558ms; SamplesPerSecond = 64201 -Finished Epoch[27 of 50]: [Training Set] TrainLossPerSample = 0.15443754; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.156798 -Starting Epoch 28: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 27 at record count 270000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2091 retries for 10000 elements (20.9%) to ensure window condition -randomordering: recached sequence for seed 27: 1656, 2946, ... - Epoch[28 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14393666; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[28 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17761412; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00581s; TotalTimePerSample = 0.02324ms; SamplesPerSecond = 43036 - Epoch[28 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15454230; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65651 - Epoch[28 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14929742; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00394s; TotalTimePerSample = 0.01574ms; SamplesPerSecond = 63516 - Epoch[28 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12484784; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65599 - Epoch[28 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14156793; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00409s; TotalTimePerSample = 0.01635ms; SamplesPerSecond = 61154 - Epoch[28 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13612256; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65616 - Epoch[28 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14138220; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00395s; TotalTimePerSample = 0.01579ms; SamplesPerSecond = 63323 - Epoch[28 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14947571; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00357s; TotalTimePerSample = 0.01430ms; SamplesPerSecond = 69949 - Epoch[28 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14680188; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65945 - Epoch[28 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17777942; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00406s; TotalTimePerSample = 0.01623ms; SamplesPerSecond = 61621 - Epoch[28 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19684912; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00382s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65513 - Epoch[28 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14749500; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 - Epoch[28 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17938940; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00381s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65685 - Epoch[28 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12751685; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00393s; TotalTimePerSample = 0.01571ms; SamplesPerSecond = 63661 - Epoch[28 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18895264; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63435 - Epoch[28 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12314355; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64053 - Epoch[28 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126514; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00379s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65876 - Epoch[28 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13606494; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00429s; TotalTimePerSample = 0.01715ms; SamplesPerSecond = 58315 - Epoch[28 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16117578; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 - Epoch[28 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15077344; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01487ms; SamplesPerSecond = 67240 - Epoch[28 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15562012; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63954 - Epoch[28 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15696167; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00380s; TotalTimePerSample = 0.01521ms; SamplesPerSecond = 65754 - Epoch[28 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13213037; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00393s; TotalTimePerSample = 0.01571ms; SamplesPerSecond = 63645 - Epoch[28 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15861768; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01443ms; SamplesPerSecond = 69290 - Epoch[28 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20295117; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63435 - Epoch[28 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12567627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00391s; TotalTimePerSample = 0.01565ms; SamplesPerSecond = 63889 - Epoch[28 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17646289; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01520ms; SamplesPerSecond = 65806 - Epoch[28 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15850049; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63467 - Epoch[28 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13273242; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01525ms; SamplesPerSecond = 65565 - Epoch[28 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12176416; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00408s; TotalTimePerSample = 0.01630ms; SamplesPerSecond = 61334 - Epoch[28 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16263672; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65668 - Epoch[28 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16976172; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00392s; TotalTimePerSample = 0.01567ms; SamplesPerSecond = 63808 - Epoch[28 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11975098; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00382s; TotalTimePerSample = 0.01530ms; SamplesPerSecond = 65376 - Epoch[28 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14589404; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00409s; TotalTimePerSample = 0.01638ms; SamplesPerSecond = 61064 - Epoch[28 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16949268; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65720 - Epoch[28 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13438086; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00438s; TotalTimePerSample = 0.01751ms; SamplesPerSecond = 57116 - Epoch[28 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15717041; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00467s; TotalTimePerSample = 0.01866ms; SamplesPerSecond = 53579 - Epoch[28 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18282764; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00450s; TotalTimePerSample = 0.01798ms; SamplesPerSecond = 55604 - Epoch[28 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16783301; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00452s; TotalTimePerSample = 0.01808ms; SamplesPerSecond = 55297 -Finished Epoch[28 of 50]: [Training Set] TrainLossPerSample = 0.15442897; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.160649 -Starting Epoch 29: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 28 at record count 280000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2057 retries for 10000 elements (20.6%) to ensure window condition -randomordering: recached sequence for seed 28: 1080, 2027, ... - Epoch[29 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13651086; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 - Epoch[29 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17676038; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00643s; TotalTimePerSample = 0.02570ms; SamplesPerSecond = 38910 - Epoch[29 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17492984; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00457s; TotalTimePerSample = 0.01828ms; SamplesPerSecond = 54692 - Epoch[29 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18197458; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51313 - Epoch[29 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16113654; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00705s; TotalTimePerSample = 0.02818ms; SamplesPerSecond = 35486 - Epoch[29 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13163928; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00472s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53011 - Epoch[29 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16208954; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00455s; TotalTimePerSample = 0.01821ms; SamplesPerSecond = 54920 - Epoch[29 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14617480; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00404s; TotalTimePerSample = 0.01617ms; SamplesPerSecond = 61850 - Epoch[29 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16572351; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53613 - Epoch[29 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20103735; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54860 - Epoch[29 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17811365; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53316 - Epoch[29 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11239307; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00455s; TotalTimePerSample = 0.01821ms; SamplesPerSecond = 54920 - Epoch[29 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13314844; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00468s; TotalTimePerSample = 0.01870ms; SamplesPerSecond = 53464 - Epoch[29 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13256396; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49125 - Epoch[29 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499634; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00456s; TotalTimePerSample = 0.01824ms; SamplesPerSecond = 54824 - Epoch[29 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11521069; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00467s; TotalTimePerSample = 0.01866ms; SamplesPerSecond = 53579 - Epoch[29 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293921; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01822ms; SamplesPerSecond = 54884 - Epoch[29 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13474438; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01875ms; SamplesPerSecond = 53327 - Epoch[29 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16396338; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00454s; TotalTimePerSample = 0.01815ms; SamplesPerSecond = 55090 - Epoch[29 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15193042; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00645s; TotalTimePerSample = 0.02582ms; SamplesPerSecond = 38735 - Epoch[29 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10718457; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00405s; TotalTimePerSample = 0.01620ms; SamplesPerSecond = 61728 - Epoch[29 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12672412; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00453s; TotalTimePerSample = 0.01812ms; SamplesPerSecond = 55199 - Epoch[29 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19824097; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01893ms; SamplesPerSecond = 52831 - Epoch[29 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17706128; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00457s; TotalTimePerSample = 0.01827ms; SamplesPerSecond = 54728 - Epoch[29 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14488696; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01884ms; SamplesPerSecond = 53067 - Epoch[29 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14674194; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00454s; TotalTimePerSample = 0.01817ms; SamplesPerSecond = 55041 - Epoch[29 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15550610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01875ms; SamplesPerSecond = 53327 - Epoch[29 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15548340; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00401s; TotalTimePerSample = 0.01605ms; SamplesPerSecond = 62313 - Epoch[29 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16021094; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00452s; TotalTimePerSample = 0.01810ms; SamplesPerSecond = 55260 - Epoch[29 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16832129; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00472s; TotalTimePerSample = 0.01889ms; SamplesPerSecond = 52932 - Epoch[29 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18285400; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54848 - Epoch[29 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12037598; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00469s; TotalTimePerSample = 0.01877ms; SamplesPerSecond = 53270 - Epoch[29 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18390674; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00454s; TotalTimePerSample = 0.01816ms; SamplesPerSecond = 55066 - Epoch[29 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11044336; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00405s; TotalTimePerSample = 0.01621ms; SamplesPerSecond = 61682 - Epoch[29 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17944385; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01869ms; SamplesPerSecond = 53498 - Epoch[29 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14682129; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01824ms; SamplesPerSecond = 54824 - Epoch[29 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17389502; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01871ms; SamplesPerSecond = 53453 - Epoch[29 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14601221; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00454s; TotalTimePerSample = 0.01817ms; SamplesPerSecond = 55029 - Epoch[29 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16214209; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00471s; TotalTimePerSample = 0.01883ms; SamplesPerSecond = 53101 - Epoch[29 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13225928; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00402s; TotalTimePerSample = 0.01609ms; SamplesPerSecond = 62142 -Finished Epoch[29 of 50]: [Training Set] TrainLossPerSample = 0.15416239; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.189019 -Starting Epoch 30: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 29 at record count 290000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1974 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 29: 914, 4227, ... - Epoch[30 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14470308; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[30 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16273665; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00717s; TotalTimePerSample = 0.02868ms; SamplesPerSecond = 34862 - Epoch[30 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12416318; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53033 - Epoch[30 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795630; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00475s; TotalTimePerSample = 0.01900ms; SamplesPerSecond = 52631 - Epoch[30 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15630731; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00459s; TotalTimePerSample = 0.01838ms; SamplesPerSecond = 54406 - Epoch[30 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12211853; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52776 - Epoch[30 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11135504; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01837ms; SamplesPerSecond = 54430 - Epoch[30 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13587048; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00476s; TotalTimePerSample = 0.01902ms; SamplesPerSecond = 52565 - Epoch[30 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17534290; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 - Epoch[30 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11606653; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52776 - Epoch[30 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14560828; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00458s; TotalTimePerSample = 0.01833ms; SamplesPerSecond = 54561 - Epoch[30 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22452795; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54159 - Epoch[30 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20647754; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52865 - Epoch[30 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13715662; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 - Epoch[30 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15057251; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01891ms; SamplesPerSecond = 52876 - Epoch[30 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16580542; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00460s; TotalTimePerSample = 0.01838ms; SamplesPerSecond = 54395 - Epoch[30 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16650293; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52764 - Epoch[30 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15484229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00460s; TotalTimePerSample = 0.01840ms; SamplesPerSecond = 54336 - Epoch[30 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16349536; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00408s; TotalTimePerSample = 0.01632ms; SamplesPerSecond = 61259 - Epoch[30 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13694678; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00472s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53011 - Epoch[30 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10007373; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54159 - Epoch[30 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14123389; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00473s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52910 - Epoch[30 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16516821; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00463s; TotalTimePerSample = 0.01851ms; SamplesPerSecond = 54030 - Epoch[30 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13568677; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00476s; TotalTimePerSample = 0.01904ms; SamplesPerSecond = 52532 - Epoch[30 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15595508; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54171 - Epoch[30 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13078223; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52399 - Epoch[30 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13739844; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00463s; TotalTimePerSample = 0.01853ms; SamplesPerSecond = 53960 - Epoch[30 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14447607; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01911ms; SamplesPerSecond = 52334 - Epoch[30 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14593066; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00460s; TotalTimePerSample = 0.01842ms; SamplesPerSecond = 54300 - Epoch[30 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13859473; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00461s; TotalTimePerSample = 0.01845ms; SamplesPerSecond = 54194 - Epoch[30 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21342383; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52764 - Epoch[30 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16210498; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01850ms; SamplesPerSecond = 54054 - Epoch[30 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20290576; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 - Epoch[30 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16057129; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01860ms; SamplesPerSecond = 53775 - Epoch[30 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21367480; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00475s; TotalTimePerSample = 0.01901ms; SamplesPerSecond = 52598 - Epoch[30 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16640332; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01858ms; SamplesPerSecond = 53809 - Epoch[30 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709473; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 - Epoch[30 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14478906; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 - Epoch[30 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17019434; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00477s; TotalTimePerSample = 0.01910ms; SamplesPerSecond = 52366 - Epoch[30 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13185107; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00463s; TotalTimePerSample = 0.01852ms; SamplesPerSecond = 54007 -Finished Epoch[30 of 50]: [Training Set] TrainLossPerSample = 0.15467171; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.189437 -Starting Epoch 31: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 30 at record count 300000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1851 retries for 10000 elements (18.5%) to ensure window condition -randomordering: recached sequence for seed 30: 3803, 3991, ... - Epoch[31 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14462451; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75826 - Epoch[31 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16309470; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00663s; TotalTimePerSample = 0.02651ms; SamplesPerSecond = 37718 - Epoch[31 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14857794; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00470s; TotalTimePerSample = 0.01879ms; SamplesPerSecond = 53225 - Epoch[31 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17301627; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00484s; TotalTimePerSample = 0.01936ms; SamplesPerSecond = 51663 - Epoch[31 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17042346; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00466s; TotalTimePerSample = 0.01863ms; SamplesPerSecond = 53682 - Epoch[31 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18859375; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01915ms; SamplesPerSecond = 52224 - Epoch[31 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667926; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53613 - Epoch[31 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18785522; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00480s; TotalTimePerSample = 0.01919ms; SamplesPerSecond = 52115 - Epoch[31 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16620203; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00411s; TotalTimePerSample = 0.01643ms; SamplesPerSecond = 60871 - Epoch[31 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14571423; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00480s; TotalTimePerSample = 0.01920ms; SamplesPerSecond = 52094 - Epoch[31 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508728; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00411s; TotalTimePerSample = 0.01645ms; SamplesPerSecond = 60782 - Epoch[31 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11401196; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01905ms; SamplesPerSecond = 52498 - Epoch[31 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11211658; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00479s; TotalTimePerSample = 0.01915ms; SamplesPerSecond = 52213 - Epoch[31 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18307996; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53521 - Epoch[31 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14014038; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01917ms; SamplesPerSecond = 52170 - Epoch[31 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18277954; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00465s; TotalTimePerSample = 0.01861ms; SamplesPerSecond = 53728 - Epoch[31 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312427; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 - Epoch[31 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11868140; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00414s; TotalTimePerSample = 0.01654ms; SamplesPerSecond = 60444 - Epoch[31 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11320850; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00465s; TotalTimePerSample = 0.01860ms; SamplesPerSecond = 53763 - Epoch[31 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12614941; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01917ms; SamplesPerSecond = 52170 - Epoch[31 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12027686; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53544 - Epoch[31 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14531152; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01918ms; SamplesPerSecond = 52148 - Epoch[31 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19766016; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00414s; TotalTimePerSample = 0.01656ms; SamplesPerSecond = 60401 - Epoch[31 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12406689; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 - Epoch[31 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17037256; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00410s; TotalTimePerSample = 0.01640ms; SamplesPerSecond = 60960 - Epoch[31 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12764600; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00641s; TotalTimePerSample = 0.02566ms; SamplesPerSecond = 38977 - Epoch[31 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12040723; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51599 - Epoch[31 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13548096; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53533 - Epoch[31 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21368213; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00482s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51910 - Epoch[31 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20563916; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00468s; TotalTimePerSample = 0.01870ms; SamplesPerSecond = 53464 - Epoch[31 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15684570; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 - Epoch[31 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17233545; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01878ms; SamplesPerSecond = 53248 - Epoch[31 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16072217; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00483s; TotalTimePerSample = 0.01932ms; SamplesPerSecond = 51749 - Epoch[31 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13155273; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00467s; TotalTimePerSample = 0.01869ms; SamplesPerSecond = 53510 - Epoch[31 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15649854; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00469s; TotalTimePerSample = 0.01877ms; SamplesPerSecond = 53270 - Epoch[31 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12080762; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00480s; TotalTimePerSample = 0.01918ms; SamplesPerSecond = 52126 - Epoch[31 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18565283; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00470s; TotalTimePerSample = 0.01880ms; SamplesPerSecond = 53202 - Epoch[31 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18446680; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00480s; TotalTimePerSample = 0.01920ms; SamplesPerSecond = 52094 - Epoch[31 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16054297; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00471s; TotalTimePerSample = 0.01882ms; SamplesPerSecond = 53123 - Epoch[31 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15127344; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51867 -Finished Epoch[31 of 50]: [Training Set] TrainLossPerSample = 0.15486005; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.190844 -Starting Epoch 32: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 31 at record count 310000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 31: 2853, 507, ... - Epoch[32 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16708499; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75895 - Epoch[32 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19162103; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00663s; TotalTimePerSample = 0.02652ms; SamplesPerSecond = 37701 - Epoch[32 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13787976; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00476s; TotalTimePerSample = 0.01903ms; SamplesPerSecond = 52543 - Epoch[32 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13813211; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00481s; TotalTimePerSample = 0.01923ms; SamplesPerSecond = 51996 - Epoch[32 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13502838; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51824 - Epoch[32 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15943768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01945ms; SamplesPerSecond = 51408 - Epoch[32 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11804175; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52910 - Epoch[32 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12086487; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51514 - Epoch[32 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15141492; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52854 - Epoch[32 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18269641; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 - Epoch[32 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14335901; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52842 - Epoch[32 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17575195; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01936ms; SamplesPerSecond = 51663 - Epoch[32 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13828113; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00474s; TotalTimePerSample = 0.01894ms; SamplesPerSecond = 52787 - Epoch[32 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15138354; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00428s; TotalTimePerSample = 0.01713ms; SamplesPerSecond = 58370 - Epoch[32 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12959351; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00470s; TotalTimePerSample = 0.01879ms; SamplesPerSecond = 53225 - Epoch[32 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16191138; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01951ms; SamplesPerSecond = 51250 - Epoch[32 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16672583; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00471s; TotalTimePerSample = 0.01884ms; SamplesPerSecond = 53089 - Epoch[32 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17047607; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00481s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52018 - Epoch[32 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14900171; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01905ms; SamplesPerSecond = 52498 - Epoch[32 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15498999; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00475s; TotalTimePerSample = 0.01898ms; SamplesPerSecond = 52675 - Epoch[32 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14057910; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00484s; TotalTimePerSample = 0.01934ms; SamplesPerSecond = 51695 - Epoch[32 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842822; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00476s; TotalTimePerSample = 0.01906ms; SamplesPerSecond = 52476 - Epoch[32 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12984058; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51599 - Epoch[32 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16722925; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01894ms; SamplesPerSecond = 52798 - Epoch[32 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21137817; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51440 - Epoch[32 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16650391; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01896ms; SamplesPerSecond = 52731 - Epoch[32 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11624414; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00486s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51398 - Epoch[32 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16824561; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01898ms; SamplesPerSecond = 52698 - Epoch[32 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17619189; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51450 - Epoch[32 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14795850; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00472s; TotalTimePerSample = 0.01889ms; SamplesPerSecond = 52943 - Epoch[32 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14788672; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51450 - Epoch[32 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19199609; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01904ms; SamplesPerSecond = 52509 - Epoch[32 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14870068; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01947ms; SamplesPerSecond = 51366 - Epoch[32 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18169238; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00416s; TotalTimePerSample = 0.01665ms; SamplesPerSecond = 60067 - Epoch[32 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13152783; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00487s; TotalTimePerSample = 0.01948ms; SamplesPerSecond = 51334 - Epoch[32 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13540674; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00472s; TotalTimePerSample = 0.01888ms; SamplesPerSecond = 52954 - Epoch[32 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15633887; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51229 - Epoch[32 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12714551; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00480s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52029 - Epoch[32 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18318164; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51282 - Epoch[32 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15918359; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01885ms; SamplesPerSecond = 53044 -Finished Epoch[32 of 50]: [Training Set] TrainLossPerSample = 0.15423338; EvalErrPerSample = 0.073899999; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.192775 -Starting Epoch 33: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 32 at record count 320000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1906 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 32: 88, 89, ... - Epoch[33 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16717963; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75941 - Epoch[33 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19270932; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00671s; TotalTimePerSample = 0.02683ms; SamplesPerSecond = 37268 - Epoch[33 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13215280; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 - Epoch[33 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12894147; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 - Epoch[33 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16403992; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 - Epoch[33 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19667297; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01942ms; SamplesPerSecond = 51503 - Epoch[33 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18192096; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00481s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51931 - Epoch[33 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11374414; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51610 - Epoch[33 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13821655; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01931ms; SamplesPerSecond = 51792 - Epoch[33 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19738550; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01961ms; SamplesPerSecond = 50989 - Epoch[33 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14261597; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 - Epoch[33 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12330664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01953ms; SamplesPerSecond = 51198 - Epoch[33 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14401660; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52290 - Epoch[33 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22224963; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51240 - Epoch[33 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19924243; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 - Epoch[33 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13060303; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 - Epoch[33 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12433960; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52268 - Epoch[33 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15537744; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01962ms; SamplesPerSecond = 50978 - Epoch[33 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14369678; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52312 - Epoch[33 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14945068; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00493s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50751 - Epoch[33 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16571777; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00420s; TotalTimePerSample = 0.01680ms; SamplesPerSecond = 59523 - Epoch[33 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12740942; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01963ms; SamplesPerSecond = 50937 - Epoch[33 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16293408; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 - Epoch[33 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15815918; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50658 - Epoch[33 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.07717480; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52301 - Epoch[33 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14254663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50813 - Epoch[33 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16696899; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01914ms; SamplesPerSecond = 52235 - Epoch[33 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19129102; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 - Epoch[33 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437451; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52268 - Epoch[33 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11403076; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 - Epoch[33 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14417578; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50823 - Epoch[33 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11433838; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50597 - Epoch[33 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23562842; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 - Epoch[33 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672705; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50535 - Epoch[33 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14627832; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 - Epoch[33 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19525000; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00493s; TotalTimePerSample = 0.01972ms; SamplesPerSecond = 50709 - Epoch[33 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12537402; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 - Epoch[33 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709619; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00492s; TotalTimePerSample = 0.01967ms; SamplesPerSecond = 50833 - Epoch[33 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15483057; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51910 - Epoch[33 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14704346; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00495s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50464 -Finished Epoch[33 of 50]: [Training Set] TrainLossPerSample = 0.15413028; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.19587 -Starting Epoch 34: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 33 at record count 330000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1929 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 33: 3804, 1788, ... - Epoch[34 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13535313; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 - Epoch[34 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352461; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00674s; TotalTimePerSample = 0.02695ms; SamplesPerSecond = 37102 - Epoch[34 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15004388; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49870 - Epoch[34 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18189130; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 - Epoch[34 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15283014; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00721s; TotalTimePerSample = 0.02884ms; SamplesPerSecond = 34678 - Epoch[34 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11324536; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00663s; TotalTimePerSample = 0.02653ms; SamplesPerSecond = 37696 - Epoch[34 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14698895; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 - Epoch[34 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13531000; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51535 - Epoch[34 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15702075; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[34 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15457910; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01931ms; SamplesPerSecond = 51792 - Epoch[34 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13761108; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00422s; TotalTimePerSample = 0.01690ms; SamplesPerSecond = 59185 - Epoch[34 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050061; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00670s; TotalTimePerSample = 0.02679ms; SamplesPerSecond = 37330 - Epoch[34 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12034814; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01947ms; SamplesPerSecond = 51366 - Epoch[34 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690234; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00500s; TotalTimePerSample = 0.01998ms; SamplesPerSecond = 50040 - Epoch[34 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14237549; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 - Epoch[34 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16746973; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00491s; TotalTimePerSample = 0.01965ms; SamplesPerSecond = 50885 - Epoch[34 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631616; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01977ms; SamplesPerSecond = 50586 - Epoch[34 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16113428; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51514 - Epoch[34 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01993ms; SamplesPerSecond = 50180 - Epoch[34 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18868311; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 - Epoch[34 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15792822; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00500s; TotalTimePerSample = 0.02001ms; SamplesPerSecond = 49970 - Epoch[34 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14762939; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51684 - Epoch[34 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09712817; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50140 - Epoch[34 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13837964; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01929ms; SamplesPerSecond = 51834 - Epoch[34 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16423242; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[34 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14903052; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 - Epoch[34 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384839; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00426s; TotalTimePerSample = 0.01706ms; SamplesPerSecond = 58616 - Epoch[34 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19276465; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01964ms; SamplesPerSecond = 50926 - Epoch[34 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16750781; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[34 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15260986; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00498s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50150 - Epoch[34 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16502051; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00503s; TotalTimePerSample = 0.02013ms; SamplesPerSecond = 49682 - Epoch[34 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13349951; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51738 - Epoch[34 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11700879; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00500s; TotalTimePerSample = 0.01998ms; SamplesPerSecond = 50050 - Epoch[34 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18962549; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 - Epoch[34 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19251318; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50150 - Epoch[34 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16371680; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00492s; TotalTimePerSample = 0.01969ms; SamplesPerSecond = 50792 - Epoch[34 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11365430; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00508s; TotalTimePerSample = 0.02031ms; SamplesPerSecond = 49231 - Epoch[34 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17088965; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00489s; TotalTimePerSample = 0.01955ms; SamplesPerSecond = 51156 - Epoch[34 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21053516; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49711 - Epoch[34 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18195605; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51440 -Finished Epoch[34 of 50]: [Training Set] TrainLossPerSample = 0.15442753; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.203513 -Starting Epoch 35: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 34 at record count 340000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1977 retries for 10000 elements (19.8%) to ensure window condition -randomordering: recached sequence for seed 34: 0, 1369, ... - Epoch[35 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17794618; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00332s; TotalTimePerSample = 0.01326ms; SamplesPerSecond = 75392 - Epoch[35 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10474553; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00681s; TotalTimePerSample = 0.02725ms; SamplesPerSecond = 36694 - Epoch[35 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15704340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00503s; TotalTimePerSample = 0.02014ms; SamplesPerSecond = 49662 - Epoch[35 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14234021; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51219 - Epoch[35 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09799567; EvalErr[0]PerSample = 0.02800000; TotalTime = 0.00502s; TotalTimePerSample = 0.02010ms; SamplesPerSecond = 49761 - Epoch[35 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13716321; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51398 - Epoch[35 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11028894; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49880 - Epoch[35 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17058649; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00490s; TotalTimePerSample = 0.01958ms; SamplesPerSecond = 51072 - Epoch[35 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17828174; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00502s; TotalTimePerSample = 0.02007ms; SamplesPerSecond = 49830 - Epoch[35 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14517395; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51303 - Epoch[35 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14962244; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02006ms; SamplesPerSecond = 49860 - Epoch[35 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16455225; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00487s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51292 - Epoch[35 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15540332; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 - Epoch[35 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12299329; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 - Epoch[35 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12371277; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49950 - Epoch[35 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619019; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 - Epoch[35 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317358; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 - Epoch[35 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13892188; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01960ms; SamplesPerSecond = 51020 - Epoch[35 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17433130; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 50000 - Epoch[35 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793555; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 50000 - Epoch[35 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13101001; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50823 - Epoch[35 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17782764; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49711 - Epoch[35 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17694824; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 - Epoch[35 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12918970; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 - Epoch[35 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15366846; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01954ms; SamplesPerSecond = 51187 - Epoch[35 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15202856; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02021ms; SamplesPerSecond = 49475 - Epoch[35 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15452832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01965ms; SamplesPerSecond = 50895 - Epoch[35 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19114917; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00443s; TotalTimePerSample = 0.01774ms; SamplesPerSecond = 56369 - Epoch[35 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16061621; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01954ms; SamplesPerSecond = 51166 - Epoch[35 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18214746; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00506s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49455 - Epoch[35 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16263184; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51041 - Epoch[35 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14526318; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49514 - Epoch[35 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10713672; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 - Epoch[35 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16995996; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00507s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49358 - Epoch[35 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540625; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00490s; TotalTimePerSample = 0.01960ms; SamplesPerSecond = 51020 - Epoch[35 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19385498; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 - Epoch[35 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18298682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 - Epoch[35 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16515527; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49154 - Epoch[35 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16277686; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 - Epoch[35 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18119238; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00505s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49465 -Finished Epoch[35 of 50]: [Training Set] TrainLossPerSample = 0.154597; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.200222 -Starting Epoch 36: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 35 at record count 350000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 35: 3511, 991, ... - Epoch[36 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13045412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75872 - Epoch[36 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16481703; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00689s; TotalTimePerSample = 0.02754ms; SamplesPerSecond = 36305 - Epoch[36 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18674973; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48704 - Epoch[36 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19172791; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50261 - Epoch[36 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13598785; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48770 - Epoch[36 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14000085; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50525 - Epoch[36 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18633856; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48713 - Epoch[36 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11492786; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50220 - Epoch[36 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17103491; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49154 - Epoch[36 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16138538; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01988ms; SamplesPerSecond = 50311 - Epoch[36 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21032043; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00444s; TotalTimePerSample = 0.01777ms; SamplesPerSecond = 56280 - Epoch[36 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16564624; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00497s; TotalTimePerSample = 0.01987ms; SamplesPerSecond = 50332 - Epoch[36 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16749756; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 - Epoch[36 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11099194; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00432s; TotalTimePerSample = 0.01727ms; SamplesPerSecond = 57910 - Epoch[36 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11533960; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 - Epoch[36 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12073120; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 - Epoch[36 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13125293; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49106 - Epoch[36 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12560693; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 - Epoch[36 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17352856; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00449s; TotalTimePerSample = 0.01794ms; SamplesPerSecond = 55741 - Epoch[36 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12549976; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50597 - Epoch[36 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17819629; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50423 - Epoch[36 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14474902; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 - Epoch[36 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16296924; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 - Epoch[36 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14012451; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01927ms; SamplesPerSecond = 51899 - Epoch[36 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14773145; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00437s; TotalTimePerSample = 0.01746ms; SamplesPerSecond = 57260 - Epoch[36 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11817139; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00722s; TotalTimePerSample = 0.02888ms; SamplesPerSecond = 34630 - Epoch[36 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14450684; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50281 - Epoch[36 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18549658; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00522s; TotalTimePerSample = 0.02086ms; SamplesPerSecond = 47929 - Epoch[36 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14738379; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00566s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44200 - Epoch[36 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21349512; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01992ms; SamplesPerSecond = 50190 - Epoch[36 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022412; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 - Epoch[36 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18741650; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50413 - Epoch[36 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13112988; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 - Epoch[36 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13533154; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01996ms; SamplesPerSecond = 50110 - Epoch[36 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13490674; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00422s; TotalTimePerSample = 0.01687ms; SamplesPerSecond = 59283 - Epoch[36 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14833740; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50525 - Epoch[36 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17256348; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01929ms; SamplesPerSecond = 51845 - Epoch[36 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16826221; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50393 - Epoch[36 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17325977; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 - Epoch[36 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15534229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50433 -Finished Epoch[36 of 50]: [Training Set] TrainLossPerSample = 0.15398593; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.201135 -Starting Epoch 37: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 36 at record count 360000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1919 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 36: 87, 1672, ... - Epoch[37 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13413799; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[37 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12221150; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00673s; TotalTimePerSample = 0.02692ms; SamplesPerSecond = 37152 - Epoch[37 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635403; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 - Epoch[37 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13580142; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 - Epoch[37 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13378632; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00497s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50261 - Epoch[37 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797546; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 - Epoch[37 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11214264; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50281 - Epoch[37 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17563239; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51546 - Epoch[37 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842529; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00438s; TotalTimePerSample = 0.01750ms; SamplesPerSecond = 57129 - Epoch[37 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718579; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 - Epoch[37 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18377405; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50362 - Epoch[37 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14247290; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01937ms; SamplesPerSecond = 51620 - Epoch[37 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15313257; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 - Epoch[37 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18710876; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[37 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15047632; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01942ms; SamplesPerSecond = 51482 - Epoch[37 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10942798; EvalErr[0]PerSample = 0.02400000; TotalTime = 0.00427s; TotalTimePerSample = 0.01707ms; SamplesPerSecond = 58589 - Epoch[37 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16937573; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50761 - Epoch[37 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17252734; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01992ms; SamplesPerSecond = 50200 - Epoch[37 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15181689; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 - Epoch[37 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17059106; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50271 - Epoch[37 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16196045; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 - Epoch[37 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18610669; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[37 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14776636; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 - Epoch[37 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13439941; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50251 - Epoch[37 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17143726; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50251 - Epoch[37 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17794141; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00425s; TotalTimePerSample = 0.01702ms; SamplesPerSecond = 58768 - Epoch[37 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16231763; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01934ms; SamplesPerSecond = 51706 - Epoch[37 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15370898; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00496s; TotalTimePerSample = 0.01985ms; SamplesPerSecond = 50382 - Epoch[37 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15689111; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 - Epoch[37 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12223779; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00492s; TotalTimePerSample = 0.01969ms; SamplesPerSecond = 50792 - Epoch[37 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16878760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49810 - Epoch[37 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10121338; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50648 - Epoch[37 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14805615; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 - Epoch[37 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16627832; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50423 - Epoch[37 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333057; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00508s; TotalTimePerSample = 0.02032ms; SamplesPerSecond = 49212 - Epoch[37 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16828662; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 - Epoch[37 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21935449; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48866 - Epoch[37 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12863428; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48590 - Epoch[37 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11302051; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48933 - Epoch[37 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16875684; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48216 -Finished Epoch[37 of 50]: [Training Set] TrainLossPerSample = 0.15362105; EvalErrPerSample = 0.071800001; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.198585 -Starting Epoch 38: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 37 at record count 370000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2090 retries for 10000 elements (20.9%) to ensure window condition -randomordering: recached sequence for seed 37: 2147, 1618, ... - Epoch[38 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15782407; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01321ms; SamplesPerSecond = 75688 - Epoch[38 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13964478; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00681s; TotalTimePerSample = 0.02722ms; SamplesPerSecond = 36732 - Epoch[38 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13224973; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49495 - Epoch[38 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14791113; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51271 - Epoch[38 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16625989; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 - Epoch[38 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777277; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51219 - Epoch[38 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18762695; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00502s; TotalTimePerSample = 0.02006ms; SamplesPerSecond = 49840 - Epoch[38 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17601624; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50220 - Epoch[38 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17659778; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50771 - Epoch[38 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15942542; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49940 - Epoch[38 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15378552; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51124 - Epoch[38 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11990942; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 - Epoch[38 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15716211; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51135 - Epoch[38 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12430591; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02003ms; SamplesPerSecond = 49930 - Epoch[38 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14166211; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01993ms; SamplesPerSecond = 50180 - Epoch[38 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19538525; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50607 - Epoch[38 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16727295; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49880 - Epoch[38 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15161816; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01953ms; SamplesPerSecond = 51208 - Epoch[38 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12691724; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 - Epoch[38 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14758423; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00491s; TotalTimePerSample = 0.01964ms; SamplesPerSecond = 50916 - Epoch[38 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266943; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 - Epoch[38 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14729663; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00500s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49960 - Epoch[38 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16603687; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49940 - Epoch[38 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13901196; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49691 - Epoch[38 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17964941; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00427s; TotalTimePerSample = 0.01707ms; SamplesPerSecond = 58589 - Epoch[38 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18162671; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49800 - Epoch[38 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12574365; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51124 - Epoch[38 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18256348; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00502s; TotalTimePerSample = 0.02009ms; SamplesPerSecond = 49771 - Epoch[38 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13491504; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45421 - Epoch[38 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16772412; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00504s; TotalTimePerSample = 0.02015ms; SamplesPerSecond = 49632 - Epoch[38 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14115820; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01995ms; SamplesPerSecond = 50130 - Epoch[38 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10757568; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00670s; TotalTimePerSample = 0.02678ms; SamplesPerSecond = 37335 - Epoch[38 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12081152; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49348 - Epoch[38 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20721875; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50771 - Epoch[38 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20681787; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00583s; TotalTimePerSample = 0.02334ms; SamplesPerSecond = 42852 - Epoch[38 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15313916; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00506s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49446 - Epoch[38 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14917236; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00504s; TotalTimePerSample = 0.02016ms; SamplesPerSecond = 49613 - Epoch[38 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18255664; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00579s; TotalTimePerSample = 0.02314ms; SamplesPerSecond = 43215 - Epoch[38 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15325146; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00557s; TotalTimePerSample = 0.02228ms; SamplesPerSecond = 44883 - Epoch[38 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12367236; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48628 -Finished Epoch[38 of 50]: [Training Set] TrainLossPerSample = 0.15423857; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.20528 -Starting Epoch 39: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 38 at record count 380000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 38: 1196, 3933, ... - Epoch[39 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17279616; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75803 - Epoch[39 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15053123; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00686s; TotalTimePerSample = 0.02743ms; SamplesPerSecond = 36459 - Epoch[39 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15340201; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 - Epoch[39 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16145346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50658 - Epoch[39 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16051062; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00681s; TotalTimePerSample = 0.02722ms; SamplesPerSecond = 36737 - Epoch[39 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14096790; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00494s; TotalTimePerSample = 0.01977ms; SamplesPerSecond = 50576 - Epoch[39 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12817004; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 - Epoch[39 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18435486; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50271 - Epoch[39 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15009888; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02018ms; SamplesPerSecond = 49554 - Epoch[39 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17215259; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00508s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49173 - Epoch[39 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19742957; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50515 - Epoch[39 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14509302; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02035ms; SamplesPerSecond = 49144 - Epoch[39 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15422522; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01981ms; SamplesPerSecond = 50474 - Epoch[39 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20461450; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00507s; TotalTimePerSample = 0.02028ms; SamplesPerSecond = 49299 - Epoch[39 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14545752; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49164 - Epoch[39 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11789624; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50342 - Epoch[39 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14740527; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00441s; TotalTimePerSample = 0.01766ms; SamplesPerSecond = 56625 - Epoch[39 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14339429; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00583s; TotalTimePerSample = 0.02331ms; SamplesPerSecond = 42896 - Epoch[39 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246411; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48638 - Epoch[39 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14868335; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00431s; TotalTimePerSample = 0.01725ms; SamplesPerSecond = 57977 - Epoch[39 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10311523; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00506s; TotalTimePerSample = 0.02023ms; SamplesPerSecond = 49426 - Epoch[39 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14912964; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 - Epoch[39 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14753906; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02080ms; SamplesPerSecond = 48086 - Epoch[39 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12303320; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 - Epoch[39 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14021680; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49164 - Epoch[39 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13175928; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00509s; TotalTimePerSample = 0.02038ms; SamplesPerSecond = 49077 - Epoch[39 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12917285; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50454 - Epoch[39 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14734375; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00508s; TotalTimePerSample = 0.02030ms; SamplesPerSecond = 49261 - Epoch[39 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13080371; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 - Epoch[39 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21033447; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49125 - Epoch[39 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17700244; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00510s; TotalTimePerSample = 0.02040ms; SamplesPerSecond = 49029 - Epoch[39 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13948145; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00497s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50352 - Epoch[39 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15833105; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00532s; TotalTimePerSample = 0.02127ms; SamplesPerSecond = 47010 - Epoch[39 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18455029; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00537s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46520 - Epoch[39 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16332812; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00537s; TotalTimePerSample = 0.02147ms; SamplesPerSecond = 46572 - Epoch[39 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16213574; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00458s; TotalTimePerSample = 0.01833ms; SamplesPerSecond = 54549 - Epoch[39 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15135254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 - Epoch[39 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19016113; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02153ms; SamplesPerSecond = 46442 - Epoch[39 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00539s; TotalTimePerSample = 0.02154ms; SamplesPerSecond = 46416 - Epoch[39 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13075830; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00537s; TotalTimePerSample = 0.02148ms; SamplesPerSecond = 46546 -Finished Epoch[39 of 50]: [Training Set] TrainLossPerSample = 0.15412679; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.206532 -Starting Epoch 40: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 39 at record count 390000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1956 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 39: 2661, 3152, ... - Epoch[40 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14161708; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01316ms; SamplesPerSecond = 76010 - Epoch[40 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14613423; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00689s; TotalTimePerSample = 0.02756ms; SamplesPerSecond = 36289 - Epoch[40 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18211203; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00447s; TotalTimePerSample = 0.01789ms; SamplesPerSecond = 55903 - Epoch[40 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17613000; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00434s; TotalTimePerSample = 0.01736ms; SamplesPerSecond = 57616 - Epoch[40 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17070685; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48875 - Epoch[40 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17630023; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00510s; TotalTimePerSample = 0.02040ms; SamplesPerSecond = 49029 - Epoch[40 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13823822; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00514s; TotalTimePerSample = 0.02057ms; SamplesPerSecond = 48619 - Epoch[40 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16955151; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 - Epoch[40 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16634717; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00512s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48790 - Epoch[40 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14256152; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00512s; TotalTimePerSample = 0.02048ms; SamplesPerSecond = 48818 - Epoch[40 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10239709; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00502s; TotalTimePerSample = 0.02009ms; SamplesPerSecond = 49771 - Epoch[40 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572424; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00513s; TotalTimePerSample = 0.02052ms; SamplesPerSecond = 48742 - Epoch[40 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15609790; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 - Epoch[40 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20184753; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48914 - Epoch[40 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12643213; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00516s; TotalTimePerSample = 0.02062ms; SamplesPerSecond = 48487 - Epoch[40 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12390137; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49810 - Epoch[40 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14508374; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00449s; TotalTimePerSample = 0.01795ms; SamplesPerSecond = 55716 - Epoch[40 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18371777; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00519s; TotalTimePerSample = 0.02076ms; SamplesPerSecond = 48160 - Epoch[40 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18318823; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00531s; TotalTimePerSample = 0.02125ms; SamplesPerSecond = 47063 - Epoch[40 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579785; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45947 - Epoch[40 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10870361; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46099 - Epoch[40 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15615601; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 - Epoch[40 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18662476; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45938 - Epoch[40 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20422046; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 - Epoch[40 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16050317; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00553s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45240 - Epoch[40 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46108 - Epoch[40 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14602197; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02167ms; SamplesPerSecond = 46151 - Epoch[40 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12734863; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 - Epoch[40 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14315967; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00541s; TotalTimePerSample = 0.02166ms; SamplesPerSecond = 46176 - Epoch[40 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14660010; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 - Epoch[40 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12748047; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00541s; TotalTimePerSample = 0.02164ms; SamplesPerSecond = 46202 - Epoch[40 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13572021; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02167ms; SamplesPerSecond = 46142 - Epoch[40 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13043604; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00541s; TotalTimePerSample = 0.02165ms; SamplesPerSecond = 46185 - Epoch[40 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14089502; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00547s; TotalTimePerSample = 0.02187ms; SamplesPerSecond = 45720 - Epoch[40 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15098340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 - Epoch[40 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14866943; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45964 - Epoch[40 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15431445; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 - Epoch[40 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20131445; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 - Epoch[40 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14877686; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45930 - Epoch[40 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12697461; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45879 -Finished Epoch[40 of 50]: [Training Set] TrainLossPerSample = 0.15357913; EvalErrPerSample = 0.071499996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.211041 -Starting Epoch 41: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 40 at record count 400000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1997 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 40: 4294, 1646, ... - Epoch[41 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15997090; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 - Epoch[41 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16715651; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00697s; TotalTimePerSample = 0.02789ms; SamplesPerSecond = 35852 - Epoch[41 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10600992; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00519s; TotalTimePerSample = 0.02077ms; SamplesPerSecond = 48151 - Epoch[41 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18155670; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00506s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49368 - Epoch[41 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19502350; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00515s; TotalTimePerSample = 0.02061ms; SamplesPerSecond = 48524 - Epoch[41 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16436865; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00517s; TotalTimePerSample = 0.02070ms; SamplesPerSecond = 48318 - Epoch[41 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13003320; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 - Epoch[41 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19080762; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00519s; TotalTimePerSample = 0.02076ms; SamplesPerSecond = 48169 - Epoch[41 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15023108; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48914 - Epoch[41 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12813550; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00518s; TotalTimePerSample = 0.02073ms; SamplesPerSecond = 48243 - Epoch[41 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16008496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48206 - Epoch[41 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13177649; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49691 - Epoch[41 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13133691; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00518s; TotalTimePerSample = 0.02071ms; SamplesPerSecond = 48290 - Epoch[41 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21506421; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48123 - Epoch[41 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11038550; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49504 - Epoch[41 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14044556; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00523s; TotalTimePerSample = 0.02091ms; SamplesPerSecond = 47819 - Epoch[41 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13908252; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00527s; TotalTimePerSample = 0.02106ms; SamplesPerSecond = 47483 - Epoch[41 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15261816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00592s; TotalTimePerSample = 0.02366ms; SamplesPerSecond = 42265 - Epoch[41 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15004224; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02200ms; SamplesPerSecond = 45462 - Epoch[41 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11603027; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 - Epoch[41 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20238867; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00546s; TotalTimePerSample = 0.02186ms; SamplesPerSecond = 45754 - Epoch[41 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12511157; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45821 - Epoch[41 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690991; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45712 - Epoch[41 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15268115; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01859ms; SamplesPerSecond = 53798 - Epoch[41 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12382983; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53625 - Epoch[41 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15059326; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00779s; TotalTimePerSample = 0.03116ms; SamplesPerSecond = 32092 - Epoch[41 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16363306; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45662 - Epoch[41 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13311108; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00464s; TotalTimePerSample = 0.01858ms; SamplesPerSecond = 53832 - Epoch[41 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17219043; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 - Epoch[41 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17049805; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 - Epoch[41 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19276025; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00549s; TotalTimePerSample = 0.02195ms; SamplesPerSecond = 45562 - Epoch[41 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14431494; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 - Epoch[41 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20596777; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 - Epoch[41 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12097510; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45537 - Epoch[41 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18849707; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00548s; TotalTimePerSample = 0.02194ms; SamplesPerSecond = 45587 - Epoch[41 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718799; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02199ms; SamplesPerSecond = 45479 - Epoch[41 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12531006; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 - Epoch[41 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15923877; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00551s; TotalTimePerSample = 0.02204ms; SamplesPerSecond = 45380 - Epoch[41 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15602734; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53418 - Epoch[41 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17051758; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00551s; TotalTimePerSample = 0.02204ms; SamplesPerSecond = 45372 -Finished Epoch[41 of 50]: [Training Set] TrainLossPerSample = 0.15404761; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.214919 -Starting Epoch 42: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 41 at record count 410000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1963 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 41: 3344, 3717, ... - Epoch[42 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15668861; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[42 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16626445; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00705s; TotalTimePerSample = 0.02819ms; SamplesPerSecond = 35471 - Epoch[42 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15020337; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 - Epoch[42 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09922888; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00512s; TotalTimePerSample = 0.02047ms; SamplesPerSecond = 48856 - Epoch[42 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15337531; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02096ms; SamplesPerSecond = 47700 - Epoch[42 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14959808; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00533s; TotalTimePerSample = 0.02132ms; SamplesPerSecond = 46913 - Epoch[42 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14924933; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48933 - Epoch[42 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15217285; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54848 - Epoch[42 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17924707; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00510s; TotalTimePerSample = 0.02041ms; SamplesPerSecond = 48990 - Epoch[42 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16403760; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02081ms; SamplesPerSecond = 48049 - Epoch[42 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12861609; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47755 - Epoch[42 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12339722; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00513s; TotalTimePerSample = 0.02052ms; SamplesPerSecond = 48742 - Epoch[42 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10131934; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47755 - Epoch[42 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18952173; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00522s; TotalTimePerSample = 0.02088ms; SamplesPerSecond = 47892 - Epoch[42 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20686011; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00515s; TotalTimePerSample = 0.02059ms; SamplesPerSecond = 48571 - Epoch[42 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16400244; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47746 - Epoch[42 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14127393; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45712 - Epoch[42 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18588232; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 - Epoch[42 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14134302; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 - Epoch[42 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14998389; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 - Epoch[42 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16024512; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47429 - Epoch[42 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11431982; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02145ms; SamplesPerSecond = 46624 - Epoch[42 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16649219; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48866 - Epoch[42 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15155249; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47609 - Epoch[42 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11818628; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00522s; TotalTimePerSample = 0.02086ms; SamplesPerSecond = 47929 - Epoch[42 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16405737; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02238ms; SamplesPerSecond = 44682 - Epoch[42 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13877539; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 - Epoch[42 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17617358; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48770 - Epoch[42 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15604688; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00527s; TotalTimePerSample = 0.02106ms; SamplesPerSecond = 47483 - Epoch[42 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15834229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47537 - Epoch[42 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21922949; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48638 - Epoch[42 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12137402; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47628 - Epoch[42 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16690039; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 - Epoch[42 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12310693; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48590 - Epoch[42 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19693359; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00457s; TotalTimePerSample = 0.01827ms; SamplesPerSecond = 54728 - Epoch[42 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18927979; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 - Epoch[42 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14653174; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47600 - Epoch[42 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14917383; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 - Epoch[42 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13983691; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48581 - Epoch[42 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14345850; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47609 -Finished Epoch[42 of 50]: [Training Set] TrainLossPerSample = 0.15380706; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.209725 -Starting Epoch 43: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 42 at record count 420000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2041 retries for 10000 elements (20.4%) to ensure window condition -randomordering: recached sequence for seed 42: 4625, 1494, ... - Epoch[43 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16346191; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00331s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75642 - Epoch[43 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13378452; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00884s; TotalTimePerSample = 0.03536ms; SamplesPerSecond = 28277 - Epoch[43 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12752466; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00529s; TotalTimePerSample = 0.02116ms; SamplesPerSecond = 47258 - Epoch[43 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16106375; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00515s; TotalTimePerSample = 0.02060ms; SamplesPerSecond = 48553 - Epoch[43 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12669000; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47447 - Epoch[43 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14844189; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00461s; TotalTimePerSample = 0.01843ms; SamplesPerSecond = 54265 - Epoch[43 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20358795; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02051ms; SamplesPerSecond = 48761 - Epoch[43 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246301; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00707s; TotalTimePerSample = 0.02828ms; SamplesPerSecond = 35360 - Epoch[43 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15258435; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48206 - Epoch[43 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14182422; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00529s; TotalTimePerSample = 0.02116ms; SamplesPerSecond = 47258 - Epoch[43 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13872412; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47232 - Epoch[43 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13299866; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00445s; TotalTimePerSample = 0.01780ms; SamplesPerSecond = 56167 - Epoch[43 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558264; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00537s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46511 - Epoch[43 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15742139; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53304 - Epoch[43 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13340625; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 - Epoch[43 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11161108; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00447s; TotalTimePerSample = 0.01789ms; SamplesPerSecond = 55903 - Epoch[43 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16127979; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 - Epoch[43 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11706860; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47330 - Epoch[43 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16475464; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00516s; TotalTimePerSample = 0.02064ms; SamplesPerSecond = 48449 - Epoch[43 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17851855; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47438 - Epoch[43 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15488403; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47241 - Epoch[43 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12714917; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00517s; TotalTimePerSample = 0.02068ms; SamplesPerSecond = 48365 - Epoch[43 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13928101; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47330 - Epoch[43 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16454639; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00531s; TotalTimePerSample = 0.02124ms; SamplesPerSecond = 47089 - Epoch[43 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19079565; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00516s; TotalTimePerSample = 0.02064ms; SamplesPerSecond = 48459 - Epoch[43 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13771436; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02121ms; SamplesPerSecond = 47152 - Epoch[43 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17899536; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00528s; TotalTimePerSample = 0.02110ms; SamplesPerSecond = 47384 - Epoch[43 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18417554; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48113 - Epoch[43 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17164062; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00531s; TotalTimePerSample = 0.02123ms; SamplesPerSecond = 47107 - Epoch[43 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17466846; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00522s; TotalTimePerSample = 0.02087ms; SamplesPerSecond = 47911 - Epoch[43 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13243311; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46728 - Epoch[43 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17387646; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00532s; TotalTimePerSample = 0.02127ms; SamplesPerSecond = 47018 - Epoch[43 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15478662; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00518s; TotalTimePerSample = 0.02071ms; SamplesPerSecond = 48281 - Epoch[43 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11948877; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02120ms; SamplesPerSecond = 47160 - Epoch[43 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15327832; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00531s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47045 - Epoch[43 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12046826; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48113 - Epoch[43 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18561133; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00458s; TotalTimePerSample = 0.01832ms; SamplesPerSecond = 54597 - Epoch[43 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17181885; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00530s; TotalTimePerSample = 0.02119ms; SamplesPerSecond = 47187 - Epoch[43 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19418848; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00522s; TotalTimePerSample = 0.02088ms; SamplesPerSecond = 47901 - Epoch[43 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716992; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00531s; TotalTimePerSample = 0.02124ms; SamplesPerSecond = 47072 -Finished Epoch[43 of 50]: [Training Set] TrainLossPerSample = 0.15374406; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.212294 -Starting Epoch 44: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 43 at record count 430000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition -randomordering: recached sequence for seed 43: 1442, 760, ... - Epoch[44 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19620923; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76126 - Epoch[44 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17284280; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00709s; TotalTimePerSample = 0.02834ms; SamplesPerSecond = 35280 - Epoch[44 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17107645; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02134ms; SamplesPerSecond = 46860 - Epoch[44 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13653772; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00528s; TotalTimePerSample = 0.02112ms; SamplesPerSecond = 47348 - Epoch[44 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549390; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00751s; TotalTimePerSample = 0.03004ms; SamplesPerSecond = 33288 - Epoch[44 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13838623; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02135ms; SamplesPerSecond = 46834 - Epoch[44 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15465302; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 - Epoch[44 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13823840; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00529s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47214 - Epoch[44 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14467957; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02138ms; SamplesPerSecond = 46763 - Epoch[44 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15305005; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47957 - Epoch[44 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902258; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00531s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47045 - Epoch[44 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13886462; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00533s; TotalTimePerSample = 0.02134ms; SamplesPerSecond = 46869 - Epoch[44 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18513098; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47966 - Epoch[44 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16015112; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00530s; TotalTimePerSample = 0.02121ms; SamplesPerSecond = 47143 - Epoch[44 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20326123; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46886 - Epoch[44 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13745190; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00522s; TotalTimePerSample = 0.02087ms; SamplesPerSecond = 47920 - Epoch[44 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10230371; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00532s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47036 - Epoch[44 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13917432; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46957 - Epoch[44 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14841895; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47957 - Epoch[44 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658423; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46948 - Epoch[44 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14894409; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00532s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47027 - Epoch[44 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14695752; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02082ms; SamplesPerSecond = 48039 - Epoch[44 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13216064; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00532s; TotalTimePerSample = 0.02128ms; SamplesPerSecond = 46983 - Epoch[44 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17407349; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46877 - Epoch[44 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13991919; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02097ms; SamplesPerSecond = 47682 - Epoch[44 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17606494; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02151ms; SamplesPerSecond = 46494 - Epoch[44 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12547949; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00536s; TotalTimePerSample = 0.02146ms; SamplesPerSecond = 46607 - Epoch[44 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12784619; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00526s; TotalTimePerSample = 0.02102ms; SamplesPerSecond = 47564 - Epoch[44 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18277832; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 - Epoch[44 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16881885; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00538s; TotalTimePerSample = 0.02153ms; SamplesPerSecond = 46442 - Epoch[44 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12812451; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00528s; TotalTimePerSample = 0.02112ms; SamplesPerSecond = 47357 - Epoch[44 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18617529; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00537s; TotalTimePerSample = 0.02146ms; SamplesPerSecond = 46598 - Epoch[44 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11412061; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00537s; TotalTimePerSample = 0.02148ms; SamplesPerSecond = 46546 - Epoch[44 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16486426; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 - Epoch[44 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672900; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00667s; TotalTimePerSample = 0.02668ms; SamplesPerSecond = 37481 - Epoch[44 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18357324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00578s; TotalTimePerSample = 0.02310ms; SamplesPerSecond = 43282 - Epoch[44 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18769873; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00572s; TotalTimePerSample = 0.02288ms; SamplesPerSecond = 43713 - Epoch[44 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12417529; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00569s; TotalTimePerSample = 0.02275ms; SamplesPerSecond = 43959 - Epoch[44 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16986035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00569s; TotalTimePerSample = 0.02278ms; SamplesPerSecond = 43905 - Epoch[44 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13162256; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00569s; TotalTimePerSample = 0.02276ms; SamplesPerSecond = 43929 -Finished Epoch[44 of 50]: [Training Set] TrainLossPerSample = 0.15353793; EvalErrPerSample = 0.074900001; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.219771 -Starting Epoch 45: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 44 at record count 440000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1862 retries for 10000 elements (18.6%) to ensure window condition -randomordering: recached sequence for seed 44: 491, 13, ... - Epoch[45 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15391052; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76057 - Epoch[45 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13992700; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00717s; TotalTimePerSample = 0.02867ms; SamplesPerSecond = 34882 - Epoch[45 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16474039; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46270 - Epoch[45 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15187094; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00541s; TotalTimePerSample = 0.02162ms; SamplesPerSecond = 46253 - Epoch[45 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20688507; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 - Epoch[45 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14095599; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00539s; TotalTimePerSample = 0.02155ms; SamplesPerSecond = 46408 - Epoch[45 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13668738; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00539s; TotalTimePerSample = 0.02154ms; SamplesPerSecond = 46416 - Epoch[45 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14179443; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 - Epoch[45 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13604297; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46468 - Epoch[45 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16773560; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02156ms; SamplesPerSecond = 46382 - Epoch[45 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12565735; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 - Epoch[45 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12209326; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46364 - Epoch[45 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16090442; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46356 - Epoch[45 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16171191; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00454s; TotalTimePerSample = 0.01816ms; SamplesPerSecond = 55078 - Epoch[45 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17052759; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46502 - Epoch[45 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16556714; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00527s; TotalTimePerSample = 0.02110ms; SamplesPerSecond = 47402 - Epoch[45 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12027563; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00543s; TotalTimePerSample = 0.02170ms; SamplesPerSecond = 46082 - Epoch[45 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14207788; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53407 - Epoch[45 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20278442; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00453s; TotalTimePerSample = 0.01812ms; SamplesPerSecond = 55199 - Epoch[45 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16695776; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02155ms; SamplesPerSecond = 46408 - Epoch[45 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14629443; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00529s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47223 - Epoch[45 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21565137; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02163ms; SamplesPerSecond = 46227 - Epoch[45 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14214185; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46356 - Epoch[45 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13090479; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00528s; TotalTimePerSample = 0.02114ms; SamplesPerSecond = 47312 - Epoch[45 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13423755; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00541s; TotalTimePerSample = 0.02165ms; SamplesPerSecond = 46193 - Epoch[45 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16339355; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46133 - Epoch[45 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16817798; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00534s; TotalTimePerSample = 0.02136ms; SamplesPerSecond = 46825 - Epoch[45 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519287; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46877 - Epoch[45 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15910303; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53407 - Epoch[45 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11289258; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47510 - Epoch[45 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549756; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00544s; TotalTimePerSample = 0.02175ms; SamplesPerSecond = 45981 - Epoch[45 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12784619; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02192ms; SamplesPerSecond = 45628 - Epoch[45 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17025049; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00466s; TotalTimePerSample = 0.01864ms; SamplesPerSecond = 53648 - Epoch[45 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18051855; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 - Epoch[45 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21135937; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00577s; TotalTimePerSample = 0.02308ms; SamplesPerSecond = 43335 - Epoch[45 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16994287; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 - Epoch[45 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11147803; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00578s; TotalTimePerSample = 0.02310ms; SamplesPerSecond = 43282 - Epoch[45 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13351807; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00574s; TotalTimePerSample = 0.02296ms; SamplesPerSecond = 43554 - Epoch[45 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15980029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00574s; TotalTimePerSample = 0.02296ms; SamplesPerSecond = 43546 - Epoch[45 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15938818; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00575s; TotalTimePerSample = 0.02298ms; SamplesPerSecond = 43516 -Finished Epoch[45 of 50]: [Training Set] TrainLossPerSample = 0.15391743; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.214193 -Starting Epoch 46: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 45 at record count 450000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1934 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 45: 1579, 1557, ... - Epoch[46 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16876230; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 - Epoch[46 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16504675; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00722s; TotalTimePerSample = 0.02888ms; SamplesPerSecond = 34630 - Epoch[46 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668588; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45964 - Epoch[46 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13980969; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 - Epoch[46 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15281616; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00530s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47205 - Epoch[46 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09885846; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00546s; TotalTimePerSample = 0.02186ms; SamplesPerSecond = 45754 - Epoch[46 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17244324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00543s; TotalTimePerSample = 0.02171ms; SamplesPerSecond = 46057 - Epoch[46 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14810022; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47241 - Epoch[46 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15425452; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45118 - Epoch[46 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18108887; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 - Epoch[46 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15925623; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00534s; TotalTimePerSample = 0.02137ms; SamplesPerSecond = 46790 - Epoch[46 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662000; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 - Epoch[46 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16391589; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 - Epoch[46 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15208911; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 - Epoch[46 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18344653; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46477 - Epoch[46 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13962915; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45829 - Epoch[46 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14502954; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45879 - Epoch[46 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631055; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02138ms; SamplesPerSecond = 46772 - Epoch[46 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16011963; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 - Epoch[46 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15244800; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00593s; TotalTimePerSample = 0.02374ms; SamplesPerSecond = 42130 - Epoch[46 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17083496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00537s; TotalTimePerSample = 0.02147ms; SamplesPerSecond = 46580 - Epoch[46 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18358936; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 - Epoch[46 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12527197; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00548s; TotalTimePerSample = 0.02192ms; SamplesPerSecond = 45628 - Epoch[46 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15015942; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02143ms; SamplesPerSecond = 46667 - Epoch[46 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13307202; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00470s; TotalTimePerSample = 0.01882ms; SamplesPerSecond = 53146 - Epoch[46 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15247827; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00785s; TotalTimePerSample = 0.03140ms; SamplesPerSecond = 31847 - Epoch[46 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14167920; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46737 - Epoch[46 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16559229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 - Epoch[46 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16666162; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 - Epoch[46 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17522510; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00534s; TotalTimePerSample = 0.02137ms; SamplesPerSecond = 46790 - Epoch[46 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11583984; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00546s; TotalTimePerSample = 0.02183ms; SamplesPerSecond = 45804 - Epoch[46 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13685254; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 - Epoch[46 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808350; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02181ms; SamplesPerSecond = 45846 - Epoch[46 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12016553; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46728 - Epoch[46 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14723242; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45938 - Epoch[46 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19884863; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00549s; TotalTimePerSample = 0.02195ms; SamplesPerSecond = 45562 - Epoch[46 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16697168; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02136ms; SamplesPerSecond = 46807 - Epoch[46 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220557; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 - Epoch[46 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15398535; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45821 - Epoch[46 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11107129; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 -Finished Epoch[46 of 50]: [Training Set] TrainLossPerSample = 0.15381378; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.220975 -Starting Epoch 47: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 46 at record count 460000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1896 retries for 10000 elements (19.0%) to ensure window condition -randomordering: recached sequence for seed 46: 375, 4056, ... - Epoch[47 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17009537; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76173 - Epoch[47 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14089816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00728s; TotalTimePerSample = 0.02912ms; SamplesPerSecond = 34335 - Epoch[47 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13973935; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02191ms; SamplesPerSecond = 45645 - Epoch[47 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14522598; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00549s; TotalTimePerSample = 0.02197ms; SamplesPerSecond = 45520 - Epoch[47 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12900201; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00535s; TotalTimePerSample = 0.02141ms; SamplesPerSecond = 46711 - Epoch[47 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16575568; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 - Epoch[47 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16477930; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00547s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45670 - Epoch[47 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17690088; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00714s; TotalTimePerSample = 0.02857ms; SamplesPerSecond = 35004 - Epoch[47 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12744287; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02198ms; SamplesPerSecond = 45495 - Epoch[47 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722363; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45603 - Epoch[47 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17214087; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 - Epoch[47 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11358752; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46459 - Epoch[47 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17516565; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45653 - Epoch[47 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12191040; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00731s; TotalTimePerSample = 0.02923ms; SamplesPerSecond = 34209 - Epoch[47 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16542603; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02164ms; SamplesPerSecond = 46210 - Epoch[47 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12420703; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00556s; TotalTimePerSample = 0.02225ms; SamplesPerSecond = 44947 - Epoch[47 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14355225; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00550s; TotalTimePerSample = 0.02198ms; SamplesPerSecond = 45487 - Epoch[47 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11818335; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00552s; TotalTimePerSample = 0.02209ms; SamplesPerSecond = 45265 - Epoch[47 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15443384; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00559s; TotalTimePerSample = 0.02234ms; SamplesPerSecond = 44754 - Epoch[47 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18573535; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00551s; TotalTimePerSample = 0.02206ms; SamplesPerSecond = 45330 - Epoch[47 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16070605; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00552s; TotalTimePerSample = 0.02208ms; SamplesPerSecond = 45289 - Epoch[47 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18047827; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46279 - Epoch[47 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12440088; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 - Epoch[47 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667993; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 - Epoch[47 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745728; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00539s; TotalTimePerSample = 0.02156ms; SamplesPerSecond = 46373 - Epoch[47 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13788281; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00553s; TotalTimePerSample = 0.02213ms; SamplesPerSecond = 45191 - Epoch[47 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17373560; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00554s; TotalTimePerSample = 0.02214ms; SamplesPerSecond = 45158 - Epoch[47 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15597192; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46270 - Epoch[47 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14745557; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02221ms; SamplesPerSecond = 45028 - Epoch[47 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13610791; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 - Epoch[47 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10932178; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00555s; TotalTimePerSample = 0.02219ms; SamplesPerSecond = 45061 - Epoch[47 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13513916; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02166ms; SamplesPerSecond = 46176 - Epoch[47 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20640479; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00553s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45240 - Epoch[47 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13302637; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45134 - Epoch[47 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15080078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00598s; TotalTimePerSample = 0.02392ms; SamplesPerSecond = 41806 - Epoch[47 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17111182; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02240ms; SamplesPerSecond = 44650 - Epoch[47 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19842529; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 - Epoch[47 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20571094; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 - Epoch[47 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13281738; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00543s; TotalTimePerSample = 0.02172ms; SamplesPerSecond = 46032 - Epoch[47 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17746826; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45118 -Finished Epoch[47 of 50]: [Training Set] TrainLossPerSample = 0.15356271; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.224735 -Starting Epoch 48: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 47 at record count 470000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1949 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 47: 2638, 2515, ... - Epoch[48 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15027994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00404s; TotalTimePerSample = 0.01615ms; SamplesPerSecond = 61911 - Epoch[48 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13497180; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00731s; TotalTimePerSample = 0.02922ms; SamplesPerSecond = 34223 - Epoch[48 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13972147; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00480s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52039 - Epoch[48 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19054150; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45947 - Epoch[48 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15590564; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02229ms; SamplesPerSecond = 44859 - Epoch[48 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17132758; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02218ms; SamplesPerSecond = 45093 - Epoch[48 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12567676; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45110 - Epoch[48 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11931079; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46108 - Epoch[48 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13617419; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00556s; TotalTimePerSample = 0.02224ms; SamplesPerSecond = 44972 - Epoch[48 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13334998; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44778 - Epoch[48 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16063586; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00552s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45257 - Epoch[48 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16104102; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 - Epoch[48 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15746460; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44738 - Epoch[48 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13254297; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02227ms; SamplesPerSecond = 44899 - Epoch[48 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18614429; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 - Epoch[48 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19013940; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02232ms; SamplesPerSecond = 44802 - Epoch[48 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13494287; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00557s; TotalTimePerSample = 0.02226ms; SamplesPerSecond = 44923 - Epoch[48 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15374585; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53418 - Epoch[48 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14510815; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52279 - Epoch[48 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17918848; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02226ms; SamplesPerSecond = 44915 - Epoch[48 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15291870; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00545s; TotalTimePerSample = 0.02181ms; SamplesPerSecond = 45854 - Epoch[48 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16817578; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02244ms; SamplesPerSecond = 44571 - Epoch[48 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11767017; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00582s; TotalTimePerSample = 0.02328ms; SamplesPerSecond = 42947 - Epoch[48 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16226343; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53316 - Epoch[48 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13237671; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02238ms; SamplesPerSecond = 44682 - Epoch[48 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15202222; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00558s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44835 - Epoch[48 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718896; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 - Epoch[48 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12673828; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 - Epoch[48 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14897900; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00559s; TotalTimePerSample = 0.02237ms; SamplesPerSecond = 44706 - Epoch[48 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13764648; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44587 - Epoch[48 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17610938; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02185ms; SamplesPerSecond = 45770 - Epoch[48 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722559; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00561s; TotalTimePerSample = 0.02245ms; SamplesPerSecond = 44539 - Epoch[48 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17907373; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44746 - Epoch[48 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13869922; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00557s; TotalTimePerSample = 0.02229ms; SamplesPerSecond = 44859 - Epoch[48 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12678125; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00550s; TotalTimePerSample = 0.02199ms; SamplesPerSecond = 45471 - Epoch[48 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16539990; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 - Epoch[48 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17748828; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44658 - Epoch[48 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19358105; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45662 - Epoch[48 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713672; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44738 - Epoch[48 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17234180; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 -Finished Epoch[48 of 50]: [Training Set] TrainLossPerSample = 0.15395074; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.221345 -Starting Epoch 49: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 48 at record count 480000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition -randomordering: recached sequence for seed 48: 1688, 1039, ... - Epoch[49 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17320860; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 - Epoch[49 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14062874; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00737s; TotalTimePerSample = 0.02946ms; SamplesPerSecond = 33939 - Epoch[49 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789914; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 - Epoch[49 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19715366; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02246ms; SamplesPerSecond = 44531 - Epoch[49 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15716534; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00774s; TotalTimePerSample = 0.03096ms; SamplesPerSecond = 32299 - Epoch[49 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14775000; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 - Epoch[49 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17860504; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44579 - Epoch[49 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17541602; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00562s; TotalTimePerSample = 0.02246ms; SamplesPerSecond = 44515 - Epoch[49 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10276807; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00547s; TotalTimePerSample = 0.02189ms; SamplesPerSecond = 45678 - Epoch[49 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12209045; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 - Epoch[49 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16249878; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00559s; TotalTimePerSample = 0.02237ms; SamplesPerSecond = 44698 - Epoch[49 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14359253; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00564s; TotalTimePerSample = 0.02255ms; SamplesPerSecond = 44349 - Epoch[49 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12310620; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 - Epoch[49 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18520605; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51303 - Epoch[49 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13575903; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00563s; TotalTimePerSample = 0.02251ms; SamplesPerSecond = 44420 - Epoch[49 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18317041; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 - Epoch[49 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14142798; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 - Epoch[49 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12793579; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 - Epoch[49 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14227441; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00573s; TotalTimePerSample = 0.02290ms; SamplesPerSecond = 43660 - Epoch[49 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14391211; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01893ms; SamplesPerSecond = 52820 - Epoch[49 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911133; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 - Epoch[49 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14438867; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00569s; TotalTimePerSample = 0.02278ms; SamplesPerSecond = 43905 - Epoch[49 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16217529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02200ms; SamplesPerSecond = 45454 - Epoch[49 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17709521; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00562s; TotalTimePerSample = 0.02248ms; SamplesPerSecond = 44483 - Epoch[49 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12703735; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02248ms; SamplesPerSecond = 44476 - Epoch[49 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15274927; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00472s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52921 - Epoch[49 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11113696; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44603 - Epoch[49 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096509; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00573s; TotalTimePerSample = 0.02291ms; SamplesPerSecond = 43645 - Epoch[49 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19092334; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00561s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44603 - Epoch[49 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14696729; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45045 - Epoch[49 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13934277; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 - Epoch[49 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13451563; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00564s; TotalTimePerSample = 0.02258ms; SamplesPerSecond = 44294 - Epoch[49 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18252490; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00556s; TotalTimePerSample = 0.02223ms; SamplesPerSecond = 44988 - Epoch[49 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15291846; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02240ms; SamplesPerSecond = 44642 - Epoch[49 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16258545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51588 - Epoch[49 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18700635; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00554s; TotalTimePerSample = 0.02218ms; SamplesPerSecond = 45093 - Epoch[49 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15848047; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02241ms; SamplesPerSecond = 44626 - Epoch[49 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19608398; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01943ms; SamplesPerSecond = 51461 - Epoch[49 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12497852; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45036 - Epoch[49 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14640137; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 -Finished Epoch[49 of 50]: [Training Set] TrainLossPerSample = 0.1532239; EvalErrPerSample = 0.073199995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.223632 -Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 49 at record count 490000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 49: 954, 533, ... - Epoch[50 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215771; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01316ms; SamplesPerSecond = 75964 - Epoch[50 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902780; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00740s; TotalTimePerSample = 0.02960ms; SamplesPerSecond = 33783 - Epoch[50 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13724939; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00568s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44029 - Epoch[50 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14850296; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00567s; TotalTimePerSample = 0.02267ms; SamplesPerSecond = 44115 - Epoch[50 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21980939; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00568s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44052 - Epoch[50 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14945911; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 - Epoch[50 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13680707; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00565s; TotalTimePerSample = 0.02258ms; SamplesPerSecond = 44286 - Epoch[50 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18415393; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00567s; TotalTimePerSample = 0.02268ms; SamplesPerSecond = 44091 - Epoch[50 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13719470; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00568s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44029 - Epoch[50 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292920; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45126 - Epoch[50 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13963062; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00566s; TotalTimePerSample = 0.02265ms; SamplesPerSecond = 44146 - Epoch[50 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12586951; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00565s; TotalTimePerSample = 0.02261ms; SamplesPerSecond = 44232 - Epoch[50 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15136499; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00566s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44200 - Epoch[50 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13051282; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00552s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45248 - Epoch[50 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12969312; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51282 - Epoch[50 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15042041; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00568s; TotalTimePerSample = 0.02273ms; SamplesPerSecond = 43990 - Epoch[50 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14371826; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02219ms; SamplesPerSecond = 45069 - Epoch[50 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08564575; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00570s; TotalTimePerSample = 0.02279ms; SamplesPerSecond = 43882 - Epoch[50 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17781812; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00567s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44060 - Epoch[50 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20411499; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00567s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44060 - Epoch[50 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17243628; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00557s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44851 - Epoch[50 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12457227; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01951ms; SamplesPerSecond = 51250 - Epoch[50 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477710; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00598s; TotalTimePerSample = 0.02394ms; SamplesPerSecond = 41778 - Epoch[50 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16080859; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00559s; TotalTimePerSample = 0.02234ms; SamplesPerSecond = 44754 - Epoch[50 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15700757; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00568s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44045 - Epoch[50 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11665210; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00567s; TotalTimePerSample = 0.02268ms; SamplesPerSecond = 44099 - Epoch[50 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17690649; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00572s; TotalTimePerSample = 0.02287ms; SamplesPerSecond = 43721 - Epoch[50 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266699; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00558s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44835 - Epoch[50 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20211230; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00581s; TotalTimePerSample = 0.02324ms; SamplesPerSecond = 43036 - Epoch[50 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14714990; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00597s; TotalTimePerSample = 0.02388ms; SamplesPerSecond = 41876 - Epoch[50 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411475; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00611s; TotalTimePerSample = 0.02445ms; SamplesPerSecond = 40896 - Epoch[50 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13433350; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00607s; TotalTimePerSample = 0.02429ms; SamplesPerSecond = 41165 - Epoch[50 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18207861; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00605s; TotalTimePerSample = 0.02419ms; SamplesPerSecond = 41342 - Epoch[50 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16985645; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00603s; TotalTimePerSample = 0.02411ms; SamplesPerSecond = 41473 - Epoch[50 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18879053; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00605s; TotalTimePerSample = 0.02418ms; SamplesPerSecond = 41356 - Epoch[50 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14025830; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00605s; TotalTimePerSample = 0.02420ms; SamplesPerSecond = 41329 - Epoch[50 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17361621; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00618s; TotalTimePerSample = 0.02472ms; SamplesPerSecond = 40459 - Epoch[50 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13023340; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02032ms; SamplesPerSecond = 49212 - Epoch[50 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19875732; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00606s; TotalTimePerSample = 0.02424ms; SamplesPerSecond = 41254 - Epoch[50 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12190576; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00605s; TotalTimePerSample = 0.02418ms; SamplesPerSecond = 41356 -Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15337785; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.229606 -CNTKCommandTrainEnd: Simple_Demo -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - - -Allocating matrices for forward propagation. - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -starting at epoch 0 counting lines to determine record count - - 603 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch -Minibatch[1]: ActualMBSize = 603 -Total Samples Evaluated = 603 -COMPLETED -=== Deleting last epoch data -==== Re-running from checkpoint -=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 makeMode=true -------------------------------------------------------------------- -Build info: - - Built time: Nov 19 2015 14:09:57 - Last modified date: Thu Nov 19 08:15:28 2015 - Built by svcphil on dphaim-26-new - Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 - Build Branch: HEAD - Build SHA1: a342184dcad3179b0e33596ac330ce724e9cee4b -------------------------------------------------------------------- -running on liana-08-w at 2015/11/19 14:21:40 -command line: -C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 makeMode=true - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=$RunDir$/models/simple.dnn -deviceId=$DeviceNumber$ -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -outputPath=$RunDir$/SimpleOutput -] -RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=-1 -makeMode=true - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -deviceId=-1 -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput -] -RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=-1 -makeMode=true - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output -configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -configparameters: cntk.config:deviceId=-1 -configparameters: cntk.config:DeviceNumber=-1 -configparameters: cntk.config:makeMode=true -configparameters: cntk.config:modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu -configparameters: cntk.config:Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] - -configparameters: cntk.config:Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput -] - -configparameters: cntk.config:traceLevel=1 -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: Simple_Demo Simple_Demo_Output -precision = float -CNTKModelPath: C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn -CNTKCommandTrainInfo: Simple_Demo : 50 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 -CNTKCommandTrainBegin: Simple_Demo -SimpleNetworkBuilder Using CPU -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt -Starting from checkpoint. Load Network From File C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn.49. - - -Allocating matrices for forward propagation. - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Allocating matrices for gradient computing -No PreCompute nodes found, skipping PreCompute step -Warning: checkpoint file is missing. learning parameters will be initialized from 0 -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting at epoch 49 counting lines to determine record count - - 10000 records found -starting epoch 49 at record count 490000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 49: 954, 533, ... - Epoch[50 of 50]-Minibatch[ 1- 10]: SamplesSeen = 250; TrainLossPerSample = 0.16219548; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.12387s; TotalTimePerSample = 0.49550ms; SamplesPerSecond = 2018 - Epoch[50 of 50]-Minibatch[ 11- 20]: SamplesSeen = 250; TrainLossPerSample = 0.15912361; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00325s; TotalTimePerSample = 0.01298ms; SamplesPerSecond = 77041 - Epoch[50 of 50]-Minibatch[ 21- 30]: SamplesSeen = 250; TrainLossPerSample = 0.13726807; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00326s; TotalTimePerSample = 0.01303ms; SamplesPerSecond = 76734 - Epoch[50 of 50]-Minibatch[ 31- 40]: SamplesSeen = 250; TrainLossPerSample = 0.14841956; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76429 - Epoch[50 of 50]-Minibatch[ 41- 50]: SamplesSeen = 250; TrainLossPerSample = 0.21969360; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 - Epoch[50 of 50]-Minibatch[ 51- 60]: SamplesSeen = 250; TrainLossPerSample = 0.14945532; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00328s; TotalTimePerSample = 0.01311ms; SamplesPerSecond = 76266 - Epoch[50 of 50]-Minibatch[ 61- 70]: SamplesSeen = 250; TrainLossPerSample = 0.13676563; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 - Epoch[50 of 50]-Minibatch[ 71- 80]: SamplesSeen = 250; TrainLossPerSample = 0.18411462; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00355s; TotalTimePerSample = 0.01420ms; SamplesPerSecond = 70442 - Epoch[50 of 50]-Minibatch[ 81- 90]: SamplesSeen = 250; TrainLossPerSample = 0.13720374; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 - Epoch[50 of 50]-Minibatch[ 91- 100]: SamplesSeen = 250; TrainLossPerSample = 0.15293506; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76452 - Epoch[50 of 50]-Minibatch[ 101- 110]: SamplesSeen = 250; TrainLossPerSample = 0.13964490; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00326s; TotalTimePerSample = 0.01306ms; SamplesPerSecond = 76593 - Epoch[50 of 50]-Minibatch[ 111- 120]: SamplesSeen = 250; TrainLossPerSample = 0.12590125; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 - Epoch[50 of 50]-Minibatch[ 121- 130]: SamplesSeen = 250; TrainLossPerSample = 0.15137183; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76312 - Epoch[50 of 50]-Minibatch[ 131- 140]: SamplesSeen = 250; TrainLossPerSample = 0.13051648; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 - Epoch[50 of 50]-Minibatch[ 141- 150]: SamplesSeen = 250; TrainLossPerSample = 0.12970435; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 - Epoch[50 of 50]-Minibatch[ 151- 160]: SamplesSeen = 250; TrainLossPerSample = 0.15042603; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00326s; TotalTimePerSample = 0.01306ms; SamplesPerSecond = 76569 - Epoch[50 of 50]-Minibatch[ 161- 170]: SamplesSeen = 250; TrainLossPerSample = 0.14372729; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 - Epoch[50 of 50]-Minibatch[ 171- 180]: SamplesSeen = 250; TrainLossPerSample = 0.08566089; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73486 - Epoch[50 of 50]-Minibatch[ 181- 190]: SamplesSeen = 250; TrainLossPerSample = 0.17779297; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00330s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75665 - Epoch[50 of 50]-Minibatch[ 191- 200]: SamplesSeen = 250; TrainLossPerSample = 0.20410034; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 - Epoch[50 of 50]-Minibatch[ 201- 210]: SamplesSeen = 250; TrainLossPerSample = 0.17243750; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79693 - Epoch[50 of 50]-Minibatch[ 211- 220]: SamplesSeen = 250; TrainLossPerSample = 0.12458472; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 - Epoch[50 of 50]-Minibatch[ 221- 230]: SamplesSeen = 250; TrainLossPerSample = 0.14477295; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[50 of 50]-Minibatch[ 231- 240]: SamplesSeen = 250; TrainLossPerSample = 0.16081299; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 - Epoch[50 of 50]-Minibatch[ 241- 250]: SamplesSeen = 250; TrainLossPerSample = 0.15700513; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 - Epoch[50 of 50]-Minibatch[ 251- 260]: SamplesSeen = 250; TrainLossPerSample = 0.11666357; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[50 of 50]-Minibatch[ 261- 270]: SamplesSeen = 250; TrainLossPerSample = 0.17689966; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 - Epoch[50 of 50]-Minibatch[ 271- 280]: SamplesSeen = 250; TrainLossPerSample = 0.12267065; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 - Epoch[50 of 50]-Minibatch[ 281- 290]: SamplesSeen = 250; TrainLossPerSample = 0.20211328; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[50 of 50]-Minibatch[ 291- 300]: SamplesSeen = 250; TrainLossPerSample = 0.14714893; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 - Epoch[50 of 50]-Minibatch[ 301- 310]: SamplesSeen = 250; TrainLossPerSample = 0.14411719; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 - Epoch[50 of 50]-Minibatch[ 311- 320]: SamplesSeen = 250; TrainLossPerSample = 0.13433789; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[50 of 50]-Minibatch[ 321- 330]: SamplesSeen = 250; TrainLossPerSample = 0.18207080; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01333ms; SamplesPerSecond = 75030 - Epoch[50 of 50]-Minibatch[ 331- 340]: SamplesSeen = 250; TrainLossPerSample = 0.16985889; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 - Epoch[50 of 50]-Minibatch[ 341- 350]: SamplesSeen = 250; TrainLossPerSample = 0.18878906; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 - Epoch[50 of 50]-Minibatch[ 351- 360]: SamplesSeen = 250; TrainLossPerSample = 0.14026221; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 - Epoch[50 of 50]-Minibatch[ 361- 370]: SamplesSeen = 250; TrainLossPerSample = 0.17361914; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 - Epoch[50 of 50]-Minibatch[ 371- 380]: SamplesSeen = 250; TrainLossPerSample = 0.13023926; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 - Epoch[50 of 50]-Minibatch[ 381- 390]: SamplesSeen = 250; TrainLossPerSample = 0.19875293; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 - Epoch[50 of 50]-Minibatch[ 391- 400]: SamplesSeen = 250; TrainLossPerSample = 0.12190723; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 -Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15337712; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.251958 -CNTKCommandTrainEnd: Simple_Demo -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - - -Allocating matrices for forward propagation. - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -starting at epoch 0 counting lines to determine record count - - 603 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch -Minibatch[1]: ActualMBSize = 603 -Total Samples Evaluated = 603 -COMPLETED +=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 +------------------------------------------------------------------- +Build info: + + Built time: Nov 19 2015 14:09:57 + Last modified date: Thu Nov 19 08:15:28 2015 + Built by svcphil on dphaim-26-new + Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 + Build Branch: HEAD + Build SHA1: a342184dcad3179b0e33596ac330ce724e9cee4b +------------------------------------------------------------------- +running on liana-08-w at 2015/11/19 14:21:28 +command line: +C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=$RunDir$/models/simple.dnn +deviceId=$DeviceNumber$ +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +outputPath=$RunDir$/SimpleOutput +] +RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +deviceId=-1 +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput +] +RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=-1 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output +configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +configparameters: cntk.config:deviceId=-1 +configparameters: cntk.config:DeviceNumber=-1 +configparameters: cntk.config:modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +configparameters: cntk.config:Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] + +configparameters: cntk.config:Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput +] + +configparameters: cntk.config:traceLevel=1 +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: Simple_Demo Simple_Demo_Output +precision = float +CNTKModelPath: C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +CNTKCommandTrainInfo: Simple_Demo : 50 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 +CNTKCommandTrainBegin: Simple_Demo +SimpleNetworkBuilder Using CPU +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + + +Allocating matrices for forward propagation. + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Allocating matrices for gradient computing + +Precomputing --> 3 PreCompute nodes found. + + NodeName: InvStdOfFeatures + NodeName: MeanOfFeatures + NodeName: Prior +starting at epoch 0 counting lines to determine record count + + 10000 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch + + +Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] + +Validating for node InvStdOfFeatures, final verification. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node MeanOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] + +Validating for node MeanOfFeatures, final verification. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node Prior. 2 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] + +Validating for node Prior, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + +randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 0: 2334, 3830, ... + +Precomputing --> Completed. + +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 +starting epoch 0 at record count 0, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 0: 2334, 3830, ... + Epoch[ 1 of 50]-Minibatch[ 1- 10 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70002374; EvalErr[0]PerSample = 0.50400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65547 + Epoch[ 1 of 50]-Minibatch[ 11- 20 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72432672; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.01197s; TotalTimePerSample = 0.04788ms; SamplesPerSecond = 20885 + Epoch[ 1 of 50]-Minibatch[ 21- 30 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69646301; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 1 of 50]-Minibatch[ 31- 40 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71841040; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 + Epoch[ 1 of 50]-Minibatch[ 41- 50 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.78310376; EvalErr[0]PerSample = 0.52800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 + Epoch[ 1 of 50]-Minibatch[ 51- 60 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73678174; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82754 + Epoch[ 1 of 50]-Minibatch[ 61- 70 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74273096; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 + Epoch[ 1 of 50]-Minibatch[ 71- 80 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69696533; EvalErr[0]PerSample = 0.44000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 + Epoch[ 1 of 50]-Minibatch[ 81- 90 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72015430; EvalErr[0]PerSample = 0.42800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 + Epoch[ 1 of 50]-Minibatch[ 91- 100 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74531885; EvalErr[0]PerSample = 0.53600000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 + Epoch[ 1 of 50]-Minibatch[ 101- 110 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73130273; EvalErr[0]PerSample = 0.56000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 + Epoch[ 1 of 50]-Minibatch[ 111- 120 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70340771; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 + Epoch[ 1 of 50]-Minibatch[ 121- 130 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70279102; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 + Epoch[ 1 of 50]-Minibatch[ 131- 140 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70393750; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 + Epoch[ 1 of 50]-Minibatch[ 141- 150 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72239941; EvalErr[0]PerSample = 0.56800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01254ms; SamplesPerSecond = 79719 + Epoch[ 1 of 50]-Minibatch[ 151- 160 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70179590; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 + Epoch[ 1 of 50]-Minibatch[ 161- 170 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71486914; EvalErr[0]PerSample = 0.54400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82863 + Epoch[ 1 of 50]-Minibatch[ 171- 180 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70239160; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 + Epoch[ 1 of 50]-Minibatch[ 181- 190 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71378516; EvalErr[0]PerSample = 0.47200000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 + Epoch[ 1 of 50]-Minibatch[ 191- 200 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70370605; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82671 + Epoch[ 1 of 50]-Minibatch[ 201- 210 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69720996; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82481 + Epoch[ 1 of 50]-Minibatch[ 211- 220 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69622168; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 + Epoch[ 1 of 50]-Minibatch[ 221- 230 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72116895; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01210ms; SamplesPerSecond = 82617 + Epoch[ 1 of 50]-Minibatch[ 231- 240 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69737695; EvalErr[0]PerSample = 0.44800000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 + Epoch[ 1 of 50]-Minibatch[ 241- 250 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70615625; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[ 1 of 50]-Minibatch[ 251- 260 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69251172; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 1 of 50]-Minibatch[ 261- 270 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70424609; EvalErr[0]PerSample = 0.46000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 + Epoch[ 1 of 50]-Minibatch[ 271- 280 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69197461; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 + Epoch[ 1 of 50]-Minibatch[ 281- 290 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69758984; EvalErr[0]PerSample = 0.50400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 + Epoch[ 1 of 50]-Minibatch[ 291- 300 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68911914; EvalErr[0]PerSample = 0.40400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 + Epoch[ 1 of 50]-Minibatch[ 301- 310 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69688281; EvalErr[0]PerSample = 0.42400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01209ms; SamplesPerSecond = 82699 + Epoch[ 1 of 50]-Minibatch[ 311- 320 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68536328; EvalErr[0]PerSample = 0.39200000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82754 + Epoch[ 1 of 50]-Minibatch[ 321- 330 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68434766; EvalErr[0]PerSample = 0.44800000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82946 + Epoch[ 1 of 50]-Minibatch[ 331- 340 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69032812; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.00301s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82918 + Epoch[ 1 of 50]-Minibatch[ 341- 350 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68711328; EvalErr[0]PerSample = 0.46400000; TotalTime = 0.00302s; TotalTimePerSample = 0.01207ms; SamplesPerSecond = 82836 + Epoch[ 1 of 50]-Minibatch[ 351- 360 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.66240039; EvalErr[0]PerSample = 0.40000000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82808 + Epoch[ 1 of 50]-Minibatch[ 361- 370 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.63556445; EvalErr[0]PerSample = 0.36000000; TotalTime = 0.00302s; TotalTimePerSample = 0.01206ms; SamplesPerSecond = 82891 + Epoch[ 1 of 50]-Minibatch[ 371- 380 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.60319531; EvalErr[0]PerSample = 0.17600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 + Epoch[ 1 of 50]-Minibatch[ 381- 390 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.55960937; EvalErr[0]PerSample = 0.14800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01208ms; SamplesPerSecond = 82781 + Epoch[ 1 of 50]-Minibatch[ 391- 400 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.46704102; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82453 +Finished Epoch[ 1 of 50]: [Training Set] TrainLossPerSample = 0.69325215; EvalErrPerSample = 0.4531; AvgLearningRatePerSample = 0.01999999955; EpochTime=0.134564 +Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 1 at record count 10000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1901 retries for 10000 elements (19.0%) to ensure window condition +randomordering: recached sequence for seed 1: 1914, 1172, ... + Epoch[ 2 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.40249194; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75803 + Epoch[ 2 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.35273859; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00481s; TotalTimePerSample = 0.01923ms; SamplesPerSecond = 51996 + Epoch[ 2 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.28230634; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 2 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.27482300; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 + Epoch[ 2 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22246130; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 + Epoch[ 2 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619019; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82535 + Epoch[ 2 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22821680; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 + Epoch[ 2 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19877063; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82481 + Epoch[ 2 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21215894; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 2 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17010425; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 + Epoch[ 2 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20611206; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80360 + Epoch[ 2 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20604297; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 2 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17485327; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 2 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15899878; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 + Epoch[ 2 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572290; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82453 + Epoch[ 2 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15648706; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 + Epoch[ 2 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16314795; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 + Epoch[ 2 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18076172; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 + Epoch[ 2 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14921509; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 + Epoch[ 2 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15605103; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 + Epoch[ 2 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15768115; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 + Epoch[ 2 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14727295; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 2 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177051; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 2 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17959912; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 + Epoch[ 2 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15195068; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 + Epoch[ 2 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14948584; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 + Epoch[ 2 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18874512; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00316s; TotalTimePerSample = 0.01265ms; SamplesPerSecond = 79038 + Epoch[ 2 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14938379; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 2 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15367627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 + Epoch[ 2 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19336426; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 + Epoch[ 2 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15837061; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 + Epoch[ 2 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789453; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 2 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14981152; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 2 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15530029; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 2 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17563477; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 + Epoch[ 2 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12291064; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 2 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12840234; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01213ms; SamplesPerSecond = 82426 + Epoch[ 2 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16160254; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 + Epoch[ 2 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19109912; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 + Epoch[ 2 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20358594; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82399 +Finished Epoch[ 2 of 50]: [Training Set] TrainLossPerSample = 0.18712991; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125498 +Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 2 at record count 20000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1912 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 2: 3447, 110, ... + Epoch[ 3 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17238383; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 + Epoch[ 3 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18818459; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 + Epoch[ 3 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15802277; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 3 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18184222; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 3 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17347736; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 3 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16783289; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 + Epoch[ 3 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11263281; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82372 + Epoch[ 3 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16322668; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 3 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20259961; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 3 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17802710; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 3 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13942529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 + Epoch[ 3 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15911414; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00343s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72971 + Epoch[ 3 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21205322; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[ 3 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20571143; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 3 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12194995; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 + Epoch[ 3 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10843677; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 3 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16375562; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 3 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14576929; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 3 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12491528; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 3 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17839331; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 + Epoch[ 3 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19846533; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 + Epoch[ 3 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716040; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82182 + Epoch[ 3 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14919946; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 3 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795898; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 3 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09909546; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 + Epoch[ 3 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14711084; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 3 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17405420; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 3 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949805; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 3 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18007227; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00301s; TotalTimePerSample = 0.01203ms; SamplesPerSecond = 83139 + Epoch[ 3 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12333936; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 + Epoch[ 3 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17010840; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 3 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13394141; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 + Epoch[ 3 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12574951; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[ 3 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14083252; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 3 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19624414; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 3 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15029590; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 3 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12545410; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 + Epoch[ 3 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17764307; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[ 3 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17303564; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 + Epoch[ 3 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17385254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 +Finished Epoch[ 3 of 50]: [Training Set] TrainLossPerSample = 0.15902165; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125763 +Starting Epoch 4: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 3 at record count 30000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1941 retries for 10000 elements (19.4%) to ensure window condition +randomordering: recached sequence for seed 3: 1741, 4693, ... + Epoch[ 4 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18574167; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74074 + Epoch[ 4 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19552863; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00665s; TotalTimePerSample = 0.02660ms; SamplesPerSecond = 37599 + Epoch[ 4 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13308151; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 4 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20122906; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47321 + Epoch[ 4 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19024731; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 4 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22164557; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 4 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17202637; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 4 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15516443; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 4 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13772925; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 + Epoch[ 4 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14771838; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 4 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15806226; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51271 + Epoch[ 4 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11111121; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 4 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17290845; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 4 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10793408; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 4 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15473315; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 4 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19119116; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 4 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12782153; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 4 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11329663; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 + Epoch[ 4 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18824927; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 4 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17514160; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 + Epoch[ 4 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18781836; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 4 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13335425; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 4 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15953931; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 4 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16786304; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 4 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16218237; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 4 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17439844; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 4 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12411182; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[ 4 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18026221; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 4 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11037988; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 4 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20034375; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 4 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14125391; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 4 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11299268; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[ 4 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14577051; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81433 + Epoch[ 4 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15335791; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 4 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15446289; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 4 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15231689; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 + Epoch[ 4 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16738574; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 4 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15105371; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00312s; TotalTimePerSample = 0.01249ms; SamplesPerSecond = 80076 + Epoch[ 4 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17064062; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 + Epoch[ 4 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15144287; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 +Finished Epoch[ 4 of 50]: [Training Set] TrainLossPerSample = 0.15853731; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131853 +Starting Epoch 5: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 4 at record count 40000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1967 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 4: 4062, 4375, ... + Epoch[ 5 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18211707; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68212 + Epoch[ 5 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270682; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00499s; TotalTimePerSample = 0.01995ms; SamplesPerSecond = 50130 + Epoch[ 5 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14021991; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 + Epoch[ 5 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13036838; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 + Epoch[ 5 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16885815; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 + Epoch[ 5 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16560083; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 + Epoch[ 5 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521637; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[ 5 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16385156; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[ 5 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15566504; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 + Epoch[ 5 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14036731; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 + Epoch[ 5 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15665149; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 + Epoch[ 5 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21973108; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00313s; TotalTimePerSample = 0.01253ms; SamplesPerSecond = 79795 + Epoch[ 5 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10951392; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 5 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17261426; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 + Epoch[ 5 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14421240; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[ 5 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18687842; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 5 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13771851; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 5 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18702417; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 5 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19803906; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 5 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16439648; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 5 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13458398; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 5 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14413330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 + Epoch[ 5 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16508472; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 5 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18465308; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00304s; TotalTimePerSample = 0.01217ms; SamplesPerSecond = 82155 + Epoch[ 5 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034399; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82590 + Epoch[ 5 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14785254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 + Epoch[ 5 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12280029; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82318 + Epoch[ 5 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14299121; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 5 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14473291; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82535 + Epoch[ 5 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13322998; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 5 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16276221; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[ 5 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17622998; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 5 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15482129; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 5 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16986865; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00303s; TotalTimePerSample = 0.01211ms; SamplesPerSecond = 82562 + Epoch[ 5 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12328027; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[ 5 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19244189; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 5 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16235889; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 5 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16860645; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82128 + Epoch[ 5 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14493848; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 5 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14430078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01214ms; SamplesPerSecond = 82345 +Finished Epoch[ 5 of 50]: [Training Set] TrainLossPerSample = 0.15854415; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.126562 +Starting Epoch 6: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 5 at record count 50000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1962 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 5: 2260, 139, ... + Epoch[ 6 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20848543; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76312 + Epoch[ 6 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18413786; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51610 + Epoch[ 6 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12884314; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 6 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17770209; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 6 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16133618; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[ 6 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17693451; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 6 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17611108; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 6 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16652490; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 6 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15625903; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 6 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13772961; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 6 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12265063; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 + Epoch[ 6 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15450598; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 + Epoch[ 6 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18095142; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 6 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13513940; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 6 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16936719; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[ 6 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14850415; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 + Epoch[ 6 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10062085; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 6 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18162427; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 6 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17896509; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 6 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14618848; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 6 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15001221; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 + Epoch[ 6 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13238550; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 6 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745386; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 6 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18064160; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00525s; TotalTimePerSample = 0.02099ms; SamplesPerSecond = 47646 + Epoch[ 6 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14728540; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[ 6 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14484814; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 + Epoch[ 6 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15216748; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[ 6 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17140381; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00303s; TotalTimePerSample = 0.01212ms; SamplesPerSecond = 82508 + Epoch[ 6 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16164160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00317s; TotalTimePerSample = 0.01268ms; SamplesPerSecond = 78864 + Epoch[ 6 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15148389; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 6 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15548877; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82047 + Epoch[ 6 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17652832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 6 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12786475; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00302s; TotalTimePerSample = 0.01209ms; SamplesPerSecond = 82699 + Epoch[ 6 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13548047; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[ 6 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13521143; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 6 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11820703; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[ 6 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17178711; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 6 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232471; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 6 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20254590; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 6 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262207; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 +Finished Epoch[ 6 of 50]: [Training Set] TrainLossPerSample = 0.15674913; EvalErrPerSample = 0.072899997; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127935 +Starting Epoch 7: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 6 at record count 60000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1971 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 6: 3864, 19, ... + Epoch[ 7 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17245078; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75941 + Epoch[ 7 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19818744; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51535 + Epoch[ 7 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468723; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 7 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19880463; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 7 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194360; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 7 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16685468; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 7 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13934058; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[ 7 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18776672; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 7 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15224536; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 7 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15151465; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 + Epoch[ 7 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009729; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 7 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14528259; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81063 + Epoch[ 7 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14854077; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 7 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16354980; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 + Epoch[ 7 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15437085; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[ 7 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713916; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[ 7 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14201392; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 7 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13360254; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 7 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034888; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 + Epoch[ 7 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11665967; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 + Epoch[ 7 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11198755; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 7 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14900244; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 + Epoch[ 7 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15741577; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 7 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14853564; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81726 + Epoch[ 7 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11856958; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 + Epoch[ 7 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631104; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[ 7 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16389893; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 7 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12061914; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00304s; TotalTimePerSample = 0.01215ms; SamplesPerSecond = 82290 + Epoch[ 7 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17542578; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[ 7 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16121924; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 + Epoch[ 7 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15175781; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 7 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14326563; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[ 7 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15750830; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 + Epoch[ 7 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21582031; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 7 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15399707; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 7 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739600; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 7 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13247070; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 + Epoch[ 7 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16683496; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81779 + Epoch[ 7 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13261475; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 7 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16716357; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 +Finished Epoch[ 7 of 50]: [Training Set] TrainLossPerSample = 0.15718038; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.125899 +Starting Epoch 8: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 7 at record count 70000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1996 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 7: 1209, 4212, ... + Epoch[ 8 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19877904; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00331s; TotalTimePerSample = 0.01325ms; SamplesPerSecond = 75460 + Epoch[ 8 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18074571; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51313 + Epoch[ 8 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12930881; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 8 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13702704; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 8 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14323309; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[ 8 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16479724; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 + Epoch[ 8 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11388129; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 + Epoch[ 8 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18741321; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 + Epoch[ 8 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15643237; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 8 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16937451; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 8 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635852; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00306s; TotalTimePerSample = 0.01223ms; SamplesPerSecond = 81752 + Epoch[ 8 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10614185; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 + Epoch[ 8 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17952051; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 + Epoch[ 8 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20380176; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 + Epoch[ 8 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14754346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 + Epoch[ 8 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15922070; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[ 8 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13510278; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81116 + Epoch[ 8 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12481543; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[ 8 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15976733; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 + Epoch[ 8 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13234937; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81566 + Epoch[ 8 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19277612; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 + Epoch[ 8 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317163; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 8 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12374902; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 + Epoch[ 8 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13515283; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 + Epoch[ 8 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19020288; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 8 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17203125; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81699 + Epoch[ 8 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18403027; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 + Epoch[ 8 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17481885; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 + Epoch[ 8 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16984912; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81619 + Epoch[ 8 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20073486; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 8 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15632080; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 + Epoch[ 8 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22257129; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[ 8 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11293311; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82209 + Epoch[ 8 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11900879; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 8 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16342529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81486 + Epoch[ 8 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16408691; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82101 + Epoch[ 8 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17154541; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 + Epoch[ 8 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16180078; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 + Epoch[ 8 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15100049; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81592 + Epoch[ 8 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14595703; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 +Finished Epoch[ 8 of 50]: [Training Set] TrainLossPerSample = 0.15926951; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.126201 +Starting Epoch 9: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 8 at record count 80000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1981 retries for 10000 elements (19.8%) to ensure window condition +randomordering: recached sequence for seed 8: 258, 4597, ... + Epoch[ 9 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13674069; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01309ms; SamplesPerSecond = 76405 + Epoch[ 9 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11702635; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 + Epoch[ 9 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842564; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 + Epoch[ 9 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499652; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02115ms; SamplesPerSecond = 47276 + Epoch[ 9 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11475745; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 + Epoch[ 9 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15994904; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[ 9 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19827142; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 + Epoch[ 9 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658093; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 + Epoch[ 9 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12600732; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 9 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13172473; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 + Epoch[ 9 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17483435; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[ 9 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15718530; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 + Epoch[ 9 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11679797; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 + Epoch[ 9 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15759924; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 + Epoch[ 9 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14018481; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 + Epoch[ 9 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17445630; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 + Epoch[ 9 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18871265; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 + Epoch[ 9 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15529590; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79239 + Epoch[ 9 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322241; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 9 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20066455; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[ 9 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18700708; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 9 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14747046; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 + Epoch[ 9 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14981421; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[ 9 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15509644; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[ 9 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13184204; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[ 9 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16714648; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82236 + Epoch[ 9 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353101; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 9 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16314990; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[ 9 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12779297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[ 9 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18806982; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 + Epoch[ 9 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16694678; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01365ms; SamplesPerSecond = 73249 + Epoch[ 9 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15696777; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[ 9 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13640088; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 + Epoch[ 9 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19312012; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[ 9 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17352100; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 + Epoch[ 9 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18453564; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[ 9 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13698291; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 + Epoch[ 9 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18448828; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81433 + Epoch[ 9 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11695068; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 + Epoch[ 9 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17289453; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81380 +Finished Epoch[ 9 of 50]: [Training Set] TrainLossPerSample = 0.15717906; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131099 +Starting Epoch 10: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 9 at record count 90000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1914 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 9: 1957, 3535, ... + Epoch[10 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15815988; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76149 + Epoch[10 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588196; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00507s; TotalTimePerSample = 0.02029ms; SamplesPerSecond = 49290 + Epoch[10 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19559683; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[10 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15650873; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 + Epoch[10 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16335553; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[10 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17638708; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 + Epoch[10 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16771643; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[10 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11975793; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01229ms; SamplesPerSecond = 81353 + Epoch[10 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15952380; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 + Epoch[10 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15673218; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[10 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17476709; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 + Epoch[10 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18008984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[10 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17350000; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[10 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12354517; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[10 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16949609; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[10 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16343677; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[10 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16678516; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81406 + Epoch[10 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11746558; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 + Epoch[10 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15827710; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[10 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17702612; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00308s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81274 + Epoch[10 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20688770; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[10 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17176245; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[10 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15878784; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[10 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20615088; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 + Epoch[10 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200415; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[10 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12214331; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 + Epoch[10 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15946631; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 + Epoch[10 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16630420; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81247 + Epoch[10 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10444971; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[10 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10899414; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81168 + Epoch[10 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15568896; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[10 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19664648; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[10 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16486670; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01231ms; SamplesPerSecond = 81221 + Epoch[10 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11217139; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[10 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12276953; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[10 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14118750; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81327 + Epoch[10 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16956396; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00322s; TotalTimePerSample = 0.01286ms; SamplesPerSecond = 77736 + Epoch[10 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18732910; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00308s; TotalTimePerSample = 0.01233ms; SamplesPerSecond = 81089 + Epoch[10 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16965137; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81142 + Epoch[10 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15103662; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81300 +Finished Epoch[10 of 50]: [Training Set] TrainLossPerSample = 0.15804678; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127015 +Starting Epoch 11: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 10 at record count 100000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1865 retries for 10000 elements (18.6%) to ensure window condition +randomordering: recached sequence for seed 10: 3356, 877, ... + Epoch[11 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19904596; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01309ms; SamplesPerSecond = 76405 + Epoch[11 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12819327; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 + Epoch[11 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16058502; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 + Epoch[11 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14434543; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 + Epoch[11 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14051538; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[11 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16834058; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00313s; TotalTimePerSample = 0.01253ms; SamplesPerSecond = 79821 + Epoch[11 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200586; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 + Epoch[11 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10635474; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80775 + Epoch[11 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13951978; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[11 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15526868; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80984 + Epoch[11 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15423694; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 80025 + Epoch[11 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15439380; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00308s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81037 + Epoch[11 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13974609; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 + Epoch[11 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10850623; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14422498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16235693; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 + Epoch[11 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18480713; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13377002; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14019043; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80932 + Epoch[11 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232812; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15934985; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16396509; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00309s; TotalTimePerSample = 0.01234ms; SamplesPerSecond = 81011 + Epoch[11 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13943994; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 + Epoch[11 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15856152; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[11 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20442163; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46720 + Epoch[11 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18395508; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 + Epoch[11 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17780688; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80463 + Epoch[11 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17736963; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 + Epoch[11 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22826904; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[11 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17714990; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01235ms; SamplesPerSecond = 80958 + Epoch[11 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14284717; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 + Epoch[11 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 + Epoch[11 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17360303; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80906 + Epoch[11 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11425732; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[11 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15241650; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 + Epoch[11 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15622266; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01225ms; SamplesPerSecond = 81645 + Epoch[11 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17193799; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 + Epoch[11 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20015576; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80932 + Epoch[11 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15971143; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 + Epoch[11 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12481396; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 +Finished Epoch[11 of 50]: [Training Set] TrainLossPerSample = 0.15740953; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.131325 +Starting Epoch 12: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 11 at record count 110000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1961 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 11: 2406, 1536, ... + Epoch[12 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15112077; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76057 + Epoch[12 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13805214; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00493s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50668 + Epoch[12 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16700012; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00307s; TotalTimePerSample = 0.01226ms; SamplesPerSecond = 81539 + Epoch[12 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11470972; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[12 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456842; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 + Epoch[12 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17456824; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 + Epoch[12 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18122705; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80256 + Epoch[12 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636694; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[12 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14702148; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 + Epoch[12 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13681128; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[12 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20395264; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80853 + Epoch[12 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15242615; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 + Epoch[12 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15183557; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80334 + Epoch[12 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13492712; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[12 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17645776; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 + Epoch[12 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13103125; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 + Epoch[12 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21384326; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[12 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15316968; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80697 + Epoch[12 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13777979; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00298s; TotalTimePerSample = 0.01193ms; SamplesPerSecond = 83836 + Epoch[12 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13050586; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 + Epoch[12 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15227588; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 + Epoch[12 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20449072; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 + Epoch[12 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19038770; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 + Epoch[12 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14301831; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 + Epoch[12 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17311987; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00309s; TotalTimePerSample = 0.01237ms; SamplesPerSecond = 80827 + Epoch[12 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12977148; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 + Epoch[12 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16124243; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 + Epoch[12 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15764600; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[12 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12035010; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 + Epoch[12 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12236279; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[12 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19375684; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01239ms; SamplesPerSecond = 80723 + Epoch[12 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13069238; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80749 + Epoch[12 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15182031; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 + Epoch[12 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16397412; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 + Epoch[12 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 + Epoch[12 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20146240; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[12 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12055029; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00307s; TotalTimePerSample = 0.01227ms; SamplesPerSecond = 81512 + Epoch[12 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16200439; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 + Epoch[12 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20963672; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[12 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15343359; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01236ms; SamplesPerSecond = 80879 +Finished Epoch[12 of 50]: [Training Set] TrainLossPerSample = 0.15737277; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.127231 +Starting Epoch 13: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 12 at record count 120000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2004 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 12: 1583, 2526, ... + Epoch[13 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12830981; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 + Epoch[13 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20211029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50231 + Epoch[13 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17840430; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79415 + Epoch[13 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15879236; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00311s; TotalTimePerSample = 0.01243ms; SamplesPerSecond = 80437 + Epoch[13 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10898322; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00402s; TotalTimePerSample = 0.01606ms; SamplesPerSecond = 62266 + Epoch[13 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17485632; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 + Epoch[13 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16192310; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80489 + Epoch[13 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312061; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01242ms; SamplesPerSecond = 80515 + Epoch[13 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13282751; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80567 + Epoch[13 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14269019; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80334 + Epoch[13 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14420691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80385 + Epoch[13 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540918; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80619 + Epoch[13 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20124243; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[13 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15036206; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80645 + Epoch[13 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10167212; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01240ms; SamplesPerSecond = 80671 + Epoch[13 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17007324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01334ms; SamplesPerSecond = 74985 + Epoch[13 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12729102; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00331s; TotalTimePerSample = 0.01325ms; SamplesPerSecond = 75460 + Epoch[13 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19446069; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01326ms; SamplesPerSecond = 75392 + Epoch[13 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15536450; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74360 + Epoch[13 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17620239; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01330ms; SamplesPerSecond = 75187 + Epoch[13 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15503101; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75075 + Epoch[13 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19150610; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74693 + Epoch[13 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14232446; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 + Epoch[13 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14458325; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01343ms; SamplesPerSecond = 74449 + Epoch[13 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18954443; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74626 + Epoch[13 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17082227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 + Epoch[13 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17740283; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00332s; TotalTimePerSample = 0.01327ms; SamplesPerSecond = 75369 + Epoch[13 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16458936; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00322s; TotalTimePerSample = 0.01287ms; SamplesPerSecond = 77712 + Epoch[13 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17239062; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79415 + Epoch[13 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14639941; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74382 + Epoch[13 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15091553; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00334s; TotalTimePerSample = 0.01334ms; SamplesPerSecond = 74962 + Epoch[13 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18959277; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75075 + Epoch[13 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15226514; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75052 + Epoch[13 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15816553; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00332s; TotalTimePerSample = 0.01328ms; SamplesPerSecond = 75278 + Epoch[13 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13357324; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00346s; TotalTimePerSample = 0.01382ms; SamplesPerSecond = 72358 + Epoch[13 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10534766; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 + Epoch[13 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20473242; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74338 + Epoch[13 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793311; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74537 + Epoch[13 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14926563; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74426 + Epoch[13 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14114600; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74404 +Finished Epoch[13 of 50]: [Training Set] TrainLossPerSample = 0.15789582; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.134401 +Starting Epoch 14: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 13 at record count 130000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 13: 503, 1097, ... + Epoch[14 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14947922; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76242 + Epoch[14 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13988226; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48704 + Epoch[14 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14457452; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00321s; TotalTimePerSample = 0.01285ms; SamplesPerSecond = 77808 + Epoch[14 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18849722; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00333s; TotalTimePerSample = 0.01332ms; SamplesPerSecond = 75052 + Epoch[14 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15743707; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00551s; TotalTimePerSample = 0.02206ms; SamplesPerSecond = 45330 + Epoch[14 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17268475; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73942 + Epoch[14 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12649329; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 + Epoch[14 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15325610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74626 + Epoch[14 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16319580; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74716 + Epoch[14 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15290247; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 + Epoch[14 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14664307; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00333s; TotalTimePerSample = 0.01330ms; SamplesPerSecond = 75165 + Epoch[14 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15380029; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01335ms; SamplesPerSecond = 74917 + Epoch[14 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13412048; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01345ms; SamplesPerSecond = 74360 + Epoch[14 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126929; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 + Epoch[14 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16534766; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 + Epoch[14 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11969702; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73399 + Epoch[14 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11787769; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74604 + Epoch[14 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12611548; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00317s; TotalTimePerSample = 0.01267ms; SamplesPerSecond = 78939 + Epoch[14 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20692041; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74493 + Epoch[14 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18696948; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 + Epoch[14 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15918701; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74738 + Epoch[14 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672168; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00334s; TotalTimePerSample = 0.01336ms; SamplesPerSecond = 74827 + Epoch[14 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16776294; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01340ms; SamplesPerSecond = 74604 + Epoch[14 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17649243; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74760 + Epoch[14 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17918311; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00332s; TotalTimePerSample = 0.01327ms; SamplesPerSecond = 75346 + Epoch[14 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18229102; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 + Epoch[14 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15990381; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00335s; TotalTimePerSample = 0.01341ms; SamplesPerSecond = 74582 + Epoch[14 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13827246; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01338ms; SamplesPerSecond = 74738 + Epoch[14 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17928223; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 + Epoch[14 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12728760; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 + Epoch[14 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15062500; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00344s; TotalTimePerSample = 0.01377ms; SamplesPerSecond = 72632 + Epoch[14 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13134277; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00322s; TotalTimePerSample = 0.01288ms; SamplesPerSecond = 77663 + Epoch[14 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15669336; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00336s; TotalTimePerSample = 0.01344ms; SamplesPerSecond = 74426 + Epoch[14 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14840771; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00318s; TotalTimePerSample = 0.01272ms; SamplesPerSecond = 78591 + Epoch[14 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14254639; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00337s; TotalTimePerSample = 0.01349ms; SamplesPerSecond = 74117 + Epoch[14 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16551855; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74693 + Epoch[14 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19324121; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00336s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74493 + Epoch[14 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17837939; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00335s; TotalTimePerSample = 0.01339ms; SamplesPerSecond = 74671 + Epoch[14 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17495312; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00334s; TotalTimePerSample = 0.01337ms; SamplesPerSecond = 74783 + Epoch[14 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13701074; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00331s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75620 +Finished Epoch[14 of 50]: [Training Set] TrainLossPerSample = 0.15705664; EvalErrPerSample = 0.074299999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.138958 +Starting Epoch 15: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 14 at record count 140000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 14: 989, 678, ... + Epoch[15 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20959497; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[15 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14418518; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00495s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50464 + Epoch[15 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18801270; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 + Epoch[15 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12507526; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 + Epoch[15 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13645209; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80256 + Epoch[15 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17137933; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 + Epoch[15 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20525153; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[15 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13449194; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 + Epoch[15 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12386584; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80411 + Epoch[15 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17504480; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00322s; TotalTimePerSample = 0.01286ms; SamplesPerSecond = 77736 + Epoch[15 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18549670; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01251ms; SamplesPerSecond = 79948 + Epoch[15 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14575098; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 + Epoch[15 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17600708; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79999 + Epoch[15 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718848; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00311s; TotalTimePerSample = 0.01244ms; SamplesPerSecond = 80360 + Epoch[15 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16285669; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00308s; TotalTimePerSample = 0.01232ms; SamplesPerSecond = 81195 + Epoch[15 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12723975; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[15 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11262451; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 + Epoch[15 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11530811; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[15 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14287646; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00313s; TotalTimePerSample = 0.01251ms; SamplesPerSecond = 79923 + Epoch[15 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19053613; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80153 + Epoch[15 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17408228; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80205 + Epoch[15 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15081714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 80000 + Epoch[15 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17837671; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80102 + Epoch[15 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13743726; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01246ms; SamplesPerSecond = 80231 + Epoch[15 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15989746; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00312s; TotalTimePerSample = 0.01248ms; SamplesPerSecond = 80128 + Epoch[15 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15915088; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 + Epoch[15 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12169214; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00312s; TotalTimePerSample = 0.01249ms; SamplesPerSecond = 80076 + Epoch[15 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19548730; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79974 + Epoch[15 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14926123; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79693 + Epoch[15 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521191; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00313s; TotalTimePerSample = 0.01250ms; SamplesPerSecond = 79974 + Epoch[15 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15001807; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 + Epoch[15 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14514063; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70641 + Epoch[15 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15551074; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00352s; TotalTimePerSample = 0.01407ms; SamplesPerSecond = 71083 + Epoch[15 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18218506; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00352s; TotalTimePerSample = 0.01408ms; SamplesPerSecond = 71042 + Epoch[15 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131006; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00362s; TotalTimePerSample = 0.01447ms; SamplesPerSecond = 69098 + Epoch[15 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15272461; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00355s; TotalTimePerSample = 0.01420ms; SamplesPerSecond = 70422 + Epoch[15 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17988477; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00347s; TotalTimePerSample = 0.01387ms; SamplesPerSecond = 72108 + Epoch[15 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15021924; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70641 + Epoch[15 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18285986; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00347s; TotalTimePerSample = 0.01389ms; SamplesPerSecond = 71983 + Epoch[15 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17852979; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 +Finished Epoch[15 of 50]: [Training Set] TrainLossPerSample = 0.15797588; EvalErrPerSample = 0.0757; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.13213 +Starting Epoch 16: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 15 at record count 150000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 15: 834, 4617, ... + Epoch[16 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18087309; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 + Epoch[16 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12064146; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48216 + Epoch[16 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13509888; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73942 + Epoch[16 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16519626; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73724 + Epoch[16 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14746753; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 + Epoch[16 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16059631; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00350s; TotalTimePerSample = 0.01401ms; SamplesPerSecond = 71367 + Epoch[16 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14891614; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 + Epoch[16 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009229; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73898 + Epoch[16 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14677124; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73768 + Epoch[16 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14683655; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74052 + Epoch[16 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14287915; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00324s; TotalTimePerSample = 0.01296ms; SamplesPerSecond = 77160 + Epoch[16 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19432043; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01352ms; SamplesPerSecond = 73986 + Epoch[16 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15663904; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 + Epoch[16 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11754077; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00322s; TotalTimePerSample = 0.01288ms; SamplesPerSecond = 77663 + Epoch[16 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14312231; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00350s; TotalTimePerSample = 0.01400ms; SamplesPerSecond = 71428 + Epoch[16 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16778052; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00338s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74074 + Epoch[16 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14940283; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00337s; TotalTimePerSample = 0.01350ms; SamplesPerSecond = 74096 + Epoch[16 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21570117; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 + Epoch[16 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11603052; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73920 + Epoch[16 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456787; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01355ms; SamplesPerSecond = 73811 + Epoch[16 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050806; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01357ms; SamplesPerSecond = 73702 + Epoch[16 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14824438; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00343s; TotalTimePerSample = 0.01373ms; SamplesPerSecond = 72822 + Epoch[16 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10748706; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 + Epoch[16 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16115039; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73443 + Epoch[16 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19790210; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00338s; TotalTimePerSample = 0.01354ms; SamplesPerSecond = 73877 + Epoch[16 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15511743; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00682s; TotalTimePerSample = 0.02728ms; SamplesPerSecond = 36656 + Epoch[16 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14571045; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01446ms; SamplesPerSecond = 69156 + Epoch[16 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14506445; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 73014 + Epoch[16 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16244141; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 + Epoch[16 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17346436; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00338s; TotalTimePerSample = 0.01353ms; SamplesPerSecond = 73898 + Epoch[16 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16367285; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73768 + Epoch[16 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09780713; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 + Epoch[16 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13693506; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01357ms; SamplesPerSecond = 73702 + Epoch[16 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16609375; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73637 + Epoch[16 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17097021; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73616 + Epoch[16 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13705225; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01356ms; SamplesPerSecond = 73724 + Epoch[16 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17503369; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01360ms; SamplesPerSecond = 73507 + Epoch[16 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18202393; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73163 + Epoch[16 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387305; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00350s; TotalTimePerSample = 0.01400ms; SamplesPerSecond = 71428 + Epoch[16 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16323730; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00343s; TotalTimePerSample = 0.01372ms; SamplesPerSecond = 72907 +Finished Epoch[16 of 50]: [Training Set] TrainLossPerSample = 0.15635659; EvalErrPerSample = 0.071899995; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.143279 +Starting Epoch 17: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 16 at record count 160000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2037 retries for 10000 elements (20.4%) to ensure window condition +randomordering: recached sequence for seed 16: 4884, 4383, ... + Epoch[17 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13506857; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 + Epoch[17 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892380; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02122ms; SamplesPerSecond = 47134 + Epoch[17 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15982581; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73206 + Epoch[17 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16914691; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73464 + Epoch[17 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17463800; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01365ms; SamplesPerSecond = 73270 + Epoch[17 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337073; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00339s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73637 + Epoch[17 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11288971; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73335 + Epoch[17 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14479626; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 + Epoch[17 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14517712; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72950 + Epoch[17 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14121558; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00359s; TotalTimePerSample = 0.01437ms; SamplesPerSecond = 69599 + Epoch[17 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12230225; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00346s; TotalTimePerSample = 0.01386ms; SamplesPerSecond = 72170 + Epoch[17 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17032593; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72928 + Epoch[17 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16533850; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00354s; TotalTimePerSample = 0.01417ms; SamplesPerSecond = 70561 + Epoch[17 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19219836; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01360ms; SamplesPerSecond = 73529 + Epoch[17 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14920825; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00341s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73399 + Epoch[17 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16778174; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 + Epoch[17 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14410010; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00340s; TotalTimePerSample = 0.01362ms; SamplesPerSecond = 73421 + Epoch[17 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096069; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73464 + Epoch[17 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15516626; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73227 + Epoch[17 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15461401; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73292 + Epoch[17 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17591846; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 + Epoch[17 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777539; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72992 + Epoch[17 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14908203; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 + Epoch[17 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18884204; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00341s; TotalTimePerSample = 0.01366ms; SamplesPerSecond = 73206 + Epoch[17 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13255542; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00353s; TotalTimePerSample = 0.01411ms; SamplesPerSecond = 70881 + Epoch[17 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16748437; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02084ms; SamplesPerSecond = 47993 + Epoch[17 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12382349; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 + Epoch[17 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15887451; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 + Epoch[17 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16871777; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00343s; TotalTimePerSample = 0.01370ms; SamplesPerSecond = 72992 + Epoch[17 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16700195; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01368ms; SamplesPerSecond = 73078 + Epoch[17 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19813770; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00341s; TotalTimePerSample = 0.01364ms; SamplesPerSecond = 73313 + Epoch[17 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13726709; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00341s; TotalTimePerSample = 0.01363ms; SamplesPerSecond = 73356 + Epoch[17 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14840186; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 + Epoch[17 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12672119; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48123 + Epoch[17 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15882520; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00344s; TotalTimePerSample = 0.01376ms; SamplesPerSecond = 72695 + Epoch[17 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16427832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70521 + Epoch[17 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18704590; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00348s; TotalTimePerSample = 0.01392ms; SamplesPerSecond = 71818 + Epoch[17 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11677686; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00335s; TotalTimePerSample = 0.01342ms; SamplesPerSecond = 74537 + Epoch[17 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16292725; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00344s; TotalTimePerSample = 0.01376ms; SamplesPerSecond = 72674 + Epoch[17 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19076270; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00343s; TotalTimePerSample = 0.01372ms; SamplesPerSecond = 72907 +Finished Epoch[17 of 50]: [Training Set] TrainLossPerSample = 0.1562067; EvalErrPerSample = 0.075300001; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.144045 +Starting Epoch 18: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 17 at record count 170000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1944 retries for 10000 elements (19.4%) to ensure window condition +randomordering: recached sequence for seed 17: 1700, 4307, ... + Epoch[18 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17602905; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76335 + Epoch[18 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12962930; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 + Epoch[18 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15791135; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 + Epoch[18 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16390161; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79592 + Epoch[18 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14452905; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01260ms; SamplesPerSecond = 79365 + Epoch[18 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12564063; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 + Epoch[18 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16398291; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79617 + Epoch[18 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15125537; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00309s; TotalTimePerSample = 0.01238ms; SamplesPerSecond = 80801 + Epoch[18 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10915930; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00316s; TotalTimePerSample = 0.01264ms; SamplesPerSecond = 79113 + Epoch[18 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14124622; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79239 + Epoch[18 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20968835; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 + Epoch[18 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13917358; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79643 + Epoch[18 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16085022; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 + Epoch[18 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549524; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79668 + Epoch[18 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13878174; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79617 + Epoch[18 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14446997; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00310s; TotalTimePerSample = 0.01241ms; SamplesPerSecond = 80593 + Epoch[18 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17112207; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00318s; TotalTimePerSample = 0.01272ms; SamplesPerSecond = 78616 + Epoch[18 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21744116; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79491 + Epoch[18 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12943896; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79592 + Epoch[18 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13674170; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79465 + Epoch[18 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15600317; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01262ms; SamplesPerSecond = 79264 + Epoch[18 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17383594; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79465 + Epoch[18 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13479980; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 + Epoch[18 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11709106; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79440 + Epoch[18 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13944995; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00397s; TotalTimePerSample = 0.01588ms; SamplesPerSecond = 62956 + Epoch[18 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22621606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00317s; TotalTimePerSample = 0.01267ms; SamplesPerSecond = 78914 + Epoch[18 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13783594; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79567 + Epoch[18 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12238086; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 + Epoch[18 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20077979; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00314s; TotalTimePerSample = 0.01256ms; SamplesPerSecond = 79643 + Epoch[18 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18251855; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 + Epoch[18 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16045117; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00315s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79491 + Epoch[18 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16636084; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00315s; TotalTimePerSample = 0.01260ms; SamplesPerSecond = 79390 + Epoch[18 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17318896; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 + Epoch[18 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13682520; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79567 + Epoch[18 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22038428; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00316s; TotalTimePerSample = 0.01265ms; SamplesPerSecond = 79063 + Epoch[18 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13850977; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00315s; TotalTimePerSample = 0.01259ms; SamplesPerSecond = 79440 + Epoch[18 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14276904; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00314s; TotalTimePerSample = 0.01258ms; SamplesPerSecond = 79516 + Epoch[18 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18119238; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00311s; TotalTimePerSample = 0.01245ms; SamplesPerSecond = 80308 + Epoch[18 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16981934; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00316s; TotalTimePerSample = 0.01263ms; SamplesPerSecond = 79189 + Epoch[18 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15691846; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00314s; TotalTimePerSample = 0.01257ms; SamplesPerSecond = 79541 +Finished Epoch[18 of 50]: [Training Set] TrainLossPerSample = 0.15709546; EvalErrPerSample = 0.074299999; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.130074 +Starting Epoch 19: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 18 at record count 180000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2073 retries for 10000 elements (20.7%) to ensure window condition +randomordering: recached sequence for seed 18: 3974, 797, ... + Epoch[19 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13168202; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76103 + Epoch[19 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13988628; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46957 + Epoch[19 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15568793; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00364s; TotalTimePerSample = 0.01455ms; SamplesPerSecond = 68719 + Epoch[19 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14448511; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00355s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70323 + Epoch[19 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13734735; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00599s; TotalTimePerSample = 0.02396ms; SamplesPerSecond = 41736 + Epoch[19 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15639966; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00357s; TotalTimePerSample = 0.01429ms; SamplesPerSecond = 69969 + Epoch[19 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15040472; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 + Epoch[19 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15769873; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72928 + Epoch[19 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16571130; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68418 + Epoch[19 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15984302; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00361s; TotalTimePerSample = 0.01446ms; SamplesPerSecond = 69156 + Epoch[19 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12869775; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70601 + Epoch[19 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007764; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01450ms; SamplesPerSecond = 68984 + Epoch[19 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13434424; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70323 + Epoch[19 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19713831; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 + Epoch[19 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16539819; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01432ms; SamplesPerSecond = 69832 + Epoch[19 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15775098; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00375s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66595 + Epoch[19 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17449683; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00339s; TotalTimePerSample = 0.01358ms; SamplesPerSecond = 73659 + Epoch[19 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18054639; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00356s; TotalTimePerSample = 0.01424ms; SamplesPerSecond = 70224 + Epoch[19 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19580371; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00364s; TotalTimePerSample = 0.01458ms; SamplesPerSecond = 68587 + Epoch[19 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17742773; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00358s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 + Epoch[19 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20143457; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00364s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68756 + Epoch[19 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12416846; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69871 + Epoch[19 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14046631; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 + Epoch[19 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20061450; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70303 + Epoch[19 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16971094; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01450ms; SamplesPerSecond = 68965 + Epoch[19 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15172412; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00355s; TotalTimePerSample = 0.01419ms; SamplesPerSecond = 70482 + Epoch[19 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13443018; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00363s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68794 + Epoch[19 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12323096; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00355s; TotalTimePerSample = 0.01421ms; SamplesPerSecond = 70363 + Epoch[19 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20002246; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01467ms; SamplesPerSecond = 68157 + Epoch[19 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14361279; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01469ms; SamplesPerSecond = 68064 + Epoch[19 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11884912; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00364s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68756 + Epoch[19 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17547803; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00359s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69715 + Epoch[19 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18262158; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68436 + Epoch[19 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11165186; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00356s; TotalTimePerSample = 0.01422ms; SamplesPerSecond = 70303 + Epoch[19 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13564307; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01359ms; SamplesPerSecond = 73572 + Epoch[19 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15268945; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01470ms; SamplesPerSecond = 68045 + Epoch[19 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16245166; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68287 + Epoch[19 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12254980; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 + Epoch[19 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14969043; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00356s; TotalTimePerSample = 0.01426ms; SamplesPerSecond = 70145 + Epoch[19 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16413672; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73486 +Finished Epoch[19 of 50]: [Training Set] TrainLossPerSample = 0.15590012; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.149183 +Starting Epoch 20: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 19 at record count 190000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1918 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 19: 549, 1755, ... + Epoch[20 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18188667; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 + Epoch[20 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519978; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 + Epoch[20 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15642200; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00343s; TotalTimePerSample = 0.01371ms; SamplesPerSecond = 72950 + Epoch[20 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12080978; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01514ms; SamplesPerSecond = 66032 + Epoch[20 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13863098; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01436ms; SamplesPerSecond = 69657 + Epoch[20 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15650378; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67934 + Epoch[20 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18442590; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69890 + Epoch[20 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13180127; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66155 + Epoch[20 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13930054; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 + Epoch[20 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16425256; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68380 + Epoch[20 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19111047; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00357s; TotalTimePerSample = 0.01427ms; SamplesPerSecond = 70067 + Epoch[20 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12297009; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 + Epoch[20 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15583582; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01437ms; SamplesPerSecond = 69579 + Epoch[20 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16438818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 + Epoch[20 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15821484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00353s; TotalTimePerSample = 0.01412ms; SamplesPerSecond = 70841 + Epoch[20 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317896; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69696 + Epoch[20 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15155737; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68418 + Epoch[20 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13631714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69676 + Epoch[20 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17534424; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 + Epoch[20 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549976; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00358s; TotalTimePerSample = 0.01433ms; SamplesPerSecond = 69773 + Epoch[20 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14183154; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00342s; TotalTimePerSample = 0.01369ms; SamplesPerSecond = 73056 + Epoch[20 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16146045; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65893 + Epoch[20 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21492310; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00357s; TotalTimePerSample = 0.01429ms; SamplesPerSecond = 69988 + Epoch[20 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13325562; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 + Epoch[20 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15456714; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00357s; TotalTimePerSample = 0.01427ms; SamplesPerSecond = 70086 + Epoch[20 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15659009; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68399 + Epoch[20 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16902222; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01438ms; SamplesPerSecond = 69560 + Epoch[20 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15981104; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00368s; TotalTimePerSample = 0.01473ms; SamplesPerSecond = 67879 + Epoch[20 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16168359; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67078 + Epoch[20 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14797168; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00365s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68399 + Epoch[20 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16445166; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00358s; TotalTimePerSample = 0.01433ms; SamplesPerSecond = 69793 + Epoch[20 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12721533; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01463ms; SamplesPerSecond = 68343 + Epoch[20 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579053; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00358s; TotalTimePerSample = 0.01430ms; SamplesPerSecond = 69910 + Epoch[20 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18053223; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67953 + Epoch[20 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540820; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00359s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 + Epoch[20 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18781592; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00382s; TotalTimePerSample = 0.01529ms; SamplesPerSecond = 65393 + Epoch[20 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19209521; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01434ms; SamplesPerSecond = 69735 + Epoch[20 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14946777; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68455 + Epoch[20 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14759277; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00358s; TotalTimePerSample = 0.01431ms; SamplesPerSecond = 69890 + Epoch[20 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13829053; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00369s; TotalTimePerSample = 0.01476ms; SamplesPerSecond = 67769 +Finished Epoch[20 of 50]: [Training Set] TrainLossPerSample = 0.15708567; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.148148 +Starting Epoch 21: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 20 at record count 200000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 20: 1080, 44, ... + Epoch[21 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12482390; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 + Epoch[21 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17546846; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00557s; TotalTimePerSample = 0.02227ms; SamplesPerSecond = 44899 + Epoch[21 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19240039; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 + Epoch[21 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12887006; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67494 + Epoch[21 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13593225; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 + Epoch[21 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17156799; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 + Epoch[21 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11016656; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00360s; TotalTimePerSample = 0.01441ms; SamplesPerSecond = 69405 + Epoch[21 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14242212; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01531ms; SamplesPerSecond = 65308 + Epoch[21 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11643091; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 + Epoch[21 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579993; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67567 + Epoch[21 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16201392; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01449ms; SamplesPerSecond = 69003 + Epoch[21 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12312732; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67658 + Epoch[21 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19885461; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00360s; TotalTimePerSample = 0.01440ms; SamplesPerSecond = 69463 + Epoch[21 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15726025; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 + Epoch[21 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12881958; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00360s; TotalTimePerSample = 0.01438ms; SamplesPerSecond = 69521 + Epoch[21 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19554468; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 + Epoch[21 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16273682; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00363s; TotalTimePerSample = 0.01451ms; SamplesPerSecond = 68927 + Epoch[21 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16055859; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01487ms; SamplesPerSecond = 67258 + Epoch[21 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18578052; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69232 + Epoch[21 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14378711; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00392s; TotalTimePerSample = 0.01568ms; SamplesPerSecond = 63791 + Epoch[21 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12746191; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00363s; TotalTimePerSample = 0.01454ms; SamplesPerSecond = 68794 + Epoch[21 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12992017; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 + Epoch[21 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16369800; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00361s; TotalTimePerSample = 0.01445ms; SamplesPerSecond = 69194 + Epoch[21 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19116650; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67403 + Epoch[21 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17885107; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 + Epoch[21 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15923657; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00619s; TotalTimePerSample = 0.02474ms; SamplesPerSecond = 40413 + Epoch[21 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16519727; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01455ms; SamplesPerSecond = 68719 + Epoch[21 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13316406; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01489ms; SamplesPerSecond = 67150 + Epoch[21 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19117871; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69079 + Epoch[21 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17145117; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67276 + Epoch[21 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20479297; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68851 + Epoch[21 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15686377; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66312 + Epoch[21 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18002539; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67060 + Epoch[21 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15793604; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67585 + Epoch[21 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999805; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01444ms; SamplesPerSecond = 69252 + Epoch[21 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12539844; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00371s; TotalTimePerSample = 0.01485ms; SamplesPerSecond = 67330 + Epoch[21 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14626563; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00362s; TotalTimePerSample = 0.01448ms; SamplesPerSecond = 69041 + Epoch[21 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21309863; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01495ms; SamplesPerSecond = 66898 + Epoch[21 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13187744; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66471 + Epoch[21 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13131494; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00370s; TotalTimePerSample = 0.01479ms; SamplesPerSecond = 67604 +Finished Epoch[21 of 50]: [Training Set] TrainLossPerSample = 0.15653156; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=0.152793 +Starting Epoch 22: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 21 at record count 210000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2012 retries for 10000 elements (20.1%) to ensure window condition +randomordering: recached sequence for seed 21: 549, 612, ... + Epoch[22 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17214368; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01320ms; SamplesPerSecond = 75757 + Epoch[22 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13493817; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44587 + Epoch[22 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23933429; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68889 + Epoch[22 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14054822; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 + Epoch[22 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13355316; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01456ms; SamplesPerSecond = 68700 + Epoch[22 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12845679; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 + Epoch[22 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13766351; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01834ms; SamplesPerSecond = 54513 + Epoch[22 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18023999; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00385s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65002 + Epoch[22 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14117981; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01460ms; SamplesPerSecond = 68474 + Epoch[22 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12569360; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67114 + Epoch[22 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22072864; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68324 + Epoch[22 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17385925; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66934 + Epoch[22 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16276953; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67458 + Epoch[22 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14364160; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 + Epoch[22 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12422998; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65945 + Epoch[22 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12879321; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00385s; TotalTimePerSample = 0.01541ms; SamplesPerSecond = 64884 + Epoch[22 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17239917; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00363s; TotalTimePerSample = 0.01452ms; SamplesPerSecond = 68851 + Epoch[22 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15272241; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 + Epoch[22 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549023; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00433s; TotalTimePerSample = 0.01732ms; SamplesPerSecond = 57736 + Epoch[22 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18117432; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01670ms; SamplesPerSecond = 59865 + Epoch[22 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13781250; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00419s; TotalTimePerSample = 0.01677ms; SamplesPerSecond = 59637 + Epoch[22 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17124634; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00433s; TotalTimePerSample = 0.01730ms; SamplesPerSecond = 57790 + Epoch[22 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14588330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01671ms; SamplesPerSecond = 59851 + Epoch[22 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15950610; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00418s; TotalTimePerSample = 0.01671ms; SamplesPerSecond = 59851 + Epoch[22 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797070; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00461s; TotalTimePerSample = 0.01845ms; SamplesPerSecond = 54194 + Epoch[22 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13840186; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00418s; TotalTimePerSample = 0.01674ms; SamplesPerSecond = 59751 + Epoch[22 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18444922; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59808 + Epoch[22 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14643213; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00432s; TotalTimePerSample = 0.01727ms; SamplesPerSecond = 57897 + Epoch[22 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11097461; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00416s; TotalTimePerSample = 0.01665ms; SamplesPerSecond = 60067 + Epoch[22 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18351416; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00422s; TotalTimePerSample = 0.01688ms; SamplesPerSecond = 59241 + Epoch[22 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17264697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00430s; TotalTimePerSample = 0.01721ms; SamplesPerSecond = 58112 + Epoch[22 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14929102; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00416s; TotalTimePerSample = 0.01666ms; SamplesPerSecond = 60038 + Epoch[22 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13344678; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59822 + Epoch[22 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13797266; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00432s; TotalTimePerSample = 0.01729ms; SamplesPerSecond = 57830 + Epoch[22 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16307861; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00417s; TotalTimePerSample = 0.01670ms; SamplesPerSecond = 59880 + Epoch[22 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19140332; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00421s; TotalTimePerSample = 0.01682ms; SamplesPerSecond = 59438 + Epoch[22 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12631348; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00433s; TotalTimePerSample = 0.01734ms; SamplesPerSecond = 57683 + Epoch[22 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13973828; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00417s; TotalTimePerSample = 0.01669ms; SamplesPerSecond = 59908 + Epoch[22 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08310840; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00418s; TotalTimePerSample = 0.01672ms; SamplesPerSecond = 59822 + Epoch[22 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21134424; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00433s; TotalTimePerSample = 0.01734ms; SamplesPerSecond = 57683 +Finished Epoch[22 of 50]: [Training Set] TrainLossPerSample = 0.15510236; EvalErrPerSample = 0.073299997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.164444 +Starting Epoch 23: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 22 at record count 220000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1950 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 22: 43, 802, ... + Epoch[23 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13851323; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 + Epoch[23 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15266893; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00556s; TotalTimePerSample = 0.02224ms; SamplesPerSecond = 44955 + Epoch[23 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15029691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 + Epoch[23 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13997247; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 + Epoch[23 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15457629; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66471 + Epoch[23 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13599609; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68306 + Epoch[23 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17177649; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 + Epoch[23 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15163049; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01468ms; SamplesPerSecond = 68119 + Epoch[23 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14814685; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00375s; TotalTimePerSample = 0.01500ms; SamplesPerSecond = 66648 + Epoch[23 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15877173; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 + Epoch[23 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19294788; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63922 + Epoch[23 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18038538; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00367s; TotalTimePerSample = 0.01467ms; SamplesPerSecond = 68175 + Epoch[23 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11728796; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00349s; TotalTimePerSample = 0.01398ms; SamplesPerSecond = 71551 + Epoch[23 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08960864; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00359s; TotalTimePerSample = 0.01435ms; SamplesPerSecond = 69676 + Epoch[23 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17279297; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01506ms; SamplesPerSecond = 66401 + Epoch[23 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18872900; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01464ms; SamplesPerSecond = 68324 + Epoch[23 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15471533; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 + Epoch[23 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15328271; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 + Epoch[23 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12188086; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 + Epoch[23 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14665259; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00368s; TotalTimePerSample = 0.01472ms; SamplesPerSecond = 67916 + Epoch[23 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20669824; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66952 + Epoch[23 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17103247; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00366s; TotalTimePerSample = 0.01465ms; SamplesPerSecond = 68250 + Epoch[23 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15786304; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44851 + Epoch[23 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745117; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00370s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67640 + Epoch[23 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12732666; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00391s; TotalTimePerSample = 0.01566ms; SamplesPerSecond = 63873 + Epoch[23 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14623828; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00365s; TotalTimePerSample = 0.01461ms; SamplesPerSecond = 68436 + Epoch[23 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15708105; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01509ms; SamplesPerSecond = 66277 + Epoch[23 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13106592; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00367s; TotalTimePerSample = 0.01468ms; SamplesPerSecond = 68138 + Epoch[23 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19996533; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00375s; TotalTimePerSample = 0.01501ms; SamplesPerSecond = 66631 + Epoch[23 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16580322; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68231 + Epoch[23 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17845557; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 + Epoch[23 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20788770; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00366s; TotalTimePerSample = 0.01462ms; SamplesPerSecond = 68380 + Epoch[23 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15010986; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00376s; TotalTimePerSample = 0.01506ms; SamplesPerSecond = 66418 + Epoch[23 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15772803; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67549 + Epoch[23 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13234326; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66295 + Epoch[23 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16466455; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00368s; TotalTimePerSample = 0.01474ms; SamplesPerSecond = 67861 + Epoch[23 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11601953; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00393s; TotalTimePerSample = 0.01572ms; SamplesPerSecond = 63597 + Epoch[23 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14522217; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00367s; TotalTimePerSample = 0.01469ms; SamplesPerSecond = 68082 + Epoch[23 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12782617; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00348s; TotalTimePerSample = 0.01392ms; SamplesPerSecond = 71859 + Epoch[23 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14913232; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 +Finished Epoch[23 of 50]: [Training Set] TrainLossPerSample = 0.15451369; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.1553 +Starting Epoch 24: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 23 at record count 230000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1952 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 23: 3227, 3145, ... + Epoch[24 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14052142; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 + Epoch[24 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11839349; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00566s; TotalTimePerSample = 0.02266ms; SamplesPerSecond = 44138 + Epoch[24 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13736136; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67677 + Epoch[24 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17809995; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00606s; TotalTimePerSample = 0.02423ms; SamplesPerSecond = 41267 + Epoch[24 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15297235; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67403 + Epoch[24 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17571277; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00382s; TotalTimePerSample = 0.01528ms; SamplesPerSecond = 65427 + Epoch[24 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19117938; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00361s; TotalTimePerSample = 0.01442ms; SamplesPerSecond = 69328 + Epoch[24 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477551; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00367s; TotalTimePerSample = 0.01466ms; SamplesPerSecond = 68194 + Epoch[24 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17912805; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66365 + Epoch[24 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13628906; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67458 + Epoch[24 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14371887; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00352s; TotalTimePerSample = 0.01407ms; SamplesPerSecond = 71083 + Epoch[24 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14074475; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01517ms; SamplesPerSecond = 65910 + Epoch[24 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15530225; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00385s; TotalTimePerSample = 0.01539ms; SamplesPerSecond = 64968 + Epoch[24 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19925366; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01511ms; SamplesPerSecond = 66190 + Epoch[24 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14178516; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00369s; TotalTimePerSample = 0.01477ms; SamplesPerSecond = 67713 + Epoch[24 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18568652; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65858 + Epoch[24 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14750757; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01836ms; SamplesPerSecond = 54478 + Epoch[24 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116553; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00395s; TotalTimePerSample = 0.01580ms; SamplesPerSecond = 63307 + Epoch[24 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13254932; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01491ms; SamplesPerSecond = 67060 + Epoch[24 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16391455; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00378s; TotalTimePerSample = 0.01513ms; SamplesPerSecond = 66085 + Epoch[24 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12146313; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00369s; TotalTimePerSample = 0.01476ms; SamplesPerSecond = 67732 + Epoch[24 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16737085; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65530 + Epoch[24 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14815918; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 + Epoch[24 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15551050; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63500 + Epoch[24 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15667847; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00371s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67476 + Epoch[24 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13440381; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65963 + Epoch[24 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17122607; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 + Epoch[24 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14292285; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65685 + Epoch[24 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688672; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00371s; TotalTimePerSample = 0.01486ms; SamplesPerSecond = 67294 + Epoch[24 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13779199; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00392s; TotalTimePerSample = 0.01568ms; SamplesPerSecond = 63791 + Epoch[24 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12947656; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01480ms; SamplesPerSecond = 67567 + Epoch[24 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18507080; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00380s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65720 + Epoch[24 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11831396; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00369s; TotalTimePerSample = 0.01478ms; SamplesPerSecond = 67677 + Epoch[24 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19608057; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00351s; TotalTimePerSample = 0.01404ms; SamplesPerSecond = 71204 + Epoch[24 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13332031; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63484 + Epoch[24 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18062402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00370s; TotalTimePerSample = 0.01482ms; SamplesPerSecond = 67494 + Epoch[24 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16128076; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00380s; TotalTimePerSample = 0.01519ms; SamplesPerSecond = 65841 + Epoch[24 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18098535; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00369s; TotalTimePerSample = 0.01477ms; SamplesPerSecond = 67695 + Epoch[24 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13035645; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65634 + Epoch[24 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14853516; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01481ms; SamplesPerSecond = 67531 +Finished Epoch[24 of 50]: [Training Set] TrainLossPerSample = 0.15456297; EvalErrPerSample = 0.073899999; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.156134 +Starting Epoch 25: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 24 at record count 240000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1932 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 24: 3407, 2083, ... + Epoch[25 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10413789; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 + Epoch[25 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15427972; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02245ms; SamplesPerSecond = 44547 + Epoch[25 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14306274; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00384s; TotalTimePerSample = 0.01536ms; SamplesPerSecond = 65087 + Epoch[25 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16880078; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00375s; TotalTimePerSample = 0.01498ms; SamplesPerSecond = 66737 + Epoch[25 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17541754; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00384s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65172 + Epoch[25 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11391406; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67222 + Epoch[25 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14680377; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00397s; TotalTimePerSample = 0.01588ms; SamplesPerSecond = 62988 + Epoch[25 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17015393; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00352s; TotalTimePerSample = 0.01406ms; SamplesPerSecond = 71103 + Epoch[25 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16514246; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00352s; TotalTimePerSample = 0.01410ms; SamplesPerSecond = 70921 + Epoch[25 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18206763; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00352s; TotalTimePerSample = 0.01408ms; SamplesPerSecond = 71002 + Epoch[25 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16409290; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00370s; TotalTimePerSample = 0.01481ms; SamplesPerSecond = 67512 + Epoch[25 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10293591; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00384s; TotalTimePerSample = 0.01535ms; SamplesPerSecond = 65155 + Epoch[25 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12705823; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00388s; TotalTimePerSample = 0.01553ms; SamplesPerSecond = 64383 + Epoch[25 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12850781; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00383s; TotalTimePerSample = 0.01530ms; SamplesPerSecond = 65342 + Epoch[25 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15889661; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00371s; TotalTimePerSample = 0.01484ms; SamplesPerSecond = 67367 + Epoch[25 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15900195; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00384s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65172 + Epoch[25 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10437720; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00372s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67114 + Epoch[25 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15870410; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00399s; TotalTimePerSample = 0.01594ms; SamplesPerSecond = 62719 + Epoch[25 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15275415; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00373s; TotalTimePerSample = 0.01493ms; SamplesPerSecond = 66988 + Epoch[25 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18503589; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00383s; TotalTimePerSample = 0.01533ms; SamplesPerSecond = 65240 + Epoch[25 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19125000; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67222 + Epoch[25 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17112036; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65257 + Epoch[25 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16705054; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01490ms; SamplesPerSecond = 67096 + Epoch[25 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16799976; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00398s; TotalTimePerSample = 0.01592ms; SamplesPerSecond = 62798 + Epoch[25 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19638110; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67186 + Epoch[25 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18267236; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00383s; TotalTimePerSample = 0.01534ms; SamplesPerSecond = 65189 + Epoch[25 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14591772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00382s; TotalTimePerSample = 0.01529ms; SamplesPerSecond = 65410 + Epoch[25 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13971826; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01545ms; SamplesPerSecond = 64733 + Epoch[25 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15680713; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64750 + Epoch[25 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572021; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65599 + Epoch[25 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16551855; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66934 + Epoch[25 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15907275; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00351s; TotalTimePerSample = 0.01406ms; SamplesPerSecond = 71143 + Epoch[25 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15407373; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00384s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65036 + Epoch[25 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14376416; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67006 + Epoch[25 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00398s; TotalTimePerSample = 0.01590ms; SamplesPerSecond = 62877 + Epoch[25 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16232422; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00374s; TotalTimePerSample = 0.01498ms; SamplesPerSecond = 66755 + Epoch[25 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14375244; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 + Epoch[25 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17384912; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67024 + Epoch[25 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12632861; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00400s; TotalTimePerSample = 0.01601ms; SamplesPerSecond = 62468 + Epoch[25 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16370215; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00384s; TotalTimePerSample = 0.01536ms; SamplesPerSecond = 65121 +Finished Epoch[25 of 50]: [Training Set] TrainLossPerSample = 0.1546814; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.15433 +Starting Epoch 26: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 25 at record count 250000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1924 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 25: 1325, 1664, ... + Epoch[26 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14296657; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76242 + Epoch[26 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21987227; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45703 + Epoch[26 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13585349; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66560 + Epoch[26 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16297119; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64783 + Epoch[26 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13785205; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00373s; TotalTimePerSample = 0.01492ms; SamplesPerSecond = 67024 + Epoch[26 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18902814; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 + Epoch[26 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11547314; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00389s; TotalTimePerSample = 0.01556ms; SamplesPerSecond = 64283 + Epoch[26 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14476709; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00384s; TotalTimePerSample = 0.01538ms; SamplesPerSecond = 65036 + Epoch[26 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18713660; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65651 + Epoch[26 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13506458; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01543ms; SamplesPerSecond = 64817 + Epoch[26 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508728; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01496ms; SamplesPerSecond = 66827 + Epoch[26 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12936316; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00400s; TotalTimePerSample = 0.01599ms; SamplesPerSecond = 62531 + Epoch[26 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12021448; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01488ms; SamplesPerSecond = 67186 + Epoch[26 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14798718; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00384s; TotalTimePerSample = 0.01535ms; SamplesPerSecond = 65138 + Epoch[26 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15024536; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00375s; TotalTimePerSample = 0.01499ms; SamplesPerSecond = 66720 + Epoch[26 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11538232; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00387s; TotalTimePerSample = 0.01547ms; SamplesPerSecond = 64649 + Epoch[26 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10707275; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01546ms; SamplesPerSecond = 64699 + Epoch[26 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11743384; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 + Epoch[26 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17169653; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66916 + Epoch[26 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17983130; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00385s; TotalTimePerSample = 0.01540ms; SamplesPerSecond = 64918 + Epoch[26 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13164038; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00354s; TotalTimePerSample = 0.01416ms; SamplesPerSecond = 70621 + Epoch[26 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16592725; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00373s; TotalTimePerSample = 0.01494ms; SamplesPerSecond = 66952 + Epoch[26 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10557300; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00400s; TotalTimePerSample = 0.01600ms; SamplesPerSecond = 62515 + Epoch[26 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13424634; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00374s; TotalTimePerSample = 0.01495ms; SamplesPerSecond = 66880 + Epoch[26 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16414233; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00623s; TotalTimePerSample = 0.02490ms; SamplesPerSecond = 40160 + Epoch[26 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18921021; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66120 + Epoch[26 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14548486; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00354s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70541 + Epoch[26 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17447925; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00401s; TotalTimePerSample = 0.01606ms; SamplesPerSecond = 62282 + Epoch[26 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19369873; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66524 + Epoch[26 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11958496; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 + Epoch[26 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12995361; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 + Epoch[26 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17100439; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63922 + Epoch[26 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16168213; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00390s; TotalTimePerSample = 0.01559ms; SamplesPerSecond = 64151 + Epoch[26 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20277344; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00386s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64834 + Epoch[26 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15511426; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01502ms; SamplesPerSecond = 66560 + Epoch[26 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22491211; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00386s; TotalTimePerSample = 0.01544ms; SamplesPerSecond = 64783 + Epoch[26 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20030127; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 + Epoch[26 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17554395; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00404s; TotalTimePerSample = 0.01616ms; SamplesPerSecond = 61865 + Epoch[26 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12255664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00374s; TotalTimePerSample = 0.01496ms; SamplesPerSecond = 66862 + Epoch[26 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12970947; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00385s; TotalTimePerSample = 0.01542ms; SamplesPerSecond = 64850 +Finished Epoch[26 of 50]: [Training Set] TrainLossPerSample = 0.15407094; EvalErrPerSample = 0.072499998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.157749 +Starting Epoch 27: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 26 at record count 260000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1927 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 26: 375, 3629, ... + Epoch[27 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12591264; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76149 + Epoch[27 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14306953; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00577s; TotalTimePerSample = 0.02308ms; SamplesPerSecond = 43335 + Epoch[27 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14663956; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00378s; TotalTimePerSample = 0.01512ms; SamplesPerSecond = 66137 + Epoch[27 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12528238; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00392s; TotalTimePerSample = 0.01569ms; SamplesPerSecond = 63742 + Epoch[27 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13291266; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01503ms; SamplesPerSecond = 66542 + Epoch[27 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14019751; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00403s; TotalTimePerSample = 0.01611ms; SamplesPerSecond = 62080 + Epoch[27 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10290942; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00355s; TotalTimePerSample = 0.01418ms; SamplesPerSecond = 70501 + Epoch[27 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13283911; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00375s; TotalTimePerSample = 0.01499ms; SamplesPerSecond = 66720 + Epoch[27 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18021509; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00402s; TotalTimePerSample = 0.01607ms; SamplesPerSecond = 62235 + Epoch[27 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16835486; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 + Epoch[27 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21902234; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00406s; TotalTimePerSample = 0.01622ms; SamplesPerSecond = 61637 + Epoch[27 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18301453; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66507 + Epoch[27 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17154932; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00388s; TotalTimePerSample = 0.01553ms; SamplesPerSecond = 64399 + Epoch[27 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14856836; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66489 + Epoch[27 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14754175; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00388s; TotalTimePerSample = 0.01552ms; SamplesPerSecond = 64449 + Epoch[27 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17677759; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 + Epoch[27 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14752319; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00400s; TotalTimePerSample = 0.01600ms; SamplesPerSecond = 62484 + Epoch[27 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12513721; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00377s; TotalTimePerSample = 0.01508ms; SamplesPerSecond = 66330 + Epoch[27 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13795337; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64069 + Epoch[27 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14083496; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00378s; TotalTimePerSample = 0.01510ms; SamplesPerSecond = 66225 + Epoch[27 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13369287; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00404s; TotalTimePerSample = 0.01615ms; SamplesPerSecond = 61911 + Epoch[27 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20411377; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00379s; TotalTimePerSample = 0.01515ms; SamplesPerSecond = 66015 + Epoch[27 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18830322; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00388s; TotalTimePerSample = 0.01554ms; SamplesPerSecond = 64366 + Epoch[27 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23027222; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01509ms; SamplesPerSecond = 66260 + Epoch[27 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13498975; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01560ms; SamplesPerSecond = 64102 + Epoch[27 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20234009; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00393s; TotalTimePerSample = 0.01572ms; SamplesPerSecond = 63597 + Epoch[27 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621289; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00387s; TotalTimePerSample = 0.01550ms; SamplesPerSecond = 64532 + Epoch[27 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10671240; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01510ms; SamplesPerSecond = 66225 + Epoch[27 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19180225; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00389s; TotalTimePerSample = 0.01557ms; SamplesPerSecond = 64234 + Epoch[27 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13920410; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01517ms; SamplesPerSecond = 65928 + Epoch[27 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12604297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00364s; TotalTimePerSample = 0.01456ms; SamplesPerSecond = 68700 + Epoch[27 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999072; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01575ms; SamplesPerSecond = 63500 + Epoch[27 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22732324; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.00376s; TotalTimePerSample = 0.01505ms; SamplesPerSecond = 66436 + Epoch[27 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10179541; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64053 + Epoch[27 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12488281; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00377s; TotalTimePerSample = 0.01507ms; SamplesPerSecond = 66348 + Epoch[27 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13114551; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00403s; TotalTimePerSample = 0.01613ms; SamplesPerSecond = 61988 + Epoch[27 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18670459; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00376s; TotalTimePerSample = 0.01504ms; SamplesPerSecond = 66489 + Epoch[27 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14991797; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00389s; TotalTimePerSample = 0.01555ms; SamplesPerSecond = 64316 + Epoch[27 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17380664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00378s; TotalTimePerSample = 0.01514ms; SamplesPerSecond = 66067 + Epoch[27 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15199316; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00389s; TotalTimePerSample = 0.01558ms; SamplesPerSecond = 64201 +Finished Epoch[27 of 50]: [Training Set] TrainLossPerSample = 0.15443754; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.156798 +Starting Epoch 28: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 27 at record count 270000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2091 retries for 10000 elements (20.9%) to ensure window condition +randomordering: recached sequence for seed 27: 1656, 2946, ... + Epoch[28 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14393666; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[28 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17761412; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00581s; TotalTimePerSample = 0.02324ms; SamplesPerSecond = 43036 + Epoch[28 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15454230; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65651 + Epoch[28 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14929742; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00394s; TotalTimePerSample = 0.01574ms; SamplesPerSecond = 63516 + Epoch[28 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12484784; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65599 + Epoch[28 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14156793; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00409s; TotalTimePerSample = 0.01635ms; SamplesPerSecond = 61154 + Epoch[28 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13612256; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00381s; TotalTimePerSample = 0.01524ms; SamplesPerSecond = 65616 + Epoch[28 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14138220; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00395s; TotalTimePerSample = 0.01579ms; SamplesPerSecond = 63323 + Epoch[28 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14947571; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00357s; TotalTimePerSample = 0.01430ms; SamplesPerSecond = 69949 + Epoch[28 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14680188; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00379s; TotalTimePerSample = 0.01516ms; SamplesPerSecond = 65945 + Epoch[28 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17777942; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00406s; TotalTimePerSample = 0.01623ms; SamplesPerSecond = 61621 + Epoch[28 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19684912; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00382s; TotalTimePerSample = 0.01526ms; SamplesPerSecond = 65513 + Epoch[28 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14749500; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00390s; TotalTimePerSample = 0.01562ms; SamplesPerSecond = 64020 + Epoch[28 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17938940; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00381s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65685 + Epoch[28 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12751685; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00393s; TotalTimePerSample = 0.01571ms; SamplesPerSecond = 63661 + Epoch[28 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18895264; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63435 + Epoch[28 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12314355; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00390s; TotalTimePerSample = 0.01561ms; SamplesPerSecond = 64053 + Epoch[28 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126514; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00379s; TotalTimePerSample = 0.01518ms; SamplesPerSecond = 65876 + Epoch[28 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13606494; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00429s; TotalTimePerSample = 0.01715ms; SamplesPerSecond = 58315 + Epoch[28 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16117578; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00383s; TotalTimePerSample = 0.01532ms; SamplesPerSecond = 65291 + Epoch[28 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15077344; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00372s; TotalTimePerSample = 0.01487ms; SamplesPerSecond = 67240 + Epoch[28 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15562012; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00391s; TotalTimePerSample = 0.01564ms; SamplesPerSecond = 63954 + Epoch[28 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15696167; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00380s; TotalTimePerSample = 0.01521ms; SamplesPerSecond = 65754 + Epoch[28 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13213037; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00393s; TotalTimePerSample = 0.01571ms; SamplesPerSecond = 63645 + Epoch[28 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15861768; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00361s; TotalTimePerSample = 0.01443ms; SamplesPerSecond = 69290 + Epoch[28 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20295117; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63435 + Epoch[28 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12567627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00391s; TotalTimePerSample = 0.01565ms; SamplesPerSecond = 63889 + Epoch[28 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17646289; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01520ms; SamplesPerSecond = 65806 + Epoch[28 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15850049; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00394s; TotalTimePerSample = 0.01576ms; SamplesPerSecond = 63467 + Epoch[28 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13273242; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00381s; TotalTimePerSample = 0.01525ms; SamplesPerSecond = 65565 + Epoch[28 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12176416; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00408s; TotalTimePerSample = 0.01630ms; SamplesPerSecond = 61334 + Epoch[28 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16263672; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00381s; TotalTimePerSample = 0.01523ms; SamplesPerSecond = 65668 + Epoch[28 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16976172; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00392s; TotalTimePerSample = 0.01567ms; SamplesPerSecond = 63808 + Epoch[28 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11975098; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00382s; TotalTimePerSample = 0.01530ms; SamplesPerSecond = 65376 + Epoch[28 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14589404; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00409s; TotalTimePerSample = 0.01638ms; SamplesPerSecond = 61064 + Epoch[28 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16949268; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00380s; TotalTimePerSample = 0.01522ms; SamplesPerSecond = 65720 + Epoch[28 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13438086; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00438s; TotalTimePerSample = 0.01751ms; SamplesPerSecond = 57116 + Epoch[28 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15717041; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00467s; TotalTimePerSample = 0.01866ms; SamplesPerSecond = 53579 + Epoch[28 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18282764; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00450s; TotalTimePerSample = 0.01798ms; SamplesPerSecond = 55604 + Epoch[28 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16783301; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00452s; TotalTimePerSample = 0.01808ms; SamplesPerSecond = 55297 +Finished Epoch[28 of 50]: [Training Set] TrainLossPerSample = 0.15442897; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.160649 +Starting Epoch 29: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 28 at record count 280000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2057 retries for 10000 elements (20.6%) to ensure window condition +randomordering: recached sequence for seed 28: 1080, 2027, ... + Epoch[29 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13651086; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 + Epoch[29 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17676038; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00643s; TotalTimePerSample = 0.02570ms; SamplesPerSecond = 38910 + Epoch[29 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17492984; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00457s; TotalTimePerSample = 0.01828ms; SamplesPerSecond = 54692 + Epoch[29 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18197458; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51313 + Epoch[29 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16113654; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00705s; TotalTimePerSample = 0.02818ms; SamplesPerSecond = 35486 + Epoch[29 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13163928; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00472s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53011 + Epoch[29 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16208954; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00455s; TotalTimePerSample = 0.01821ms; SamplesPerSecond = 54920 + Epoch[29 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14617480; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00404s; TotalTimePerSample = 0.01617ms; SamplesPerSecond = 61850 + Epoch[29 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16572351; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53613 + Epoch[29 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20103735; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54860 + Epoch[29 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17811365; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53316 + Epoch[29 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11239307; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00455s; TotalTimePerSample = 0.01821ms; SamplesPerSecond = 54920 + Epoch[29 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13314844; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00468s; TotalTimePerSample = 0.01870ms; SamplesPerSecond = 53464 + Epoch[29 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13256396; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49125 + Epoch[29 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499634; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00456s; TotalTimePerSample = 0.01824ms; SamplesPerSecond = 54824 + Epoch[29 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11521069; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00467s; TotalTimePerSample = 0.01866ms; SamplesPerSecond = 53579 + Epoch[29 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293921; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01822ms; SamplesPerSecond = 54884 + Epoch[29 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13474438; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01875ms; SamplesPerSecond = 53327 + Epoch[29 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16396338; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00454s; TotalTimePerSample = 0.01815ms; SamplesPerSecond = 55090 + Epoch[29 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15193042; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00645s; TotalTimePerSample = 0.02582ms; SamplesPerSecond = 38735 + Epoch[29 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10718457; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00405s; TotalTimePerSample = 0.01620ms; SamplesPerSecond = 61728 + Epoch[29 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12672412; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00453s; TotalTimePerSample = 0.01812ms; SamplesPerSecond = 55199 + Epoch[29 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19824097; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01893ms; SamplesPerSecond = 52831 + Epoch[29 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17706128; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00457s; TotalTimePerSample = 0.01827ms; SamplesPerSecond = 54728 + Epoch[29 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14488696; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01884ms; SamplesPerSecond = 53067 + Epoch[29 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14674194; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00454s; TotalTimePerSample = 0.01817ms; SamplesPerSecond = 55041 + Epoch[29 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15550610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01875ms; SamplesPerSecond = 53327 + Epoch[29 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15548340; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00401s; TotalTimePerSample = 0.01605ms; SamplesPerSecond = 62313 + Epoch[29 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16021094; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00452s; TotalTimePerSample = 0.01810ms; SamplesPerSecond = 55260 + Epoch[29 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16832129; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00472s; TotalTimePerSample = 0.01889ms; SamplesPerSecond = 52932 + Epoch[29 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18285400; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54848 + Epoch[29 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12037598; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00469s; TotalTimePerSample = 0.01877ms; SamplesPerSecond = 53270 + Epoch[29 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18390674; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00454s; TotalTimePerSample = 0.01816ms; SamplesPerSecond = 55066 + Epoch[29 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11044336; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00405s; TotalTimePerSample = 0.01621ms; SamplesPerSecond = 61682 + Epoch[29 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17944385; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01869ms; SamplesPerSecond = 53498 + Epoch[29 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14682129; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01824ms; SamplesPerSecond = 54824 + Epoch[29 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17389502; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01871ms; SamplesPerSecond = 53453 + Epoch[29 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14601221; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00454s; TotalTimePerSample = 0.01817ms; SamplesPerSecond = 55029 + Epoch[29 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16214209; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00471s; TotalTimePerSample = 0.01883ms; SamplesPerSecond = 53101 + Epoch[29 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13225928; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00402s; TotalTimePerSample = 0.01609ms; SamplesPerSecond = 62142 +Finished Epoch[29 of 50]: [Training Set] TrainLossPerSample = 0.15416239; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.189019 +Starting Epoch 30: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 29 at record count 290000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1974 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 29: 914, 4227, ... + Epoch[30 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14470308; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[30 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16273665; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00717s; TotalTimePerSample = 0.02868ms; SamplesPerSecond = 34862 + Epoch[30 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12416318; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53033 + Epoch[30 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795630; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00475s; TotalTimePerSample = 0.01900ms; SamplesPerSecond = 52631 + Epoch[30 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15630731; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00459s; TotalTimePerSample = 0.01838ms; SamplesPerSecond = 54406 + Epoch[30 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12211853; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52776 + Epoch[30 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11135504; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00459s; TotalTimePerSample = 0.01837ms; SamplesPerSecond = 54430 + Epoch[30 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13587048; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00476s; TotalTimePerSample = 0.01902ms; SamplesPerSecond = 52565 + Epoch[30 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17534290; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 + Epoch[30 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11606653; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52776 + Epoch[30 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14560828; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00458s; TotalTimePerSample = 0.01833ms; SamplesPerSecond = 54561 + Epoch[30 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22452795; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54159 + Epoch[30 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20647754; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52865 + Epoch[30 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13715662; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 + Epoch[30 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15057251; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01891ms; SamplesPerSecond = 52876 + Epoch[30 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16580542; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00460s; TotalTimePerSample = 0.01838ms; SamplesPerSecond = 54395 + Epoch[30 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16650293; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52764 + Epoch[30 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15484229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00460s; TotalTimePerSample = 0.01840ms; SamplesPerSecond = 54336 + Epoch[30 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16349536; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00408s; TotalTimePerSample = 0.01632ms; SamplesPerSecond = 61259 + Epoch[30 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13694678; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00472s; TotalTimePerSample = 0.01886ms; SamplesPerSecond = 53011 + Epoch[30 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10007373; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54159 + Epoch[30 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14123389; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00473s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52910 + Epoch[30 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16516821; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00463s; TotalTimePerSample = 0.01851ms; SamplesPerSecond = 54030 + Epoch[30 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13568677; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00476s; TotalTimePerSample = 0.01904ms; SamplesPerSecond = 52532 + Epoch[30 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15595508; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01846ms; SamplesPerSecond = 54171 + Epoch[30 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13078223; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52399 + Epoch[30 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13739844; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00463s; TotalTimePerSample = 0.01853ms; SamplesPerSecond = 53960 + Epoch[30 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14447607; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01911ms; SamplesPerSecond = 52334 + Epoch[30 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14593066; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00460s; TotalTimePerSample = 0.01842ms; SamplesPerSecond = 54300 + Epoch[30 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13859473; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00461s; TotalTimePerSample = 0.01845ms; SamplesPerSecond = 54194 + Epoch[30 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21342383; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00474s; TotalTimePerSample = 0.01895ms; SamplesPerSecond = 52764 + Epoch[30 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16210498; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01850ms; SamplesPerSecond = 54054 + Epoch[30 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20290576; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 + Epoch[30 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16057129; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01860ms; SamplesPerSecond = 53775 + Epoch[30 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21367480; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00475s; TotalTimePerSample = 0.01901ms; SamplesPerSecond = 52598 + Epoch[30 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16640332; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01858ms; SamplesPerSecond = 53809 + Epoch[30 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709473; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 + Epoch[30 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14478906; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00462s; TotalTimePerSample = 0.01847ms; SamplesPerSecond = 54135 + Epoch[30 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17019434; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00477s; TotalTimePerSample = 0.01910ms; SamplesPerSecond = 52366 + Epoch[30 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13185107; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00463s; TotalTimePerSample = 0.01852ms; SamplesPerSecond = 54007 +Finished Epoch[30 of 50]: [Training Set] TrainLossPerSample = 0.15467171; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.189437 +Starting Epoch 31: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 30 at record count 300000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1851 retries for 10000 elements (18.5%) to ensure window condition +randomordering: recached sequence for seed 30: 3803, 3991, ... + Epoch[31 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14462451; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75826 + Epoch[31 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16309470; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00663s; TotalTimePerSample = 0.02651ms; SamplesPerSecond = 37718 + Epoch[31 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14857794; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00470s; TotalTimePerSample = 0.01879ms; SamplesPerSecond = 53225 + Epoch[31 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17301627; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00484s; TotalTimePerSample = 0.01936ms; SamplesPerSecond = 51663 + Epoch[31 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17042346; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00466s; TotalTimePerSample = 0.01863ms; SamplesPerSecond = 53682 + Epoch[31 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18859375; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01915ms; SamplesPerSecond = 52224 + Epoch[31 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667926; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53613 + Epoch[31 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18785522; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00480s; TotalTimePerSample = 0.01919ms; SamplesPerSecond = 52115 + Epoch[31 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16620203; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00411s; TotalTimePerSample = 0.01643ms; SamplesPerSecond = 60871 + Epoch[31 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14571423; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00480s; TotalTimePerSample = 0.01920ms; SamplesPerSecond = 52094 + Epoch[31 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15508728; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00411s; TotalTimePerSample = 0.01645ms; SamplesPerSecond = 60782 + Epoch[31 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11401196; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01905ms; SamplesPerSecond = 52498 + Epoch[31 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11211658; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00479s; TotalTimePerSample = 0.01915ms; SamplesPerSecond = 52213 + Epoch[31 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18307996; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53521 + Epoch[31 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14014038; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01917ms; SamplesPerSecond = 52170 + Epoch[31 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18277954; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00465s; TotalTimePerSample = 0.01861ms; SamplesPerSecond = 53728 + Epoch[31 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312427; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 + Epoch[31 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11868140; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00414s; TotalTimePerSample = 0.01654ms; SamplesPerSecond = 60444 + Epoch[31 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11320850; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00465s; TotalTimePerSample = 0.01860ms; SamplesPerSecond = 53763 + Epoch[31 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12614941; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01917ms; SamplesPerSecond = 52170 + Epoch[31 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12027686; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53544 + Epoch[31 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14531152; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01918ms; SamplesPerSecond = 52148 + Epoch[31 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19766016; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00414s; TotalTimePerSample = 0.01656ms; SamplesPerSecond = 60401 + Epoch[31 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12406689; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 + Epoch[31 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17037256; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00410s; TotalTimePerSample = 0.01640ms; SamplesPerSecond = 60960 + Epoch[31 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12764600; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00641s; TotalTimePerSample = 0.02566ms; SamplesPerSecond = 38977 + Epoch[31 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12040723; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51599 + Epoch[31 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13548096; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00467s; TotalTimePerSample = 0.01868ms; SamplesPerSecond = 53533 + Epoch[31 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21368213; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00482s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51910 + Epoch[31 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20563916; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00468s; TotalTimePerSample = 0.01870ms; SamplesPerSecond = 53464 + Epoch[31 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15684570; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 + Epoch[31 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17233545; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01878ms; SamplesPerSecond = 53248 + Epoch[31 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16072217; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00483s; TotalTimePerSample = 0.01932ms; SamplesPerSecond = 51749 + Epoch[31 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13155273; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00467s; TotalTimePerSample = 0.01869ms; SamplesPerSecond = 53510 + Epoch[31 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15649854; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00469s; TotalTimePerSample = 0.01877ms; SamplesPerSecond = 53270 + Epoch[31 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12080762; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00480s; TotalTimePerSample = 0.01918ms; SamplesPerSecond = 52126 + Epoch[31 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18565283; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00470s; TotalTimePerSample = 0.01880ms; SamplesPerSecond = 53202 + Epoch[31 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18446680; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00480s; TotalTimePerSample = 0.01920ms; SamplesPerSecond = 52094 + Epoch[31 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16054297; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00471s; TotalTimePerSample = 0.01882ms; SamplesPerSecond = 53123 + Epoch[31 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15127344; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51867 +Finished Epoch[31 of 50]: [Training Set] TrainLossPerSample = 0.15486005; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.190844 +Starting Epoch 32: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 31 at record count 310000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 31: 2853, 507, ... + Epoch[32 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16708499; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75895 + Epoch[32 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19162103; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00663s; TotalTimePerSample = 0.02652ms; SamplesPerSecond = 37701 + Epoch[32 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13787976; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00476s; TotalTimePerSample = 0.01903ms; SamplesPerSecond = 52543 + Epoch[32 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13813211; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00481s; TotalTimePerSample = 0.01923ms; SamplesPerSecond = 51996 + Epoch[32 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13502838; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51824 + Epoch[32 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15943768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01945ms; SamplesPerSecond = 51408 + Epoch[32 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11804175; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52910 + Epoch[32 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12086487; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51514 + Epoch[32 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15141492; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52854 + Epoch[32 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18269641; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 + Epoch[32 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14335901; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00473s; TotalTimePerSample = 0.01892ms; SamplesPerSecond = 52842 + Epoch[32 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17575195; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01936ms; SamplesPerSecond = 51663 + Epoch[32 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13828113; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00474s; TotalTimePerSample = 0.01894ms; SamplesPerSecond = 52787 + Epoch[32 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15138354; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00428s; TotalTimePerSample = 0.01713ms; SamplesPerSecond = 58370 + Epoch[32 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12959351; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00470s; TotalTimePerSample = 0.01879ms; SamplesPerSecond = 53225 + Epoch[32 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16191138; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01951ms; SamplesPerSecond = 51250 + Epoch[32 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16672583; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00471s; TotalTimePerSample = 0.01884ms; SamplesPerSecond = 53089 + Epoch[32 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17047607; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00481s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52018 + Epoch[32 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14900171; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01905ms; SamplesPerSecond = 52498 + Epoch[32 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15498999; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00475s; TotalTimePerSample = 0.01898ms; SamplesPerSecond = 52675 + Epoch[32 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14057910; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00484s; TotalTimePerSample = 0.01934ms; SamplesPerSecond = 51695 + Epoch[32 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842822; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00476s; TotalTimePerSample = 0.01906ms; SamplesPerSecond = 52476 + Epoch[32 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12984058; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51599 + Epoch[32 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16722925; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01894ms; SamplesPerSecond = 52798 + Epoch[32 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21137817; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51440 + Epoch[32 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16650391; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01896ms; SamplesPerSecond = 52731 + Epoch[32 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11624414; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00486s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51398 + Epoch[32 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16824561; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00474s; TotalTimePerSample = 0.01898ms; SamplesPerSecond = 52698 + Epoch[32 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17619189; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51450 + Epoch[32 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14795850; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00472s; TotalTimePerSample = 0.01889ms; SamplesPerSecond = 52943 + Epoch[32 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14788672; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51450 + Epoch[32 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19199609; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00476s; TotalTimePerSample = 0.01904ms; SamplesPerSecond = 52509 + Epoch[32 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14870068; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01947ms; SamplesPerSecond = 51366 + Epoch[32 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18169238; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00416s; TotalTimePerSample = 0.01665ms; SamplesPerSecond = 60067 + Epoch[32 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13152783; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00487s; TotalTimePerSample = 0.01948ms; SamplesPerSecond = 51334 + Epoch[32 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13540674; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00472s; TotalTimePerSample = 0.01888ms; SamplesPerSecond = 52954 + Epoch[32 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15633887; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51229 + Epoch[32 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12714551; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00480s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52029 + Epoch[32 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18318164; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51282 + Epoch[32 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15918359; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00471s; TotalTimePerSample = 0.01885ms; SamplesPerSecond = 53044 +Finished Epoch[32 of 50]: [Training Set] TrainLossPerSample = 0.15423338; EvalErrPerSample = 0.073899999; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.192775 +Starting Epoch 33: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 32 at record count 320000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1906 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 32: 88, 89, ... + Epoch[33 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16717963; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75941 + Epoch[33 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19270932; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00671s; TotalTimePerSample = 0.02683ms; SamplesPerSecond = 37268 + Epoch[33 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13215280; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 + Epoch[33 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12894147; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 + Epoch[33 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16403992; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 + Epoch[33 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19667297; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01942ms; SamplesPerSecond = 51503 + Epoch[33 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18192096; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00481s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51931 + Epoch[33 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11374414; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51610 + Epoch[33 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13821655; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01931ms; SamplesPerSecond = 51792 + Epoch[33 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19738550; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01961ms; SamplesPerSecond = 50989 + Epoch[33 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14261597; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 + Epoch[33 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12330664; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01953ms; SamplesPerSecond = 51198 + Epoch[33 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14401660; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52290 + Epoch[33 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22224963; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51240 + Epoch[33 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19924243; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 + Epoch[33 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13060303; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 + Epoch[33 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12433960; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52268 + Epoch[33 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15537744; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01962ms; SamplesPerSecond = 50978 + Epoch[33 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14369678; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52312 + Epoch[33 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14945068; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00493s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50751 + Epoch[33 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16571777; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00420s; TotalTimePerSample = 0.01680ms; SamplesPerSecond = 59523 + Epoch[33 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12740942; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01963ms; SamplesPerSecond = 50937 + Epoch[33 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16293408; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 + Epoch[33 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15815918; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50658 + Epoch[33 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.07717480; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01912ms; SamplesPerSecond = 52301 + Epoch[33 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14254663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50813 + Epoch[33 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16696899; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01914ms; SamplesPerSecond = 52235 + Epoch[33 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19129102; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 + Epoch[33 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437451; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52268 + Epoch[33 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11403076; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 + Epoch[33 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14417578; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50823 + Epoch[33 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11433838; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50597 + Epoch[33 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23562842; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52202 + Epoch[33 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672705; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50535 + Epoch[33 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14627832; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 + Epoch[33 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19525000; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00493s; TotalTimePerSample = 0.01972ms; SamplesPerSecond = 50709 + Epoch[33 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12537402; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00479s; TotalTimePerSample = 0.01916ms; SamplesPerSecond = 52181 + Epoch[33 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709619; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00492s; TotalTimePerSample = 0.01967ms; SamplesPerSecond = 50833 + Epoch[33 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15483057; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01926ms; SamplesPerSecond = 51910 + Epoch[33 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14704346; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00495s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50464 +Finished Epoch[33 of 50]: [Training Set] TrainLossPerSample = 0.15413028; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.19587 +Starting Epoch 34: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 33 at record count 330000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1929 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 33: 3804, 1788, ... + Epoch[34 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13535313; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 + Epoch[34 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352461; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00674s; TotalTimePerSample = 0.02695ms; SamplesPerSecond = 37102 + Epoch[34 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15004388; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49870 + Epoch[34 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18189130; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 + Epoch[34 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15283014; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00721s; TotalTimePerSample = 0.02884ms; SamplesPerSecond = 34678 + Epoch[34 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11324536; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00663s; TotalTimePerSample = 0.02653ms; SamplesPerSecond = 37696 + Epoch[34 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14698895; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 + Epoch[34 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13531000; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51535 + Epoch[34 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15702075; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[34 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15457910; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01931ms; SamplesPerSecond = 51792 + Epoch[34 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13761108; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00422s; TotalTimePerSample = 0.01690ms; SamplesPerSecond = 59185 + Epoch[34 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050061; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.00670s; TotalTimePerSample = 0.02679ms; SamplesPerSecond = 37330 + Epoch[34 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12034814; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01947ms; SamplesPerSecond = 51366 + Epoch[34 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690234; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00500s; TotalTimePerSample = 0.01998ms; SamplesPerSecond = 50040 + Epoch[34 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14237549; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 + Epoch[34 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16746973; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00491s; TotalTimePerSample = 0.01965ms; SamplesPerSecond = 50885 + Epoch[34 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631616; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01977ms; SamplesPerSecond = 50586 + Epoch[34 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16113428; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51514 + Epoch[34 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14549487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01993ms; SamplesPerSecond = 50180 + Epoch[34 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18868311; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 + Epoch[34 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15792822; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00500s; TotalTimePerSample = 0.02001ms; SamplesPerSecond = 49970 + Epoch[34 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14762939; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51684 + Epoch[34 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09712817; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50140 + Epoch[34 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13837964; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01929ms; SamplesPerSecond = 51834 + Epoch[34 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16423242; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[34 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14903052; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 + Epoch[34 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384839; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00426s; TotalTimePerSample = 0.01706ms; SamplesPerSecond = 58616 + Epoch[34 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19276465; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01964ms; SamplesPerSecond = 50926 + Epoch[34 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16750781; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[34 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15260986; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00498s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50150 + Epoch[34 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16502051; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00503s; TotalTimePerSample = 0.02013ms; SamplesPerSecond = 49682 + Epoch[34 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13349951; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51738 + Epoch[34 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11700879; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00500s; TotalTimePerSample = 0.01998ms; SamplesPerSecond = 50050 + Epoch[34 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18962549; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 + Epoch[34 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19251318; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01994ms; SamplesPerSecond = 50150 + Epoch[34 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16371680; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00492s; TotalTimePerSample = 0.01969ms; SamplesPerSecond = 50792 + Epoch[34 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11365430; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00508s; TotalTimePerSample = 0.02031ms; SamplesPerSecond = 49231 + Epoch[34 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17088965; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00489s; TotalTimePerSample = 0.01955ms; SamplesPerSecond = 51156 + Epoch[34 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21053516; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49711 + Epoch[34 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18195605; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01944ms; SamplesPerSecond = 51440 +Finished Epoch[34 of 50]: [Training Set] TrainLossPerSample = 0.15442753; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.203513 +Starting Epoch 35: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 34 at record count 340000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1977 retries for 10000 elements (19.8%) to ensure window condition +randomordering: recached sequence for seed 34: 0, 1369, ... + Epoch[35 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17794618; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00332s; TotalTimePerSample = 0.01326ms; SamplesPerSecond = 75392 + Epoch[35 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10474553; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00681s; TotalTimePerSample = 0.02725ms; SamplesPerSecond = 36694 + Epoch[35 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15704340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00503s; TotalTimePerSample = 0.02014ms; SamplesPerSecond = 49662 + Epoch[35 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14234021; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51219 + Epoch[35 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09799567; EvalErr[0]PerSample = 0.02800000; TotalTime = 0.00502s; TotalTimePerSample = 0.02010ms; SamplesPerSecond = 49761 + Epoch[35 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13716321; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51398 + Epoch[35 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11028894; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49880 + Epoch[35 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17058649; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00490s; TotalTimePerSample = 0.01958ms; SamplesPerSecond = 51072 + Epoch[35 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17828174; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00502s; TotalTimePerSample = 0.02007ms; SamplesPerSecond = 49830 + Epoch[35 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14517395; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51303 + Epoch[35 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14962244; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02006ms; SamplesPerSecond = 49860 + Epoch[35 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16455225; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00487s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51292 + Epoch[35 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15540332; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 + Epoch[35 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12299329; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 + Epoch[35 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12371277; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49950 + Epoch[35 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619019; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 + Epoch[35 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317358; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 + Epoch[35 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13892188; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01960ms; SamplesPerSecond = 51020 + Epoch[35 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17433130; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 50000 + Epoch[35 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793555; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 50000 + Epoch[35 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13101001; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00492s; TotalTimePerSample = 0.01968ms; SamplesPerSecond = 50823 + Epoch[35 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17782764; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49711 + Epoch[35 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17694824; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51051 + Epoch[35 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12918970; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 + Epoch[35 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15366846; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01954ms; SamplesPerSecond = 51187 + Epoch[35 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15202856; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02021ms; SamplesPerSecond = 49475 + Epoch[35 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15452832; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00491s; TotalTimePerSample = 0.01965ms; SamplesPerSecond = 50895 + Epoch[35 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19114917; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00443s; TotalTimePerSample = 0.01774ms; SamplesPerSecond = 56369 + Epoch[35 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16061621; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01954ms; SamplesPerSecond = 51166 + Epoch[35 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18214746; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00506s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49455 + Epoch[35 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16263184; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00490s; TotalTimePerSample = 0.01959ms; SamplesPerSecond = 51041 + Epoch[35 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14526318; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49514 + Epoch[35 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10713672; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00489s; TotalTimePerSample = 0.01957ms; SamplesPerSecond = 51103 + Epoch[35 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16995996; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00507s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49358 + Epoch[35 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540625; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00490s; TotalTimePerSample = 0.01960ms; SamplesPerSecond = 51020 + Epoch[35 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19385498; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 + Epoch[35 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18298682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00493s; TotalTimePerSample = 0.01971ms; SamplesPerSecond = 50740 + Epoch[35 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16515527; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49154 + Epoch[35 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16277686; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 + Epoch[35 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18119238; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00505s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49465 +Finished Epoch[35 of 50]: [Training Set] TrainLossPerSample = 0.154597; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.200222 +Starting Epoch 36: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 35 at record count 350000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 35: 3511, 991, ... + Epoch[36 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13045412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75872 + Epoch[36 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16481703; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00689s; TotalTimePerSample = 0.02754ms; SamplesPerSecond = 36305 + Epoch[36 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18674973; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48704 + Epoch[36 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19172791; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50261 + Epoch[36 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13598785; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48770 + Epoch[36 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14000085; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50525 + Epoch[36 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18633856; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00513s; TotalTimePerSample = 0.02053ms; SamplesPerSecond = 48713 + Epoch[36 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11492786; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50220 + Epoch[36 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17103491; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49154 + Epoch[36 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16138538; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01988ms; SamplesPerSecond = 50311 + Epoch[36 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21032043; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00444s; TotalTimePerSample = 0.01777ms; SamplesPerSecond = 56280 + Epoch[36 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16564624; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00497s; TotalTimePerSample = 0.01987ms; SamplesPerSecond = 50332 + Epoch[36 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16749756; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 + Epoch[36 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11099194; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00432s; TotalTimePerSample = 0.01727ms; SamplesPerSecond = 57910 + Epoch[36 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11533960; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 + Epoch[36 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12073120; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 + Epoch[36 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13125293; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49106 + Epoch[36 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12560693; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 + Epoch[36 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17352856; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00449s; TotalTimePerSample = 0.01794ms; SamplesPerSecond = 55741 + Epoch[36 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12549976; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50597 + Epoch[36 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17819629; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50423 + Epoch[36 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14474902; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01928ms; SamplesPerSecond = 51856 + Epoch[36 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16296924; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50617 + Epoch[36 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14012451; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00482s; TotalTimePerSample = 0.01927ms; SamplesPerSecond = 51899 + Epoch[36 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14773145; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00437s; TotalTimePerSample = 0.01746ms; SamplesPerSecond = 57260 + Epoch[36 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11817139; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00722s; TotalTimePerSample = 0.02888ms; SamplesPerSecond = 34630 + Epoch[36 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14450684; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50281 + Epoch[36 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18549658; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00522s; TotalTimePerSample = 0.02086ms; SamplesPerSecond = 47929 + Epoch[36 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14738379; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00566s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44200 + Epoch[36 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21349512; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01992ms; SamplesPerSecond = 50190 + Epoch[36 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022412; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 + Epoch[36 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18741650; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50413 + Epoch[36 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13112988; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00485s; TotalTimePerSample = 0.01941ms; SamplesPerSecond = 51525 + Epoch[36 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13533154; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01996ms; SamplesPerSecond = 50110 + Epoch[36 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13490674; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00422s; TotalTimePerSample = 0.01687ms; SamplesPerSecond = 59283 + Epoch[36 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14833740; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00495s; TotalTimePerSample = 0.01979ms; SamplesPerSecond = 50525 + Epoch[36 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17256348; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00482s; TotalTimePerSample = 0.01929ms; SamplesPerSecond = 51845 + Epoch[36 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16826221; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50393 + Epoch[36 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17325977; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 + Epoch[36 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15534229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50433 +Finished Epoch[36 of 50]: [Training Set] TrainLossPerSample = 0.15398593; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.201135 +Starting Epoch 37: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 36 at record count 360000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1919 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 36: 87, 1672, ... + Epoch[37 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13413799; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[37 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12221150; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00673s; TotalTimePerSample = 0.02692ms; SamplesPerSecond = 37152 + Epoch[37 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635403; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 + Epoch[37 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13580142; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00487s; TotalTimePerSample = 0.01946ms; SamplesPerSecond = 51376 + Epoch[37 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13378632; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00497s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50261 + Epoch[37 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797546; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 + Epoch[37 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11214264; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50281 + Epoch[37 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17563239; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00485s; TotalTimePerSample = 0.01940ms; SamplesPerSecond = 51546 + Epoch[37 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13842529; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00438s; TotalTimePerSample = 0.01750ms; SamplesPerSecond = 57129 + Epoch[37 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718579; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00483s; TotalTimePerSample = 0.01930ms; SamplesPerSecond = 51813 + Epoch[37 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18377405; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50362 + Epoch[37 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14247290; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01937ms; SamplesPerSecond = 51620 + Epoch[37 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15313257; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01984ms; SamplesPerSecond = 50403 + Epoch[37 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18710876; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[37 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15047632; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00486s; TotalTimePerSample = 0.01942ms; SamplesPerSecond = 51482 + Epoch[37 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10942798; EvalErr[0]PerSample = 0.02400000; TotalTime = 0.00427s; TotalTimePerSample = 0.01707ms; SamplesPerSecond = 58589 + Epoch[37 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16937573; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50761 + Epoch[37 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17252734; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01992ms; SamplesPerSecond = 50200 + Epoch[37 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15181689; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 + Epoch[37 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17059106; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50271 + Epoch[37 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16196045; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00483s; TotalTimePerSample = 0.01933ms; SamplesPerSecond = 51727 + Epoch[37 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18610669; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[37 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14776636; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00485s; TotalTimePerSample = 0.01939ms; SamplesPerSecond = 51567 + Epoch[37 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13439941; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50251 + Epoch[37 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17143726; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50251 + Epoch[37 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17794141; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00425s; TotalTimePerSample = 0.01702ms; SamplesPerSecond = 58768 + Epoch[37 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16231763; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00484s; TotalTimePerSample = 0.01934ms; SamplesPerSecond = 51706 + Epoch[37 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15370898; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00496s; TotalTimePerSample = 0.01985ms; SamplesPerSecond = 50382 + Epoch[37 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15689111; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00500s; TotalTimePerSample = 0.02000ms; SamplesPerSecond = 49990 + Epoch[37 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12223779; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00492s; TotalTimePerSample = 0.01969ms; SamplesPerSecond = 50792 + Epoch[37 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16878760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49810 + Epoch[37 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10121338; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50648 + Epoch[37 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14805615; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 + Epoch[37 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16627832; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00496s; TotalTimePerSample = 0.01983ms; SamplesPerSecond = 50423 + Epoch[37 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333057; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00508s; TotalTimePerSample = 0.02032ms; SamplesPerSecond = 49212 + Epoch[37 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16828662; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02027ms; SamplesPerSecond = 49338 + Epoch[37 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21935449; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48866 + Epoch[37 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12863428; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48590 + Epoch[37 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11302051; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48933 + Epoch[37 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16875684; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48216 +Finished Epoch[37 of 50]: [Training Set] TrainLossPerSample = 0.15362105; EvalErrPerSample = 0.071800001; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.198585 +Starting Epoch 38: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 37 at record count 370000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2090 retries for 10000 elements (20.9%) to ensure window condition +randomordering: recached sequence for seed 37: 2147, 1618, ... + Epoch[38 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15782407; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01321ms; SamplesPerSecond = 75688 + Epoch[38 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13964478; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00681s; TotalTimePerSample = 0.02722ms; SamplesPerSecond = 36732 + Epoch[38 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13224973; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49495 + Epoch[38 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14791113; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51271 + Epoch[38 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16625989; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 + Epoch[38 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777277; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01952ms; SamplesPerSecond = 51219 + Epoch[38 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18762695; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00502s; TotalTimePerSample = 0.02006ms; SamplesPerSecond = 49840 + Epoch[38 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17601624; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00498s; TotalTimePerSample = 0.01991ms; SamplesPerSecond = 50220 + Epoch[38 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17659778; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50771 + Epoch[38 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15942542; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49940 + Epoch[38 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15378552; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51124 + Epoch[38 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11990942; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 + Epoch[38 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15716211; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51135 + Epoch[38 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12430591; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02003ms; SamplesPerSecond = 49930 + Epoch[38 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14166211; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00498s; TotalTimePerSample = 0.01993ms; SamplesPerSecond = 50180 + Epoch[38 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19538525; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00494s; TotalTimePerSample = 0.01976ms; SamplesPerSecond = 50607 + Epoch[38 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16727295; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02005ms; SamplesPerSecond = 49880 + Epoch[38 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15161816; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00488s; TotalTimePerSample = 0.01953ms; SamplesPerSecond = 51208 + Epoch[38 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12691724; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 + Epoch[38 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14758423; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00491s; TotalTimePerSample = 0.01964ms; SamplesPerSecond = 50916 + Epoch[38 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266943; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49900 + Epoch[38 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14729663; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00500s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49960 + Epoch[38 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16603687; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00501s; TotalTimePerSample = 0.02002ms; SamplesPerSecond = 49940 + Epoch[38 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13901196; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49691 + Epoch[38 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17964941; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00427s; TotalTimePerSample = 0.01707ms; SamplesPerSecond = 58589 + Epoch[38 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18162671; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49800 + Epoch[38 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12574365; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00489s; TotalTimePerSample = 0.01956ms; SamplesPerSecond = 51124 + Epoch[38 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18256348; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00502s; TotalTimePerSample = 0.02009ms; SamplesPerSecond = 49771 + Epoch[38 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13491504; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45421 + Epoch[38 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16772412; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00504s; TotalTimePerSample = 0.02015ms; SamplesPerSecond = 49632 + Epoch[38 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14115820; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00499s; TotalTimePerSample = 0.01995ms; SamplesPerSecond = 50130 + Epoch[38 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10757568; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00670s; TotalTimePerSample = 0.02678ms; SamplesPerSecond = 37335 + Epoch[38 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12081152; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00507s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49348 + Epoch[38 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20721875; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00492s; TotalTimePerSample = 0.01970ms; SamplesPerSecond = 50771 + Epoch[38 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20681787; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00583s; TotalTimePerSample = 0.02334ms; SamplesPerSecond = 42852 + Epoch[38 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15313916; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00506s; TotalTimePerSample = 0.02022ms; SamplesPerSecond = 49446 + Epoch[38 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14917236; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00504s; TotalTimePerSample = 0.02016ms; SamplesPerSecond = 49613 + Epoch[38 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18255664; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00579s; TotalTimePerSample = 0.02314ms; SamplesPerSecond = 43215 + Epoch[38 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15325146; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00557s; TotalTimePerSample = 0.02228ms; SamplesPerSecond = 44883 + Epoch[38 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12367236; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48628 +Finished Epoch[38 of 50]: [Training Set] TrainLossPerSample = 0.15423857; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.20528 +Starting Epoch 39: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 38 at record count 380000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 38: 1196, 3933, ... + Epoch[39 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17279616; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00330s; TotalTimePerSample = 0.01319ms; SamplesPerSecond = 75803 + Epoch[39 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15053123; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00686s; TotalTimePerSample = 0.02743ms; SamplesPerSecond = 36459 + Epoch[39 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15340201; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 + Epoch[39 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16145346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00494s; TotalTimePerSample = 0.01974ms; SamplesPerSecond = 50658 + Epoch[39 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16051062; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00681s; TotalTimePerSample = 0.02722ms; SamplesPerSecond = 36737 + Epoch[39 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14096790; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00494s; TotalTimePerSample = 0.01977ms; SamplesPerSecond = 50576 + Epoch[39 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12817004; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02033ms; SamplesPerSecond = 49193 + Epoch[39 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18435486; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00497s; TotalTimePerSample = 0.01989ms; SamplesPerSecond = 50271 + Epoch[39 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15009888; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02018ms; SamplesPerSecond = 49554 + Epoch[39 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17215259; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00508s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49173 + Epoch[39 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19742957; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50515 + Epoch[39 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14509302; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02035ms; SamplesPerSecond = 49144 + Epoch[39 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15422522; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00495s; TotalTimePerSample = 0.01981ms; SamplesPerSecond = 50474 + Epoch[39 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20461450; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00507s; TotalTimePerSample = 0.02028ms; SamplesPerSecond = 49299 + Epoch[39 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14545752; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49164 + Epoch[39 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11789624; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00497s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50342 + Epoch[39 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14740527; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00441s; TotalTimePerSample = 0.01766ms; SamplesPerSecond = 56625 + Epoch[39 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14339429; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00583s; TotalTimePerSample = 0.02331ms; SamplesPerSecond = 42896 + Epoch[39 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246411; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48638 + Epoch[39 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14868335; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00431s; TotalTimePerSample = 0.01725ms; SamplesPerSecond = 57977 + Epoch[39 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10311523; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00506s; TotalTimePerSample = 0.02023ms; SamplesPerSecond = 49426 + Epoch[39 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14912964; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00495s; TotalTimePerSample = 0.01980ms; SamplesPerSecond = 50505 + Epoch[39 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14753906; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02080ms; SamplesPerSecond = 48086 + Epoch[39 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12303320; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 + Epoch[39 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14021680; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00509s; TotalTimePerSample = 0.02034ms; SamplesPerSecond = 49164 + Epoch[39 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13175928; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00509s; TotalTimePerSample = 0.02038ms; SamplesPerSecond = 49077 + Epoch[39 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12917285; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00496s; TotalTimePerSample = 0.01982ms; SamplesPerSecond = 50454 + Epoch[39 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14734375; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00508s; TotalTimePerSample = 0.02030ms; SamplesPerSecond = 49261 + Epoch[39 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13080371; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00498s; TotalTimePerSample = 0.01990ms; SamplesPerSecond = 50241 + Epoch[39 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21033447; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00509s; TotalTimePerSample = 0.02036ms; SamplesPerSecond = 49125 + Epoch[39 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17700244; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00510s; TotalTimePerSample = 0.02040ms; SamplesPerSecond = 49029 + Epoch[39 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13948145; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00497s; TotalTimePerSample = 0.01986ms; SamplesPerSecond = 50352 + Epoch[39 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15833105; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00532s; TotalTimePerSample = 0.02127ms; SamplesPerSecond = 47010 + Epoch[39 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18455029; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00537s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46520 + Epoch[39 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16332812; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00537s; TotalTimePerSample = 0.02147ms; SamplesPerSecond = 46572 + Epoch[39 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16213574; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00458s; TotalTimePerSample = 0.01833ms; SamplesPerSecond = 54549 + Epoch[39 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15135254; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 + Epoch[39 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19016113; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02153ms; SamplesPerSecond = 46442 + Epoch[39 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00539s; TotalTimePerSample = 0.02154ms; SamplesPerSecond = 46416 + Epoch[39 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13075830; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00537s; TotalTimePerSample = 0.02148ms; SamplesPerSecond = 46546 +Finished Epoch[39 of 50]: [Training Set] TrainLossPerSample = 0.15412679; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.206532 +Starting Epoch 40: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 39 at record count 390000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1956 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 39: 2661, 3152, ... + Epoch[40 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14161708; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01316ms; SamplesPerSecond = 76010 + Epoch[40 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14613423; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00689s; TotalTimePerSample = 0.02756ms; SamplesPerSecond = 36289 + Epoch[40 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18211203; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00447s; TotalTimePerSample = 0.01789ms; SamplesPerSecond = 55903 + Epoch[40 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17613000; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00434s; TotalTimePerSample = 0.01736ms; SamplesPerSecond = 57616 + Epoch[40 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17070685; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48875 + Epoch[40 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17630023; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00510s; TotalTimePerSample = 0.02040ms; SamplesPerSecond = 49029 + Epoch[40 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13823822; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00514s; TotalTimePerSample = 0.02057ms; SamplesPerSecond = 48619 + Epoch[40 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16955151; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00499s; TotalTimePerSample = 0.01997ms; SamplesPerSecond = 50070 + Epoch[40 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16634717; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00512s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48790 + Epoch[40 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14256152; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00512s; TotalTimePerSample = 0.02048ms; SamplesPerSecond = 48818 + Epoch[40 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10239709; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00502s; TotalTimePerSample = 0.02009ms; SamplesPerSecond = 49771 + Epoch[40 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15572424; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00513s; TotalTimePerSample = 0.02052ms; SamplesPerSecond = 48742 + Epoch[40 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15609790; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00501s; TotalTimePerSample = 0.02004ms; SamplesPerSecond = 49890 + Epoch[40 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20184753; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48914 + Epoch[40 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12643213; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00516s; TotalTimePerSample = 0.02062ms; SamplesPerSecond = 48487 + Epoch[40 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12390137; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00502s; TotalTimePerSample = 0.02008ms; SamplesPerSecond = 49810 + Epoch[40 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14508374; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00449s; TotalTimePerSample = 0.01795ms; SamplesPerSecond = 55716 + Epoch[40 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18371777; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00519s; TotalTimePerSample = 0.02076ms; SamplesPerSecond = 48160 + Epoch[40 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18318823; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00531s; TotalTimePerSample = 0.02125ms; SamplesPerSecond = 47063 + Epoch[40 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579785; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45947 + Epoch[40 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10870361; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46099 + Epoch[40 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15615601; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 + Epoch[40 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18662476; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45938 + Epoch[40 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20422046; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 + Epoch[40 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16050317; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00553s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45240 + Epoch[40 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46108 + Epoch[40 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14602197; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02167ms; SamplesPerSecond = 46151 + Epoch[40 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12734863; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 + Epoch[40 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14315967; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00541s; TotalTimePerSample = 0.02166ms; SamplesPerSecond = 46176 + Epoch[40 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14660010; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 + Epoch[40 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12748047; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00541s; TotalTimePerSample = 0.02164ms; SamplesPerSecond = 46202 + Epoch[40 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13572021; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02167ms; SamplesPerSecond = 46142 + Epoch[40 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13043604; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00541s; TotalTimePerSample = 0.02165ms; SamplesPerSecond = 46185 + Epoch[40 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14089502; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00547s; TotalTimePerSample = 0.02187ms; SamplesPerSecond = 45720 + Epoch[40 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15098340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46116 + Epoch[40 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14866943; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45964 + Epoch[40 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15431445; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 + Epoch[40 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20131445; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 + Epoch[40 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14877686; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45930 + Epoch[40 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12697461; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45879 +Finished Epoch[40 of 50]: [Training Set] TrainLossPerSample = 0.15357913; EvalErrPerSample = 0.071499996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.211041 +Starting Epoch 41: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 40 at record count 400000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1997 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 40: 4294, 1646, ... + Epoch[41 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15997090; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00330s; TotalTimePerSample = 0.01318ms; SamplesPerSecond = 75849 + Epoch[41 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16715651; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00697s; TotalTimePerSample = 0.02789ms; SamplesPerSecond = 35852 + Epoch[41 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10600992; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00519s; TotalTimePerSample = 0.02077ms; SamplesPerSecond = 48151 + Epoch[41 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18155670; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00506s; TotalTimePerSample = 0.02026ms; SamplesPerSecond = 49368 + Epoch[41 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19502350; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00515s; TotalTimePerSample = 0.02061ms; SamplesPerSecond = 48524 + Epoch[41 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16436865; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00517s; TotalTimePerSample = 0.02070ms; SamplesPerSecond = 48318 + Epoch[41 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13003320; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00504s; TotalTimePerSample = 0.02017ms; SamplesPerSecond = 49583 + Epoch[41 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19080762; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00519s; TotalTimePerSample = 0.02076ms; SamplesPerSecond = 48169 + Epoch[41 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15023108; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48914 + Epoch[41 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12813550; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00518s; TotalTimePerSample = 0.02073ms; SamplesPerSecond = 48243 + Epoch[41 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16008496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48206 + Epoch[41 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13177649; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00503s; TotalTimePerSample = 0.02012ms; SamplesPerSecond = 49691 + Epoch[41 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13133691; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00518s; TotalTimePerSample = 0.02071ms; SamplesPerSecond = 48290 + Epoch[41 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21506421; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48123 + Epoch[41 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11038550; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00505s; TotalTimePerSample = 0.02020ms; SamplesPerSecond = 49504 + Epoch[41 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14044556; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00523s; TotalTimePerSample = 0.02091ms; SamplesPerSecond = 47819 + Epoch[41 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13908252; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00527s; TotalTimePerSample = 0.02106ms; SamplesPerSecond = 47483 + Epoch[41 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15261816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00592s; TotalTimePerSample = 0.02366ms; SamplesPerSecond = 42265 + Epoch[41 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15004224; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02200ms; SamplesPerSecond = 45462 + Epoch[41 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11603027; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 + Epoch[41 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20238867; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00546s; TotalTimePerSample = 0.02186ms; SamplesPerSecond = 45754 + Epoch[41 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12511157; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45821 + Epoch[41 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690991; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45712 + Epoch[41 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15268115; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00465s; TotalTimePerSample = 0.01859ms; SamplesPerSecond = 53798 + Epoch[41 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12382983; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00466s; TotalTimePerSample = 0.01865ms; SamplesPerSecond = 53625 + Epoch[41 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15059326; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00779s; TotalTimePerSample = 0.03116ms; SamplesPerSecond = 32092 + Epoch[41 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16363306; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45662 + Epoch[41 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13311108; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00464s; TotalTimePerSample = 0.01858ms; SamplesPerSecond = 53832 + Epoch[41 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17219043; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 + Epoch[41 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17049805; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 + Epoch[41 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19276025; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00549s; TotalTimePerSample = 0.02195ms; SamplesPerSecond = 45562 + Epoch[41 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14431494; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 + Epoch[41 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20596777; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 + Epoch[41 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12097510; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45537 + Epoch[41 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18849707; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00548s; TotalTimePerSample = 0.02194ms; SamplesPerSecond = 45587 + Epoch[41 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718799; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02199ms; SamplesPerSecond = 45479 + Epoch[41 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12531006; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 + Epoch[41 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15923877; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00551s; TotalTimePerSample = 0.02204ms; SamplesPerSecond = 45380 + Epoch[41 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15602734; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53418 + Epoch[41 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17051758; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00551s; TotalTimePerSample = 0.02204ms; SamplesPerSecond = 45372 +Finished Epoch[41 of 50]: [Training Set] TrainLossPerSample = 0.15404761; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.214919 +Starting Epoch 42: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 41 at record count 410000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1963 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 41: 3344, 3717, ... + Epoch[42 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15668861; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[42 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16626445; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00705s; TotalTimePerSample = 0.02819ms; SamplesPerSecond = 35471 + Epoch[42 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15020337; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 + Epoch[42 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09922888; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00512s; TotalTimePerSample = 0.02047ms; SamplesPerSecond = 48856 + Epoch[42 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15337531; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02096ms; SamplesPerSecond = 47700 + Epoch[42 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14959808; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00533s; TotalTimePerSample = 0.02132ms; SamplesPerSecond = 46913 + Epoch[42 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14924933; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00511s; TotalTimePerSample = 0.02044ms; SamplesPerSecond = 48933 + Epoch[42 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15217285; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00456s; TotalTimePerSample = 0.01823ms; SamplesPerSecond = 54848 + Epoch[42 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17924707; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00510s; TotalTimePerSample = 0.02041ms; SamplesPerSecond = 48990 + Epoch[42 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16403760; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02081ms; SamplesPerSecond = 48049 + Epoch[42 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12861609; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47755 + Epoch[42 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12339722; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00513s; TotalTimePerSample = 0.02052ms; SamplesPerSecond = 48742 + Epoch[42 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10131934; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47755 + Epoch[42 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18952173; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00522s; TotalTimePerSample = 0.02088ms; SamplesPerSecond = 47892 + Epoch[42 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20686011; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00515s; TotalTimePerSample = 0.02059ms; SamplesPerSecond = 48571 + Epoch[42 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16400244; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00524s; TotalTimePerSample = 0.02094ms; SamplesPerSecond = 47746 + Epoch[42 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14127393; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00547s; TotalTimePerSample = 0.02188ms; SamplesPerSecond = 45712 + Epoch[42 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18588232; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 + Epoch[42 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14134302; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 + Epoch[42 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14998389; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 + Epoch[42 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16024512; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47429 + Epoch[42 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11431982; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02145ms; SamplesPerSecond = 46624 + Epoch[42 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16649219; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00512s; TotalTimePerSample = 0.02046ms; SamplesPerSecond = 48866 + Epoch[42 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15155249; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47609 + Epoch[42 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11818628; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00522s; TotalTimePerSample = 0.02086ms; SamplesPerSecond = 47929 + Epoch[42 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16405737; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02238ms; SamplesPerSecond = 44682 + Epoch[42 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13877539; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 + Epoch[42 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17617358; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02050ms; SamplesPerSecond = 48770 + Epoch[42 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15604688; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00527s; TotalTimePerSample = 0.02106ms; SamplesPerSecond = 47483 + Epoch[42 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15834229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47537 + Epoch[42 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21922949; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00514s; TotalTimePerSample = 0.02056ms; SamplesPerSecond = 48638 + Epoch[42 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12137402; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47628 + Epoch[42 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16690039; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00526s; TotalTimePerSample = 0.02104ms; SamplesPerSecond = 47528 + Epoch[42 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12310693; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48590 + Epoch[42 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19693359; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00457s; TotalTimePerSample = 0.01827ms; SamplesPerSecond = 54728 + Epoch[42 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18927979; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00511s; TotalTimePerSample = 0.02045ms; SamplesPerSecond = 48894 + Epoch[42 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14653174; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47600 + Epoch[42 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14917383; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 + Epoch[42 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13983691; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00515s; TotalTimePerSample = 0.02058ms; SamplesPerSecond = 48581 + Epoch[42 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14345850; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00525s; TotalTimePerSample = 0.02100ms; SamplesPerSecond = 47609 +Finished Epoch[42 of 50]: [Training Set] TrainLossPerSample = 0.15380706; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.209725 +Starting Epoch 43: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 42 at record count 420000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2041 retries for 10000 elements (20.4%) to ensure window condition +randomordering: recached sequence for seed 42: 4625, 1494, ... + Epoch[43 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16346191; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00331s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75642 + Epoch[43 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13378452; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00884s; TotalTimePerSample = 0.03536ms; SamplesPerSecond = 28277 + Epoch[43 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12752466; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00529s; TotalTimePerSample = 0.02116ms; SamplesPerSecond = 47258 + Epoch[43 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16106375; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00515s; TotalTimePerSample = 0.02060ms; SamplesPerSecond = 48553 + Epoch[43 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12669000; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47447 + Epoch[43 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14844189; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00461s; TotalTimePerSample = 0.01843ms; SamplesPerSecond = 54265 + Epoch[43 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20358795; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00513s; TotalTimePerSample = 0.02051ms; SamplesPerSecond = 48761 + Epoch[43 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246301; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00707s; TotalTimePerSample = 0.02828ms; SamplesPerSecond = 35360 + Epoch[43 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15258435; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00519s; TotalTimePerSample = 0.02074ms; SamplesPerSecond = 48206 + Epoch[43 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14182422; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00529s; TotalTimePerSample = 0.02116ms; SamplesPerSecond = 47258 + Epoch[43 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13872412; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47232 + Epoch[43 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13299866; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00445s; TotalTimePerSample = 0.01780ms; SamplesPerSecond = 56167 + Epoch[43 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558264; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00537s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46511 + Epoch[43 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15742139; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53304 + Epoch[43 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13340625; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 + Epoch[43 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11161108; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00447s; TotalTimePerSample = 0.01789ms; SamplesPerSecond = 55903 + Epoch[43 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16127979; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47501 + Epoch[43 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11706860; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47330 + Epoch[43 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16475464; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00516s; TotalTimePerSample = 0.02064ms; SamplesPerSecond = 48449 + Epoch[43 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17851855; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00527s; TotalTimePerSample = 0.02108ms; SamplesPerSecond = 47438 + Epoch[43 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15488403; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47241 + Epoch[43 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12714917; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00517s; TotalTimePerSample = 0.02068ms; SamplesPerSecond = 48365 + Epoch[43 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13928101; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00528s; TotalTimePerSample = 0.02113ms; SamplesPerSecond = 47330 + Epoch[43 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16454639; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00531s; TotalTimePerSample = 0.02124ms; SamplesPerSecond = 47089 + Epoch[43 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19079565; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00516s; TotalTimePerSample = 0.02064ms; SamplesPerSecond = 48459 + Epoch[43 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13771436; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02121ms; SamplesPerSecond = 47152 + Epoch[43 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17899536; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00528s; TotalTimePerSample = 0.02110ms; SamplesPerSecond = 47384 + Epoch[43 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18417554; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48113 + Epoch[43 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17164062; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00531s; TotalTimePerSample = 0.02123ms; SamplesPerSecond = 47107 + Epoch[43 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17466846; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00522s; TotalTimePerSample = 0.02087ms; SamplesPerSecond = 47911 + Epoch[43 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13243311; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46728 + Epoch[43 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17387646; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00532s; TotalTimePerSample = 0.02127ms; SamplesPerSecond = 47018 + Epoch[43 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15478662; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00518s; TotalTimePerSample = 0.02071ms; SamplesPerSecond = 48281 + Epoch[43 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11948877; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00530s; TotalTimePerSample = 0.02120ms; SamplesPerSecond = 47160 + Epoch[43 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15327832; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00531s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47045 + Epoch[43 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12046826; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02078ms; SamplesPerSecond = 48113 + Epoch[43 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18561133; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00458s; TotalTimePerSample = 0.01832ms; SamplesPerSecond = 54597 + Epoch[43 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17181885; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00530s; TotalTimePerSample = 0.02119ms; SamplesPerSecond = 47187 + Epoch[43 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19418848; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00522s; TotalTimePerSample = 0.02088ms; SamplesPerSecond = 47901 + Epoch[43 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716992; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00531s; TotalTimePerSample = 0.02124ms; SamplesPerSecond = 47072 +Finished Epoch[43 of 50]: [Training Set] TrainLossPerSample = 0.15374406; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.212294 +Starting Epoch 44: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 43 at record count 430000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition +randomordering: recached sequence for seed 43: 1442, 760, ... + Epoch[44 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19620923; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76126 + Epoch[44 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17284280; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00709s; TotalTimePerSample = 0.02834ms; SamplesPerSecond = 35280 + Epoch[44 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17107645; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02134ms; SamplesPerSecond = 46860 + Epoch[44 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13653772; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00528s; TotalTimePerSample = 0.02112ms; SamplesPerSecond = 47348 + Epoch[44 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549390; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00751s; TotalTimePerSample = 0.03004ms; SamplesPerSecond = 33288 + Epoch[44 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13838623; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02135ms; SamplesPerSecond = 46834 + Epoch[44 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15465302; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 + Epoch[44 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13823840; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00529s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47214 + Epoch[44 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14467957; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02138ms; SamplesPerSecond = 46763 + Epoch[44 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15305005; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47957 + Epoch[44 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902258; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00531s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47045 + Epoch[44 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13886462; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00533s; TotalTimePerSample = 0.02134ms; SamplesPerSecond = 46869 + Epoch[44 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18513098; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47966 + Epoch[44 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16015112; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00530s; TotalTimePerSample = 0.02121ms; SamplesPerSecond = 47143 + Epoch[44 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20326123; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46886 + Epoch[44 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13745190; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00522s; TotalTimePerSample = 0.02087ms; SamplesPerSecond = 47920 + Epoch[44 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10230371; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00532s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47036 + Epoch[44 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13917432; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46957 + Epoch[44 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14841895; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00521s; TotalTimePerSample = 0.02085ms; SamplesPerSecond = 47957 + Epoch[44 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658423; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00532s; TotalTimePerSample = 0.02130ms; SamplesPerSecond = 46948 + Epoch[44 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14894409; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00532s; TotalTimePerSample = 0.02126ms; SamplesPerSecond = 47027 + Epoch[44 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14695752; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00520s; TotalTimePerSample = 0.02082ms; SamplesPerSecond = 48039 + Epoch[44 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13216064; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00532s; TotalTimePerSample = 0.02128ms; SamplesPerSecond = 46983 + Epoch[44 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17407349; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46877 + Epoch[44 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13991919; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00524s; TotalTimePerSample = 0.02097ms; SamplesPerSecond = 47682 + Epoch[44 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17606494; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02151ms; SamplesPerSecond = 46494 + Epoch[44 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12547949; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00536s; TotalTimePerSample = 0.02146ms; SamplesPerSecond = 46607 + Epoch[44 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12784619; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00526s; TotalTimePerSample = 0.02102ms; SamplesPerSecond = 47564 + Epoch[44 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18277832; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 + Epoch[44 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16881885; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00538s; TotalTimePerSample = 0.02153ms; SamplesPerSecond = 46442 + Epoch[44 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12812451; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00528s; TotalTimePerSample = 0.02112ms; SamplesPerSecond = 47357 + Epoch[44 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18617529; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00537s; TotalTimePerSample = 0.02146ms; SamplesPerSecond = 46598 + Epoch[44 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11412061; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00537s; TotalTimePerSample = 0.02148ms; SamplesPerSecond = 46546 + Epoch[44 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16486426; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 + Epoch[44 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13672900; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00667s; TotalTimePerSample = 0.02668ms; SamplesPerSecond = 37481 + Epoch[44 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18357324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00578s; TotalTimePerSample = 0.02310ms; SamplesPerSecond = 43282 + Epoch[44 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18769873; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00572s; TotalTimePerSample = 0.02288ms; SamplesPerSecond = 43713 + Epoch[44 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12417529; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00569s; TotalTimePerSample = 0.02275ms; SamplesPerSecond = 43959 + Epoch[44 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16986035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00569s; TotalTimePerSample = 0.02278ms; SamplesPerSecond = 43905 + Epoch[44 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13162256; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00569s; TotalTimePerSample = 0.02276ms; SamplesPerSecond = 43929 +Finished Epoch[44 of 50]: [Training Set] TrainLossPerSample = 0.15353793; EvalErrPerSample = 0.074900001; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.219771 +Starting Epoch 45: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 44 at record count 440000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1862 retries for 10000 elements (18.6%) to ensure window condition +randomordering: recached sequence for seed 44: 491, 13, ... + Epoch[45 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15391052; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76057 + Epoch[45 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13992700; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00717s; TotalTimePerSample = 0.02867ms; SamplesPerSecond = 34882 + Epoch[45 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16474039; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46270 + Epoch[45 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15187094; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00541s; TotalTimePerSample = 0.02162ms; SamplesPerSecond = 46253 + Epoch[45 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20688507; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 + Epoch[45 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14095599; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00539s; TotalTimePerSample = 0.02155ms; SamplesPerSecond = 46408 + Epoch[45 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13668738; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00539s; TotalTimePerSample = 0.02154ms; SamplesPerSecond = 46416 + Epoch[45 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14179443; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00525s; TotalTimePerSample = 0.02101ms; SamplesPerSecond = 47591 + Epoch[45 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13604297; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46468 + Epoch[45 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16773560; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02156ms; SamplesPerSecond = 46382 + Epoch[45 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12565735; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02103ms; SamplesPerSecond = 47546 + Epoch[45 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12209326; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46364 + Epoch[45 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16090442; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46356 + Epoch[45 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16171191; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00454s; TotalTimePerSample = 0.01816ms; SamplesPerSecond = 55078 + Epoch[45 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17052759; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00538s; TotalTimePerSample = 0.02150ms; SamplesPerSecond = 46502 + Epoch[45 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16556714; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00527s; TotalTimePerSample = 0.02110ms; SamplesPerSecond = 47402 + Epoch[45 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12027563; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00543s; TotalTimePerSample = 0.02170ms; SamplesPerSecond = 46082 + Epoch[45 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14207788; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53407 + Epoch[45 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20278442; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00453s; TotalTimePerSample = 0.01812ms; SamplesPerSecond = 55199 + Epoch[45 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16695776; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00539s; TotalTimePerSample = 0.02155ms; SamplesPerSecond = 46408 + Epoch[45 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14629443; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00529s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47223 + Epoch[45 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21565137; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02163ms; SamplesPerSecond = 46227 + Epoch[45 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14214185; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00539s; TotalTimePerSample = 0.02157ms; SamplesPerSecond = 46356 + Epoch[45 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13090479; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00528s; TotalTimePerSample = 0.02114ms; SamplesPerSecond = 47312 + Epoch[45 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13423755; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00541s; TotalTimePerSample = 0.02165ms; SamplesPerSecond = 46193 + Epoch[45 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16339355; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00542s; TotalTimePerSample = 0.02168ms; SamplesPerSecond = 46133 + Epoch[45 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16817798; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00534s; TotalTimePerSample = 0.02136ms; SamplesPerSecond = 46825 + Epoch[45 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519287; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00533s; TotalTimePerSample = 0.02133ms; SamplesPerSecond = 46877 + Epoch[45 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15910303; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53407 + Epoch[45 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11289258; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00526s; TotalTimePerSample = 0.02105ms; SamplesPerSecond = 47510 + Epoch[45 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549756; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00544s; TotalTimePerSample = 0.02175ms; SamplesPerSecond = 45981 + Epoch[45 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12784619; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02192ms; SamplesPerSecond = 45628 + Epoch[45 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17025049; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00466s; TotalTimePerSample = 0.01864ms; SamplesPerSecond = 53648 + Epoch[45 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18051855; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 + Epoch[45 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21135937; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00577s; TotalTimePerSample = 0.02308ms; SamplesPerSecond = 43335 + Epoch[45 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16994287; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00484s; TotalTimePerSample = 0.01935ms; SamplesPerSecond = 51674 + Epoch[45 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11147803; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00578s; TotalTimePerSample = 0.02310ms; SamplesPerSecond = 43282 + Epoch[45 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13351807; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00574s; TotalTimePerSample = 0.02296ms; SamplesPerSecond = 43554 + Epoch[45 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15980029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00574s; TotalTimePerSample = 0.02296ms; SamplesPerSecond = 43546 + Epoch[45 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15938818; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00575s; TotalTimePerSample = 0.02298ms; SamplesPerSecond = 43516 +Finished Epoch[45 of 50]: [Training Set] TrainLossPerSample = 0.15391743; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.214193 +Starting Epoch 46: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 45 at record count 450000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1934 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 45: 1579, 1557, ... + Epoch[46 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16876230; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01317ms; SamplesPerSecond = 75918 + Epoch[46 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16504675; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00722s; TotalTimePerSample = 0.02888ms; SamplesPerSecond = 34630 + Epoch[46 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668588; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45964 + Epoch[46 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13980969; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45888 + Epoch[46 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15281616; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00530s; TotalTimePerSample = 0.02118ms; SamplesPerSecond = 47205 + Epoch[46 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09885846; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00546s; TotalTimePerSample = 0.02186ms; SamplesPerSecond = 45754 + Epoch[46 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17244324; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00543s; TotalTimePerSample = 0.02171ms; SamplesPerSecond = 46057 + Epoch[46 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14810022; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00529s; TotalTimePerSample = 0.02117ms; SamplesPerSecond = 47241 + Epoch[46 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15425452; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45118 + Epoch[46 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18108887; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 + Epoch[46 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15925623; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00534s; TotalTimePerSample = 0.02137ms; SamplesPerSecond = 46790 + Epoch[46 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662000; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 + Epoch[46 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16391589; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 + Epoch[46 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15208911; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 + Epoch[46 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18344653; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46477 + Epoch[46 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13962915; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45829 + Epoch[46 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14502954; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45879 + Epoch[46 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15631055; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00535s; TotalTimePerSample = 0.02138ms; SamplesPerSecond = 46772 + Epoch[46 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16011963; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 + Epoch[46 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15244800; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00593s; TotalTimePerSample = 0.02374ms; SamplesPerSecond = 42130 + Epoch[46 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17083496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00537s; TotalTimePerSample = 0.02147ms; SamplesPerSecond = 46580 + Epoch[46 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18358936; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 + Epoch[46 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12527197; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00548s; TotalTimePerSample = 0.02192ms; SamplesPerSecond = 45628 + Epoch[46 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15015942; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00536s; TotalTimePerSample = 0.02143ms; SamplesPerSecond = 46667 + Epoch[46 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13307202; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00470s; TotalTimePerSample = 0.01882ms; SamplesPerSecond = 53146 + Epoch[46 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15247827; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00785s; TotalTimePerSample = 0.03140ms; SamplesPerSecond = 31847 + Epoch[46 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14167920; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46737 + Epoch[46 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16559229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00549s; TotalTimePerSample = 0.02196ms; SamplesPerSecond = 45529 + Epoch[46 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16666162; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02184ms; SamplesPerSecond = 45787 + Epoch[46 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17522510; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00534s; TotalTimePerSample = 0.02137ms; SamplesPerSecond = 46790 + Epoch[46 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11583984; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00546s; TotalTimePerSample = 0.02183ms; SamplesPerSecond = 45804 + Epoch[46 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13685254; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 + Epoch[46 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808350; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02181ms; SamplesPerSecond = 45846 + Epoch[46 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12016553; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00535s; TotalTimePerSample = 0.02140ms; SamplesPerSecond = 46728 + Epoch[46 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14723242; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00544s; TotalTimePerSample = 0.02177ms; SamplesPerSecond = 45938 + Epoch[46 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19884863; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00549s; TotalTimePerSample = 0.02195ms; SamplesPerSecond = 45562 + Epoch[46 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16697168; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00534s; TotalTimePerSample = 0.02136ms; SamplesPerSecond = 46807 + Epoch[46 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220557; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 + Epoch[46 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15398535; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02182ms; SamplesPerSecond = 45821 + Epoch[46 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11107129; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00536s; TotalTimePerSample = 0.02144ms; SamplesPerSecond = 46641 +Finished Epoch[46 of 50]: [Training Set] TrainLossPerSample = 0.15381378; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.220975 +Starting Epoch 47: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 46 at record count 460000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1896 retries for 10000 elements (19.0%) to ensure window condition +randomordering: recached sequence for seed 46: 375, 4056, ... + Epoch[47 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17009537; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00328s; TotalTimePerSample = 0.01313ms; SamplesPerSecond = 76173 + Epoch[47 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14089816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00728s; TotalTimePerSample = 0.02912ms; SamplesPerSecond = 34335 + Epoch[47 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13973935; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02191ms; SamplesPerSecond = 45645 + Epoch[47 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14522598; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00549s; TotalTimePerSample = 0.02197ms; SamplesPerSecond = 45520 + Epoch[47 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12900201; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00535s; TotalTimePerSample = 0.02141ms; SamplesPerSecond = 46711 + Epoch[47 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16575568; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 + Epoch[47 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16477930; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00547s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45670 + Epoch[47 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17690088; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00714s; TotalTimePerSample = 0.02857ms; SamplesPerSecond = 35004 + Epoch[47 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12744287; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02198ms; SamplesPerSecond = 45495 + Epoch[47 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722363; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45603 + Epoch[47 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17214087; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00551s; TotalTimePerSample = 0.02202ms; SamplesPerSecond = 45413 + Epoch[47 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11358752; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00538s; TotalTimePerSample = 0.02152ms; SamplesPerSecond = 46459 + Epoch[47 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17516565; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45653 + Epoch[47 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12191040; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00731s; TotalTimePerSample = 0.02923ms; SamplesPerSecond = 34209 + Epoch[47 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16542603; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02164ms; SamplesPerSecond = 46210 + Epoch[47 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12420703; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00556s; TotalTimePerSample = 0.02225ms; SamplesPerSecond = 44947 + Epoch[47 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14355225; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00550s; TotalTimePerSample = 0.02198ms; SamplesPerSecond = 45487 + Epoch[47 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11818335; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00552s; TotalTimePerSample = 0.02209ms; SamplesPerSecond = 45265 + Epoch[47 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15443384; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00559s; TotalTimePerSample = 0.02234ms; SamplesPerSecond = 44754 + Epoch[47 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18573535; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00551s; TotalTimePerSample = 0.02206ms; SamplesPerSecond = 45330 + Epoch[47 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16070605; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00552s; TotalTimePerSample = 0.02208ms; SamplesPerSecond = 45289 + Epoch[47 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18047827; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46279 + Epoch[47 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12440088; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00477s; TotalTimePerSample = 0.01908ms; SamplesPerSecond = 52410 + Epoch[47 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667993; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 + Epoch[47 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16745728; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00539s; TotalTimePerSample = 0.02156ms; SamplesPerSecond = 46373 + Epoch[47 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13788281; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00553s; TotalTimePerSample = 0.02213ms; SamplesPerSecond = 45191 + Epoch[47 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17373560; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00554s; TotalTimePerSample = 0.02214ms; SamplesPerSecond = 45158 + Epoch[47 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15597192; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00540s; TotalTimePerSample = 0.02161ms; SamplesPerSecond = 46270 + Epoch[47 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14745557; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02221ms; SamplesPerSecond = 45028 + Epoch[47 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13610791; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 + Epoch[47 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10932178; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00555s; TotalTimePerSample = 0.02219ms; SamplesPerSecond = 45061 + Epoch[47 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13513916; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00541s; TotalTimePerSample = 0.02166ms; SamplesPerSecond = 46176 + Epoch[47 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20640479; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.00553s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45240 + Epoch[47 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13302637; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45134 + Epoch[47 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15080078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00598s; TotalTimePerSample = 0.02392ms; SamplesPerSecond = 41806 + Epoch[47 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17111182; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02240ms; SamplesPerSecond = 44650 + Epoch[47 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19842529; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 + Epoch[47 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20571094; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45053 + Epoch[47 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13281738; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00543s; TotalTimePerSample = 0.02172ms; SamplesPerSecond = 46032 + Epoch[47 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17746826; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45118 +Finished Epoch[47 of 50]: [Training Set] TrainLossPerSample = 0.15356271; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.224735 +Starting Epoch 48: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 47 at record count 470000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1949 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 47: 2638, 2515, ... + Epoch[48 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15027994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00404s; TotalTimePerSample = 0.01615ms; SamplesPerSecond = 61911 + Epoch[48 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13497180; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00731s; TotalTimePerSample = 0.02922ms; SamplesPerSecond = 34223 + Epoch[48 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13972147; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00480s; TotalTimePerSample = 0.01922ms; SamplesPerSecond = 52039 + Epoch[48 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19054150; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00544s; TotalTimePerSample = 0.02176ms; SamplesPerSecond = 45947 + Epoch[48 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15590564; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02229ms; SamplesPerSecond = 44859 + Epoch[48 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17132758; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00554s; TotalTimePerSample = 0.02218ms; SamplesPerSecond = 45093 + Epoch[48 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12567676; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45110 + Epoch[48 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11931079; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00542s; TotalTimePerSample = 0.02169ms; SamplesPerSecond = 46108 + Epoch[48 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13617419; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00556s; TotalTimePerSample = 0.02224ms; SamplesPerSecond = 44972 + Epoch[48 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13334998; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44778 + Epoch[48 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16063586; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00552s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45257 + Epoch[48 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16104102; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 + Epoch[48 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15746460; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44738 + Epoch[48 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13254297; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02227ms; SamplesPerSecond = 44899 + Epoch[48 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18614429; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00545s; TotalTimePerSample = 0.02179ms; SamplesPerSecond = 45896 + Epoch[48 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19013940; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02232ms; SamplesPerSecond = 44802 + Epoch[48 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13494287; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00557s; TotalTimePerSample = 0.02226ms; SamplesPerSecond = 44923 + Epoch[48 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15374585; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00468s; TotalTimePerSample = 0.01872ms; SamplesPerSecond = 53418 + Epoch[48 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14510815; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00478s; TotalTimePerSample = 0.01913ms; SamplesPerSecond = 52279 + Epoch[48 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17918848; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00557s; TotalTimePerSample = 0.02226ms; SamplesPerSecond = 44915 + Epoch[48 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15291870; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00545s; TotalTimePerSample = 0.02181ms; SamplesPerSecond = 45854 + Epoch[48 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16817578; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02244ms; SamplesPerSecond = 44571 + Epoch[48 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11767017; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00582s; TotalTimePerSample = 0.02328ms; SamplesPerSecond = 42947 + Epoch[48 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16226343; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00469s; TotalTimePerSample = 0.01876ms; SamplesPerSecond = 53316 + Epoch[48 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13237671; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02238ms; SamplesPerSecond = 44682 + Epoch[48 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15202222; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00558s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44835 + Epoch[48 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718896; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.00545s; TotalTimePerSample = 0.02180ms; SamplesPerSecond = 45871 + Epoch[48 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12673828; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 + Epoch[48 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14897900; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00559s; TotalTimePerSample = 0.02237ms; SamplesPerSecond = 44706 + Epoch[48 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13764648; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44587 + Epoch[48 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17610938; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00546s; TotalTimePerSample = 0.02185ms; SamplesPerSecond = 45770 + Epoch[48 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722559; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00561s; TotalTimePerSample = 0.02245ms; SamplesPerSecond = 44539 + Epoch[48 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17907373; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44746 + Epoch[48 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13869922; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00557s; TotalTimePerSample = 0.02229ms; SamplesPerSecond = 44859 + Epoch[48 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12678125; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00550s; TotalTimePerSample = 0.02199ms; SamplesPerSecond = 45471 + Epoch[48 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16539990; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 + Epoch[48 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17748828; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44658 + Epoch[48 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19358105; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00548s; TotalTimePerSample = 0.02190ms; SamplesPerSecond = 45662 + Epoch[48 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713672; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00559s; TotalTimePerSample = 0.02235ms; SamplesPerSecond = 44738 + Epoch[48 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17234180; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 +Finished Epoch[48 of 50]: [Training Set] TrainLossPerSample = 0.15395074; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.221345 +Starting Epoch 49: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 48 at record count 480000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition +randomordering: recached sequence for seed 48: 1688, 1039, ... + Epoch[49 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17320860; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01314ms; SamplesPerSecond = 76080 + Epoch[49 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14062874; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00737s; TotalTimePerSample = 0.02946ms; SamplesPerSecond = 33939 + Epoch[49 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789914; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 + Epoch[49 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19715366; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02246ms; SamplesPerSecond = 44531 + Epoch[49 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15716534; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00774s; TotalTimePerSample = 0.03096ms; SamplesPerSecond = 32299 + Epoch[49 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14775000; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 + Epoch[49 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17860504; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00561s; TotalTimePerSample = 0.02243ms; SamplesPerSecond = 44579 + Epoch[49 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17541602; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00562s; TotalTimePerSample = 0.02246ms; SamplesPerSecond = 44515 + Epoch[49 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10276807; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00547s; TotalTimePerSample = 0.02189ms; SamplesPerSecond = 45678 + Epoch[49 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12209045; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 + Epoch[49 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16249878; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00559s; TotalTimePerSample = 0.02237ms; SamplesPerSecond = 44698 + Epoch[49 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14359253; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00564s; TotalTimePerSample = 0.02255ms; SamplesPerSecond = 44349 + Epoch[49 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12310620; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00548s; TotalTimePerSample = 0.02193ms; SamplesPerSecond = 45595 + Epoch[49 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18520605; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00487s; TotalTimePerSample = 0.01949ms; SamplesPerSecond = 51303 + Epoch[49 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13575903; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00563s; TotalTimePerSample = 0.02251ms; SamplesPerSecond = 44420 + Epoch[49 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18317041; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00550s; TotalTimePerSample = 0.02201ms; SamplesPerSecond = 45438 + Epoch[49 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14142798; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 + Epoch[49 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12793579; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02239ms; SamplesPerSecond = 44666 + Epoch[49 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14227441; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00573s; TotalTimePerSample = 0.02290ms; SamplesPerSecond = 43660 + Epoch[49 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14391211; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00473s; TotalTimePerSample = 0.01893ms; SamplesPerSecond = 52820 + Epoch[49 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911133; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44610 + Epoch[49 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14438867; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00569s; TotalTimePerSample = 0.02278ms; SamplesPerSecond = 43905 + Epoch[49 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16217529; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00550s; TotalTimePerSample = 0.02200ms; SamplesPerSecond = 45454 + Epoch[49 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17709521; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00562s; TotalTimePerSample = 0.02248ms; SamplesPerSecond = 44483 + Epoch[49 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12703735; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02248ms; SamplesPerSecond = 44476 + Epoch[49 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15274927; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00472s; TotalTimePerSample = 0.01890ms; SamplesPerSecond = 52921 + Epoch[49 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11113696; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00561s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44603 + Epoch[49 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096509; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.00573s; TotalTimePerSample = 0.02291ms; SamplesPerSecond = 43645 + Epoch[49 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19092334; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.00561s; TotalTimePerSample = 0.02242ms; SamplesPerSecond = 44603 + Epoch[49 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14696729; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45045 + Epoch[49 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13934277; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00562s; TotalTimePerSample = 0.02247ms; SamplesPerSecond = 44507 + Epoch[49 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13451563; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00564s; TotalTimePerSample = 0.02258ms; SamplesPerSecond = 44294 + Epoch[49 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18252490; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00556s; TotalTimePerSample = 0.02223ms; SamplesPerSecond = 44988 + Epoch[49 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15291846; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00560s; TotalTimePerSample = 0.02240ms; SamplesPerSecond = 44642 + Epoch[49 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16258545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00485s; TotalTimePerSample = 0.01938ms; SamplesPerSecond = 51588 + Epoch[49 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18700635; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00554s; TotalTimePerSample = 0.02218ms; SamplesPerSecond = 45093 + Epoch[49 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15848047; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.00560s; TotalTimePerSample = 0.02241ms; SamplesPerSecond = 44626 + Epoch[49 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19608398; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.00486s; TotalTimePerSample = 0.01943ms; SamplesPerSecond = 51461 + Epoch[49 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12497852; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00555s; TotalTimePerSample = 0.02220ms; SamplesPerSecond = 45036 + Epoch[49 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14640137; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00558s; TotalTimePerSample = 0.02233ms; SamplesPerSecond = 44786 +Finished Epoch[49 of 50]: [Training Set] TrainLossPerSample = 0.1532239; EvalErrPerSample = 0.073199995; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.223632 +Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 49 at record count 490000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 49: 954, 533, ... + Epoch[50 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215771; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00329s; TotalTimePerSample = 0.01316ms; SamplesPerSecond = 75964 + Epoch[50 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902780; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00740s; TotalTimePerSample = 0.02960ms; SamplesPerSecond = 33783 + Epoch[50 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13724939; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00568s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44029 + Epoch[50 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14850296; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00567s; TotalTimePerSample = 0.02267ms; SamplesPerSecond = 44115 + Epoch[50 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21980939; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00568s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44052 + Epoch[50 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14945911; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00554s; TotalTimePerSample = 0.02217ms; SamplesPerSecond = 45101 + Epoch[50 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13680707; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00565s; TotalTimePerSample = 0.02258ms; SamplesPerSecond = 44286 + Epoch[50 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18415393; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00567s; TotalTimePerSample = 0.02268ms; SamplesPerSecond = 44091 + Epoch[50 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13719470; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00568s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44029 + Epoch[50 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292920; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00554s; TotalTimePerSample = 0.02216ms; SamplesPerSecond = 45126 + Epoch[50 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13963062; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00566s; TotalTimePerSample = 0.02265ms; SamplesPerSecond = 44146 + Epoch[50 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12586951; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00565s; TotalTimePerSample = 0.02261ms; SamplesPerSecond = 44232 + Epoch[50 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15136499; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00566s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44200 + Epoch[50 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13051282; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00552s; TotalTimePerSample = 0.02210ms; SamplesPerSecond = 45248 + Epoch[50 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12969312; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00488s; TotalTimePerSample = 0.01950ms; SamplesPerSecond = 51282 + Epoch[50 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15042041; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00568s; TotalTimePerSample = 0.02273ms; SamplesPerSecond = 43990 + Epoch[50 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14371826; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00555s; TotalTimePerSample = 0.02219ms; SamplesPerSecond = 45069 + Epoch[50 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08564575; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00570s; TotalTimePerSample = 0.02279ms; SamplesPerSecond = 43882 + Epoch[50 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17781812; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00567s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44060 + Epoch[50 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20411499; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00567s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44060 + Epoch[50 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17243628; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00557s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44851 + Epoch[50 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12457227; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00488s; TotalTimePerSample = 0.01951ms; SamplesPerSecond = 51250 + Epoch[50 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477710; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00598s; TotalTimePerSample = 0.02394ms; SamplesPerSecond = 41778 + Epoch[50 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16080859; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00559s; TotalTimePerSample = 0.02234ms; SamplesPerSecond = 44754 + Epoch[50 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15700757; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00568s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44045 + Epoch[50 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11665210; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00567s; TotalTimePerSample = 0.02268ms; SamplesPerSecond = 44099 + Epoch[50 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17690649; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00572s; TotalTimePerSample = 0.02287ms; SamplesPerSecond = 43721 + Epoch[50 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266699; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00558s; TotalTimePerSample = 0.02230ms; SamplesPerSecond = 44835 + Epoch[50 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20211230; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00581s; TotalTimePerSample = 0.02324ms; SamplesPerSecond = 43036 + Epoch[50 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14714990; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00597s; TotalTimePerSample = 0.02388ms; SamplesPerSecond = 41876 + Epoch[50 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411475; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00611s; TotalTimePerSample = 0.02445ms; SamplesPerSecond = 40896 + Epoch[50 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13433350; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00607s; TotalTimePerSample = 0.02429ms; SamplesPerSecond = 41165 + Epoch[50 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18207861; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00605s; TotalTimePerSample = 0.02419ms; SamplesPerSecond = 41342 + Epoch[50 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16985645; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00603s; TotalTimePerSample = 0.02411ms; SamplesPerSecond = 41473 + Epoch[50 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18879053; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00605s; TotalTimePerSample = 0.02418ms; SamplesPerSecond = 41356 + Epoch[50 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14025830; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00605s; TotalTimePerSample = 0.02420ms; SamplesPerSecond = 41329 + Epoch[50 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17361621; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00618s; TotalTimePerSample = 0.02472ms; SamplesPerSecond = 40459 + Epoch[50 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13023340; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00508s; TotalTimePerSample = 0.02032ms; SamplesPerSecond = 49212 + Epoch[50 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19875732; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00606s; TotalTimePerSample = 0.02424ms; SamplesPerSecond = 41254 + Epoch[50 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12190576; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00605s; TotalTimePerSample = 0.02418ms; SamplesPerSecond = 41356 +Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15337785; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.229606 +CNTKCommandTrainEnd: Simple_Demo +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + + +Allocating matrices for forward propagation. + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +starting at epoch 0 counting lines to determine record count + + 603 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch +Minibatch[1]: ActualMBSize = 603 +Total Samples Evaluated = 603 +COMPLETED +=== Deleting last epoch data +==== Re-running from checkpoint +=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 makeMode=true +------------------------------------------------------------------- +Build info: + + Built time: Nov 19 2015 14:09:57 + Last modified date: Thu Nov 19 08:15:28 2015 + Built by svcphil on dphaim-26-new + Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 + Build Branch: HEAD + Build SHA1: a342184dcad3179b0e33596ac330ce724e9cee4b +------------------------------------------------------------------- +running on liana-08-w at 2015/11/19 14:21:40 +command line: +C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=-1 makeMode=true + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=$RunDir$/models/simple.dnn +deviceId=$DeviceNumber$ +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +outputPath=$RunDir$/SimpleOutput +] +RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=-1 +makeMode=true + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +deviceId=-1 +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput +] +RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=-1 +makeMode=true + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output +configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +configparameters: cntk.config:deviceId=-1 +configparameters: cntk.config:DeviceNumber=-1 +configparameters: cntk.config:makeMode=true +configparameters: cntk.config:modelPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu +configparameters: cntk.config:Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] + +configparameters: cntk.config:Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/SimpleOutput +] + +configparameters: cntk.config:traceLevel=1 +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: Simple_Demo Simple_Demo_Output +precision = float +CNTKModelPath: C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn +CNTKCommandTrainInfo: Simple_Demo : 50 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 +CNTKCommandTrainBegin: Simple_Demo +SimpleNetworkBuilder Using CPU +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt +Starting from checkpoint. Load Network From File C:\windows\TEMP\cntk-test-20151119141632.280376\Speech_Simple@release_cpu/models/simple.dnn.49. + + +Allocating matrices for forward propagation. + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Allocating matrices for gradient computing +No PreCompute nodes found, skipping PreCompute step +Warning: checkpoint file is missing. learning parameters will be initialized from 0 +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting at epoch 49 counting lines to determine record count + + 10000 records found +starting epoch 49 at record count 490000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 49: 954, 533, ... + Epoch[50 of 50]-Minibatch[ 1- 10]: SamplesSeen = 250; TrainLossPerSample = 0.16219548; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.12387s; TotalTimePerSample = 0.49550ms; SamplesPerSecond = 2018 + Epoch[50 of 50]-Minibatch[ 11- 20]: SamplesSeen = 250; TrainLossPerSample = 0.15912361; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00325s; TotalTimePerSample = 0.01298ms; SamplesPerSecond = 77041 + Epoch[50 of 50]-Minibatch[ 21- 30]: SamplesSeen = 250; TrainLossPerSample = 0.13726807; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00326s; TotalTimePerSample = 0.01303ms; SamplesPerSecond = 76734 + Epoch[50 of 50]-Minibatch[ 31- 40]: SamplesSeen = 250; TrainLossPerSample = 0.14841956; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76429 + Epoch[50 of 50]-Minibatch[ 41- 50]: SamplesSeen = 250; TrainLossPerSample = 0.21969360; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 + Epoch[50 of 50]-Minibatch[ 51- 60]: SamplesSeen = 250; TrainLossPerSample = 0.14945532; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00328s; TotalTimePerSample = 0.01311ms; SamplesPerSecond = 76266 + Epoch[50 of 50]-Minibatch[ 61- 70]: SamplesSeen = 250; TrainLossPerSample = 0.13676563; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 + Epoch[50 of 50]-Minibatch[ 71- 80]: SamplesSeen = 250; TrainLossPerSample = 0.18411462; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.00355s; TotalTimePerSample = 0.01420ms; SamplesPerSecond = 70442 + Epoch[50 of 50]-Minibatch[ 81- 90]: SamplesSeen = 250; TrainLossPerSample = 0.13720374; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00342s; TotalTimePerSample = 0.01367ms; SamplesPerSecond = 73142 + Epoch[50 of 50]-Minibatch[ 91- 100]: SamplesSeen = 250; TrainLossPerSample = 0.15293506; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76452 + Epoch[50 of 50]-Minibatch[ 101- 110]: SamplesSeen = 250; TrainLossPerSample = 0.13964490; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00326s; TotalTimePerSample = 0.01306ms; SamplesPerSecond = 76593 + Epoch[50 of 50]-Minibatch[ 111- 120]: SamplesSeen = 250; TrainLossPerSample = 0.12590125; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00329s; TotalTimePerSample = 0.01315ms; SamplesPerSecond = 76034 + Epoch[50 of 50]-Minibatch[ 121- 130]: SamplesSeen = 250; TrainLossPerSample = 0.15137183; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76312 + Epoch[50 of 50]-Minibatch[ 131- 140]: SamplesSeen = 250; TrainLossPerSample = 0.13051648; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00328s; TotalTimePerSample = 0.01312ms; SamplesPerSecond = 76219 + Epoch[50 of 50]-Minibatch[ 141- 150]: SamplesSeen = 250; TrainLossPerSample = 0.12970435; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 + Epoch[50 of 50]-Minibatch[ 151- 160]: SamplesSeen = 250; TrainLossPerSample = 0.15042603; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00326s; TotalTimePerSample = 0.01306ms; SamplesPerSecond = 76569 + Epoch[50 of 50]-Minibatch[ 161- 170]: SamplesSeen = 250; TrainLossPerSample = 0.14372729; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00327s; TotalTimePerSample = 0.01308ms; SamplesPerSecond = 76475 + Epoch[50 of 50]-Minibatch[ 171- 180]: SamplesSeen = 250; TrainLossPerSample = 0.08566089; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.00340s; TotalTimePerSample = 0.01361ms; SamplesPerSecond = 73486 + Epoch[50 of 50]-Minibatch[ 181- 190]: SamplesSeen = 250; TrainLossPerSample = 0.17779297; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00330s; TotalTimePerSample = 0.01322ms; SamplesPerSecond = 75665 + Epoch[50 of 50]-Minibatch[ 191- 200]: SamplesSeen = 250; TrainLossPerSample = 0.20410034; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00327s; TotalTimePerSample = 0.01310ms; SamplesPerSecond = 76359 + Epoch[50 of 50]-Minibatch[ 201- 210]: SamplesSeen = 250; TrainLossPerSample = 0.17243750; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00314s; TotalTimePerSample = 0.01255ms; SamplesPerSecond = 79693 + Epoch[50 of 50]-Minibatch[ 211- 220]: SamplesSeen = 250; TrainLossPerSample = 0.12458472; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81994 + Epoch[50 of 50]-Minibatch[ 221- 230]: SamplesSeen = 250; TrainLossPerSample = 0.14477295; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[50 of 50]-Minibatch[ 231- 240]: SamplesSeen = 250; TrainLossPerSample = 0.16081299; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81806 + Epoch[50 of 50]-Minibatch[ 241- 250]: SamplesSeen = 250; TrainLossPerSample = 0.15700513; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81886 + Epoch[50 of 50]-Minibatch[ 251- 260]: SamplesSeen = 250; TrainLossPerSample = 0.11666357; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[50 of 50]-Minibatch[ 261- 270]: SamplesSeen = 250; TrainLossPerSample = 0.17689966; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81940 + Epoch[50 of 50]-Minibatch[ 271- 280]: SamplesSeen = 250; TrainLossPerSample = 0.12267065; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01221ms; SamplesPerSecond = 81913 + Epoch[50 of 50]-Minibatch[ 281- 290]: SamplesSeen = 250; TrainLossPerSample = 0.20211328; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[50 of 50]-Minibatch[ 291- 300]: SamplesSeen = 250; TrainLossPerSample = 0.14714893; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.00307s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81459 + Epoch[50 of 50]-Minibatch[ 301- 310]: SamplesSeen = 250; TrainLossPerSample = 0.14411719; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01219ms; SamplesPerSecond = 82020 + Epoch[50 of 50]-Minibatch[ 311- 320]: SamplesSeen = 250; TrainLossPerSample = 0.13433789; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[50 of 50]-Minibatch[ 321- 330]: SamplesSeen = 250; TrainLossPerSample = 0.18207080; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00333s; TotalTimePerSample = 0.01333ms; SamplesPerSecond = 75030 + Epoch[50 of 50]-Minibatch[ 331- 340]: SamplesSeen = 250; TrainLossPerSample = 0.16985889; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81833 + Epoch[50 of 50]-Minibatch[ 341- 350]: SamplesSeen = 250; TrainLossPerSample = 0.18878906; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.00312s; TotalTimePerSample = 0.01247ms; SamplesPerSecond = 80179 + Epoch[50 of 50]-Minibatch[ 351- 360]: SamplesSeen = 250; TrainLossPerSample = 0.14026221; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01218ms; SamplesPerSecond = 82074 + Epoch[50 of 50]-Minibatch[ 361- 370]: SamplesSeen = 250; TrainLossPerSample = 0.17361914; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.00304s; TotalTimePerSample = 0.01216ms; SamplesPerSecond = 82263 + Epoch[50 of 50]-Minibatch[ 371- 380]: SamplesSeen = 250; TrainLossPerSample = 0.13023926; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.00305s; TotalTimePerSample = 0.01220ms; SamplesPerSecond = 81967 + Epoch[50 of 50]-Minibatch[ 381- 390]: SamplesSeen = 250; TrainLossPerSample = 0.19875293; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.00305s; TotalTimePerSample = 0.01222ms; SamplesPerSecond = 81859 + Epoch[50 of 50]-Minibatch[ 391- 400]: SamplesSeen = 250; TrainLossPerSample = 0.12190723; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.00306s; TotalTimePerSample = 0.01224ms; SamplesPerSecond = 81672 +Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15337712; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=0.251958 +CNTKCommandTrainEnd: Simple_Demo +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + + +Allocating matrices for forward propagation. + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +starting at epoch 0 counting lines to determine record count + + 603 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch +Minibatch[1]: ActualMBSize = 603 +Total Samples Evaluated = 603 +COMPLETED diff --git a/Tests/EndToEndTests/Speech/Simple/baseline.windows.gpu.txt b/Tests/EndToEndTests/Speech/Simple/baseline.windows.gpu.txt index 04cf43893ab0..2134a6b93e60 100644 --- a/Tests/EndToEndTests/Speech/Simple/baseline.windows.gpu.txt +++ b/Tests/EndToEndTests/Speech/Simple/baseline.windows.gpu.txt @@ -1,4977 +1,4977 @@ -=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/debug/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 -------------------------------------------------------------------- -Build info: - - Built time: Nov 19 2015 12:01:19 - Last modified date: Thu Nov 19 08:15:43 2015 - Built by svcphil on liana-08-w - Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 - Build Branch: HEAD - Build SHA1: 70db50387b8998cf0f444e0164d4e4473a027bc9 -------------------------------------------------------------------- -running on DPHAIM-25 at 2015/11/19 12:07:34 -command line: -C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\debug\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=$RunDir$/models/simple.dnn -deviceId=$DeviceNumber$ -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -outputPath=$RunDir$/SimpleOutput -] -RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -deviceId=-1 -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput -] -RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=0 - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output -configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -configparameters: cntk.config:deviceId=0 -configparameters: cntk.config:DeviceNumber=-1 -configparameters: cntk.config:modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -configparameters: cntk.config:Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] - -configparameters: cntk.config:Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput -] - -configparameters: cntk.config:traceLevel=1 -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: Simple_Demo Simple_Demo_Output -precision = float -CNTKModelPath: C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -CNTKCommandTrainInfo: Simple_Demo : 50 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 -CNTKCommandTrainBegin: Simple_Demo -SimpleNetworkBuilder Using GPU 0 -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt -SetUniformRandomValue (GPU): creating curand object with seed 1 - - -Allocating matrices for forward propagation. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 3], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) -B2[2, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) -B1[50, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) -B0[50, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 3], MeanOfFeatures[0, 0], InvStdOfFeatures[0, 0]) -InvStdOfFeatures[0, 0] = InvStdDev(features[2, 3]) -MeanOfFeatures[0, 0] = Mean(features[2, 3]) -features[2, 3] = InputValue -W0[50, 2] = LearnableParameter -W1[50, 50] = LearnableParameter -W2[2, 50] = LearnableParameter -labels[2, 3] = InputValue - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Allocating matrices for gradient computing - -Precomputing --> 3 PreCompute nodes found. - - NodeName: InvStdOfFeatures - NodeName: MeanOfFeatures - NodeName: Prior -starting at epoch 0 counting lines to determine record count - - 10000 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch - - -Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] - -Validating for node InvStdOfFeatures, final verification. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node MeanOfFeatures. 2 nodes to process in pass 1. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] - -Validating for node MeanOfFeatures, final verification. - -Validating --> features = InputValue -> [2, MBSize 3] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - - - -Validating for node Prior. 2 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] - -Validating for node Prior, final verification. - -Validating --> labels = InputValue -> [2, MBSize 3] -Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] - -1 out of 2 nodes do not share the minibatch layout with the input data. - -randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 0: 2334, 3830, ... - -Precomputing --> Completed. - -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 -starting epoch 0 at record count 0, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 0: 2334, 3830, ... - Epoch[ 1 of 50]-Minibatch[ 1- 10 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70131116; EvalErr[0]PerSample = 0.53200000; TotalTime = 0.05255s; TotalTimePerSample = 0.21018ms; SamplesPerSecond = 4757 - Epoch[ 1 of 50]-Minibatch[ 11- 20 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72596228; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.04638s; TotalTimePerSample = 0.18551ms; SamplesPerSecond = 5390 - Epoch[ 1 of 50]-Minibatch[ 21- 30 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69576807; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04638s; TotalTimePerSample = 0.18553ms; SamplesPerSecond = 5390 - Epoch[ 1 of 50]-Minibatch[ 31- 40 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71764063; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.04635s; TotalTimePerSample = 0.18540ms; SamplesPerSecond = 5393 - Epoch[ 1 of 50]-Minibatch[ 41- 50 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.78249609; EvalErr[0]PerSample = 0.52800000; TotalTime = 0.04637s; TotalTimePerSample = 0.18548ms; SamplesPerSecond = 5391 - Epoch[ 1 of 50]-Minibatch[ 51- 60 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73651172; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04616s; TotalTimePerSample = 0.18464ms; SamplesPerSecond = 5415 - Epoch[ 1 of 50]-Minibatch[ 61- 70 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74346777; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04641s; TotalTimePerSample = 0.18565ms; SamplesPerSecond = 5386 - Epoch[ 1 of 50]-Minibatch[ 71- 80 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69752002; EvalErr[0]PerSample = 0.44000000; TotalTime = 0.04656s; TotalTimePerSample = 0.18625ms; SamplesPerSecond = 5369 - Epoch[ 1 of 50]-Minibatch[ 81- 90 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72111279; EvalErr[0]PerSample = 0.42800000; TotalTime = 0.04641s; TotalTimePerSample = 0.18564ms; SamplesPerSecond = 5386 - Epoch[ 1 of 50]-Minibatch[ 91- 100 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74622461; EvalErr[0]PerSample = 0.53600000; TotalTime = 0.04619s; TotalTimePerSample = 0.18476ms; SamplesPerSecond = 5412 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 1 of 50]-Minibatch[ 101- 110 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73008838; EvalErr[0]PerSample = 0.56000000; TotalTime = 0.04623s; TotalTimePerSample = 0.18494ms; SamplesPerSecond = 5407 - Epoch[ 1 of 50]-Minibatch[ 111- 120 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70292676; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.04617s; TotalTimePerSample = 0.18468ms; SamplesPerSecond = 5414 - Epoch[ 1 of 50]-Minibatch[ 121- 130 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70272168; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.04620s; TotalTimePerSample = 0.18481ms; SamplesPerSecond = 5411 - Epoch[ 1 of 50]-Minibatch[ 131- 140 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70346289; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04661s; TotalTimePerSample = 0.18644ms; SamplesPerSecond = 5363 - Epoch[ 1 of 50]-Minibatch[ 141- 150 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72254395; EvalErr[0]PerSample = 0.56800000; TotalTime = 0.04630s; TotalTimePerSample = 0.18521ms; SamplesPerSecond = 5399 - Epoch[ 1 of 50]-Minibatch[ 151- 160 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70169727; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04638s; TotalTimePerSample = 0.18550ms; SamplesPerSecond = 5390 - Epoch[ 1 of 50]-Minibatch[ 161- 170 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71473145; EvalErr[0]PerSample = 0.54400000; TotalTime = 0.04632s; TotalTimePerSample = 0.18530ms; SamplesPerSecond = 5396 - Epoch[ 1 of 50]-Minibatch[ 171- 180 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70216016; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04652s; TotalTimePerSample = 0.18610ms; SamplesPerSecond = 5373 - Epoch[ 1 of 50]-Minibatch[ 181- 190 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71398242; EvalErr[0]PerSample = 0.47200000; TotalTime = 0.04629s; TotalTimePerSample = 0.18516ms; SamplesPerSecond = 5400 - Epoch[ 1 of 50]-Minibatch[ 191- 200 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70411133; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.04662s; TotalTimePerSample = 0.18647ms; SamplesPerSecond = 5362 - Epoch[ 1 of 50]-Minibatch[ 201- 210 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69730371; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04618s; TotalTimePerSample = 0.18472ms; SamplesPerSecond = 5413 - Epoch[ 1 of 50]-Minibatch[ 211- 220 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69658789; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.04609s; TotalTimePerSample = 0.18438ms; SamplesPerSecond = 5423 - Epoch[ 1 of 50]-Minibatch[ 221- 230 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72137207; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04643s; TotalTimePerSample = 0.18571ms; SamplesPerSecond = 5384 - Epoch[ 1 of 50]-Minibatch[ 231- 240 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69762695; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04609s; TotalTimePerSample = 0.18437ms; SamplesPerSecond = 5423 - Epoch[ 1 of 50]-Minibatch[ 241- 250 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70667187; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04669s; TotalTimePerSample = 0.18677ms; SamplesPerSecond = 5354 - Epoch[ 1 of 50]-Minibatch[ 251- 260 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69341797; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04603s; TotalTimePerSample = 0.18414ms; SamplesPerSecond = 5430 - Epoch[ 1 of 50]-Minibatch[ 261- 270 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70505273; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.04626s; TotalTimePerSample = 0.18502ms; SamplesPerSecond = 5404 - Epoch[ 1 of 50]-Minibatch[ 271- 280 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69313086; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04635s; TotalTimePerSample = 0.18540ms; SamplesPerSecond = 5393 - Epoch[ 1 of 50]-Minibatch[ 281- 290 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69931641; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.04609s; TotalTimePerSample = 0.18438ms; SamplesPerSecond = 5423 - Epoch[ 1 of 50]-Minibatch[ 291- 300 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69199219; EvalErr[0]PerSample = 0.41600000; TotalTime = 0.04622s; TotalTimePerSample = 0.18489ms; SamplesPerSecond = 5408 - Epoch[ 1 of 50]-Minibatch[ 301- 310 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69941211; EvalErr[0]PerSample = 0.46000000; TotalTime = 0.04640s; TotalTimePerSample = 0.18560ms; SamplesPerSecond = 5387 - Epoch[ 1 of 50]-Minibatch[ 311- 320 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68918164; EvalErr[0]PerSample = 0.42000000; TotalTime = 0.04616s; TotalTimePerSample = 0.18465ms; SamplesPerSecond = 5415 - Epoch[ 1 of 50]-Minibatch[ 321- 330 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69079492; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04636s; TotalTimePerSample = 0.18545ms; SamplesPerSecond = 5392 - Epoch[ 1 of 50]-Minibatch[ 331- 340 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70002344; EvalErr[0]PerSample = 0.51600000; TotalTime = 0.04637s; TotalTimePerSample = 0.18547ms; SamplesPerSecond = 5391 - Epoch[ 1 of 50]-Minibatch[ 341- 350 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69928320; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04604s; TotalTimePerSample = 0.18415ms; SamplesPerSecond = 5430 - Epoch[ 1 of 50]-Minibatch[ 351- 360 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68106836; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.04641s; TotalTimePerSample = 0.18563ms; SamplesPerSecond = 5387 - Epoch[ 1 of 50]-Minibatch[ 361- 370 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.66569336; EvalErr[0]PerSample = 0.42400000; TotalTime = 0.04607s; TotalTimePerSample = 0.18426ms; SamplesPerSecond = 5426 - Epoch[ 1 of 50]-Minibatch[ 371- 380 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.64908203; EvalErr[0]PerSample = 0.22400000; TotalTime = 0.04640s; TotalTimePerSample = 0.18558ms; SamplesPerSecond = 5388 - Epoch[ 1 of 50]-Minibatch[ 381- 390 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.63724609; EvalErr[0]PerSample = 0.31200000; TotalTime = 0.04647s; TotalTimePerSample = 0.18586ms; SamplesPerSecond = 5380 - Epoch[ 1 of 50]-Minibatch[ 391- 400 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.58329297; EvalErr[0]PerSample = 0.26000000; TotalTime = 0.04644s; TotalTimePerSample = 0.18575ms; SamplesPerSecond = 5383 -Finished Epoch[ 1 of 50]: [Training Set] TrainLossPerSample = 0.70159978; EvalErrPerSample = 0.47079998; AvgLearningRatePerSample = 0.01999999955; EpochTime=1.867046 -Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 1 at record count 10000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1901 retries for 10000 elements (19.0%) to ensure window condition -randomordering: recached sequence for seed 1: 1914, 1172, ... - Epoch[ 2 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.54827979; EvalErr[0]PerSample = 0.21600000; TotalTime = 0.04982s; TotalTimePerSample = 0.19928ms; SamplesPerSecond = 5018 - Epoch[ 2 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.48939734; EvalErr[0]PerSample = 0.18000000; TotalTime = 0.04622s; TotalTimePerSample = 0.18486ms; SamplesPerSecond = 5409 - Epoch[ 2 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.43074292; EvalErr[0]PerSample = 0.16000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18278ms; SamplesPerSecond = 5470 - Epoch[ 2 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.39592285; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04581s; TotalTimePerSample = 0.18324ms; SamplesPerSecond = 5457 - Epoch[ 2 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.32346033; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 - Epoch[ 2 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.27213037; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 - Epoch[ 2 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.28338940; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 - Epoch[ 2 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.24400537; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04607s; TotalTimePerSample = 0.18430ms; SamplesPerSecond = 5426 - Epoch[ 2 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23811084; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04625s; TotalTimePerSample = 0.18500ms; SamplesPerSecond = 5405 - Epoch[ 2 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19497119; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04598s; TotalTimePerSample = 0.18392ms; SamplesPerSecond = 5437 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 2 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22167163; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04615s; TotalTimePerSample = 0.18459ms; SamplesPerSecond = 5417 - Epoch[ 2 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21802466; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18330ms; SamplesPerSecond = 5455 - Epoch[ 2 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18628687; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04581s; TotalTimePerSample = 0.18325ms; SamplesPerSecond = 5457 - Epoch[ 2 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16806738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 - Epoch[ 2 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353809; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 - Epoch[ 2 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16384961; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04572s; TotalTimePerSample = 0.18286ms; SamplesPerSecond = 5468 - Epoch[ 2 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16741260; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18332ms; SamplesPerSecond = 5454 - Epoch[ 2 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18466162; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04660s; TotalTimePerSample = 0.18638ms; SamplesPerSecond = 5365 - Epoch[ 2 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15171387; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04774s; TotalTimePerSample = 0.19094ms; SamplesPerSecond = 5237 - Epoch[ 2 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688721; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04611s; TotalTimePerSample = 0.18442ms; SamplesPerSecond = 5422 - Epoch[ 2 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15986084; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04646s; TotalTimePerSample = 0.18586ms; SamplesPerSecond = 5380 - Epoch[ 2 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14824854; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18365ms; SamplesPerSecond = 5445 - Epoch[ 2 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15344580; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04577s; TotalTimePerSample = 0.18307ms; SamplesPerSecond = 5462 - Epoch[ 2 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18035303; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04574s; TotalTimePerSample = 0.18295ms; SamplesPerSecond = 5466 - Epoch[ 2 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15277344; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04579s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 - Epoch[ 2 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15023730; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04592s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 - Epoch[ 2 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19006250; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 - Epoch[ 2 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15078857; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04616s; TotalTimePerSample = 0.18464ms; SamplesPerSecond = 5415 - Epoch[ 2 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15495068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04555s; TotalTimePerSample = 0.18218ms; SamplesPerSecond = 5488 - Epoch[ 2 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19428369; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04596s; TotalTimePerSample = 0.18383ms; SamplesPerSecond = 5439 - Epoch[ 2 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15784766; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04643s; TotalTimePerSample = 0.18572ms; SamplesPerSecond = 5384 - Epoch[ 2 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16802686; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 - Epoch[ 2 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14867822; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18373ms; SamplesPerSecond = 5442 - Epoch[ 2 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15570654; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 - Epoch[ 2 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17597510; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04555s; TotalTimePerSample = 0.18222ms; SamplesPerSecond = 5487 - Epoch[ 2 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12306738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 - Epoch[ 2 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12854980; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 - Epoch[ 2 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16306689; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04547s; TotalTimePerSample = 0.18187ms; SamplesPerSecond = 5498 - Epoch[ 2 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19146924; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 - Epoch[ 2 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20339941; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04573s; TotalTimePerSample = 0.18294ms; SamplesPerSecond = 5466 -Finished Epoch[ 2 of 50]: [Training Set] TrainLossPerSample = 0.21133289; EvalErrPerSample = 0.082400002; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.84572 -Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 2 at record count 20000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1912 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 2: 3447, 110, ... - Epoch[ 3 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17365886; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.05013s; TotalTimePerSample = 0.20052ms; SamplesPerSecond = 4987 - Epoch[ 3 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18830190; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04683s; TotalTimePerSample = 0.18732ms; SamplesPerSecond = 5338 - Epoch[ 3 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15952179; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04577s; TotalTimePerSample = 0.18308ms; SamplesPerSecond = 5461 - Epoch[ 3 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18096484; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04566s; TotalTimePerSample = 0.18265ms; SamplesPerSecond = 5475 - Epoch[ 3 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17355847; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04594s; TotalTimePerSample = 0.18375ms; SamplesPerSecond = 5442 - Epoch[ 3 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16889929; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 - Epoch[ 3 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11249597; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04567s; TotalTimePerSample = 0.18269ms; SamplesPerSecond = 5473 - Epoch[ 3 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16278979; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 - Epoch[ 3 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20447998; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04585s; TotalTimePerSample = 0.18339ms; SamplesPerSecond = 5452 - Epoch[ 3 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17866296; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18306ms; SamplesPerSecond = 5462 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 3 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13869763; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 - Epoch[ 3 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15972412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18333ms; SamplesPerSecond = 5454 - Epoch[ 3 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21236255; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04586s; TotalTimePerSample = 0.18345ms; SamplesPerSecond = 5451 - Epoch[ 3 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20550391; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04610s; TotalTimePerSample = 0.18441ms; SamplesPerSecond = 5422 - Epoch[ 3 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12160645; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 - Epoch[ 3 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10832666; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04583s; TotalTimePerSample = 0.18334ms; SamplesPerSecond = 5454 - Epoch[ 3 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16376050; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18334ms; SamplesPerSecond = 5454 - Epoch[ 3 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14577954; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 - Epoch[ 3 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12433838; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04597s; TotalTimePerSample = 0.18386ms; SamplesPerSecond = 5438 - Epoch[ 3 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17770288; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04595s; TotalTimePerSample = 0.18380ms; SamplesPerSecond = 5440 - Epoch[ 3 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19776099; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04644s; TotalTimePerSample = 0.18577ms; SamplesPerSecond = 5382 - Epoch[ 3 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14767725; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04691s; TotalTimePerSample = 0.18766ms; SamplesPerSecond = 5328 - Epoch[ 3 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904053; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04668s; TotalTimePerSample = 0.18672ms; SamplesPerSecond = 5355 - Epoch[ 3 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795532; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04613s; TotalTimePerSample = 0.18452ms; SamplesPerSecond = 5419 - Epoch[ 3 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09892163; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18311ms; SamplesPerSecond = 5461 - Epoch[ 3 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716260; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 - Epoch[ 3 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17355420; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18311ms; SamplesPerSecond = 5461 - Epoch[ 3 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15936279; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04597s; TotalTimePerSample = 0.18389ms; SamplesPerSecond = 5437 - Epoch[ 3 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17972363; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 - Epoch[ 3 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12260254; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 - Epoch[ 3 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17041846; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04598s; TotalTimePerSample = 0.18391ms; SamplesPerSecond = 5437 - Epoch[ 3 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13416553; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 - Epoch[ 3 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12525977; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 - Epoch[ 3 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14119385; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 - Epoch[ 3 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19594287; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04609s; TotalTimePerSample = 0.18436ms; SamplesPerSecond = 5424 - Epoch[ 3 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15041064; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04566s; TotalTimePerSample = 0.18263ms; SamplesPerSecond = 5475 - Epoch[ 3 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12593604; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04581s; TotalTimePerSample = 0.18324ms; SamplesPerSecond = 5457 - Epoch[ 3 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17783594; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04584s; TotalTimePerSample = 0.18336ms; SamplesPerSecond = 5453 - Epoch[ 3 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17358057; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04573s; TotalTimePerSample = 0.18291ms; SamplesPerSecond = 5467 - Epoch[ 3 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17433789; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04598s; TotalTimePerSample = 0.18391ms; SamplesPerSecond = 5437 -Finished Epoch[ 3 of 50]: [Training Set] TrainLossPerSample = 0.15909949; EvalErrPerSample = 0.075800002; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.845274 -Starting Epoch 4: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 3 at record count 30000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1941 retries for 10000 elements (19.4%) to ensure window condition -randomordering: recached sequence for seed 3: 1741, 4693, ... - Epoch[ 4 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18529663; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04986s; TotalTimePerSample = 0.19942ms; SamplesPerSecond = 5014 - Epoch[ 4 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19594196; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04609s; TotalTimePerSample = 0.18437ms; SamplesPerSecond = 5423 - Epoch[ 4 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13145807; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 - Epoch[ 4 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20137177; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 - Epoch[ 4 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18951855; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04590s; TotalTimePerSample = 0.18361ms; SamplesPerSecond = 5446 - Epoch[ 4 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22155804; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04584s; TotalTimePerSample = 0.18335ms; SamplesPerSecond = 5454 - Epoch[ 4 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034131; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04561s; TotalTimePerSample = 0.18246ms; SamplesPerSecond = 5480 - Epoch[ 4 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521619; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04605s; TotalTimePerSample = 0.18419ms; SamplesPerSecond = 5429 - Epoch[ 4 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13807910; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18314ms; SamplesPerSecond = 5460 - Epoch[ 4 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14815063; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04588s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 4 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15757410; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04568s; TotalTimePerSample = 0.18271ms; SamplesPerSecond = 5473 - Epoch[ 4 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11073975; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04593s; TotalTimePerSample = 0.18371ms; SamplesPerSecond = 5443 - Epoch[ 4 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17341187; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18310ms; SamplesPerSecond = 5461 - Epoch[ 4 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10827295; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04595s; TotalTimePerSample = 0.18381ms; SamplesPerSecond = 5440 - Epoch[ 4 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15454028; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 - Epoch[ 4 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19133398; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18306ms; SamplesPerSecond = 5462 - Epoch[ 4 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12807007; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04647s; TotalTimePerSample = 0.18590ms; SamplesPerSecond = 5379 - Epoch[ 4 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11360352; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04590s; TotalTimePerSample = 0.18361ms; SamplesPerSecond = 5446 - Epoch[ 4 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18848999; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5463 - Epoch[ 4 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17556250; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04630s; TotalTimePerSample = 0.18518ms; SamplesPerSecond = 5400 - Epoch[ 4 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18725269; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04588s; TotalTimePerSample = 0.18351ms; SamplesPerSecond = 5449 - Epoch[ 4 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13380542; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04573s; TotalTimePerSample = 0.18293ms; SamplesPerSecond = 5466 - Epoch[ 4 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15871045; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04569s; TotalTimePerSample = 0.18276ms; SamplesPerSecond = 5471 - Epoch[ 4 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789551; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18280ms; SamplesPerSecond = 5470 - Epoch[ 4 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16187354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04601s; TotalTimePerSample = 0.18403ms; SamplesPerSecond = 5433 - Epoch[ 4 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17491016; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18346ms; SamplesPerSecond = 5450 - Epoch[ 4 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12453125; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04605s; TotalTimePerSample = 0.18418ms; SamplesPerSecond = 5429 - Epoch[ 4 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17954883; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04576s; TotalTimePerSample = 0.18305ms; SamplesPerSecond = 5462 - Epoch[ 4 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11063574; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04561s; TotalTimePerSample = 0.18244ms; SamplesPerSecond = 5481 - Epoch[ 4 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20003271; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 - Epoch[ 4 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14218066; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04588s; TotalTimePerSample = 0.18350ms; SamplesPerSecond = 5449 - Epoch[ 4 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11288135; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04594s; TotalTimePerSample = 0.18375ms; SamplesPerSecond = 5442 - Epoch[ 4 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14597998; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 - Epoch[ 4 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15331152; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 - Epoch[ 4 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15427734; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04573s; TotalTimePerSample = 0.18294ms; SamplesPerSecond = 5466 - Epoch[ 4 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15157959; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18366ms; SamplesPerSecond = 5444 - Epoch[ 4 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16704346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04557s; TotalTimePerSample = 0.18229ms; SamplesPerSecond = 5485 - Epoch[ 4 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15075293; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04585s; TotalTimePerSample = 0.18341ms; SamplesPerSecond = 5452 - Epoch[ 4 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16991504; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 - Epoch[ 4 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15195361; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18350ms; SamplesPerSecond = 5449 -Finished Epoch[ 4 of 50]: [Training Set] TrainLossPerSample = 0.15844007; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.841954 -Starting Epoch 5: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 4 at record count 40000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1967 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 4: 4062, 4375, ... - Epoch[ 5 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18240567; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.05004s; TotalTimePerSample = 0.20018ms; SamplesPerSecond = 4995 - Epoch[ 5 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17254541; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18383ms; SamplesPerSecond = 5439 - Epoch[ 5 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14018625; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04579s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 - Epoch[ 5 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13022693; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18362ms; SamplesPerSecond = 5446 - Epoch[ 5 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16820844; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04571s; TotalTimePerSample = 0.18283ms; SamplesPerSecond = 5469 - Epoch[ 5 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16504120; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04593s; TotalTimePerSample = 0.18373ms; SamplesPerSecond = 5442 - Epoch[ 5 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15489612; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04613s; TotalTimePerSample = 0.18451ms; SamplesPerSecond = 5419 - Epoch[ 5 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387878; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 - Epoch[ 5 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15578931; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04621s; TotalTimePerSample = 0.18483ms; SamplesPerSecond = 5410 - Epoch[ 5 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14024121; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04571s; TotalTimePerSample = 0.18283ms; SamplesPerSecond = 5469 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 5 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15645557; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04587s; TotalTimePerSample = 0.18349ms; SamplesPerSecond = 5449 - Epoch[ 5 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21965906; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 - Epoch[ 5 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10941675; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04577s; TotalTimePerSample = 0.18308ms; SamplesPerSecond = 5461 - Epoch[ 5 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17199731; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04562s; TotalTimePerSample = 0.18250ms; SamplesPerSecond = 5479 - Epoch[ 5 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14458350; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04585s; TotalTimePerSample = 0.18342ms; SamplesPerSecond = 5452 - Epoch[ 5 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18683936; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 - Epoch[ 5 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13792139; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04620s; TotalTimePerSample = 0.18482ms; SamplesPerSecond = 5410 - Epoch[ 5 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18643384; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04597s; TotalTimePerSample = 0.18388ms; SamplesPerSecond = 5438 - Epoch[ 5 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19757935; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04565s; TotalTimePerSample = 0.18258ms; SamplesPerSecond = 5477 - Epoch[ 5 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16441406; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04648s; TotalTimePerSample = 0.18590ms; SamplesPerSecond = 5379 - Epoch[ 5 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13472876; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04613s; TotalTimePerSample = 0.18453ms; SamplesPerSecond = 5419 - Epoch[ 5 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14302734; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04604s; TotalTimePerSample = 0.18417ms; SamplesPerSecond = 5429 - Epoch[ 5 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16659668; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04609s; TotalTimePerSample = 0.18436ms; SamplesPerSecond = 5424 - Epoch[ 5 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18445850; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04589s; TotalTimePerSample = 0.18355ms; SamplesPerSecond = 5448 - Epoch[ 5 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17099609; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 - Epoch[ 5 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14728320; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04564s; TotalTimePerSample = 0.18254ms; SamplesPerSecond = 5478 - Epoch[ 5 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12275146; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 - Epoch[ 5 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14349561; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04552s; TotalTimePerSample = 0.18206ms; SamplesPerSecond = 5492 - Epoch[ 5 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519629; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04608s; TotalTimePerSample = 0.18432ms; SamplesPerSecond = 5425 - Epoch[ 5 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13344775; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04566s; TotalTimePerSample = 0.18264ms; SamplesPerSecond = 5475 - Epoch[ 5 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16332129; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 - Epoch[ 5 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17570459; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 - Epoch[ 5 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15510986; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04598s; TotalTimePerSample = 0.18394ms; SamplesPerSecond = 5436 - Epoch[ 5 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16931592; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04607s; TotalTimePerSample = 0.18426ms; SamplesPerSecond = 5426 - Epoch[ 5 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12283789; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04581s; TotalTimePerSample = 0.18323ms; SamplesPerSecond = 5457 - Epoch[ 5 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19195605; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04603s; TotalTimePerSample = 0.18413ms; SamplesPerSecond = 5431 - Epoch[ 5 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16223145; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04577s; TotalTimePerSample = 0.18309ms; SamplesPerSecond = 5461 - Epoch[ 5 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16847412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18332ms; SamplesPerSecond = 5454 - Epoch[ 5 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14448291; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04579s; TotalTimePerSample = 0.18317ms; SamplesPerSecond = 5459 - Epoch[ 5 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384033; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04612s; TotalTimePerSample = 0.18448ms; SamplesPerSecond = 5420 -Finished Epoch[ 5 of 50]: [Training Set] TrainLossPerSample = 0.15844938; EvalErrPerSample = 0.076099999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.843669 -Starting Epoch 6: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 5 at record count 50000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1962 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 5: 2260, 139, ... - Epoch[ 6 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20859395; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.05013s; TotalTimePerSample = 0.20051ms; SamplesPerSecond = 4987 - Epoch[ 6 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18441814; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04564s; TotalTimePerSample = 0.18258ms; SamplesPerSecond = 5477 - Epoch[ 6 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12928479; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 - Epoch[ 6 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17763599; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 - Epoch[ 6 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16105701; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04556s; TotalTimePerSample = 0.18226ms; SamplesPerSecond = 5486 - Epoch[ 6 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17642236; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04586s; TotalTimePerSample = 0.18343ms; SamplesPerSecond = 5451 - Epoch[ 6 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17646826; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 - Epoch[ 6 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635876; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04575s; TotalTimePerSample = 0.18300ms; SamplesPerSecond = 5464 - Epoch[ 6 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15607031; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04591s; TotalTimePerSample = 0.18362ms; SamplesPerSecond = 5445 - Epoch[ 6 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13740442; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04598s; TotalTimePerSample = 0.18390ms; SamplesPerSecond = 5437 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 6 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12236243; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 - Epoch[ 6 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15398157; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04594s; TotalTimePerSample = 0.18378ms; SamplesPerSecond = 5441 - Epoch[ 6 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18065393; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 - Epoch[ 6 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13495654; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04558s; TotalTimePerSample = 0.18232ms; SamplesPerSecond = 5484 - Epoch[ 6 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16918359; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04563s; TotalTimePerSample = 0.18252ms; SamplesPerSecond = 5478 - Epoch[ 6 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14874463; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04569s; TotalTimePerSample = 0.18277ms; SamplesPerSecond = 5471 - Epoch[ 6 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10122803; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04589s; TotalTimePerSample = 0.18357ms; SamplesPerSecond = 5447 - Epoch[ 6 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18160791; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04553s; TotalTimePerSample = 0.18212ms; SamplesPerSecond = 5491 - Epoch[ 6 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17919165; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04594s; TotalTimePerSample = 0.18376ms; SamplesPerSecond = 5441 - Epoch[ 6 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14641138; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04576s; TotalTimePerSample = 0.18303ms; SamplesPerSecond = 5463 - Epoch[ 6 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14951001; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04568s; TotalTimePerSample = 0.18274ms; SamplesPerSecond = 5472 - Epoch[ 6 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13207251; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04595s; TotalTimePerSample = 0.18381ms; SamplesPerSecond = 5440 - Epoch[ 6 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16790063; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18382ms; SamplesPerSecond = 5440 - Epoch[ 6 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18087573; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04601s; TotalTimePerSample = 0.18402ms; SamplesPerSecond = 5434 - Epoch[ 6 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667212; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 - Epoch[ 6 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14512085; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 - Epoch[ 6 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15134570; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 - Epoch[ 6 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17105615; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04585s; TotalTimePerSample = 0.18340ms; SamplesPerSecond = 5452 - Epoch[ 6 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16167139; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04566s; TotalTimePerSample = 0.18264ms; SamplesPerSecond = 5475 - Epoch[ 6 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245068; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04604s; TotalTimePerSample = 0.18416ms; SamplesPerSecond = 5429 - Epoch[ 6 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15437012; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04623s; TotalTimePerSample = 0.18490ms; SamplesPerSecond = 5408 - Epoch[ 6 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17593652; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04636s; TotalTimePerSample = 0.18544ms; SamplesPerSecond = 5392 - Epoch[ 6 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12771338; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04628s; TotalTimePerSample = 0.18512ms; SamplesPerSecond = 5401 - Epoch[ 6 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13509131; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04632s; TotalTimePerSample = 0.18527ms; SamplesPerSecond = 5397 - Epoch[ 6 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512305; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04568s; TotalTimePerSample = 0.18273ms; SamplesPerSecond = 5472 - Epoch[ 6 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11810596; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04586s; TotalTimePerSample = 0.18345ms; SamplesPerSecond = 5451 - Epoch[ 6 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17171484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04573s; TotalTimePerSample = 0.18292ms; SamplesPerSecond = 5466 - Epoch[ 6 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215918; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 - Epoch[ 6 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20186230; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04596s; TotalTimePerSample = 0.18385ms; SamplesPerSecond = 5439 - Epoch[ 6 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13284375; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 -Finished Epoch[ 6 of 50]: [Training Set] TrainLossPerSample = 0.1566408; EvalErrPerSample = 0.072700001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.842141 -Starting Epoch 7: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 6 at record count 60000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1971 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 6: 3864, 19, ... - Epoch[ 7 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17176042; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04996s; TotalTimePerSample = 0.19982ms; SamplesPerSecond = 5004 - Epoch[ 7 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19883070; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18330ms; SamplesPerSecond = 5455 - Epoch[ 7 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15464130; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 - Epoch[ 7 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19855670; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04582s; TotalTimePerSample = 0.18327ms; SamplesPerSecond = 5456 - Epoch[ 7 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194446; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04594s; TotalTimePerSample = 0.18377ms; SamplesPerSecond = 5441 - Epoch[ 7 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16680231; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04566s; TotalTimePerSample = 0.18265ms; SamplesPerSecond = 5475 - Epoch[ 7 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13895239; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04595s; TotalTimePerSample = 0.18379ms; SamplesPerSecond = 5440 - Epoch[ 7 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18752332; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 - Epoch[ 7 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15161487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04621s; TotalTimePerSample = 0.18483ms; SamplesPerSecond = 5410 - Epoch[ 7 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15141785; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18314ms; SamplesPerSecond = 5460 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 7 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19026770; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04581s; TotalTimePerSample = 0.18326ms; SamplesPerSecond = 5456 - Epoch[ 7 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14526355; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04632s; TotalTimePerSample = 0.18530ms; SamplesPerSecond = 5396 - Epoch[ 7 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14874353; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04556s; TotalTimePerSample = 0.18226ms; SamplesPerSecond = 5486 - Epoch[ 7 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16310034; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04592s; TotalTimePerSample = 0.18368ms; SamplesPerSecond = 5444 - Epoch[ 7 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15433960; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04604s; TotalTimePerSample = 0.18414ms; SamplesPerSecond = 5430 - Epoch[ 7 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17733911; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 - Epoch[ 7 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14193359; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 - Epoch[ 7 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13347632; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04601s; TotalTimePerSample = 0.18403ms; SamplesPerSecond = 5433 - Epoch[ 7 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16971606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04568s; TotalTimePerSample = 0.18271ms; SamplesPerSecond = 5473 - Epoch[ 7 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11671191; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 - Epoch[ 7 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11164014; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04573s; TotalTimePerSample = 0.18291ms; SamplesPerSecond = 5467 - Epoch[ 7 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14871704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04623s; TotalTimePerSample = 0.18491ms; SamplesPerSecond = 5408 - Epoch[ 7 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15746533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04569s; TotalTimePerSample = 0.18278ms; SamplesPerSecond = 5471 - Epoch[ 7 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14831201; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04595s; TotalTimePerSample = 0.18379ms; SamplesPerSecond = 5440 - Epoch[ 7 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11845239; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 - Epoch[ 7 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15587183; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04613s; TotalTimePerSample = 0.18452ms; SamplesPerSecond = 5419 - Epoch[ 7 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16354224; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04600s; TotalTimePerSample = 0.18401ms; SamplesPerSecond = 5434 - Epoch[ 7 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12038428; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04590s; TotalTimePerSample = 0.18359ms; SamplesPerSecond = 5446 - Epoch[ 7 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17588135; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18382ms; SamplesPerSecond = 5440 - Epoch[ 7 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116455; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04571s; TotalTimePerSample = 0.18286ms; SamplesPerSecond = 5468 - Epoch[ 7 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131299; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04607s; TotalTimePerSample = 0.18428ms; SamplesPerSecond = 5426 - Epoch[ 7 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14321484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04562s; TotalTimePerSample = 0.18248ms; SamplesPerSecond = 5480 - Epoch[ 7 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15750977; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18280ms; SamplesPerSecond = 5470 - Epoch[ 7 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21607275; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04590s; TotalTimePerSample = 0.18360ms; SamplesPerSecond = 5446 - Epoch[ 7 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15346533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04560s; TotalTimePerSample = 0.18240ms; SamplesPerSecond = 5482 - Epoch[ 7 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713916; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04580s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 - Epoch[ 7 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262793; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 - Epoch[ 7 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16701270; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04584s; TotalTimePerSample = 0.18338ms; SamplesPerSecond = 5453 - Epoch[ 7 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13210449; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 - Epoch[ 7 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16644580; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04575s; TotalTimePerSample = 0.18301ms; SamplesPerSecond = 5464 -Finished Epoch[ 7 of 50]: [Training Set] TrainLossPerSample = 0.15703182; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.842136 -Starting Epoch 8: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 7 at record count 70000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1996 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 7: 1209, 4212, ... - Epoch[ 8 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19911723; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04874s; TotalTimePerSample = 0.19498ms; SamplesPerSecond = 5128 - Epoch[ 8 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18027156; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[ 8 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12933987; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[ 8 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13678201; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[ 8 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14297876; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[ 8 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16435925; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[ 8 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11406915; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[ 8 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18803101; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17985ms; SamplesPerSecond = 5560 - Epoch[ 8 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662817; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 - Epoch[ 8 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16887158; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 8 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16543518; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[ 8 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10627991; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[ 8 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17848193; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[ 8 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20434363; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[ 8 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718237; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[ 8 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15909668; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[ 8 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549097; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[ 8 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12482495; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[ 8 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15946558; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[ 8 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13230127; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 - Epoch[ 8 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19255151; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[ 8 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16289160; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 - Epoch[ 8 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12351147; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[ 8 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13493799; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[ 8 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009839; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5623 - Epoch[ 8 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17191382; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[ 8 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18348926; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[ 8 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17455273; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[ 8 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16983203; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[ 8 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20043896; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 - Epoch[ 8 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15542236; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5566 - Epoch[ 8 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22219385; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[ 8 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11237598; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[ 8 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11935156; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[ 8 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16266797; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 - Epoch[ 8 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16463818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[ 8 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17111035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[ 8 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16134229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 - Epoch[ 8 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15073584; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[ 8 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14591309; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 -Finished Epoch[ 8 of 50]: [Training Set] TrainLossPerSample = 0.15908301; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.794473 -Starting Epoch 9: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 8 at record count 80000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1981 retries for 10000 elements (19.8%) to ensure window condition -randomordering: recached sequence for seed 8: 258, 4597, ... - Epoch[ 9 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636783; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04892s; TotalTimePerSample = 0.19570ms; SamplesPerSecond = 5109 - Epoch[ 9 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11709901; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04505s; TotalTimePerSample = 0.18021ms; SamplesPerSecond = 5549 - Epoch[ 9 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13778328; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[ 9 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17506451; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[ 9 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11473816; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[ 9 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16009601; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[ 9 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19787012; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[ 9 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17618396; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[ 9 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12594666; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[ 9 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13145190; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[ 9 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17528821; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[ 9 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15709998; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04497s; TotalTimePerSample = 0.17990ms; SamplesPerSecond = 5558 - Epoch[ 9 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11661719; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[ 9 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15737024; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[ 9 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14008496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[ 9 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17420557; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[ 9 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18854102; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 - Epoch[ 9 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15494727; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[ 9 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14329468; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[ 9 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20086182; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[ 9 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18686084; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[ 9 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14726855; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[ 9 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15037939; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[ 9 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15442334; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[ 9 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13163672; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[ 9 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16719629; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[ 9 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16306079; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[ 9 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16246484; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 - Epoch[ 9 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12757666; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[ 9 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18803369; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17953ms; SamplesPerSecond = 5570 - Epoch[ 9 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16672803; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[ 9 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15702393; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[ 9 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13608740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[ 9 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19304834; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[ 9 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17301758; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04522s; TotalTimePerSample = 0.18088ms; SamplesPerSecond = 5528 - Epoch[ 9 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18412305; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[ 9 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13706201; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 - Epoch[ 9 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18411182; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[ 9 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11667383; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[ 9 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17274463; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 -Finished Epoch[ 9 of 50]: [Training Set] TrainLossPerSample = 0.15701085; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796622 -Starting Epoch 10: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 9 at record count 90000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1914 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 9: 1957, 3535, ... - Epoch[10 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795653; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04872s; TotalTimePerSample = 0.19487ms; SamplesPerSecond = 5131 - Epoch[10 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15608952; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 - Epoch[10 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19516843; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[10 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15641345; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[10 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16329114; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[10 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17629742; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[10 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16732440; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04442s; TotalTimePerSample = 0.17770ms; SamplesPerSecond = 5627 - Epoch[10 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950903; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5581 - Epoch[10 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949792; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[10 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15610913; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[10 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17446240; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[10 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17979675; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 - Epoch[10 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17328748; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[10 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12340747; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[10 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16953101; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[10 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317700; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 - Epoch[10 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16655737; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 - Epoch[10 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11744604; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[10 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15822021; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 - Epoch[10 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668506; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04444s; TotalTimePerSample = 0.17776ms; SamplesPerSecond = 5625 - Epoch[10 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20659570; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[10 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17137329; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[10 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15892773; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[10 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20601440; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[10 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13179663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[10 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12188379; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[10 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15935791; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 - Epoch[10 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16524219; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 - Epoch[10 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10420312; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[10 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10876855; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[10 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15545703; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[10 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19641748; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[10 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16463574; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[10 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11221289; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[10 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266650; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[10 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14136572; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[10 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16945117; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[10 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18718945; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04447s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 - Epoch[10 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16933008; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04522s; TotalTimePerSample = 0.18086ms; SamplesPerSecond = 5529 - Epoch[10 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15086816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 -Finished Epoch[10 of 50]: [Training Set] TrainLossPerSample = 0.15784962; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.794615 -Starting Epoch 11: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 10 at record count 100000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1865 retries for 10000 elements (18.6%) to ensure window condition -randomordering: recached sequence for seed 10: 3356, 877, ... - Epoch[11 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19867067; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04867s; TotalTimePerSample = 0.19468ms; SamplesPerSecond = 5136 - Epoch[11 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12824931; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[11 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16003491; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 - Epoch[11 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14388751; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 - Epoch[11 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14035968; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 - Epoch[11 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16900494; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[11 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200269; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[11 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10663000; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[11 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13941113; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[11 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15507104; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[11 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15361768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[11 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15443665; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[11 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13960754; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[11 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10843152; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[11 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14440918; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[11 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16181763; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[11 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18465283; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[11 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352783; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[11 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022241; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[11 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16155518; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[11 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15919336; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17977ms; SamplesPerSecond = 5562 - Epoch[11 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16321704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04512s; TotalTimePerSample = 0.18049ms; SamplesPerSecond = 5540 - Epoch[11 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13922144; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[11 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15851660; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[11 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20419971; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[11 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18372168; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[11 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739282; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[11 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17705762; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[11 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22723096; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 - Epoch[11 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668066; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5624 - Epoch[11 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14230176; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[11 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17164600; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[11 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17324609; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17970ms; SamplesPerSecond = 5564 - Epoch[11 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11411230; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[11 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15184570; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[11 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15611523; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[11 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17217090; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[11 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19968115; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[11 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15998193; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17951ms; SamplesPerSecond = 5570 - Epoch[11 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12471484; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 -Finished Epoch[11 of 50]: [Training Set] TrainLossPerSample = 0.15719619; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795227 -Starting Epoch 12: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 11 at record count 110000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1961 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 11: 2406, 1536, ... - Epoch[12 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15143643; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04863s; TotalTimePerSample = 0.19454ms; SamplesPerSecond = 5140 - Epoch[12 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13797897; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 - Epoch[12 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16665747; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[12 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11482568; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[12 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15434119; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[12 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17429492; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[12 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18050989; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[12 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13679346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[12 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14641870; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 - Epoch[12 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636035; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[12 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20385144; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04522s; TotalTimePerSample = 0.18089ms; SamplesPerSecond = 5528 - Epoch[12 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15216943; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04531s; TotalTimePerSample = 0.18124ms; SamplesPerSecond = 5517 - Epoch[12 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15158472; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04509s; TotalTimePerSample = 0.18035ms; SamplesPerSecond = 5544 - Epoch[12 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13478882; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[12 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17630396; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[12 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13077051; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 - Epoch[12 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21419189; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[12 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15287256; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[12 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13726001; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[12 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13043091; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[12 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15200464; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5559 - Epoch[12 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20407959; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[12 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19046216; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[12 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14278735; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[12 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17253809; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5586 - Epoch[12 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12954492; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04491s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5566 - Epoch[12 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16107983; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[12 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15740039; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[12 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12038916; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[12 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12200781; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[12 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19328613; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 - Epoch[12 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13074707; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 - Epoch[12 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15184863; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04502s; TotalTimePerSample = 0.18009ms; SamplesPerSecond = 5552 - Epoch[12 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16412109; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[12 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16518799; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[12 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20125488; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[12 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12034131; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 - Epoch[12 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16150488; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[12 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20943555; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[12 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333301; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 -Finished Epoch[12 of 50]: [Training Set] TrainLossPerSample = 0.15717989; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796178 -Starting Epoch 13: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 12 at record count 120000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2004 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 12: 1583, 2526, ... - Epoch[13 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12885901; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04862s; TotalTimePerSample = 0.19446ms; SamplesPerSecond = 5142 - Epoch[13 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20166235; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[13 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17811789; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04499s; TotalTimePerSample = 0.17997ms; SamplesPerSecond = 5556 - Epoch[13 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15837650; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[13 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10881372; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[13 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17468298; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[13 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16144019; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[13 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17298315; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04532s; TotalTimePerSample = 0.18128ms; SamplesPerSecond = 5516 - Epoch[13 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13253101; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[13 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14265967; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[13 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[13 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540137; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[13 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20036560; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[13 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15000488; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 - Epoch[13 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10153320; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[13 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16928491; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 - Epoch[13 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12664990; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[13 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19418799; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[13 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15498315; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04489s; TotalTimePerSample = 0.17957ms; SamplesPerSecond = 5568 - Epoch[13 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17595093; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5613 - Epoch[13 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15520947; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 - Epoch[13 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19142041; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[13 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14185376; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[13 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14463403; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[13 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18891724; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[13 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17073340; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 - Epoch[13 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17670361; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[13 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16432715; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[13 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220020; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[13 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14603711; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[13 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15039844; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17911ms; SamplesPerSecond = 5583 - Epoch[13 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18936670; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 - Epoch[13 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15210645; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 - Epoch[13 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15780615; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[13 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13348975; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 - Epoch[13 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10566113; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[13 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20451074; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[13 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14747998; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[13 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904688; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[13 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14080127; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 -Finished Epoch[13 of 50]: [Training Set] TrainLossPerSample = 0.15763268; EvalErrPerSample = 0.074999996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796521 -Starting Epoch 14: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 13 at record count 130000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 13: 503, 1097, ... - Epoch[14 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911017; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04887s; TotalTimePerSample = 0.19550ms; SamplesPerSecond = 5115 - Epoch[14 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13950354; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 - Epoch[14 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14423825; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04447s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 - Epoch[14 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18848502; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[14 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15733173; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[14 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17221399; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[14 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12658374; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[14 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15295520; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[14 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16327393; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[14 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15271497; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[14 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14662256; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[14 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15376038; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04498s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 - Epoch[14 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13387573; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[14 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126245; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[14 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16492456; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[14 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11945190; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[14 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11764478; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[14 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12589526; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[14 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20678247; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[14 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18653369; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[14 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15909570; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 - Epoch[14 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13653760; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[14 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16765674; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[14 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17644312; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[14 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17960864; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[14 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18134204; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17792ms; SamplesPerSecond = 5620 - Epoch[14 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15984668; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[14 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13785986; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[14 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17912354; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[14 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12685059; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[14 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15034229; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[14 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13104297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[14 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15666602; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[14 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14833740; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[14 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14219971; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[14 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16518359; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[14 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19329785; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[14 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797607; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[14 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17447119; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17809ms; SamplesPerSecond = 5615 - Epoch[14 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13668359; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 -Finished Epoch[14 of 50]: [Training Set] TrainLossPerSample = 0.15684323; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.793499 -Starting Epoch 15: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 14 at record count 140000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 14: 989, 678, ... - Epoch[15 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20957314; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04872s; TotalTimePerSample = 0.19487ms; SamplesPerSecond = 5131 - Epoch[15 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14468547; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[15 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18774567; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[15 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12493726; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 - Epoch[15 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13638898; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[15 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17119073; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04503s; TotalTimePerSample = 0.18010ms; SamplesPerSecond = 5552 - Epoch[15 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20540869; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[15 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13395593; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[15 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12372241; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[15 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17486792; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[15 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18510693; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04490s; TotalTimePerSample = 0.17960ms; SamplesPerSecond = 5567 - Epoch[15 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14575623; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[15 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17531445; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[15 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13697461; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[15 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16259937; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[15 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12684058; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17991ms; SamplesPerSecond = 5558 - Epoch[15 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11239185; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04500s; TotalTimePerSample = 0.17998ms; SamplesPerSecond = 5556 - Epoch[15 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11496558; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[15 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14266089; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[15 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19046021; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[15 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17349243; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[15 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15107007; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17801ms; SamplesPerSecond = 5617 - Epoch[15 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17756396; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[15 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13699707; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04500s; TotalTimePerSample = 0.18000ms; SamplesPerSecond = 5555 - Epoch[15 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16005298; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04521s; TotalTimePerSample = 0.18085ms; SamplesPerSecond = 5529 - Epoch[15 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15821387; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04518s; TotalTimePerSample = 0.18074ms; SamplesPerSecond = 5532 - Epoch[15 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12138208; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 - Epoch[15 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19546973; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 - Epoch[15 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14879150; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 - Epoch[15 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15509131; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04534s; TotalTimePerSample = 0.18137ms; SamplesPerSecond = 5513 - Epoch[15 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14998779; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[15 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14487354; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[15 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15503076; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[15 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18194482; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[15 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15081738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[15 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245752; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[15 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17944727; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[15 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14993506; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[15 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18263428; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 - Epoch[15 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17803271; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 -Finished Epoch[15 of 50]: [Training Set] TrainLossPerSample = 0.15772082; EvalErrPerSample = 0.075899996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.79833 -Starting Epoch 16: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 15 at record count 150000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 15: 834, 4617, ... - Epoch[16 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18061586; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04880s; TotalTimePerSample = 0.19519ms; SamplesPerSecond = 5123 - Epoch[16 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12055733; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13487866; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16472366; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14694202; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[16 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16010132; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[16 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14952625; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[16 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18979016; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[16 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14659521; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[16 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14648743; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[16 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14281995; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04443s; TotalTimePerSample = 0.17773ms; SamplesPerSecond = 5626 - Epoch[16 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19394141; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15640967; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[16 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11770435; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[16 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14273438; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[16 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16738745; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5562 - Epoch[16 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14881226; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[16 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21560669; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[16 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11582544; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 - Epoch[16 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15476562; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[16 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20032373; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04500s; TotalTimePerSample = 0.17999ms; SamplesPerSecond = 5555 - Epoch[16 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14822192; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04490s; TotalTimePerSample = 0.17961ms; SamplesPerSecond = 5567 - Epoch[16 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10779248; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[16 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16034375; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 - Epoch[16 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19777490; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 - Epoch[16 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15483081; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[16 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14559521; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[16 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14460498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04517s; TotalTimePerSample = 0.18068ms; SamplesPerSecond = 5534 - Epoch[16 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16182080; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04541s; TotalTimePerSample = 0.18164ms; SamplesPerSecond = 5505 - Epoch[16 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17307129; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04521s; TotalTimePerSample = 0.18083ms; SamplesPerSecond = 5529 - Epoch[16 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16362549; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[16 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09776416; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13648877; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 - Epoch[16 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16567480; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04506s; TotalTimePerSample = 0.18025ms; SamplesPerSecond = 5547 - Epoch[16 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17059229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[16 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13688623; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[16 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17496973; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5557 - Epoch[16 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18197900; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[16 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353174; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[16 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16290332; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 -Finished Epoch[16 of 50]: [Training Set] TrainLossPerSample = 0.15612552; EvalErrPerSample = 0.072099999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796983 -Starting Epoch 17: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 16 at record count 160000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2037 retries for 10000 elements (20.4%) to ensure window condition -randomordering: recached sequence for seed 16: 4884, 4383, ... - Epoch[17 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13465163; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04819s; TotalTimePerSample = 0.19278ms; SamplesPerSecond = 5187 - Epoch[17 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17920305; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 - Epoch[17 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15974200; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04434s; TotalTimePerSample = 0.17737ms; SamplesPerSecond = 5637 - Epoch[17 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16869012; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04431s; TotalTimePerSample = 0.17725ms; SamplesPerSecond = 5641 - Epoch[17 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17422345; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 - Epoch[17 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16351520; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04427s; TotalTimePerSample = 0.17708ms; SamplesPerSecond = 5647 - Epoch[17 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11277435; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04432s; TotalTimePerSample = 0.17726ms; SamplesPerSecond = 5641 - Epoch[17 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14457776; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04417s; TotalTimePerSample = 0.17669ms; SamplesPerSecond = 5659 - Epoch[17 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14491663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04428s; TotalTimePerSample = 0.17712ms; SamplesPerSecond = 5645 - Epoch[17 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14099316; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[17 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12237012; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04408s; TotalTimePerSample = 0.17632ms; SamplesPerSecond = 5671 - Epoch[17 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17041846; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04424s; TotalTimePerSample = 0.17695ms; SamplesPerSecond = 5651 - Epoch[17 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16491626; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04407s; TotalTimePerSample = 0.17630ms; SamplesPerSecond = 5672 - Epoch[17 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19155933; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04426s; TotalTimePerSample = 0.17705ms; SamplesPerSecond = 5648 - Epoch[17 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904541; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[17 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16747656; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04440s; TotalTimePerSample = 0.17762ms; SamplesPerSecond = 5630 - Epoch[17 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14390161; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04422s; TotalTimePerSample = 0.17690ms; SamplesPerSecond = 5653 - Epoch[17 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13106763; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04423s; TotalTimePerSample = 0.17694ms; SamplesPerSecond = 5651 - Epoch[17 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15458960; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04426s; TotalTimePerSample = 0.17706ms; SamplesPerSecond = 5647 - Epoch[17 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15445386; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17674ms; SamplesPerSecond = 5658 - Epoch[17 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17567090; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04413s; TotalTimePerSample = 0.17652ms; SamplesPerSecond = 5665 - Epoch[17 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14774487; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04442s; TotalTimePerSample = 0.17767ms; SamplesPerSecond = 5628 - Epoch[17 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14936938; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04425s; TotalTimePerSample = 0.17701ms; SamplesPerSecond = 5649 - Epoch[17 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18822534; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04406s; TotalTimePerSample = 0.17625ms; SamplesPerSecond = 5673 - Epoch[17 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13277808; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[17 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16678833; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17780ms; SamplesPerSecond = 5624 - Epoch[17 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12372900; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 - Epoch[17 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904199; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04414s; TotalTimePerSample = 0.17655ms; SamplesPerSecond = 5664 - Epoch[17 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16823633; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04416s; TotalTimePerSample = 0.17665ms; SamplesPerSecond = 5660 - Epoch[17 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16629639; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04421s; TotalTimePerSample = 0.17684ms; SamplesPerSecond = 5654 - Epoch[17 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19804639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04413s; TotalTimePerSample = 0.17651ms; SamplesPerSecond = 5665 - Epoch[17 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13714258; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04430s; TotalTimePerSample = 0.17719ms; SamplesPerSecond = 5643 - Epoch[17 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14821777; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04396s; TotalTimePerSample = 0.17583ms; SamplesPerSecond = 5687 - Epoch[17 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12661865; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04417s; TotalTimePerSample = 0.17667ms; SamplesPerSecond = 5660 - Epoch[17 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15863330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04421s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 - Epoch[17 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16433936; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04413s; TotalTimePerSample = 0.17653ms; SamplesPerSecond = 5664 - Epoch[17 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18692627; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04443s; TotalTimePerSample = 0.17772ms; SamplesPerSecond = 5626 - Epoch[17 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11682324; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04425s; TotalTimePerSample = 0.17699ms; SamplesPerSecond = 5650 - Epoch[17 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16239307; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04408s; TotalTimePerSample = 0.17632ms; SamplesPerSecond = 5671 - Epoch[17 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19032471; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04413s; TotalTimePerSample = 0.17650ms; SamplesPerSecond = 5665 -Finished Epoch[17 of 50]: [Training Set] TrainLossPerSample = 0.15601081; EvalErrPerSample = 0.075300001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.776697 -Starting Epoch 18: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 17 at record count 170000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1944 retries for 10000 elements (19.4%) to ensure window condition -randomordering: recached sequence for seed 17: 1700, 4307, ... - Epoch[18 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17588332; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04867s; TotalTimePerSample = 0.19470ms; SamplesPerSecond = 5136 - Epoch[18 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12923604; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5575 - Epoch[18 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15792795; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[18 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16389697; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[18 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437134; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[18 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12547186; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[18 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16366309; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 - Epoch[18 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15109570; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 - Epoch[18 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10915527; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[18 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14117944; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[18 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20950757; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[18 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13919482; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[18 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16089600; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[18 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512463; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 - Epoch[18 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13849780; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[18 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5593 - Epoch[18 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17097339; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[18 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21734570; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[18 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12918726; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[18 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643921; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[18 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588525; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[18 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17428369; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[18 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13475342; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[18 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11663159; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[18 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13888208; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[18 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22669946; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[18 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13757300; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[18 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12249902; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[18 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050586; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 - Epoch[18 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18216162; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04489s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 - Epoch[18 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16056787; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[18 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16622314; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04525s; TotalTimePerSample = 0.18102ms; SamplesPerSecond = 5524 - Epoch[18 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17283984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04501s; TotalTimePerSample = 0.18006ms; SamplesPerSecond = 5553 - Epoch[18 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13685938; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[18 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21995117; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[18 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13811914; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[18 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14204834; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[18 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18141699; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[18 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16959912; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[18 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15641895; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 -Finished Epoch[18 of 50]: [Training Set] TrainLossPerSample = 0.15692703; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795097 -Starting Epoch 19: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 18 at record count 180000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2073 retries for 10000 elements (20.7%) to ensure window condition -randomordering: recached sequence for seed 18: 3974, 797, ... - Epoch[19 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13207307; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04877s; TotalTimePerSample = 0.19509ms; SamplesPerSecond = 5125 - Epoch[19 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13981007; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 - Epoch[19 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15596564; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[19 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14441849; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[19 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13712158; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[19 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15629230; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[19 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15038147; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 - Epoch[19 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722290; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5573 - Epoch[19 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579480; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[19 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15973206; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[19 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12839282; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 - Epoch[19 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15963623; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[19 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13392346; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04508s; TotalTimePerSample = 0.18034ms; SamplesPerSecond = 5545 - Epoch[19 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19707300; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[19 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16536011; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[19 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15737549; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[19 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17403125; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[19 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17994727; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[19 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19541064; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 - Epoch[19 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17745801; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 - Epoch[19 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20086279; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[19 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12403540; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[19 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14028516; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 - Epoch[19 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19981592; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[19 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16951782; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17819ms; SamplesPerSecond = 5611 - Epoch[19 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15190747; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[19 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13389893; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[19 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12309375; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[19 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19979102; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[19 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322949; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[19 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11870898; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[19 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17556445; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[19 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18256982; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[19 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11174219; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04503s; TotalTimePerSample = 0.18010ms; SamplesPerSecond = 5552 - Epoch[19 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13577148; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04495s; TotalTimePerSample = 0.17982ms; SamplesPerSecond = 5561 - Epoch[19 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292432; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[19 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16210498; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[19 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12245996; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[19 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14956982; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 - Epoch[19 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16419238; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 -Finished Epoch[19 of 50]: [Training Set] TrainLossPerSample = 0.15573667; EvalErrPerSample = 0.074900001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796807 -Starting Epoch 20: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 19 at record count 190000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1918 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 19: 549, 1755, ... - Epoch[20 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18164697; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04850s; TotalTimePerSample = 0.19399ms; SamplesPerSecond = 5154 - Epoch[20 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14521094; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04441s; TotalTimePerSample = 0.17763ms; SamplesPerSecond = 5629 - Epoch[20 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15597580; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 - Epoch[20 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12063333; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[20 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13853162; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 - Epoch[20 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621198; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[20 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18415680; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[20 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13171667; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[20 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13916663; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[20 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387109; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[20 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19112891; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[20 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12275732; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 - Epoch[20 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15608887; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 - Epoch[20 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16431812; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[20 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15825244; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[20 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16326318; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[20 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15132129; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 - Epoch[20 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13621021; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[20 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17509082; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[20 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14556982; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[20 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14166553; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[20 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16108887; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17774ms; SamplesPerSecond = 5626 - Epoch[20 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21493335; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[20 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13326514; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 - Epoch[20 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15445020; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[20 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15669336; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[20 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16875928; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[20 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007568; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[20 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16125879; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[20 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14758447; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[20 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16430664; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[20 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12669043; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[20 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553271; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[20 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18063574; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[20 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14528320; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[20 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18753271; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[20 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19194580; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[20 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14930615; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[20 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14721973; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[20 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13791357; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 -Finished Epoch[20 of 50]: [Training Set] TrainLossPerSample = 0.15693159; EvalErrPerSample = 0.074999996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.793192 -Starting Epoch 21: learning rate per sample = 0.008000 effective momentum = 0.900000 -starting epoch 20 at record count 200000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 20: 1080, 44, ... - Epoch[21 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12496954; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04870s; TotalTimePerSample = 0.19481ms; SamplesPerSecond = 5133 - Epoch[21 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17533447; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[21 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19186441; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[21 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12946396; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[21 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13574353; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[21 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139471; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 - Epoch[21 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10968701; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[21 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14205054; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 - Epoch[21 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11600293; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 - Epoch[21 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16565967; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[21 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16167261; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04510s; TotalTimePerSample = 0.18038ms; SamplesPerSecond = 5543 - Epoch[21 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12315039; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[21 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19873743; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 - Epoch[21 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15707446; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[21 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12886548; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[21 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19557129; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[21 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16244556; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[21 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16032031; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[21 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18584741; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17807ms; SamplesPerSecond = 5615 - Epoch[21 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14336938; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[21 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12707104; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04507s; TotalTimePerSample = 0.18030ms; SamplesPerSecond = 5546 - Epoch[21 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13008472; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5563 - Epoch[21 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317065; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[21 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19089893; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 - Epoch[21 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17855615; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[21 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15948315; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 - Epoch[21 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16497705; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[21 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13283838; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[21 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19128271; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[21 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139648; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[21 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20447852; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[21 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15682861; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[21 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17994922; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[21 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15734082; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[21 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11994775; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 - Epoch[21 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12581787; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[21 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14635156; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[21 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21252393; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 - Epoch[21 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13149316; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[21 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13085107; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04538s; TotalTimePerSample = 0.18152ms; SamplesPerSecond = 5508 -Finished Epoch[21 of 50]: [Training Set] TrainLossPerSample = 0.15636417; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795501 -Starting Epoch 22: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 21 at record count 210000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2012 retries for 10000 elements (20.1%) to ensure window condition -randomordering: recached sequence for seed 21: 549, 612, ... - Epoch[22 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17249321; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04856s; TotalTimePerSample = 0.19426ms; SamplesPerSecond = 5147 - Epoch[22 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13486766; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 - Epoch[22 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23927286; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 - Epoch[22 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14058728; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[22 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13351758; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[22 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12863477; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[22 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13745990; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[22 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18005945; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[22 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14101172; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[22 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12557007; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[22 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22041040; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[22 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17405029; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17815ms; SamplesPerSecond = 5613 - Epoch[22 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16253601; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5578 - Epoch[22 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14346411; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 - Epoch[22 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12405127; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[22 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12869604; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[22 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17248706; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[22 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15263428; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[22 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14513379; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[22 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18100293; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[22 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13791406; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[22 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17094653; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[22 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14585889; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 - Epoch[22 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15929150; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[22 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17747388; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[22 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13812695; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[22 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18408325; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[22 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14613623; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[22 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11093994; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[22 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18320898; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[22 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17237891; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[22 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14933740; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 - Epoch[22 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352490; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[22 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13781689; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[22 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16293164; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[22 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19111816; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[22 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12595020; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[22 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13964209; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[22 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08325732; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[22 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21109717; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 -Finished Epoch[22 of 50]: [Training Set] TrainLossPerSample = 0.15497439; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794121 -Starting Epoch 23: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 22 at record count 220000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1950 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 22: 43, 802, ... - Epoch[23 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13861447; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04862s; TotalTimePerSample = 0.19449ms; SamplesPerSecond = 5141 - Epoch[23 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15250851; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[23 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15022876; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 - Epoch[23 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13989343; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 - Epoch[23 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15462238; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[23 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13589734; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[23 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17117334; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04495s; TotalTimePerSample = 0.17980ms; SamplesPerSecond = 5561 - Epoch[23 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15149866; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 - Epoch[23 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14791724; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[23 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15872766; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[23 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19306750; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[23 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18024060; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[23 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11721790; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[23 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08968396; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[23 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17251343; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[23 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18874536; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[23 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15459204; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[23 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15327686; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[23 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12174170; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[23 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14656714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 - Epoch[23 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20618359; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[23 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17090063; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[23 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15778906; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[23 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16741211; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[23 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12689673; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[23 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14608740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[23 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15718481; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[23 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13117432; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[23 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19989893; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[23 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16584961; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17975ms; SamplesPerSecond = 5563 - Epoch[23 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17821826; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[23 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20771924; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 - Epoch[23 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14987598; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[23 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15761572; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 - Epoch[23 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13198340; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[23 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16465576; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[23 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11607617; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[23 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14489258; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 - Epoch[23 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12753369; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[23 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904980; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 -Finished Epoch[23 of 50]: [Training Set] TrainLossPerSample = 0.15439315; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795236 -Starting Epoch 24: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 23 at record count 230000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1952 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 23: 3227, 3145, ... - Epoch[24 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14031882; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04848s; TotalTimePerSample = 0.19392ms; SamplesPerSecond = 5156 - Epoch[24 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11842618; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[24 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13751770; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[24 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17792755; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[24 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292828; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[24 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17554633; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[24 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19090387; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[24 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14506067; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[24 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17882153; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 - Epoch[24 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13650439; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[24 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14354358; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 - Epoch[24 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14098999; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 - Epoch[24 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15522107; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[24 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19907764; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[24 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14186401; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[24 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18555957; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[24 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14737354; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[24 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16094092; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[24 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13224023; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[24 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16364185; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 - Epoch[24 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12108496; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[24 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16725464; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[24 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14817480; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[24 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15528418; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[24 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15678467; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[24 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13416504; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[24 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17107593; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[24 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14259326; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[24 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15677246; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[24 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13764355; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[24 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12931787; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[24 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18501465; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 - Epoch[24 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11815723; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[24 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19544434; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[24 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13329053; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5575 - Epoch[24 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18037500; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[24 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16110352; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[24 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18099561; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 - Epoch[24 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13044922; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5617 - Epoch[24 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14814795; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 -Finished Epoch[24 of 50]: [Training Set] TrainLossPerSample = 0.15443842; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793236 -Starting Epoch 25: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 24 at record count 240000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1932 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 24: 3407, 2083, ... - Epoch[25 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10401460; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04858s; TotalTimePerSample = 0.19434ms; SamplesPerSecond = 5145 - Epoch[25 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15403728; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[25 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14292963; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[25 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16843939; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[25 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17557404; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 - Epoch[25 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11396613; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 - Epoch[25 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14637885; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5581 - Epoch[25 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17022577; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[25 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16505737; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[25 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18194116; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[25 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16375818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[25 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10269714; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[25 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12660144; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[25 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12848438; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[25 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15899890; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[25 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15867456; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 - Epoch[25 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10445361; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17967ms; SamplesPerSecond = 5565 - Epoch[25 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15862817; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[25 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15271533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 - Epoch[25 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18500146; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[25 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19119116; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[25 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17116187; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 - Epoch[25 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16713135; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04530s; TotalTimePerSample = 0.18121ms; SamplesPerSecond = 5518 - Epoch[25 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16805591; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04537s; TotalTimePerSample = 0.18148ms; SamplesPerSecond = 5510 - Epoch[25 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619067; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04521s; TotalTimePerSample = 0.18086ms; SamplesPerSecond = 5529 - Epoch[25 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18263086; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04437s; TotalTimePerSample = 0.17748ms; SamplesPerSecond = 5634 - Epoch[25 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14543237; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[25 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13985742; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[25 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15661035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[25 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15545068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[25 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16540723; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[25 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15906396; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[25 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15388477; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[25 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14379590; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[25 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15475977; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[25 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16222705; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04495s; TotalTimePerSample = 0.17980ms; SamplesPerSecond = 5561 - Epoch[25 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14356641; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[25 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17399707; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[25 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12595068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[25 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16378369; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04495s; TotalTimePerSample = 0.17979ms; SamplesPerSecond = 5562 -Finished Epoch[25 of 50]: [Training Set] TrainLossPerSample = 0.15456817; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798305 -Starting Epoch 26: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 25 at record count 250000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1924 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 25: 1325, 1664, ... - Epoch[26 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14272597; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04866s; TotalTimePerSample = 0.19465ms; SamplesPerSecond = 5137 - Epoch[26 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21971495; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[26 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13571994; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 - Epoch[26 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16259573; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[26 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13755927; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[26 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18914001; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[26 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11552826; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[26 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14476648; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[26 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18686255; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[26 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13507581; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[26 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15479773; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[26 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12926501; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[26 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12030884; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[26 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14794409; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[26 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15031055; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 - Epoch[26 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11519922; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[26 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10703442; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[26 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11749365; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[26 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17174731; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[26 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17957764; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17933ms; SamplesPerSecond = 5576 - Epoch[26 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13146729; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04504s; TotalTimePerSample = 0.18017ms; SamplesPerSecond = 5550 - Epoch[26 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16610107; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5572 - Epoch[26 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10538916; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[26 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13408008; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[26 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16431982; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[26 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18921313; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[26 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14537109; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[26 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17432764; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17793ms; SamplesPerSecond = 5620 - Epoch[26 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19397510; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[26 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950000; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[26 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13004541; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[26 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17084473; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[26 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16149268; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18015ms; SamplesPerSecond = 5550 - Epoch[26 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20250391; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 - Epoch[26 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15491113; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[26 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22479639; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[26 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20011865; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[26 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17540625; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[26 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12219189; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 - Epoch[26 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12968066; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 -Finished Epoch[26 of 50]: [Training Set] TrainLossPerSample = 0.15397759; EvalErrPerSample = 0.072999999; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79425 -Starting Epoch 27: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 26 at record count 260000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1927 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 26: 375, 3629, ... - Epoch[27 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12588452; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04864s; TotalTimePerSample = 0.19455ms; SamplesPerSecond = 5140 - Epoch[27 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14300961; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[27 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14642679; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[27 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12520630; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[27 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13292584; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[27 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14024603; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[27 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10274500; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[27 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13299634; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[27 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17985303; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[27 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16833691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[27 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21882690; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17911ms; SamplesPerSecond = 5583 - Epoch[27 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18278040; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[27 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17143176; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[27 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14857495; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[27 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14733496; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 - Epoch[27 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17670459; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[27 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14757007; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[27 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12504517; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[27 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13788232; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[27 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14063794; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[27 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13357983; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[27 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20383594; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[27 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18813232; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[27 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23009766; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.04492s; TotalTimePerSample = 0.17970ms; SamplesPerSecond = 5564 - Epoch[27 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13490405; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 - Epoch[27 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20232202; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 - Epoch[27 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15602759; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[27 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10659521; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[27 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19164111; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[27 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13914014; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[27 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12599121; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[27 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11994922; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[27 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22676465; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[27 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10160986; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[27 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12502100; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[27 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096729; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[27 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18643994; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[27 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14990186; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 - Epoch[27 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17400000; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 - Epoch[27 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15188770; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 -Finished Epoch[27 of 50]: [Training Set] TrainLossPerSample = 0.1543307; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79564 -Starting Epoch 28: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 27 at record count 270000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2091 retries for 10000 elements (20.9%) to ensure window condition -randomordering: recached sequence for seed 27: 1656, 2946, ... - Epoch[28 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384726; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04867s; TotalTimePerSample = 0.19468ms; SamplesPerSecond = 5136 - Epoch[28 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17758655; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[28 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15442941; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[28 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907935; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[28 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12490643; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[28 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14148981; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[28 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13621857; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[28 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14112085; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[28 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14918579; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[28 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690503; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[28 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17772888; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[28 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19665735; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[28 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14749402; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[28 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17928772; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[28 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12745068; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[28 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18881909; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[28 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12280054; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[28 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19135083; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[28 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13609644; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 - Epoch[28 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16096045; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 - Epoch[28 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15061768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[28 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15546216; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[28 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15687354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04507s; TotalTimePerSample = 0.18029ms; SamplesPerSecond = 5546 - Epoch[28 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13221436; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[28 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15861523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 - Epoch[28 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20283716; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[28 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12571924; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[28 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17611768; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[28 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15847119; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[28 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13257910; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5610 - Epoch[28 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12166602; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 - Epoch[28 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16247754; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[28 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16935352; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 - Epoch[28 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11980615; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[28 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14580957; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[28 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16940039; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 - Epoch[28 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13439014; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 - Epoch[28 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15725049; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[28 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18272949; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[28 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16753027; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 -Finished Epoch[28 of 50]: [Training Set] TrainLossPerSample = 0.1543334; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794768 -Starting Epoch 29: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 28 at record count 280000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2057 retries for 10000 elements (20.6%) to ensure window condition -randomordering: recached sequence for seed 28: 1080, 2027, ... - Epoch[29 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643460; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04866s; TotalTimePerSample = 0.19463ms; SamplesPerSecond = 5137 - Epoch[29 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17673239; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04516s; TotalTimePerSample = 0.18062ms; SamplesPerSecond = 5536 - Epoch[29 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17501965; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[29 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18176697; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[29 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16094989; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[29 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13162311; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[29 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16178186; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[29 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14593005; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[29 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553857; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[29 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20095337; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[29 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17819421; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17954ms; SamplesPerSecond = 5569 - Epoch[29 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11219897; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[29 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13306409; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04498s; TotalTimePerSample = 0.17990ms; SamplesPerSecond = 5558 - Epoch[29 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262695; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[29 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499731; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[29 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11501489; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[29 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293115; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[29 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13474609; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[29 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16392187; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[29 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15163599; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[29 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10707788; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[29 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12640332; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[29 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19808716; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[29 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17678784; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[29 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14488403; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[29 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14671704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[29 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15546655; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[29 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15554980; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[29 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16030908; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[29 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16830664; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[29 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18280859; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[29 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12016406; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[29 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18406348; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 - Epoch[29 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11061035; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[29 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17913623; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[29 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14679883; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[29 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17387402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[29 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14572705; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[29 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16189697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 - Epoch[29 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13214697; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 -Finished Epoch[29 of 50]: [Training Set] TrainLossPerSample = 0.15407194; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795556 -Starting Epoch 30: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 29 at record count 290000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1974 retries for 10000 elements (19.7%) to ensure window condition -randomordering: recached sequence for seed 29: 914, 4227, ... - Epoch[30 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14465047; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04847s; TotalTimePerSample = 0.19386ms; SamplesPerSecond = 5158 - Epoch[30 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16279860; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[30 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12415610; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[30 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15766827; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[30 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15625421; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[30 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12182874; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[30 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11108282; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[30 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13562219; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[30 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17527539; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[30 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11627612; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[30 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14553870; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[30 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22418823; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[30 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20641150; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[30 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13691345; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[30 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15052173; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[30 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558325; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[30 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16657935; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 - Epoch[30 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15470361; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[30 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16345532; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[30 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13692334; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[30 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10039355; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[30 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14082397; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[30 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16516626; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 - Epoch[30 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13545752; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04445s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 - Epoch[30 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15586499; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[30 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13069263; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 - Epoch[30 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13733008; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[30 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14450171; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 - Epoch[30 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14557178; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[30 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13832080; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[30 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21320020; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[30 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16202344; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[30 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20276807; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[30 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16053955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 - Epoch[30 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21381396; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[30 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16618750; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 - Epoch[30 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16696973; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 - Epoch[30 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14461035; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[30 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17023926; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[30 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13199170; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 -Finished Epoch[30 of 50]: [Training Set] TrainLossPerSample = 0.15457246; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793778 -Starting Epoch 31: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 30 at record count 300000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1851 retries for 10000 elements (18.5%) to ensure window condition -randomordering: recached sequence for seed 30: 3803, 3991, ... - Epoch[31 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14464336; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04872s; TotalTimePerSample = 0.19488ms; SamplesPerSecond = 5131 - Epoch[31 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16304344; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[31 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14846210; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 - Epoch[31 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17292856; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[31 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17017865; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[31 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18828259; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[31 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14660876; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[31 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18798572; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[31 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16629407; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[31 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14552612; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[31 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15519067; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 - Epoch[31 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11363782; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[31 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11201221; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[31 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18323889; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 - Epoch[31 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14004224; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[31 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18273169; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5562 - Epoch[31 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17291748; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[31 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11845776; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[31 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11312305; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[31 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12602563; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5579 - Epoch[31 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12005713; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18017ms; SamplesPerSecond = 5550 - Epoch[31 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14552344; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 - Epoch[31 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19756104; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04542s; TotalTimePerSample = 0.18168ms; SamplesPerSecond = 5504 - Epoch[31 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12402295; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04513s; TotalTimePerSample = 0.18052ms; SamplesPerSecond = 5539 - Epoch[31 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17038525; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[31 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12756250; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[31 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12024561; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[31 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13515845; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 - Epoch[31 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21380908; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[31 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20564746; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[31 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15676807; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[31 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17230371; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[31 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16065967; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 - Epoch[31 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13149805; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[31 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15634863; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04454s; TotalTimePerSample = 0.17815ms; SamplesPerSecond = 5613 - Epoch[31 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12047559; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[31 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18569824; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 - Epoch[31 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18437402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[31 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16043848; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[31 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15113330; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 -Finished Epoch[31 of 50]: [Training Set] TrainLossPerSample = 0.15477504; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795784 -Starting Epoch 32: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 31 at record count 310000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 31: 2853, 507, ... - Epoch[32 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16708208; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04899s; TotalTimePerSample = 0.19597ms; SamplesPerSecond = 5102 - Epoch[32 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19161845; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[32 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13780560; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 - Epoch[32 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13825052; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[32 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13485834; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[32 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949799; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[32 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11792560; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[32 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12071558; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[32 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131982; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[32 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18264221; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5623 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[32 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14340527; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[32 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17570520; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[32 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13826721; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 - Epoch[32 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15120520; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[32 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12938940; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 - Epoch[32 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16178320; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[32 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16653662; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[32 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17045508; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04490s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[32 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907202; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 - Epoch[32 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15496533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[32 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14055078; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[32 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13834326; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[32 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12971484; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[32 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16714795; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[32 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21098340; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[32 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16618188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 - Epoch[32 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11618066; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[32 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16812061; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[32 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17626123; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[32 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793311; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[32 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14779639; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 - Epoch[32 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19174854; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[32 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14862061; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[32 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18168115; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17799ms; SamplesPerSecond = 5618 - Epoch[32 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13130322; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 - Epoch[32 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13535596; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 - Epoch[32 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15619971; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[32 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12708936; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[32 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18303760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[32 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904688; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 -Finished Epoch[32 of 50]: [Training Set] TrainLossPerSample = 0.15414494; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793314 -Starting Epoch 33: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 32 at record count 320000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1906 retries for 10000 elements (19.1%) to ensure window condition -randomordering: recached sequence for seed 32: 88, 89, ... - Epoch[33 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16716551; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04884s; TotalTimePerSample = 0.19535ms; SamplesPerSecond = 5118 - Epoch[33 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19286800; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 - Epoch[33 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13189536; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[33 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12907028; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[33 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16408899; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[33 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19647198; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04493s; TotalTimePerSample = 0.17972ms; SamplesPerSecond = 5564 - Epoch[33 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18189166; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[33 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11359680; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 - Epoch[33 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13805994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 - Epoch[33 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19713013; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[33 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14238354; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[33 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12312415; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[33 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14386133; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[33 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22228809; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04515s; TotalTimePerSample = 0.18059ms; SamplesPerSecond = 5537 - Epoch[33 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19910229; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[33 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13059497; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[33 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12431787; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04435s; TotalTimePerSample = 0.17738ms; SamplesPerSecond = 5637 - Epoch[33 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15514819; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04431s; TotalTimePerSample = 0.17725ms; SamplesPerSecond = 5641 - Epoch[33 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14358105; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[33 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14937158; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[33 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16569067; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[33 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12747168; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[33 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16297607; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[33 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15803052; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[33 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.07712256; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[33 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14247266; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[33 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709619; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[33 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19128711; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5589 - Epoch[33 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14422900; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[33 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11404785; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 - Epoch[33 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14415771; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[33 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11419043; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 - Epoch[33 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23558252; EvalErr[0]PerSample = 0.13600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[33 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13658057; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[33 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14604687; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[33 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19530811; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 - Epoch[33 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12498535; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[33 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16718457; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[33 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468311; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[33 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14720410; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 -Finished Epoch[33 of 50]: [Training Set] TrainLossPerSample = 0.15405898; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795919 -Starting Epoch 34: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 33 at record count 330000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1929 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 33: 3804, 1788, ... - Epoch[34 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13529294; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04900s; TotalTimePerSample = 0.19601ms; SamplesPerSecond = 5101 - Epoch[34 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13338748; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04513s; TotalTimePerSample = 0.18052ms; SamplesPerSecond = 5539 - Epoch[34 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14988202; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[34 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18168158; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[34 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15277631; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[34 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11326105; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[34 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14692413; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[34 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512140; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[34 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688416; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[34 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15452649; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[34 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13762524; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[34 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20037817; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 - Epoch[34 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12037317; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 - Epoch[34 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14686340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[34 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14223120; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[34 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16736865; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[34 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15634155; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[34 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16114111; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[34 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14548047; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 - Epoch[34 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18879370; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[34 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15783008; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[34 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14752661; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[34 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09680249; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[34 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13845166; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 - Epoch[34 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16419336; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[34 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14899731; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[34 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14379956; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[34 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19261401; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 - Epoch[34 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16730078; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[34 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15251025; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[34 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16484766; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[34 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13353027; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[34 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11693164; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 - Epoch[34 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18941309; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[34 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19238916; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[34 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16366406; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[34 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11366504; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[34 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17076514; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[34 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21054834; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[34 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18187646; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 -Finished Epoch[34 of 50]: [Training Set] TrainLossPerSample = 0.15434977; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794518 -Starting Epoch 35: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 34 at record count 340000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1977 retries for 10000 elements (19.8%) to ensure window condition -randomordering: recached sequence for seed 34: 0, 1369, ... - Epoch[35 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17786343; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04858s; TotalTimePerSample = 0.19432ms; SamplesPerSecond = 5146 - Epoch[35 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10489035; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[35 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15705469; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[35 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14206171; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 - Epoch[35 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09796198; EvalErr[0]PerSample = 0.02800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[35 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13728088; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[35 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11030792; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 - Epoch[35 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17046454; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[35 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17805920; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[35 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14502661; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[35 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14964685; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[35 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442212; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[35 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15535950; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[35 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12269214; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 - Epoch[35 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12346741; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[35 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19613354; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[35 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16324268; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[35 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13881152; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 - Epoch[35 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17424585; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[35 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777832; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[35 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13090381; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[35 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17800317; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 - Epoch[35 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17693579; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[35 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12922388; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[35 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15329004; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04496s; TotalTimePerSample = 0.17982ms; SamplesPerSecond = 5560 - Epoch[35 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15193213; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[35 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15441504; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[35 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19106250; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04503s; TotalTimePerSample = 0.18013ms; SamplesPerSecond = 5551 - Epoch[35 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16050244; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04533s; TotalTimePerSample = 0.18132ms; SamplesPerSecond = 5515 - Epoch[35 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18208252; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[35 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16271045; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 - Epoch[35 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14521729; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[35 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10702100; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[35 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16960010; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 - Epoch[35 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14514160; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[35 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19403418; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 - Epoch[35 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18302832; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[35 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16520020; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 - Epoch[35 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16275586; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[35 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18107520; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 -Finished Epoch[35 of 50]: [Training Set] TrainLossPerSample = 0.15452266; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.796423 -Starting Epoch 36: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 35 at record count 350000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 35: 3511, 991, ... - Epoch[36 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13047495; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04854s; TotalTimePerSample = 0.19418ms; SamplesPerSecond = 5149 - Epoch[36 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16482227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[36 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18663434; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[36 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19181180; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 - Epoch[36 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13587238; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[36 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14003510; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 - Epoch[36 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18628754; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[36 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11480872; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 - Epoch[36 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17090662; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 - Epoch[36 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116858; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[36 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21025952; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 - Epoch[36 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558655; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[36 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16749841; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 - Epoch[36 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11105835; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 - Epoch[36 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11523584; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[36 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12071509; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 - Epoch[36 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13111694; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[36 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12550391; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[36 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17317896; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 - Epoch[36 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12554370; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[36 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17823950; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[36 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14454175; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17933ms; SamplesPerSecond = 5576 - Epoch[36 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16279858; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[36 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14005908; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[36 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14763037; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[36 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11815405; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[36 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14461572; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[36 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18522266; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[36 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14727051; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[36 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21325684; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[36 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022363; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[36 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18725537; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 - Epoch[36 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13104980; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5557 - Epoch[36 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13527734; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[36 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13485742; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[36 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14839697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04548s; TotalTimePerSample = 0.18190ms; SamplesPerSecond = 5497 - Epoch[36 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17245850; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[36 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16834473; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04448s; TotalTimePerSample = 0.17793ms; SamplesPerSecond = 5620 - Epoch[36 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312891; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 - Epoch[36 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15522168; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 -Finished Epoch[36 of 50]: [Training Set] TrainLossPerSample = 0.15391307; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794126 -Starting Epoch 37: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 36 at record count 360000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1919 retries for 10000 elements (19.2%) to ensure window condition -randomordering: recached sequence for seed 36: 87, 1672, ... - Epoch[37 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13406784; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04857s; TotalTimePerSample = 0.19426ms; SamplesPerSecond = 5147 - Epoch[37 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12213620; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 - Epoch[37 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16642133; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 - Epoch[37 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13588062; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 - Epoch[37 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13375146; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[37 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17796021; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[37 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11213745; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[37 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17547949; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[37 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13824939; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 - Epoch[37 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14724207; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[37 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18361682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[37 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14241943; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[37 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15306934; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[37 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18684570; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[37 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15043994; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 - Epoch[37 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10936426; EvalErr[0]PerSample = 0.02400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[37 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16943872; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[37 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270752; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 - Epoch[37 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177393; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[37 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17062476; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[37 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16197144; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[37 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18620679; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[37 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14755078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 - Epoch[37 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13411182; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[37 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17147534; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[37 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17776978; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17985ms; SamplesPerSecond = 5560 - Epoch[37 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16226855; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[37 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15357520; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[37 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15677686; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[37 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12218408; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04511s; TotalTimePerSample = 0.18042ms; SamplesPerSecond = 5542 - Epoch[37 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16878760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04530s; TotalTimePerSample = 0.18118ms; SamplesPerSecond = 5519 - Epoch[37 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10114111; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04509s; TotalTimePerSample = 0.18036ms; SamplesPerSecond = 5544 - Epoch[37 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14782568; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[37 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16620752; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17819ms; SamplesPerSecond = 5612 - Epoch[37 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15330420; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04437s; TotalTimePerSample = 0.17747ms; SamplesPerSecond = 5634 - Epoch[37 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16823730; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[37 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21951025; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 - Epoch[37 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12842187; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[37 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11264844; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[37 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16844629; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 -Finished Epoch[37 of 50]: [Training Set] TrainLossPerSample = 0.15355118; EvalErrPerSample = 0.071999997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.797248 -Starting Epoch 38: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 37 at record count 370000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2090 retries for 10000 elements (20.9%) to ensure window condition -randomordering: recached sequence for seed 37: 2147, 1618, ... - Epoch[38 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15777213; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04858s; TotalTimePerSample = 0.19433ms; SamplesPerSecond = 5145 - Epoch[38 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13953687; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[38 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13222168; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[38 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14788760; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[38 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16613861; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[38 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14783533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[38 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18742883; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[38 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17585291; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[38 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658301; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[38 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15941370; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[38 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15363379; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[38 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999939; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[38 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15738574; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17963ms; SamplesPerSecond = 5566 - Epoch[38 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12428113; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[38 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14158398; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[38 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19533032; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[38 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16717017; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[38 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15142261; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[38 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12683936; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 - Epoch[38 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14768604; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04492s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5565 - Epoch[38 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12246387; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[38 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14735449; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5579 - Epoch[38 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16589087; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[38 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13883545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[38 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17940845; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04540s; TotalTimePerSample = 0.18161ms; SamplesPerSecond = 5506 - Epoch[38 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18145972; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[38 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12568311; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04492s; TotalTimePerSample = 0.17968ms; SamplesPerSecond = 5565 - Epoch[38 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18243408; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[38 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13476904; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[38 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16763135; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 - Epoch[38 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14108545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[38 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10751416; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[38 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12097266; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[38 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20723926; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[38 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20658691; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[38 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15319434; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[38 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14894971; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[38 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18262549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[38 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15305811; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[38 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12360889; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 -Finished Epoch[38 of 50]: [Training Set] TrainLossPerSample = 0.15416922; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.796034 -Starting Epoch 39: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 38 at record count 380000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 38: 1196, 3933, ... - Epoch[39 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17274179; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04880s; TotalTimePerSample = 0.19518ms; SamplesPerSecond = 5123 - Epoch[39 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15039322; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04509s; TotalTimePerSample = 0.18035ms; SamplesPerSecond = 5544 - Epoch[39 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15331415; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 - Epoch[39 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16131561; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 - Epoch[39 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16033606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[39 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14100433; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5572 - Epoch[39 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12815009; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[39 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18430042; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04502s; TotalTimePerSample = 0.18008ms; SamplesPerSecond = 5553 - Epoch[39 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15005725; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[39 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220642; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[39 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19733789; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 - Epoch[39 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14495154; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[39 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15419104; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17996ms; SamplesPerSecond = 5556 - Epoch[39 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20454956; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[39 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14535596; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 - Epoch[39 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11774902; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[39 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718091; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[39 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14335718; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[39 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15269067; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 - Epoch[39 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14877075; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[39 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10336499; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[39 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907422; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 - Epoch[39 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14744800; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[39 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12295947; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04506s; TotalTimePerSample = 0.18026ms; SamplesPerSecond = 5547 - Epoch[39 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14030884; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[39 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13191162; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 - Epoch[39 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12906909; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[39 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14715698; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 - Epoch[39 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13055420; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5580 - Epoch[39 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21046387; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[39 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17694287; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 - Epoch[39 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13921973; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[39 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15824512; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[39 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18433203; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 - Epoch[39 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337988; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5559 - Epoch[39 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194678; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[39 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131396; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[39 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18984082; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[39 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16435498; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[39 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13071533; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 -Finished Epoch[39 of 50]: [Training Set] TrainLossPerSample = 0.15406391; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798211 -Starting Epoch 40: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 39 at record count 390000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1956 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 39: 2661, 3152, ... - Epoch[40 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14158043; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04863s; TotalTimePerSample = 0.19453ms; SamplesPerSecond = 5140 - Epoch[40 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14605721; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[40 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18199774; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[40 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17594177; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[40 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17060614; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[40 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17618237; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[40 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13822772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 - Epoch[40 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16948486; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 - Epoch[40 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16614709; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 - Epoch[40 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14237219; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[40 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10226660; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[40 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15575549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[40 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15612415; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 - Epoch[40 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20174915; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[40 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12652759; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[40 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12409521; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[40 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14512671; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17987ms; SamplesPerSecond = 5559 - Epoch[40 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18385840; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[40 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18300562; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[40 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16562012; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 - Epoch[40 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10861694; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[40 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15586011; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[40 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18680933; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[40 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20423071; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 - Epoch[40 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16040210; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5613 - Epoch[40 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14412622; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[40 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14596484; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[40 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12723340; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17953ms; SamplesPerSecond = 5570 - Epoch[40 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322510; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04502s; TotalTimePerSample = 0.18009ms; SamplesPerSecond = 5552 - Epoch[40 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14650244; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5590 - Epoch[40 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12752783; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 - Epoch[40 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13561084; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 - Epoch[40 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13040381; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[40 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14084570; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[40 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15090723; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[40 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14863672; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 - Epoch[40 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15431055; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[40 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20134863; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[40 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14854785; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[40 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12673096; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5571 -Finished Epoch[40 of 50]: [Training Set] TrainLossPerSample = 0.15351419; EvalErrPerSample = 0.0713; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79461 -Starting Epoch 41: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 40 at record count 400000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1997 retries for 10000 elements (20.0%) to ensure window condition -randomordering: recached sequence for seed 40: 4294, 1646, ... - Epoch[41 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16006821; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04815s; TotalTimePerSample = 0.19261ms; SamplesPerSecond = 5191 - Epoch[41 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16724875; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04422s; TotalTimePerSample = 0.17690ms; SamplesPerSecond = 5652 - Epoch[41 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10595062; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 - Epoch[41 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18145435; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[41 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19507477; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 - Epoch[41 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16439099; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04427s; TotalTimePerSample = 0.17710ms; SamplesPerSecond = 5646 - Epoch[41 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13010004; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04424s; TotalTimePerSample = 0.17696ms; SamplesPerSecond = 5650 - Epoch[41 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19075085; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04426s; TotalTimePerSample = 0.17706ms; SamplesPerSecond = 5647 - Epoch[41 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15007227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04412s; TotalTimePerSample = 0.17649ms; SamplesPerSecond = 5666 - Epoch[41 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12810120; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04416s; TotalTimePerSample = 0.17662ms; SamplesPerSecond = 5661 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[41 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15982971; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04438s; TotalTimePerSample = 0.17751ms; SamplesPerSecond = 5633 - Epoch[41 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13172498; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04421s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 - Epoch[41 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13100256; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04436s; TotalTimePerSample = 0.17744ms; SamplesPerSecond = 5635 - Epoch[41 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21527686; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04423s; TotalTimePerSample = 0.17692ms; SamplesPerSecond = 5652 - Epoch[41 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11033472; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04434s; TotalTimePerSample = 0.17736ms; SamplesPerSecond = 5638 - Epoch[41 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14036792; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17677ms; SamplesPerSecond = 5657 - Epoch[41 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13896533; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04416s; TotalTimePerSample = 0.17664ms; SamplesPerSecond = 5661 - Epoch[41 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246411; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04412s; TotalTimePerSample = 0.17648ms; SamplesPerSecond = 5666 - Epoch[41 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14996655; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04429s; TotalTimePerSample = 0.17717ms; SamplesPerSecond = 5644 - Epoch[41 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11589673; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17811ms; SamplesPerSecond = 5614 - Epoch[41 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20237646; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 - Epoch[41 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12496899; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[41 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14686450; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04420s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 - Epoch[41 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15260010; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04432s; TotalTimePerSample = 0.17728ms; SamplesPerSecond = 5640 - Epoch[41 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12362109; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 - Epoch[41 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15063843; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04419s; TotalTimePerSample = 0.17677ms; SamplesPerSecond = 5657 - Epoch[41 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16341724; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[41 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13300439; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 - Epoch[41 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17211768; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04434s; TotalTimePerSample = 0.17738ms; SamplesPerSecond = 5637 - Epoch[41 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17051416; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04418s; TotalTimePerSample = 0.17672ms; SamplesPerSecond = 5658 - Epoch[41 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19277441; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04427s; TotalTimePerSample = 0.17709ms; SamplesPerSecond = 5646 - Epoch[41 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14424658; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04415s; TotalTimePerSample = 0.17660ms; SamplesPerSecond = 5662 - Epoch[41 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20588477; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04447s; TotalTimePerSample = 0.17787ms; SamplesPerSecond = 5622 - Epoch[41 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12091211; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04427s; TotalTimePerSample = 0.17708ms; SamplesPerSecond = 5647 - Epoch[41 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18827930; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04431s; TotalTimePerSample = 0.17724ms; SamplesPerSecond = 5642 - Epoch[41 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13721533; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04425s; TotalTimePerSample = 0.17698ms; SamplesPerSecond = 5650 - Epoch[41 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12533447; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04425s; TotalTimePerSample = 0.17700ms; SamplesPerSecond = 5649 - Epoch[41 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904785; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04424s; TotalTimePerSample = 0.17695ms; SamplesPerSecond = 5651 - Epoch[41 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15600879; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04426s; TotalTimePerSample = 0.17704ms; SamplesPerSecond = 5648 - Epoch[41 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17057568; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04429s; TotalTimePerSample = 0.17716ms; SamplesPerSecond = 5644 -Finished Epoch[41 of 50]: [Training Set] TrainLossPerSample = 0.1539861; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.778478 -Starting Epoch 42: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 41 at record count 410000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1963 retries for 10000 elements (19.6%) to ensure window condition -randomordering: recached sequence for seed 41: 3344, 3717, ... - Epoch[42 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15670647; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04866s; TotalTimePerSample = 0.19462ms; SamplesPerSecond = 5138 - Epoch[42 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16601924; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[42 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15016824; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 - Epoch[42 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09896414; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04507s; TotalTimePerSample = 0.18028ms; SamplesPerSecond = 5546 - Epoch[42 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15355798; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[42 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14959656; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[42 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14912543; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[42 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15205188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[42 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17921887; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 - Epoch[42 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16415247; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04510s; TotalTimePerSample = 0.18040ms; SamplesPerSecond = 5543 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[42 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12882190; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[42 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12317432; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[42 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10102087; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[42 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18998999; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[42 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20682227; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04494s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 - Epoch[42 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16399731; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[42 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14118408; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[42 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18587280; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[42 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14129785; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[42 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14985107; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 - Epoch[42 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16006787; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[42 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11417261; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[42 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16649072; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[42 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15156982; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[42 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11802979; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 - Epoch[42 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16400879; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[42 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13871289; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17957ms; SamplesPerSecond = 5568 - Epoch[42 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17601343; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 - Epoch[42 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15601025; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 - Epoch[42 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15830273; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[42 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21906494; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[42 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12150439; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 - Epoch[42 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16681006; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 - Epoch[42 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12314014; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[42 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19674365; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[42 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18909473; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[42 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14651758; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[42 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14916064; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[42 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13985498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[42 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14332861; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5571 -Finished Epoch[42 of 50]: [Training Set] TrainLossPerSample = 0.1537548; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795267 -Starting Epoch 43: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 42 at record count 420000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 2041 retries for 10000 elements (20.4%) to ensure window condition -randomordering: recached sequence for seed 42: 4625, 1494, ... - Epoch[43 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337048; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04868s; TotalTimePerSample = 0.19472ms; SamplesPerSecond = 5135 - Epoch[43 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13375079; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[43 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12735876; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[43 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16123383; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 - Epoch[43 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12667511; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 - Epoch[43 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14843439; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 - Epoch[43 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20357190; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[43 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245459; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 - Epoch[43 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15251233; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 - Epoch[43 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14170276; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[43 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13866748; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 - Epoch[43 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13282947; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04518s; TotalTimePerSample = 0.18070ms; SamplesPerSecond = 5533 - Epoch[43 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16564197; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04535s; TotalTimePerSample = 0.18140ms; SamplesPerSecond = 5512 - Epoch[43 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15745947; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04536s; TotalTimePerSample = 0.18144ms; SamplesPerSecond = 5511 - Epoch[43 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13340820; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 - Epoch[43 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11160010; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[43 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16105322; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[43 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11706250; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[43 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442944; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 - Epoch[43 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17853076; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[43 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15478223; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[43 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12719238; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 - Epoch[43 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13923169; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[43 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16462354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[43 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19056055; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[43 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13770239; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[43 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892383; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 - Epoch[43 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18419531; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5586 - Epoch[43 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17152490; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 - Epoch[43 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17453027; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 - Epoch[43 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13219531; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[43 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17414697; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[43 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15467627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[43 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950830; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[43 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333545; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 - Epoch[43 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12053564; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[43 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18559570; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[43 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17155273; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[43 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19393018; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[43 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14711523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 -Finished Epoch[43 of 50]: [Training Set] TrainLossPerSample = 0.15369016; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798963 -Starting Epoch 44: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 43 at record count 430000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition -randomordering: recached sequence for seed 43: 1442, 760, ... - Epoch[44 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19626430; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04860s; TotalTimePerSample = 0.19441ms; SamplesPerSecond = 5143 - Epoch[44 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17290984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 - Epoch[44 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17088531; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[44 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643585; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[44 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13539447; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[44 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13834625; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[44 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468256; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 - Epoch[44 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808887; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 - Epoch[44 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14468994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 - Epoch[44 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15306775; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[44 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15897144; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[44 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13882825; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[44 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18498499; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18016ms; SamplesPerSecond = 5550 - Epoch[44 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007202; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 - Epoch[44 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20337402; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17987ms; SamplesPerSecond = 5559 - Epoch[44 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13722144; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[44 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10227490; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 - Epoch[44 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13890625; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[44 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14858911; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 - Epoch[44 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17651758; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[44 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14888232; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[44 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14692041; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 - Epoch[44 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13211938; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[44 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17383594; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04535s; TotalTimePerSample = 0.18142ms; SamplesPerSecond = 5512 - Epoch[44 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13997656; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[44 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17595068; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 - Epoch[44 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12542529; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[44 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12775000; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04437s; TotalTimePerSample = 0.17749ms; SamplesPerSecond = 5634 - Epoch[44 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18241943; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 - Epoch[44 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16874072; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04501s; TotalTimePerSample = 0.18005ms; SamplesPerSecond = 5553 - Epoch[44 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12801904; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04502s; TotalTimePerSample = 0.18008ms; SamplesPerSecond = 5553 - Epoch[44 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18607813; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04516s; TotalTimePerSample = 0.18064ms; SamplesPerSecond = 5535 - Epoch[44 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11408203; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04513s; TotalTimePerSample = 0.18051ms; SamplesPerSecond = 5539 - Epoch[44 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16484082; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04513s; TotalTimePerSample = 0.18051ms; SamplesPerSecond = 5539 - Epoch[44 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13667822; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04530s; TotalTimePerSample = 0.18120ms; SamplesPerSecond = 5518 - Epoch[44 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18358496; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04543s; TotalTimePerSample = 0.18173ms; SamplesPerSecond = 5502 - Epoch[44 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18765576; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04509s; TotalTimePerSample = 0.18038ms; SamplesPerSecond = 5543 - Epoch[44 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12407764; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17949ms; SamplesPerSecond = 5571 - Epoch[44 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16974805; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 - Epoch[44 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13159570; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 -Finished Epoch[44 of 50]: [Training Set] TrainLossPerSample = 0.15347216; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.800242 -Starting Epoch 45: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 44 at record count 440000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1862 retries for 10000 elements (18.6%) to ensure window condition -randomordering: recached sequence for seed 44: 491, 13, ... - Epoch[45 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15387369; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04887s; TotalTimePerSample = 0.19549ms; SamplesPerSecond = 5115 - Epoch[45 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13984473; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[45 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16473340; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[45 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177023; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 - Epoch[45 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20702142; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 - Epoch[45 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14098303; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 - Epoch[45 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13669562; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 - Epoch[45 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14183423; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[45 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13592822; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 - Epoch[45 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16768311; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[45 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12566309; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 - Epoch[45 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12210388; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[45 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16080310; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 - Epoch[45 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16150610; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[45 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17048022; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[45 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558862; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[45 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12026343; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[45 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14211206; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[45 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20260547; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[45 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16693872; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[45 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14628687; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04340s; TotalTimePerSample = 0.17360ms; SamplesPerSecond = 5760 - Epoch[45 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21542749; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04214s; TotalTimePerSample = 0.16854ms; SamplesPerSecond = 5933 - Epoch[45 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14195264; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04224s; TotalTimePerSample = 0.16897ms; SamplesPerSecond = 5918 - Epoch[45 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13079199; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04243s; TotalTimePerSample = 0.16971ms; SamplesPerSecond = 5892 - Epoch[45 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13407056; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04223s; TotalTimePerSample = 0.16892ms; SamplesPerSecond = 5920 - Epoch[45 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16342114; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04232s; TotalTimePerSample = 0.16929ms; SamplesPerSecond = 5906 - Epoch[45 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16815112; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04236s; TotalTimePerSample = 0.16945ms; SamplesPerSecond = 5901 - Epoch[45 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14529932; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04237s; TotalTimePerSample = 0.16948ms; SamplesPerSecond = 5900 - Epoch[45 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902930; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04235s; TotalTimePerSample = 0.16939ms; SamplesPerSecond = 5903 - Epoch[45 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11276465; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04243s; TotalTimePerSample = 0.16971ms; SamplesPerSecond = 5892 - Epoch[45 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13555859; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04236s; TotalTimePerSample = 0.16945ms; SamplesPerSecond = 5901 - Epoch[45 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12780225; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04217s; TotalTimePerSample = 0.16866ms; SamplesPerSecond = 5928 - Epoch[45 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17016504; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04227s; TotalTimePerSample = 0.16908ms; SamplesPerSecond = 5914 - Epoch[45 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18041113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04233s; TotalTimePerSample = 0.16931ms; SamplesPerSecond = 5906 - Epoch[45 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21134766; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04237s; TotalTimePerSample = 0.16948ms; SamplesPerSecond = 5900 - Epoch[45 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16988965; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04232s; TotalTimePerSample = 0.16928ms; SamplesPerSecond = 5907 - Epoch[45 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11136670; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04210s; TotalTimePerSample = 0.16841ms; SamplesPerSecond = 5937 - Epoch[45 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13335352; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04230s; TotalTimePerSample = 0.16918ms; SamplesPerSecond = 5910 - Epoch[45 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15975391; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04226s; TotalTimePerSample = 0.16904ms; SamplesPerSecond = 5915 - Epoch[45 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15928564; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04238s; TotalTimePerSample = 0.16953ms; SamplesPerSecond = 5898 -Finished Epoch[45 of 50]: [Training Set] TrainLossPerSample = 0.15386404; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.748834 -Starting Epoch 46: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 45 at record count 450000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1934 retries for 10000 elements (19.3%) to ensure window condition -randomordering: recached sequence for seed 45: 1579, 1557, ... - Epoch[46 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16873199; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04903s; TotalTimePerSample = 0.19613ms; SamplesPerSecond = 5098 - Epoch[46 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16506183; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[46 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17666888; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[46 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13984259; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 - Epoch[46 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15266638; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[46 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09864636; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 - Epoch[46 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270251; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 - Epoch[46 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14790149; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 - Epoch[46 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15422278; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[46 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18104761; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5624 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[46 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15919177; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[46 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662683; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5558 - Epoch[46 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16385181; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 - Epoch[46 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15194409; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[46 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18345337; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 - Epoch[46 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13968359; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[46 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14504272; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[46 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621167; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[46 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15997559; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17967ms; SamplesPerSecond = 5565 - Epoch[46 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15241724; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17809ms; SamplesPerSecond = 5615 - Epoch[46 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17079468; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 - Epoch[46 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18360156; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04505s; TotalTimePerSample = 0.18019ms; SamplesPerSecond = 5549 - Epoch[46 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12528809; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 - Epoch[46 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15011523; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[46 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13309790; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 - Epoch[46 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15234912; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04514s; TotalTimePerSample = 0.18054ms; SamplesPerSecond = 5538 - Epoch[46 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14155371; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04585s; TotalTimePerSample = 0.18341ms; SamplesPerSecond = 5452 - Epoch[46 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16554199; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04535s; TotalTimePerSample = 0.18140ms; SamplesPerSecond = 5512 - Epoch[46 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16656445; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04512s; TotalTimePerSample = 0.18050ms; SamplesPerSecond = 5540 - Epoch[46 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17517920; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[46 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11573779; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[46 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13670752; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[46 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808984; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[46 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11996289; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[46 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716162; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[46 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19882568; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5589 - Epoch[46 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16697510; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 - Epoch[46 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17219482; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 - Epoch[46 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15390625; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04433s; TotalTimePerSample = 0.17731ms; SamplesPerSecond = 5639 - Epoch[46 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11087256; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 -Finished Epoch[46 of 50]: [Training Set] TrainLossPerSample = 0.15376027; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79819 -Starting Epoch 47: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 46 at record count 460000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1896 retries for 10000 elements (19.0%) to ensure window condition -randomordering: recached sequence for seed 46: 375, 4056, ... - Epoch[47 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17008383; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04862s; TotalTimePerSample = 0.19448ms; SamplesPerSecond = 5141 - Epoch[47 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14077335; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 - Epoch[47 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13972415; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[47 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14504892; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04495s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 - Epoch[47 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12919092; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 - Epoch[47 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16585364; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[47 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16475818; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 - Epoch[47 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17667603; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 - Epoch[47 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12736060; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 - Epoch[47 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15712415; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5610 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[47 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17211584; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 - Epoch[47 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11364697; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[47 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17509961; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 - Epoch[47 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12182776; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 - Epoch[47 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16530200; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 - Epoch[47 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12411670; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04515s; TotalTimePerSample = 0.18062ms; SamplesPerSecond = 5536 - Epoch[47 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14344165; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[47 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11813501; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[47 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15428955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 - Epoch[47 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18582031; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 - Epoch[47 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16063818; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 - Epoch[47 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18036328; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 - Epoch[47 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12445923; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 - Epoch[47 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14665308; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 - Epoch[47 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16736230; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 - Epoch[47 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13780688; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 - Epoch[47 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17362231; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17965ms; SamplesPerSecond = 5566 - Epoch[47 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15593262; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 - Epoch[47 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14730029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 - Epoch[47 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13614795; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04447s; TotalTimePerSample = 0.17789ms; SamplesPerSecond = 5621 - Epoch[47 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10945850; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04447s; TotalTimePerSample = 0.17788ms; SamplesPerSecond = 5621 - Epoch[47 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13528076; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[47 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20589160; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 - Epoch[47 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13290430; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[47 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15068262; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 - Epoch[47 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17109180; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 - Epoch[47 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19824805; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 - Epoch[47 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20577832; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 - Epoch[47 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13286084; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 - Epoch[47 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17738281; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 -Finished Epoch[47 of 50]: [Training Set] TrainLossPerSample = 0.15350637; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79638 -Starting Epoch 48: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 47 at record count 470000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1949 retries for 10000 elements (19.5%) to ensure window condition -randomordering: recached sequence for seed 47: 2638, 2515, ... - Epoch[48 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15027005; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04850s; TotalTimePerSample = 0.19398ms; SamplesPerSecond = 5155 - Epoch[48 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13481610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 - Epoch[48 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13959433; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5623 - Epoch[48 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19054303; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[48 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588031; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 - Epoch[48 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17125250; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[48 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12560059; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 - Epoch[48 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11920215; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[48 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13620642; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[48 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13336023; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04501s; TotalTimePerSample = 0.18004ms; SamplesPerSecond = 5554 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[48 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16068066; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 - Epoch[48 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16086951; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04442s; TotalTimePerSample = 0.17770ms; SamplesPerSecond = 5627 - Epoch[48 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15739978; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04443s; TotalTimePerSample = 0.17773ms; SamplesPerSecond = 5626 - Epoch[48 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13238123; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 - Epoch[48 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18616211; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 - Epoch[48 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19020459; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 - Epoch[48 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13482642; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[48 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15368921; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 - Epoch[48 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14498755; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[48 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892627; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 - Epoch[48 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293530; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[48 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16804004; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 - Epoch[48 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11757837; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[48 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215381; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[48 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13225903; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 - Epoch[48 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15199707; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 - Epoch[48 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13713770; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 - Epoch[48 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12650757; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 - Epoch[48 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14910400; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 - Epoch[48 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13762646; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04445s; TotalTimePerSample = 0.17780ms; SamplesPerSecond = 5624 - Epoch[48 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17600146; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 - Epoch[48 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15713623; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 - Epoch[48 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17898389; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17776ms; SamplesPerSecond = 5625 - Epoch[48 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13861963; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 - Epoch[48 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12689941; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[48 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16532910; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 - Epoch[48 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739404; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 - Epoch[48 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19358691; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[48 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17704639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 - Epoch[48 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17228711; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 -Finished Epoch[48 of 50]: [Training Set] TrainLossPerSample = 0.15388691; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793014 -Starting Epoch 49: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 48 at record count 480000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition -randomordering: recached sequence for seed 48: 1688, 1039, ... - Epoch[49 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17310571; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04871s; TotalTimePerSample = 0.19484ms; SamplesPerSecond = 5132 - Epoch[49 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14044775; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 - Epoch[49 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16788113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 - Epoch[49 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19724570; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 - Epoch[49 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15709772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 - Epoch[49 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14763385; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 - Epoch[49 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17850745; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 - Epoch[49 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17535193; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 - Epoch[49 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10281067; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 - Epoch[49 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12199316; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[49 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16238562; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[49 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14362817; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04444s; TotalTimePerSample = 0.17777ms; SamplesPerSecond = 5625 - Epoch[49 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12313818; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 - Epoch[49 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18512012; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17983ms; SamplesPerSecond = 5560 - Epoch[49 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13569580; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17811ms; SamplesPerSecond = 5614 - Epoch[49 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18304980; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 - Epoch[49 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14132764; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 - Epoch[49 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12797217; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 - Epoch[49 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14221387; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 - Epoch[49 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14380859; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 - Epoch[49 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911719; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04439s; TotalTimePerSample = 0.17757ms; SamplesPerSecond = 5631 - Epoch[49 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14431519; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 - Epoch[49 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16214160; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 - Epoch[49 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17706299; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 - Epoch[49 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12700317; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 - Epoch[49 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15265112; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17954ms; SamplesPerSecond = 5569 - Epoch[49 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11090063; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5580 - Epoch[49 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13091309; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 - Epoch[49 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19095850; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[49 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14681836; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 - Epoch[49 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13930273; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 - Epoch[49 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13441406; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[49 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18254639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 - Epoch[49 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15285645; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 - Epoch[49 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16258301; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[49 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18678027; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 - Epoch[49 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15848584; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 - Epoch[49 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19601270; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 - Epoch[49 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12490137; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5622 - Epoch[49 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14639160; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 -Finished Epoch[49 of 50]: [Training Set] TrainLossPerSample = 0.15316428; EvalErrPerSample = 0.073299997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795359 -Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting epoch 49 at record count 490000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 49: 954, 533, ... - Epoch[50 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215799; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04846s; TotalTimePerSample = 0.19383ms; SamplesPerSecond = 5159 - Epoch[50 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15890320; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[50 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13722400; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 - Epoch[50 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14856100; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[50 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21979169; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 - Epoch[50 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14950995; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[50 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13682397; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04500s; TotalTimePerSample = 0.18002ms; SamplesPerSecond = 5554 - Epoch[50 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18404150; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 - Epoch[50 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718176; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 - Epoch[50 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15286511; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17807ms; SamplesPerSecond = 5615 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[50 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13951636; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 - Epoch[50 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12581262; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17778ms; SamplesPerSecond = 5624 - Epoch[50 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15118884; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 - Epoch[50 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13054846; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 - Epoch[50 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12965796; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 - Epoch[50 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15022876; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 - Epoch[50 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14381396; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 - Epoch[50 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08574292; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 - Epoch[50 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17754126; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 - Epoch[50 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20406201; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04496s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5560 - Epoch[50 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17242920; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04447s; TotalTimePerSample = 0.17789ms; SamplesPerSecond = 5621 - Epoch[50 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12447583; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17778ms; SamplesPerSecond = 5624 - Epoch[50 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477441; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[50 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16081299; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 - Epoch[50 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15686450; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 - Epoch[50 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11660156; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 - Epoch[50 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17683594; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 - Epoch[50 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12256201; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 - Epoch[50 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20207324; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 - Epoch[50 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14701660; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 - Epoch[50 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14405566; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 - Epoch[50 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13429590; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 - Epoch[50 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18196924; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 - Epoch[50 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16981592; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 - Epoch[50 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18866113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 - Epoch[50 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14027246; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 - Epoch[50 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17351709; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 - Epoch[50 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13018750; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 - Epoch[50 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19874121; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 - Epoch[50 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12178613; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 -Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15332304; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794151 -CNTKCommandTrainEnd: Simple_Demo -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - - -Allocating matrices for forward propagation. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) -B2[2, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) -B1[50, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) -B0[50, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) -MeanOfFeatures[2, 1] = Mean(features[2, 0]) -features[2, 0] = InputValue -W0[50, 2] = LearnableParameter -W1[50, 50] = LearnableParameter -W2[2, 50] = LearnableParameter -labels[2, 0] = InputValue - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -starting at epoch 0 counting lines to determine record count - - 603 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch -Minibatch[1]: ActualMBSize = 603 -Total Samples Evaluated = 603 -COMPLETED -=== Deleting last epoch data -==== Re-running from checkpoint -=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/debug/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 makeMode=true -------------------------------------------------------------------- -Build info: - - Built time: Nov 19 2015 12:01:19 - Last modified date: Thu Nov 19 08:15:43 2015 - Built by svcphil on liana-08-w - Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ - CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 - Build Branch: HEAD - Build SHA1: 70db50387b8998cf0f444e0164d4e4473a027bc9 -------------------------------------------------------------------- -running on DPHAIM-25 at 2015/11/19 12:09:08 -command line: -C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\debug\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 makeMode=true - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=$RunDir$/models/simple.dnn -deviceId=$DeviceNumber$ -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=$DataDir$/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -outputPath=$RunDir$/SimpleOutput -] -RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=0 -makeMode=true - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -command=Simple_Demo:Simple_Demo_Output -DeviceNumber=-1 -precision=float -modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -deviceId=-1 -outputNodeNames=ScaledLogLikelihood -traceLevel=1 -Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] -Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput -] -RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -DeviceId=0 -makeMode=true - -<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< - ->>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> -configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output -configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple -configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data -configparameters: cntk.config:deviceId=0 -configparameters: cntk.config:DeviceNumber=-1 -configparameters: cntk.config:makeMode=true -configparameters: cntk.config:modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood -configparameters: cntk.config:precision=float -configparameters: cntk.config:RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu -configparameters: cntk.config:Simple_Demo=[ - action=train - SimpleNetworkBuilder=[ - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - SGD=[ - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt - miniBatchMode=Partial - randomize=Auto - verbosity=1 - features=[ -dim=2 -start=0 - ] - labels=[ -start=2 -dim=1 -labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -] - -configparameters: cntk.config:Simple_Demo_Output=[ - action=write - reader=[ - readerType=UCIFastReader - file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt - ] - ] -outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput -] - -configparameters: cntk.config:traceLevel=1 -<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< -command: Simple_Demo Simple_Demo_Output -precision = float -CNTKModelPath: C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn -CNTKCommandTrainInfo: Simple_Demo : 50 -CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 -CNTKCommandTrainBegin: Simple_Demo -SimpleNetworkBuilder Using GPU 0 -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt -Starting from checkpoint. Load Network From File C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn.49. - - -Allocating matrices for forward propagation. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) -B2[2, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) -B1[50, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) -B0[50, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) -MeanOfFeatures[2, 1] = Mean(features[2, 0]) -features[2, 0] = InputValue -W0[50, 2] = LearnableParameter -W1[50, 50] = LearnableParameter -W2[2, 50] = LearnableParameter -labels[2, 0] = InputValue - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -GetTrainCriterionNodes ... -GetEvalCriterionNodes ... - - -Allocating matrices for gradient computing -No PreCompute nodes found, skipping PreCompute step -Warning: checkpoint file is missing. learning parameters will be initialized from 0 -Set Max Temp Mem Size For Convolution Nodes to 0 samples. -Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 -starting at epoch 49 counting lines to determine record count - - 10000 records found -starting epoch 49 at record count 490000, and file position 0 -already there from last epoch - -Starting minibatch loop. -randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition -randomordering: recached sequence for seed 49: 954, 533, ... - Epoch[50 of 50]-Minibatch[ 1- 10]: SamplesSeen = 250; TrainLossPerSample = 0.16219635; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.35556s; TotalTimePerSample = 1.42223ms; SamplesPerSecond = 703 - Epoch[50 of 50]-Minibatch[ 11- 20]: SamplesSeen = 250; TrainLossPerSample = 0.15900116; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04740s; TotalTimePerSample = 0.18962ms; SamplesPerSecond = 5273 - Epoch[50 of 50]-Minibatch[ 21- 30]: SamplesSeen = 250; TrainLossPerSample = 0.13724194; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04737s; TotalTimePerSample = 0.18949ms; SamplesPerSecond = 5277 - Epoch[50 of 50]-Minibatch[ 31- 40]: SamplesSeen = 250; TrainLossPerSample = 0.14847681; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04733s; TotalTimePerSample = 0.18931ms; SamplesPerSecond = 5282 - Epoch[50 of 50]-Minibatch[ 41- 50]: SamplesSeen = 250; TrainLossPerSample = 0.21968488; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04727s; TotalTimePerSample = 0.18908ms; SamplesPerSecond = 5288 - Epoch[50 of 50]-Minibatch[ 51- 60]: SamplesSeen = 250; TrainLossPerSample = 0.14950464; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04719s; TotalTimePerSample = 0.18875ms; SamplesPerSecond = 5298 - Epoch[50 of 50]-Minibatch[ 61- 70]: SamplesSeen = 250; TrainLossPerSample = 0.13677826; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04733s; TotalTimePerSample = 0.18931ms; SamplesPerSecond = 5282 - Epoch[50 of 50]-Minibatch[ 71- 80]: SamplesSeen = 250; TrainLossPerSample = 0.18400269; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04721s; TotalTimePerSample = 0.18884ms; SamplesPerSecond = 5295 - Epoch[50 of 50]-Minibatch[ 81- 90]: SamplesSeen = 250; TrainLossPerSample = 0.13719153; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04712s; TotalTimePerSample = 0.18848ms; SamplesPerSecond = 5305 - Epoch[50 of 50]-Minibatch[ 91- 100]: SamplesSeen = 250; TrainLossPerSample = 0.15287048; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04767s; TotalTimePerSample = 0.19069ms; SamplesPerSecond = 5244 -WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. - Epoch[50 of 50]-Minibatch[ 101- 110]: SamplesSeen = 250; TrainLossPerSample = 0.13952893; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04729s; TotalTimePerSample = 0.18916ms; SamplesPerSecond = 5286 - Epoch[50 of 50]-Minibatch[ 111- 120]: SamplesSeen = 250; TrainLossPerSample = 0.12584436; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04724s; TotalTimePerSample = 0.18896ms; SamplesPerSecond = 5292 - Epoch[50 of 50]-Minibatch[ 121- 130]: SamplesSeen = 250; TrainLossPerSample = 0.15119482; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04701s; TotalTimePerSample = 0.18803ms; SamplesPerSecond = 5318 - Epoch[50 of 50]-Minibatch[ 131- 140]: SamplesSeen = 250; TrainLossPerSample = 0.13055371; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04721s; TotalTimePerSample = 0.18884ms; SamplesPerSecond = 5295 - Epoch[50 of 50]-Minibatch[ 141- 150]: SamplesSeen = 250; TrainLossPerSample = 0.12967017; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04719s; TotalTimePerSample = 0.18876ms; SamplesPerSecond = 5297 - Epoch[50 of 50]-Minibatch[ 151- 160]: SamplesSeen = 250; TrainLossPerSample = 0.15023340; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04749s; TotalTimePerSample = 0.18996ms; SamplesPerSecond = 5264 - Epoch[50 of 50]-Minibatch[ 161- 170]: SamplesSeen = 250; TrainLossPerSample = 0.14382349; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04699s; TotalTimePerSample = 0.18797ms; SamplesPerSecond = 5320 - Epoch[50 of 50]-Minibatch[ 171- 180]: SamplesSeen = 250; TrainLossPerSample = 0.08575903; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04714s; TotalTimePerSample = 0.18858ms; SamplesPerSecond = 5302 - Epoch[50 of 50]-Minibatch[ 181- 190]: SamplesSeen = 250; TrainLossPerSample = 0.17751733; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04711s; TotalTimePerSample = 0.18845ms; SamplesPerSecond = 5306 - Epoch[50 of 50]-Minibatch[ 191- 200]: SamplesSeen = 250; TrainLossPerSample = 0.20404810; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04745s; TotalTimePerSample = 0.18980ms; SamplesPerSecond = 5268 - Epoch[50 of 50]-Minibatch[ 201- 210]: SamplesSeen = 250; TrainLossPerSample = 0.17242993; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04755s; TotalTimePerSample = 0.19020ms; SamplesPerSecond = 5257 - Epoch[50 of 50]-Minibatch[ 211- 220]: SamplesSeen = 250; TrainLossPerSample = 0.12448657; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04743s; TotalTimePerSample = 0.18970ms; SamplesPerSecond = 5271 - Epoch[50 of 50]-Minibatch[ 221- 230]: SamplesSeen = 250; TrainLossPerSample = 0.14477075; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04730s; TotalTimePerSample = 0.18921ms; SamplesPerSecond = 5285 - Epoch[50 of 50]-Minibatch[ 231- 240]: SamplesSeen = 250; TrainLossPerSample = 0.16081714; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04736s; TotalTimePerSample = 0.18945ms; SamplesPerSecond = 5278 - Epoch[50 of 50]-Minibatch[ 241- 250]: SamplesSeen = 250; TrainLossPerSample = 0.15686157; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04742s; TotalTimePerSample = 0.18970ms; SamplesPerSecond = 5271 - Epoch[50 of 50]-Minibatch[ 251- 260]: SamplesSeen = 250; TrainLossPerSample = 0.11661328; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04693s; TotalTimePerSample = 0.18772ms; SamplesPerSecond = 5327 - Epoch[50 of 50]-Minibatch[ 261- 270]: SamplesSeen = 250; TrainLossPerSample = 0.17682983; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04731s; TotalTimePerSample = 0.18925ms; SamplesPerSecond = 5283 - Epoch[50 of 50]-Minibatch[ 271- 280]: SamplesSeen = 250; TrainLossPerSample = 0.12256445; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04750s; TotalTimePerSample = 0.18999ms; SamplesPerSecond = 5263 - Epoch[50 of 50]-Minibatch[ 281- 290]: SamplesSeen = 250; TrainLossPerSample = 0.20207422; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04712s; TotalTimePerSample = 0.18847ms; SamplesPerSecond = 5305 - Epoch[50 of 50]-Minibatch[ 291- 300]: SamplesSeen = 250; TrainLossPerSample = 0.14701465; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04783s; TotalTimePerSample = 0.19133ms; SamplesPerSecond = 5226 - Epoch[50 of 50]-Minibatch[ 301- 310]: SamplesSeen = 250; TrainLossPerSample = 0.14405762; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04732s; TotalTimePerSample = 0.18928ms; SamplesPerSecond = 5283 - Epoch[50 of 50]-Minibatch[ 311- 320]: SamplesSeen = 250; TrainLossPerSample = 0.13430029; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04707s; TotalTimePerSample = 0.18826ms; SamplesPerSecond = 5311 - Epoch[50 of 50]-Minibatch[ 321- 330]: SamplesSeen = 250; TrainLossPerSample = 0.18196143; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04719s; TotalTimePerSample = 0.18876ms; SamplesPerSecond = 5297 - Epoch[50 of 50]-Minibatch[ 331- 340]: SamplesSeen = 250; TrainLossPerSample = 0.16981787; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04731s; TotalTimePerSample = 0.18924ms; SamplesPerSecond = 5284 - Epoch[50 of 50]-Minibatch[ 341- 350]: SamplesSeen = 250; TrainLossPerSample = 0.18865869; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04708s; TotalTimePerSample = 0.18832ms; SamplesPerSecond = 5310 - Epoch[50 of 50]-Minibatch[ 351- 360]: SamplesSeen = 250; TrainLossPerSample = 0.14027637; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04707s; TotalTimePerSample = 0.18828ms; SamplesPerSecond = 5311 - Epoch[50 of 50]-Minibatch[ 361- 370]: SamplesSeen = 250; TrainLossPerSample = 0.17351953; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04706s; TotalTimePerSample = 0.18825ms; SamplesPerSecond = 5312 - Epoch[50 of 50]-Minibatch[ 371- 380]: SamplesSeen = 250; TrainLossPerSample = 0.13019482; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04705s; TotalTimePerSample = 0.18820ms; SamplesPerSecond = 5313 - Epoch[50 of 50]-Minibatch[ 381- 390]: SamplesSeen = 250; TrainLossPerSample = 0.19873682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04735s; TotalTimePerSample = 0.18940ms; SamplesPerSecond = 5279 - Epoch[50 of 50]-Minibatch[ 391- 400]: SamplesSeen = 250; TrainLossPerSample = 0.12178662; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04755s; TotalTimePerSample = 0.19019ms; SamplesPerSecond = 5257 -Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15332235; EvalErrPerSample = 0.073799998; AvgLearningRatePerSample = 0.00400000019; EpochTime=2.209268 -CNTKCommandTrainEnd: Simple_Demo -reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt - - -Allocating matrices for forward propagation. - - -Printing Gradient Computation Node Order ... - -CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) -HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) -B2[2, 1] = LearnableParameter -W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) -H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) -W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) -B1[50, 1] = LearnableParameter -W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) -H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) -W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) -B0[50, 1] = LearnableParameter -W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) -MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) -MeanOfFeatures[2, 1] = Mean(features[2, 0]) -features[2, 0] = InputValue -W0[50, 2] = LearnableParameter -W1[50, 50] = LearnableParameter -W2[2, 50] = LearnableParameter -labels[2, 0] = InputValue - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node CrossEntropyWithSoftmax, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -Validating for node ScaledLogLikelihood, final verification. - -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] -Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] -Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] - -10 out of 22 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - - - -Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -Validating for node EvalErrorPrediction, final verification. - -Validating --> labels = InputValue -> [2, MBSize 0] -Validating --> W2 = LearnableParameter -> [2, 50] -Validating --> W1 = LearnableParameter -> [50, 50] -Validating --> W0 = LearnableParameter -> [50, 2] -Validating --> features = InputValue -> [2, MBSize 0] -Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] -Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] -Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] -Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] -Validating --> B0 = LearnableParameter -> [50, 1] -Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] -Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> B1 = LearnableParameter -> [50, 1] -Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] -Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] -Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] -Validating --> B2 = LearnableParameter -> [2, 1] -Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] -Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] - -9 out of 20 nodes do not share the minibatch layout with the input data. - -starting at epoch 0 counting lines to determine record count - - 603 records found -starting epoch 0 at record count 0, and file position 0 -already there from last epoch -Minibatch[1]: ActualMBSize = 603 -Total Samples Evaluated = 603 -COMPLETED +=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/debug/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 +------------------------------------------------------------------- +Build info: + + Built time: Nov 19 2015 12:01:19 + Last modified date: Thu Nov 19 08:15:43 2015 + Built by svcphil on liana-08-w + Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 + Build Branch: HEAD + Build SHA1: 70db50387b8998cf0f444e0164d4e4473a027bc9 +------------------------------------------------------------------- +running on DPHAIM-25 at 2015/11/19 12:07:34 +command line: +C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\debug\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=$RunDir$/models/simple.dnn +deviceId=$DeviceNumber$ +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +outputPath=$RunDir$/SimpleOutput +] +RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +deviceId=-1 +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput +] +RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=0 + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output +configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +configparameters: cntk.config:deviceId=0 +configparameters: cntk.config:DeviceNumber=-1 +configparameters: cntk.config:modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +configparameters: cntk.config:Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] + +configparameters: cntk.config:Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput +] + +configparameters: cntk.config:traceLevel=1 +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: Simple_Demo Simple_Demo_Output +precision = float +CNTKModelPath: C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +CNTKCommandTrainInfo: Simple_Demo : 50 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 +CNTKCommandTrainBegin: Simple_Demo +SimpleNetworkBuilder Using GPU 0 +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt +SetUniformRandomValue (GPU): creating curand object with seed 1 + + +Allocating matrices for forward propagation. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 3], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) +B2[2, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) +B1[50, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) +B0[50, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 3], MeanOfFeatures[0, 0], InvStdOfFeatures[0, 0]) +InvStdOfFeatures[0, 0] = InvStdDev(features[2, 3]) +MeanOfFeatures[0, 0] = Mean(features[2, 3]) +features[2, 3] = InputValue +W0[50, 2] = LearnableParameter +W1[50, 50] = LearnableParameter +W2[2, 50] = LearnableParameter +labels[2, 3] = InputValue + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 3], LogOfPrior[2, 1]) -> [2, MBSize 3] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 3], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 3] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 3]) -> [50, MBSize 3] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 3], B0[50, 1]) -> [50, MBSize 3] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 3], B1[50, 1]) -> [50, MBSize 3] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 3]) -> [50, MBSize 3] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 3]) -> [2, MBSize 3] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 3], B2[2, 1]) -> [2, MBSize 3] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 3], HLast[2, MBSize 3]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Allocating matrices for gradient computing + +Precomputing --> 3 PreCompute nodes found. + + NodeName: InvStdOfFeatures + NodeName: MeanOfFeatures + NodeName: Prior +starting at epoch 0 counting lines to determine record count + + 10000 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch + + +Validating for node InvStdOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] + +Validating for node InvStdOfFeatures, final verification. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node MeanOfFeatures. 2 nodes to process in pass 1. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] + +Validating for node MeanOfFeatures, final verification. + +Validating --> features = InputValue -> [2, MBSize 3] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + + + +Validating for node Prior. 2 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] + +Validating for node Prior, final verification. + +Validating --> labels = InputValue -> [2, MBSize 3] +Validating --> Prior = Mean(labels[2, MBSize 3]) -> [2, 1] + +1 out of 2 nodes do not share the minibatch layout with the input data. + +randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 0: 2334, 3830, ... + +Precomputing --> Completed. + +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 +starting epoch 0 at record count 0, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1989 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 0: 2334, 3830, ... + Epoch[ 1 of 50]-Minibatch[ 1- 10 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70131116; EvalErr[0]PerSample = 0.53200000; TotalTime = 0.05255s; TotalTimePerSample = 0.21018ms; SamplesPerSecond = 4757 + Epoch[ 1 of 50]-Minibatch[ 11- 20 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72596228; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.04638s; TotalTimePerSample = 0.18551ms; SamplesPerSecond = 5390 + Epoch[ 1 of 50]-Minibatch[ 21- 30 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69576807; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04638s; TotalTimePerSample = 0.18553ms; SamplesPerSecond = 5390 + Epoch[ 1 of 50]-Minibatch[ 31- 40 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71764063; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.04635s; TotalTimePerSample = 0.18540ms; SamplesPerSecond = 5393 + Epoch[ 1 of 50]-Minibatch[ 41- 50 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.78249609; EvalErr[0]PerSample = 0.52800000; TotalTime = 0.04637s; TotalTimePerSample = 0.18548ms; SamplesPerSecond = 5391 + Epoch[ 1 of 50]-Minibatch[ 51- 60 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73651172; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04616s; TotalTimePerSample = 0.18464ms; SamplesPerSecond = 5415 + Epoch[ 1 of 50]-Minibatch[ 61- 70 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74346777; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04641s; TotalTimePerSample = 0.18565ms; SamplesPerSecond = 5386 + Epoch[ 1 of 50]-Minibatch[ 71- 80 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69752002; EvalErr[0]PerSample = 0.44000000; TotalTime = 0.04656s; TotalTimePerSample = 0.18625ms; SamplesPerSecond = 5369 + Epoch[ 1 of 50]-Minibatch[ 81- 90 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72111279; EvalErr[0]PerSample = 0.42800000; TotalTime = 0.04641s; TotalTimePerSample = 0.18564ms; SamplesPerSecond = 5386 + Epoch[ 1 of 50]-Minibatch[ 91- 100 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.74622461; EvalErr[0]PerSample = 0.53600000; TotalTime = 0.04619s; TotalTimePerSample = 0.18476ms; SamplesPerSecond = 5412 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 1 of 50]-Minibatch[ 101- 110 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.73008838; EvalErr[0]PerSample = 0.56000000; TotalTime = 0.04623s; TotalTimePerSample = 0.18494ms; SamplesPerSecond = 5407 + Epoch[ 1 of 50]-Minibatch[ 111- 120 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70292676; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.04617s; TotalTimePerSample = 0.18468ms; SamplesPerSecond = 5414 + Epoch[ 1 of 50]-Minibatch[ 121- 130 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70272168; EvalErr[0]PerSample = 0.50800000; TotalTime = 0.04620s; TotalTimePerSample = 0.18481ms; SamplesPerSecond = 5411 + Epoch[ 1 of 50]-Minibatch[ 131- 140 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70346289; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04661s; TotalTimePerSample = 0.18644ms; SamplesPerSecond = 5363 + Epoch[ 1 of 50]-Minibatch[ 141- 150 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72254395; EvalErr[0]PerSample = 0.56800000; TotalTime = 0.04630s; TotalTimePerSample = 0.18521ms; SamplesPerSecond = 5399 + Epoch[ 1 of 50]-Minibatch[ 151- 160 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70169727; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04638s; TotalTimePerSample = 0.18550ms; SamplesPerSecond = 5390 + Epoch[ 1 of 50]-Minibatch[ 161- 170 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71473145; EvalErr[0]PerSample = 0.54400000; TotalTime = 0.04632s; TotalTimePerSample = 0.18530ms; SamplesPerSecond = 5396 + Epoch[ 1 of 50]-Minibatch[ 171- 180 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70216016; EvalErr[0]PerSample = 0.48400000; TotalTime = 0.04652s; TotalTimePerSample = 0.18610ms; SamplesPerSecond = 5373 + Epoch[ 1 of 50]-Minibatch[ 181- 190 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.71398242; EvalErr[0]PerSample = 0.47200000; TotalTime = 0.04629s; TotalTimePerSample = 0.18516ms; SamplesPerSecond = 5400 + Epoch[ 1 of 50]-Minibatch[ 191- 200 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70411133; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.04662s; TotalTimePerSample = 0.18647ms; SamplesPerSecond = 5362 + Epoch[ 1 of 50]-Minibatch[ 201- 210 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69730371; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04618s; TotalTimePerSample = 0.18472ms; SamplesPerSecond = 5413 + Epoch[ 1 of 50]-Minibatch[ 211- 220 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69658789; EvalErr[0]PerSample = 0.47600000; TotalTime = 0.04609s; TotalTimePerSample = 0.18438ms; SamplesPerSecond = 5423 + Epoch[ 1 of 50]-Minibatch[ 221- 230 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.72137207; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04643s; TotalTimePerSample = 0.18571ms; SamplesPerSecond = 5384 + Epoch[ 1 of 50]-Minibatch[ 231- 240 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69762695; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04609s; TotalTimePerSample = 0.18437ms; SamplesPerSecond = 5423 + Epoch[ 1 of 50]-Minibatch[ 241- 250 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70667187; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04669s; TotalTimePerSample = 0.18677ms; SamplesPerSecond = 5354 + Epoch[ 1 of 50]-Minibatch[ 251- 260 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69341797; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04603s; TotalTimePerSample = 0.18414ms; SamplesPerSecond = 5430 + Epoch[ 1 of 50]-Minibatch[ 261- 270 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70505273; EvalErr[0]PerSample = 0.46800000; TotalTime = 0.04626s; TotalTimePerSample = 0.18502ms; SamplesPerSecond = 5404 + Epoch[ 1 of 50]-Minibatch[ 271- 280 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69313086; EvalErr[0]PerSample = 0.49600000; TotalTime = 0.04635s; TotalTimePerSample = 0.18540ms; SamplesPerSecond = 5393 + Epoch[ 1 of 50]-Minibatch[ 281- 290 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69931641; EvalErr[0]PerSample = 0.52400000; TotalTime = 0.04609s; TotalTimePerSample = 0.18438ms; SamplesPerSecond = 5423 + Epoch[ 1 of 50]-Minibatch[ 291- 300 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69199219; EvalErr[0]PerSample = 0.41600000; TotalTime = 0.04622s; TotalTimePerSample = 0.18489ms; SamplesPerSecond = 5408 + Epoch[ 1 of 50]-Minibatch[ 301- 310 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69941211; EvalErr[0]PerSample = 0.46000000; TotalTime = 0.04640s; TotalTimePerSample = 0.18560ms; SamplesPerSecond = 5387 + Epoch[ 1 of 50]-Minibatch[ 311- 320 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68918164; EvalErr[0]PerSample = 0.42000000; TotalTime = 0.04616s; TotalTimePerSample = 0.18465ms; SamplesPerSecond = 5415 + Epoch[ 1 of 50]-Minibatch[ 321- 330 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69079492; EvalErr[0]PerSample = 0.48000000; TotalTime = 0.04636s; TotalTimePerSample = 0.18545ms; SamplesPerSecond = 5392 + Epoch[ 1 of 50]-Minibatch[ 331- 340 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.70002344; EvalErr[0]PerSample = 0.51600000; TotalTime = 0.04637s; TotalTimePerSample = 0.18547ms; SamplesPerSecond = 5391 + Epoch[ 1 of 50]-Minibatch[ 341- 350 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.69928320; EvalErr[0]PerSample = 0.50000000; TotalTime = 0.04604s; TotalTimePerSample = 0.18415ms; SamplesPerSecond = 5430 + Epoch[ 1 of 50]-Minibatch[ 351- 360 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.68106836; EvalErr[0]PerSample = 0.44400000; TotalTime = 0.04641s; TotalTimePerSample = 0.18563ms; SamplesPerSecond = 5387 + Epoch[ 1 of 50]-Minibatch[ 361- 370 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.66569336; EvalErr[0]PerSample = 0.42400000; TotalTime = 0.04607s; TotalTimePerSample = 0.18426ms; SamplesPerSecond = 5426 + Epoch[ 1 of 50]-Minibatch[ 371- 380 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.64908203; EvalErr[0]PerSample = 0.22400000; TotalTime = 0.04640s; TotalTimePerSample = 0.18558ms; SamplesPerSecond = 5388 + Epoch[ 1 of 50]-Minibatch[ 381- 390 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.63724609; EvalErr[0]PerSample = 0.31200000; TotalTime = 0.04647s; TotalTimePerSample = 0.18586ms; SamplesPerSecond = 5380 + Epoch[ 1 of 50]-Minibatch[ 391- 400 of 0%]: SamplesSeen = 250; TrainLossPerSample = 0.58329297; EvalErr[0]PerSample = 0.26000000; TotalTime = 0.04644s; TotalTimePerSample = 0.18575ms; SamplesPerSecond = 5383 +Finished Epoch[ 1 of 50]: [Training Set] TrainLossPerSample = 0.70159978; EvalErrPerSample = 0.47079998; AvgLearningRatePerSample = 0.01999999955; EpochTime=1.867046 +Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 1 at record count 10000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1901 retries for 10000 elements (19.0%) to ensure window condition +randomordering: recached sequence for seed 1: 1914, 1172, ... + Epoch[ 2 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.54827979; EvalErr[0]PerSample = 0.21600000; TotalTime = 0.04982s; TotalTimePerSample = 0.19928ms; SamplesPerSecond = 5018 + Epoch[ 2 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.48939734; EvalErr[0]PerSample = 0.18000000; TotalTime = 0.04622s; TotalTimePerSample = 0.18486ms; SamplesPerSecond = 5409 + Epoch[ 2 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.43074292; EvalErr[0]PerSample = 0.16000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18278ms; SamplesPerSecond = 5470 + Epoch[ 2 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.39592285; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04581s; TotalTimePerSample = 0.18324ms; SamplesPerSecond = 5457 + Epoch[ 2 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.32346033; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 + Epoch[ 2 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.27213037; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 + Epoch[ 2 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.28338940; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 + Epoch[ 2 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.24400537; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04607s; TotalTimePerSample = 0.18430ms; SamplesPerSecond = 5426 + Epoch[ 2 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23811084; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04625s; TotalTimePerSample = 0.18500ms; SamplesPerSecond = 5405 + Epoch[ 2 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19497119; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04598s; TotalTimePerSample = 0.18392ms; SamplesPerSecond = 5437 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 2 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22167163; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04615s; TotalTimePerSample = 0.18459ms; SamplesPerSecond = 5417 + Epoch[ 2 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21802466; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18330ms; SamplesPerSecond = 5455 + Epoch[ 2 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18628687; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04581s; TotalTimePerSample = 0.18325ms; SamplesPerSecond = 5457 + Epoch[ 2 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16806738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 + Epoch[ 2 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353809; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 + Epoch[ 2 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16384961; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04572s; TotalTimePerSample = 0.18286ms; SamplesPerSecond = 5468 + Epoch[ 2 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16741260; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18332ms; SamplesPerSecond = 5454 + Epoch[ 2 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18466162; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04660s; TotalTimePerSample = 0.18638ms; SamplesPerSecond = 5365 + Epoch[ 2 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15171387; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04774s; TotalTimePerSample = 0.19094ms; SamplesPerSecond = 5237 + Epoch[ 2 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688721; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04611s; TotalTimePerSample = 0.18442ms; SamplesPerSecond = 5422 + Epoch[ 2 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15986084; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04646s; TotalTimePerSample = 0.18586ms; SamplesPerSecond = 5380 + Epoch[ 2 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14824854; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18365ms; SamplesPerSecond = 5445 + Epoch[ 2 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15344580; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04577s; TotalTimePerSample = 0.18307ms; SamplesPerSecond = 5462 + Epoch[ 2 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18035303; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04574s; TotalTimePerSample = 0.18295ms; SamplesPerSecond = 5466 + Epoch[ 2 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15277344; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04579s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 + Epoch[ 2 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15023730; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04592s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 + Epoch[ 2 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19006250; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 + Epoch[ 2 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15078857; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04616s; TotalTimePerSample = 0.18464ms; SamplesPerSecond = 5415 + Epoch[ 2 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15495068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04555s; TotalTimePerSample = 0.18218ms; SamplesPerSecond = 5488 + Epoch[ 2 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19428369; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04596s; TotalTimePerSample = 0.18383ms; SamplesPerSecond = 5439 + Epoch[ 2 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15784766; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04643s; TotalTimePerSample = 0.18572ms; SamplesPerSecond = 5384 + Epoch[ 2 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16802686; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 + Epoch[ 2 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14867822; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18373ms; SamplesPerSecond = 5442 + Epoch[ 2 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15570654; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 + Epoch[ 2 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17597510; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04555s; TotalTimePerSample = 0.18222ms; SamplesPerSecond = 5487 + Epoch[ 2 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12306738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 + Epoch[ 2 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12854980; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 + Epoch[ 2 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16306689; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04547s; TotalTimePerSample = 0.18187ms; SamplesPerSecond = 5498 + Epoch[ 2 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19146924; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 + Epoch[ 2 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20339941; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04573s; TotalTimePerSample = 0.18294ms; SamplesPerSecond = 5466 +Finished Epoch[ 2 of 50]: [Training Set] TrainLossPerSample = 0.21133289; EvalErrPerSample = 0.082400002; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.84572 +Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 2 at record count 20000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1912 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 2: 3447, 110, ... + Epoch[ 3 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17365886; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.05013s; TotalTimePerSample = 0.20052ms; SamplesPerSecond = 4987 + Epoch[ 3 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18830190; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04683s; TotalTimePerSample = 0.18732ms; SamplesPerSecond = 5338 + Epoch[ 3 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15952179; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04577s; TotalTimePerSample = 0.18308ms; SamplesPerSecond = 5461 + Epoch[ 3 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18096484; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04566s; TotalTimePerSample = 0.18265ms; SamplesPerSecond = 5475 + Epoch[ 3 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17355847; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04594s; TotalTimePerSample = 0.18375ms; SamplesPerSecond = 5442 + Epoch[ 3 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16889929; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 + Epoch[ 3 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11249597; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04567s; TotalTimePerSample = 0.18269ms; SamplesPerSecond = 5473 + Epoch[ 3 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16278979; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 + Epoch[ 3 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20447998; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04585s; TotalTimePerSample = 0.18339ms; SamplesPerSecond = 5452 + Epoch[ 3 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17866296; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18306ms; SamplesPerSecond = 5462 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 3 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13869763; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 + Epoch[ 3 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15972412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18333ms; SamplesPerSecond = 5454 + Epoch[ 3 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21236255; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04586s; TotalTimePerSample = 0.18345ms; SamplesPerSecond = 5451 + Epoch[ 3 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20550391; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04610s; TotalTimePerSample = 0.18441ms; SamplesPerSecond = 5422 + Epoch[ 3 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12160645; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 + Epoch[ 3 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10832666; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04583s; TotalTimePerSample = 0.18334ms; SamplesPerSecond = 5454 + Epoch[ 3 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16376050; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18334ms; SamplesPerSecond = 5454 + Epoch[ 3 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14577954; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04575s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5464 + Epoch[ 3 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12433838; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04597s; TotalTimePerSample = 0.18386ms; SamplesPerSecond = 5438 + Epoch[ 3 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17770288; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04595s; TotalTimePerSample = 0.18380ms; SamplesPerSecond = 5440 + Epoch[ 3 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19776099; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04644s; TotalTimePerSample = 0.18577ms; SamplesPerSecond = 5382 + Epoch[ 3 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14767725; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04691s; TotalTimePerSample = 0.18766ms; SamplesPerSecond = 5328 + Epoch[ 3 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904053; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04668s; TotalTimePerSample = 0.18672ms; SamplesPerSecond = 5355 + Epoch[ 3 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795532; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04613s; TotalTimePerSample = 0.18452ms; SamplesPerSecond = 5419 + Epoch[ 3 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09892163; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18311ms; SamplesPerSecond = 5461 + Epoch[ 3 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716260; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 + Epoch[ 3 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17355420; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18311ms; SamplesPerSecond = 5461 + Epoch[ 3 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15936279; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04597s; TotalTimePerSample = 0.18389ms; SamplesPerSecond = 5437 + Epoch[ 3 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17972363; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 + Epoch[ 3 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12260254; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 + Epoch[ 3 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17041846; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04598s; TotalTimePerSample = 0.18391ms; SamplesPerSecond = 5437 + Epoch[ 3 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13416553; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 + Epoch[ 3 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12525977; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 + Epoch[ 3 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14119385; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 + Epoch[ 3 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19594287; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04609s; TotalTimePerSample = 0.18436ms; SamplesPerSecond = 5424 + Epoch[ 3 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15041064; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04566s; TotalTimePerSample = 0.18263ms; SamplesPerSecond = 5475 + Epoch[ 3 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12593604; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04581s; TotalTimePerSample = 0.18324ms; SamplesPerSecond = 5457 + Epoch[ 3 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17783594; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04584s; TotalTimePerSample = 0.18336ms; SamplesPerSecond = 5453 + Epoch[ 3 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17358057; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04573s; TotalTimePerSample = 0.18291ms; SamplesPerSecond = 5467 + Epoch[ 3 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17433789; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04598s; TotalTimePerSample = 0.18391ms; SamplesPerSecond = 5437 +Finished Epoch[ 3 of 50]: [Training Set] TrainLossPerSample = 0.15909949; EvalErrPerSample = 0.075800002; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.845274 +Starting Epoch 4: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 3 at record count 30000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1941 retries for 10000 elements (19.4%) to ensure window condition +randomordering: recached sequence for seed 3: 1741, 4693, ... + Epoch[ 4 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18529663; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04986s; TotalTimePerSample = 0.19942ms; SamplesPerSecond = 5014 + Epoch[ 4 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19594196; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04609s; TotalTimePerSample = 0.18437ms; SamplesPerSecond = 5423 + Epoch[ 4 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13145807; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 + Epoch[ 4 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20137177; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 + Epoch[ 4 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18951855; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04590s; TotalTimePerSample = 0.18361ms; SamplesPerSecond = 5446 + Epoch[ 4 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22155804; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04584s; TotalTimePerSample = 0.18335ms; SamplesPerSecond = 5454 + Epoch[ 4 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17034131; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04561s; TotalTimePerSample = 0.18246ms; SamplesPerSecond = 5480 + Epoch[ 4 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15521619; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04605s; TotalTimePerSample = 0.18419ms; SamplesPerSecond = 5429 + Epoch[ 4 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13807910; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18314ms; SamplesPerSecond = 5460 + Epoch[ 4 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14815063; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04588s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 4 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15757410; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04568s; TotalTimePerSample = 0.18271ms; SamplesPerSecond = 5473 + Epoch[ 4 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11073975; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04593s; TotalTimePerSample = 0.18371ms; SamplesPerSecond = 5443 + Epoch[ 4 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17341187; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04578s; TotalTimePerSample = 0.18310ms; SamplesPerSecond = 5461 + Epoch[ 4 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10827295; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04595s; TotalTimePerSample = 0.18381ms; SamplesPerSecond = 5440 + Epoch[ 4 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15454028; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 + Epoch[ 4 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19133398; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18306ms; SamplesPerSecond = 5462 + Epoch[ 4 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12807007; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04647s; TotalTimePerSample = 0.18590ms; SamplesPerSecond = 5379 + Epoch[ 4 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11360352; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04590s; TotalTimePerSample = 0.18361ms; SamplesPerSecond = 5446 + Epoch[ 4 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18848999; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04576s; TotalTimePerSample = 0.18302ms; SamplesPerSecond = 5463 + Epoch[ 4 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17556250; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04630s; TotalTimePerSample = 0.18518ms; SamplesPerSecond = 5400 + Epoch[ 4 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18725269; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04588s; TotalTimePerSample = 0.18351ms; SamplesPerSecond = 5449 + Epoch[ 4 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13380542; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04573s; TotalTimePerSample = 0.18293ms; SamplesPerSecond = 5466 + Epoch[ 4 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15871045; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04569s; TotalTimePerSample = 0.18276ms; SamplesPerSecond = 5471 + Epoch[ 4 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16789551; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18280ms; SamplesPerSecond = 5470 + Epoch[ 4 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16187354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04601s; TotalTimePerSample = 0.18403ms; SamplesPerSecond = 5433 + Epoch[ 4 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17491016; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18346ms; SamplesPerSecond = 5450 + Epoch[ 4 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12453125; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04605s; TotalTimePerSample = 0.18418ms; SamplesPerSecond = 5429 + Epoch[ 4 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17954883; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04576s; TotalTimePerSample = 0.18305ms; SamplesPerSecond = 5462 + Epoch[ 4 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11063574; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04561s; TotalTimePerSample = 0.18244ms; SamplesPerSecond = 5481 + Epoch[ 4 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20003271; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 + Epoch[ 4 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14218066; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04588s; TotalTimePerSample = 0.18350ms; SamplesPerSecond = 5449 + Epoch[ 4 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11288135; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04594s; TotalTimePerSample = 0.18375ms; SamplesPerSecond = 5442 + Epoch[ 4 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14597998; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 + Epoch[ 4 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15331152; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 + Epoch[ 4 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15427734; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04573s; TotalTimePerSample = 0.18294ms; SamplesPerSecond = 5466 + Epoch[ 4 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15157959; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18366ms; SamplesPerSecond = 5444 + Epoch[ 4 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16704346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04557s; TotalTimePerSample = 0.18229ms; SamplesPerSecond = 5485 + Epoch[ 4 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15075293; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04585s; TotalTimePerSample = 0.18341ms; SamplesPerSecond = 5452 + Epoch[ 4 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16991504; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 + Epoch[ 4 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15195361; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18350ms; SamplesPerSecond = 5449 +Finished Epoch[ 4 of 50]: [Training Set] TrainLossPerSample = 0.15844007; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.841954 +Starting Epoch 5: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 4 at record count 40000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1967 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 4: 4062, 4375, ... + Epoch[ 5 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18240567; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.05004s; TotalTimePerSample = 0.20018ms; SamplesPerSecond = 4995 + Epoch[ 5 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17254541; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18383ms; SamplesPerSecond = 5439 + Epoch[ 5 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14018625; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04579s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 + Epoch[ 5 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13022693; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18362ms; SamplesPerSecond = 5446 + Epoch[ 5 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16820844; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04571s; TotalTimePerSample = 0.18283ms; SamplesPerSecond = 5469 + Epoch[ 5 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16504120; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04593s; TotalTimePerSample = 0.18373ms; SamplesPerSecond = 5442 + Epoch[ 5 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15489612; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04613s; TotalTimePerSample = 0.18451ms; SamplesPerSecond = 5419 + Epoch[ 5 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387878; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 + Epoch[ 5 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15578931; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04621s; TotalTimePerSample = 0.18483ms; SamplesPerSecond = 5410 + Epoch[ 5 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14024121; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04571s; TotalTimePerSample = 0.18283ms; SamplesPerSecond = 5469 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 5 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15645557; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04587s; TotalTimePerSample = 0.18349ms; SamplesPerSecond = 5449 + Epoch[ 5 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21965906; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 + Epoch[ 5 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10941675; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04577s; TotalTimePerSample = 0.18308ms; SamplesPerSecond = 5461 + Epoch[ 5 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17199731; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04562s; TotalTimePerSample = 0.18250ms; SamplesPerSecond = 5479 + Epoch[ 5 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14458350; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04585s; TotalTimePerSample = 0.18342ms; SamplesPerSecond = 5452 + Epoch[ 5 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18683936; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 + Epoch[ 5 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13792139; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04620s; TotalTimePerSample = 0.18482ms; SamplesPerSecond = 5410 + Epoch[ 5 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18643384; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04597s; TotalTimePerSample = 0.18388ms; SamplesPerSecond = 5438 + Epoch[ 5 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19757935; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04565s; TotalTimePerSample = 0.18258ms; SamplesPerSecond = 5477 + Epoch[ 5 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16441406; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04648s; TotalTimePerSample = 0.18590ms; SamplesPerSecond = 5379 + Epoch[ 5 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13472876; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04613s; TotalTimePerSample = 0.18453ms; SamplesPerSecond = 5419 + Epoch[ 5 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14302734; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04604s; TotalTimePerSample = 0.18417ms; SamplesPerSecond = 5429 + Epoch[ 5 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16659668; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04609s; TotalTimePerSample = 0.18436ms; SamplesPerSecond = 5424 + Epoch[ 5 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18445850; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04589s; TotalTimePerSample = 0.18355ms; SamplesPerSecond = 5448 + Epoch[ 5 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17099609; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04593s; TotalTimePerSample = 0.18370ms; SamplesPerSecond = 5443 + Epoch[ 5 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14728320; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04564s; TotalTimePerSample = 0.18254ms; SamplesPerSecond = 5478 + Epoch[ 5 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12275146; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 + Epoch[ 5 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14349561; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04552s; TotalTimePerSample = 0.18206ms; SamplesPerSecond = 5492 + Epoch[ 5 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14519629; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04608s; TotalTimePerSample = 0.18432ms; SamplesPerSecond = 5425 + Epoch[ 5 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13344775; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04566s; TotalTimePerSample = 0.18264ms; SamplesPerSecond = 5475 + Epoch[ 5 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16332129; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04589s; TotalTimePerSample = 0.18354ms; SamplesPerSecond = 5448 + Epoch[ 5 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17570459; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04565s; TotalTimePerSample = 0.18260ms; SamplesPerSecond = 5476 + Epoch[ 5 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15510986; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04598s; TotalTimePerSample = 0.18394ms; SamplesPerSecond = 5436 + Epoch[ 5 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16931592; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04607s; TotalTimePerSample = 0.18426ms; SamplesPerSecond = 5426 + Epoch[ 5 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12283789; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04581s; TotalTimePerSample = 0.18323ms; SamplesPerSecond = 5457 + Epoch[ 5 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19195605; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04603s; TotalTimePerSample = 0.18413ms; SamplesPerSecond = 5431 + Epoch[ 5 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16223145; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04577s; TotalTimePerSample = 0.18309ms; SamplesPerSecond = 5461 + Epoch[ 5 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16847412; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04583s; TotalTimePerSample = 0.18332ms; SamplesPerSecond = 5454 + Epoch[ 5 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14448291; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04579s; TotalTimePerSample = 0.18317ms; SamplesPerSecond = 5459 + Epoch[ 5 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384033; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04612s; TotalTimePerSample = 0.18448ms; SamplesPerSecond = 5420 +Finished Epoch[ 5 of 50]: [Training Set] TrainLossPerSample = 0.15844938; EvalErrPerSample = 0.076099999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.843669 +Starting Epoch 6: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 5 at record count 50000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1962 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 5: 2260, 139, ... + Epoch[ 6 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20859395; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.05013s; TotalTimePerSample = 0.20051ms; SamplesPerSecond = 4987 + Epoch[ 6 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18441814; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04564s; TotalTimePerSample = 0.18258ms; SamplesPerSecond = 5477 + Epoch[ 6 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12928479; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04587s; TotalTimePerSample = 0.18348ms; SamplesPerSecond = 5450 + Epoch[ 6 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17763599; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 + Epoch[ 6 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16105701; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04556s; TotalTimePerSample = 0.18226ms; SamplesPerSecond = 5486 + Epoch[ 6 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17642236; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04586s; TotalTimePerSample = 0.18343ms; SamplesPerSecond = 5451 + Epoch[ 6 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17646826; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 + Epoch[ 6 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16635876; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04575s; TotalTimePerSample = 0.18300ms; SamplesPerSecond = 5464 + Epoch[ 6 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15607031; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04591s; TotalTimePerSample = 0.18362ms; SamplesPerSecond = 5445 + Epoch[ 6 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13740442; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04598s; TotalTimePerSample = 0.18390ms; SamplesPerSecond = 5437 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 6 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12236243; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 + Epoch[ 6 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15398157; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04594s; TotalTimePerSample = 0.18378ms; SamplesPerSecond = 5441 + Epoch[ 6 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18065393; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 + Epoch[ 6 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13495654; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04558s; TotalTimePerSample = 0.18232ms; SamplesPerSecond = 5484 + Epoch[ 6 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16918359; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04563s; TotalTimePerSample = 0.18252ms; SamplesPerSecond = 5478 + Epoch[ 6 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14874463; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04569s; TotalTimePerSample = 0.18277ms; SamplesPerSecond = 5471 + Epoch[ 6 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10122803; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04589s; TotalTimePerSample = 0.18357ms; SamplesPerSecond = 5447 + Epoch[ 6 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18160791; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04553s; TotalTimePerSample = 0.18212ms; SamplesPerSecond = 5491 + Epoch[ 6 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17919165; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04594s; TotalTimePerSample = 0.18376ms; SamplesPerSecond = 5441 + Epoch[ 6 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14641138; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04576s; TotalTimePerSample = 0.18303ms; SamplesPerSecond = 5463 + Epoch[ 6 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14951001; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04568s; TotalTimePerSample = 0.18274ms; SamplesPerSecond = 5472 + Epoch[ 6 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13207251; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04595s; TotalTimePerSample = 0.18381ms; SamplesPerSecond = 5440 + Epoch[ 6 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16790063; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18382ms; SamplesPerSecond = 5440 + Epoch[ 6 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18087573; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04601s; TotalTimePerSample = 0.18402ms; SamplesPerSecond = 5434 + Epoch[ 6 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14667212; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04591s; TotalTimePerSample = 0.18364ms; SamplesPerSecond = 5445 + Epoch[ 6 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14512085; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 + Epoch[ 6 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15134570; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 + Epoch[ 6 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17105615; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04585s; TotalTimePerSample = 0.18340ms; SamplesPerSecond = 5452 + Epoch[ 6 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16167139; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04566s; TotalTimePerSample = 0.18264ms; SamplesPerSecond = 5475 + Epoch[ 6 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245068; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04604s; TotalTimePerSample = 0.18416ms; SamplesPerSecond = 5429 + Epoch[ 6 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15437012; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04623s; TotalTimePerSample = 0.18490ms; SamplesPerSecond = 5408 + Epoch[ 6 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17593652; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04636s; TotalTimePerSample = 0.18544ms; SamplesPerSecond = 5392 + Epoch[ 6 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12771338; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04628s; TotalTimePerSample = 0.18512ms; SamplesPerSecond = 5401 + Epoch[ 6 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13509131; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04632s; TotalTimePerSample = 0.18527ms; SamplesPerSecond = 5397 + Epoch[ 6 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512305; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04568s; TotalTimePerSample = 0.18273ms; SamplesPerSecond = 5472 + Epoch[ 6 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11810596; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04586s; TotalTimePerSample = 0.18345ms; SamplesPerSecond = 5451 + Epoch[ 6 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17171484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04573s; TotalTimePerSample = 0.18292ms; SamplesPerSecond = 5466 + Epoch[ 6 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215918; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04571s; TotalTimePerSample = 0.18284ms; SamplesPerSecond = 5469 + Epoch[ 6 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20186230; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04596s; TotalTimePerSample = 0.18385ms; SamplesPerSecond = 5439 + Epoch[ 6 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13284375; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04582s; TotalTimePerSample = 0.18328ms; SamplesPerSecond = 5456 +Finished Epoch[ 6 of 50]: [Training Set] TrainLossPerSample = 0.1566408; EvalErrPerSample = 0.072700001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.842141 +Starting Epoch 7: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 6 at record count 60000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1971 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 6: 3864, 19, ... + Epoch[ 7 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17176042; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04996s; TotalTimePerSample = 0.19982ms; SamplesPerSecond = 5004 + Epoch[ 7 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19883070; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18330ms; SamplesPerSecond = 5455 + Epoch[ 7 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15464130; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04578s; TotalTimePerSample = 0.18313ms; SamplesPerSecond = 5460 + Epoch[ 7 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19855670; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04582s; TotalTimePerSample = 0.18327ms; SamplesPerSecond = 5456 + Epoch[ 7 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194446; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04594s; TotalTimePerSample = 0.18377ms; SamplesPerSecond = 5441 + Epoch[ 7 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16680231; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04566s; TotalTimePerSample = 0.18265ms; SamplesPerSecond = 5475 + Epoch[ 7 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13895239; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04595s; TotalTimePerSample = 0.18379ms; SamplesPerSecond = 5440 + Epoch[ 7 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18752332; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04579s; TotalTimePerSample = 0.18316ms; SamplesPerSecond = 5459 + Epoch[ 7 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15161487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04621s; TotalTimePerSample = 0.18483ms; SamplesPerSecond = 5410 + Epoch[ 7 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15141785; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04578s; TotalTimePerSample = 0.18314ms; SamplesPerSecond = 5460 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 7 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19026770; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04581s; TotalTimePerSample = 0.18326ms; SamplesPerSecond = 5456 + Epoch[ 7 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14526355; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04632s; TotalTimePerSample = 0.18530ms; SamplesPerSecond = 5396 + Epoch[ 7 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14874353; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04556s; TotalTimePerSample = 0.18226ms; SamplesPerSecond = 5486 + Epoch[ 7 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16310034; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04592s; TotalTimePerSample = 0.18368ms; SamplesPerSecond = 5444 + Epoch[ 7 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15433960; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04604s; TotalTimePerSample = 0.18414ms; SamplesPerSecond = 5430 + Epoch[ 7 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17733911; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04600s; TotalTimePerSample = 0.18400ms; SamplesPerSecond = 5434 + Epoch[ 7 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14193359; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04580s; TotalTimePerSample = 0.18320ms; SamplesPerSecond = 5458 + Epoch[ 7 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13347632; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04601s; TotalTimePerSample = 0.18403ms; SamplesPerSecond = 5433 + Epoch[ 7 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16971606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04568s; TotalTimePerSample = 0.18271ms; SamplesPerSecond = 5473 + Epoch[ 7 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11671191; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04570s; TotalTimePerSample = 0.18279ms; SamplesPerSecond = 5470 + Epoch[ 7 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11164014; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04573s; TotalTimePerSample = 0.18291ms; SamplesPerSecond = 5467 + Epoch[ 7 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14871704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04623s; TotalTimePerSample = 0.18491ms; SamplesPerSecond = 5408 + Epoch[ 7 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15746533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04569s; TotalTimePerSample = 0.18278ms; SamplesPerSecond = 5471 + Epoch[ 7 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14831201; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04595s; TotalTimePerSample = 0.18379ms; SamplesPerSecond = 5440 + Epoch[ 7 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11845239; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04587s; TotalTimePerSample = 0.18347ms; SamplesPerSecond = 5450 + Epoch[ 7 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15587183; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04613s; TotalTimePerSample = 0.18452ms; SamplesPerSecond = 5419 + Epoch[ 7 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16354224; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04600s; TotalTimePerSample = 0.18401ms; SamplesPerSecond = 5434 + Epoch[ 7 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12038428; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04590s; TotalTimePerSample = 0.18359ms; SamplesPerSecond = 5446 + Epoch[ 7 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17588135; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04596s; TotalTimePerSample = 0.18382ms; SamplesPerSecond = 5440 + Epoch[ 7 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116455; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04571s; TotalTimePerSample = 0.18286ms; SamplesPerSecond = 5468 + Epoch[ 7 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131299; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04607s; TotalTimePerSample = 0.18428ms; SamplesPerSecond = 5426 + Epoch[ 7 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14321484; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04562s; TotalTimePerSample = 0.18248ms; SamplesPerSecond = 5480 + Epoch[ 7 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15750977; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04570s; TotalTimePerSample = 0.18280ms; SamplesPerSecond = 5470 + Epoch[ 7 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21607275; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04590s; TotalTimePerSample = 0.18360ms; SamplesPerSecond = 5446 + Epoch[ 7 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15346533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04560s; TotalTimePerSample = 0.18240ms; SamplesPerSecond = 5482 + Epoch[ 7 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17713916; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04580s; TotalTimePerSample = 0.18318ms; SamplesPerSecond = 5459 + Epoch[ 7 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262793; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04583s; TotalTimePerSample = 0.18331ms; SamplesPerSecond = 5455 + Epoch[ 7 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16701270; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04584s; TotalTimePerSample = 0.18338ms; SamplesPerSecond = 5453 + Epoch[ 7 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13210449; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04574s; TotalTimePerSample = 0.18296ms; SamplesPerSecond = 5465 + Epoch[ 7 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16644580; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04575s; TotalTimePerSample = 0.18301ms; SamplesPerSecond = 5464 +Finished Epoch[ 7 of 50]: [Training Set] TrainLossPerSample = 0.15703182; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.842136 +Starting Epoch 8: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 7 at record count 70000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1996 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 7: 1209, 4212, ... + Epoch[ 8 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19911723; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04874s; TotalTimePerSample = 0.19498ms; SamplesPerSecond = 5128 + Epoch[ 8 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18027156; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[ 8 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12933987; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[ 8 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13678201; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[ 8 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14297876; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[ 8 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16435925; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[ 8 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11406915; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[ 8 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18803101; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17985ms; SamplesPerSecond = 5560 + Epoch[ 8 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662817; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 + Epoch[ 8 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16887158; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 8 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16543518; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[ 8 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10627991; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[ 8 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17848193; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[ 8 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20434363; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[ 8 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718237; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[ 8 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15909668; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[ 8 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13549097; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[ 8 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12482495; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[ 8 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15946558; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[ 8 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13230127; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 + Epoch[ 8 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19255151; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[ 8 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16289160; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 + Epoch[ 8 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12351147; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[ 8 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13493799; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[ 8 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19009839; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5623 + Epoch[ 8 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17191382; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[ 8 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18348926; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[ 8 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17455273; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[ 8 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16983203; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[ 8 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20043896; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 + Epoch[ 8 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15542236; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5566 + Epoch[ 8 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22219385; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[ 8 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11237598; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[ 8 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11935156; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[ 8 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16266797; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 + Epoch[ 8 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16463818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[ 8 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17111035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[ 8 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16134229; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 + Epoch[ 8 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15073584; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[ 8 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14591309; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 +Finished Epoch[ 8 of 50]: [Training Set] TrainLossPerSample = 0.15908301; EvalErrPerSample = 0.075399995; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.794473 +Starting Epoch 9: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 8 at record count 80000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1981 retries for 10000 elements (19.8%) to ensure window condition +randomordering: recached sequence for seed 8: 258, 4597, ... + Epoch[ 9 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636783; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04892s; TotalTimePerSample = 0.19570ms; SamplesPerSecond = 5109 + Epoch[ 9 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11709901; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04505s; TotalTimePerSample = 0.18021ms; SamplesPerSecond = 5549 + Epoch[ 9 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13778328; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[ 9 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17506451; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[ 9 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11473816; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[ 9 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16009601; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[ 9 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19787012; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[ 9 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17618396; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[ 9 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12594666; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[ 9 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13145190; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[ 9 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17528821; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[ 9 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15709998; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04497s; TotalTimePerSample = 0.17990ms; SamplesPerSecond = 5558 + Epoch[ 9 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11661719; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[ 9 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15737024; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[ 9 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14008496; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[ 9 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17420557; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[ 9 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18854102; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 + Epoch[ 9 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15494727; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[ 9 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14329468; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[ 9 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20086182; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[ 9 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18686084; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[ 9 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14726855; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[ 9 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15037939; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[ 9 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15442334; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[ 9 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13163672; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[ 9 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16719629; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[ 9 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16306079; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[ 9 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16246484; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 + Epoch[ 9 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12757666; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[ 9 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18803369; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17953ms; SamplesPerSecond = 5570 + Epoch[ 9 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16672803; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[ 9 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15702393; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[ 9 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13608740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[ 9 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19304834; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[ 9 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17301758; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04522s; TotalTimePerSample = 0.18088ms; SamplesPerSecond = 5528 + Epoch[ 9 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18412305; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[ 9 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13706201; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 + Epoch[ 9 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18411182; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[ 9 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11667383; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[ 9 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17274463; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 +Finished Epoch[ 9 of 50]: [Training Set] TrainLossPerSample = 0.15701085; EvalErrPerSample = 0.074699998; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796622 +Starting Epoch 10: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 9 at record count 90000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1914 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 9: 1957, 3535, ... + Epoch[10 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15795653; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04872s; TotalTimePerSample = 0.19487ms; SamplesPerSecond = 5131 + Epoch[10 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15608952; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 + Epoch[10 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19516843; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[10 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15641345; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[10 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16329114; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[10 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17629742; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[10 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16732440; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04442s; TotalTimePerSample = 0.17770ms; SamplesPerSecond = 5627 + Epoch[10 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950903; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5581 + Epoch[10 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949792; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[10 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15610913; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[10 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17446240; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[10 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17979675; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 + Epoch[10 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17328748; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[10 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12340747; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[10 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16953101; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[10 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317700; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 + Epoch[10 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16655737; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 + Epoch[10 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11744604; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[10 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15822021; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 + Epoch[10 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668506; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04444s; TotalTimePerSample = 0.17776ms; SamplesPerSecond = 5625 + Epoch[10 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20659570; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[10 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17137329; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[10 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15892773; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[10 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20601440; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[10 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13179663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[10 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12188379; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[10 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15935791; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 + Epoch[10 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16524219; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 + Epoch[10 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10420312; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[10 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10876855; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[10 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15545703; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[10 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19641748; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[10 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16463574; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[10 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11221289; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[10 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12266650; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[10 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14136572; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[10 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16945117; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[10 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18718945; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04447s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 + Epoch[10 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16933008; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04522s; TotalTimePerSample = 0.18086ms; SamplesPerSecond = 5529 + Epoch[10 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15086816; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 +Finished Epoch[10 of 50]: [Training Set] TrainLossPerSample = 0.15784962; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.794615 +Starting Epoch 11: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 10 at record count 100000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1865 retries for 10000 elements (18.6%) to ensure window condition +randomordering: recached sequence for seed 10: 3356, 877, ... + Epoch[11 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19867067; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04867s; TotalTimePerSample = 0.19468ms; SamplesPerSecond = 5136 + Epoch[11 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12824931; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[11 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16003491; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 + Epoch[11 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14388751; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 + Epoch[11 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14035968; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 + Epoch[11 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16900494; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[11 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13200269; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[11 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10663000; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[11 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13941113; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[11 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15507104; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[11 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15361768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[11 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15443665; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[11 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13960754; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[11 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10843152; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[11 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14440918; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[11 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16181763; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[11 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18465283; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[11 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352783; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[11 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022241; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[11 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16155518; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[11 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15919336; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17977ms; SamplesPerSecond = 5562 + Epoch[11 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16321704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04512s; TotalTimePerSample = 0.18049ms; SamplesPerSecond = 5540 + Epoch[11 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13922144; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[11 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15851660; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[11 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20419971; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[11 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18372168; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[11 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739282; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[11 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17705762; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[11 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22723096; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 + Epoch[11 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17668066; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5624 + Epoch[11 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14230176; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[11 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17164600; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[11 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17324609; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17970ms; SamplesPerSecond = 5564 + Epoch[11 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11411230; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[11 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15184570; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[11 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15611523; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[11 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17217090; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[11 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19968115; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[11 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15998193; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17951ms; SamplesPerSecond = 5570 + Epoch[11 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12471484; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 +Finished Epoch[11 of 50]: [Training Set] TrainLossPerSample = 0.15719619; EvalErrPerSample = 0.075199999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795227 +Starting Epoch 12: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 11 at record count 110000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1961 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 11: 2406, 1536, ... + Epoch[12 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15143643; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04863s; TotalTimePerSample = 0.19454ms; SamplesPerSecond = 5140 + Epoch[12 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13797897; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 + Epoch[12 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16665747; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[12 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11482568; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[12 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15434119; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[12 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17429492; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[12 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18050989; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[12 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13679346; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[12 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14641870; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 + Epoch[12 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13636035; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[12 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20385144; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04522s; TotalTimePerSample = 0.18089ms; SamplesPerSecond = 5528 + Epoch[12 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15216943; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04531s; TotalTimePerSample = 0.18124ms; SamplesPerSecond = 5517 + Epoch[12 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15158472; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04509s; TotalTimePerSample = 0.18035ms; SamplesPerSecond = 5544 + Epoch[12 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13478882; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[12 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17630396; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[12 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13077051; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 + Epoch[12 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21419189; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[12 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15287256; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[12 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13726001; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[12 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13043091; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[12 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15200464; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5559 + Epoch[12 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20407959; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[12 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19046216; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[12 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14278735; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[12 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17253809; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5586 + Epoch[12 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12954492; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04491s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5566 + Epoch[12 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16107983; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[12 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15740039; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[12 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12038916; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[12 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12200781; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[12 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19328613; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 + Epoch[12 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13074707; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 + Epoch[12 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15184863; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04502s; TotalTimePerSample = 0.18009ms; SamplesPerSecond = 5552 + Epoch[12 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16412109; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[12 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16518799; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[12 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20125488; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[12 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12034131; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 + Epoch[12 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16150488; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[12 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20943555; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[12 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333301; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 +Finished Epoch[12 of 50]: [Training Set] TrainLossPerSample = 0.15717989; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796178 +Starting Epoch 13: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 12 at record count 120000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2004 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 12: 1583, 2526, ... + Epoch[13 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12885901; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04862s; TotalTimePerSample = 0.19446ms; SamplesPerSecond = 5142 + Epoch[13 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20166235; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[13 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17811789; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04499s; TotalTimePerSample = 0.17997ms; SamplesPerSecond = 5556 + Epoch[13 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15837650; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[13 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10881372; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[13 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17468298; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[13 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16144019; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[13 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17298315; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04532s; TotalTimePerSample = 0.18128ms; SamplesPerSecond = 5516 + Epoch[13 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13253101; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[13 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14265967; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[13 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411487; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[13 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14540137; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[13 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20036560; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[13 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15000488; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 + Epoch[13 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10153320; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[13 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16928491; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 + Epoch[13 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12664990; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[13 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19418799; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[13 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15498315; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04489s; TotalTimePerSample = 0.17957ms; SamplesPerSecond = 5568 + Epoch[13 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17595093; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5613 + Epoch[13 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15520947; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 + Epoch[13 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19142041; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[13 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14185376; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[13 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14463403; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[13 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18891724; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[13 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17073340; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 + Epoch[13 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17670361; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[13 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16432715; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[13 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220020; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[13 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14603711; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[13 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15039844; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17911ms; SamplesPerSecond = 5583 + Epoch[13 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18936670; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 + Epoch[13 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15210645; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 + Epoch[13 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15780615; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[13 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13348975; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 + Epoch[13 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10566113; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[13 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20451074; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[13 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14747998; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[13 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904688; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[13 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14080127; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 +Finished Epoch[13 of 50]: [Training Set] TrainLossPerSample = 0.15763268; EvalErrPerSample = 0.074999996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796521 +Starting Epoch 14: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 13 at record count 130000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 13: 503, 1097, ... + Epoch[14 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911017; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04887s; TotalTimePerSample = 0.19550ms; SamplesPerSecond = 5115 + Epoch[14 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13950354; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 + Epoch[14 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14423825; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04447s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 + Epoch[14 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18848502; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[14 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15733173; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[14 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17221399; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[14 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12658374; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[14 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15295520; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[14 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16327393; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[14 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15271497; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[14 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14662256; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[14 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15376038; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04498s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 + Epoch[14 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13387573; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[14 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19126245; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[14 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16492456; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[14 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11945190; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[14 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11764478; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[14 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12589526; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[14 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20678247; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[14 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18653369; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[14 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15909570; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 + Epoch[14 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13653760; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[14 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16765674; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[14 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17644312; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[14 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17960864; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[14 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18134204; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17792ms; SamplesPerSecond = 5620 + Epoch[14 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15984668; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[14 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13785986; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[14 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17912354; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[14 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12685059; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[14 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15034229; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[14 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13104297; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[14 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15666602; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[14 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14833740; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[14 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14219971; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[14 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16518359; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[14 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19329785; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[14 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17797607; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[14 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17447119; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17809ms; SamplesPerSecond = 5615 + Epoch[14 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13668359; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 +Finished Epoch[14 of 50]: [Training Set] TrainLossPerSample = 0.15684323; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.793499 +Starting Epoch 15: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 14 at record count 140000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 14: 989, 678, ... + Epoch[15 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20957314; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04872s; TotalTimePerSample = 0.19487ms; SamplesPerSecond = 5131 + Epoch[15 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14468547; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[15 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18774567; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[15 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12493726; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 + Epoch[15 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13638898; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[15 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17119073; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04503s; TotalTimePerSample = 0.18010ms; SamplesPerSecond = 5552 + Epoch[15 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20540869; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[15 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13395593; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[15 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12372241; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[15 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17486792; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[15 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18510693; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04490s; TotalTimePerSample = 0.17960ms; SamplesPerSecond = 5567 + Epoch[15 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14575623; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[15 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17531445; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[15 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13697461; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[15 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16259937; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[15 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12684058; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17991ms; SamplesPerSecond = 5558 + Epoch[15 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11239185; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04500s; TotalTimePerSample = 0.17998ms; SamplesPerSecond = 5556 + Epoch[15 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11496558; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[15 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14266089; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[15 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19046021; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[15 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17349243; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[15 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15107007; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17801ms; SamplesPerSecond = 5617 + Epoch[15 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17756396; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[15 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13699707; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04500s; TotalTimePerSample = 0.18000ms; SamplesPerSecond = 5555 + Epoch[15 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16005298; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04521s; TotalTimePerSample = 0.18085ms; SamplesPerSecond = 5529 + Epoch[15 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15821387; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04518s; TotalTimePerSample = 0.18074ms; SamplesPerSecond = 5532 + Epoch[15 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12138208; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 + Epoch[15 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19546973; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 + Epoch[15 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14879150; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 + Epoch[15 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15509131; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04534s; TotalTimePerSample = 0.18137ms; SamplesPerSecond = 5513 + Epoch[15 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14998779; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[15 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14487354; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[15 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15503076; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[15 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18194482; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[15 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15081738; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[15 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245752; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[15 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17944727; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[15 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14993506; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[15 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18263428; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 + Epoch[15 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17803271; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 +Finished Epoch[15 of 50]: [Training Set] TrainLossPerSample = 0.15772082; EvalErrPerSample = 0.075899996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.79833 +Starting Epoch 16: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 15 at record count 150000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1959 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 15: 834, 4617, ... + Epoch[16 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18061586; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04880s; TotalTimePerSample = 0.19519ms; SamplesPerSecond = 5123 + Epoch[16 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12055733; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13487866; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16472366; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14694202; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[16 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16010132; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[16 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14952625; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[16 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18979016; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[16 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14659521; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[16 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14648743; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[16 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14281995; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04443s; TotalTimePerSample = 0.17773ms; SamplesPerSecond = 5626 + Epoch[16 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19394141; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15640967; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[16 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11770435; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[16 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14273438; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[16 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16738745; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5562 + Epoch[16 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14881226; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[16 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21560669; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[16 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11582544; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 + Epoch[16 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15476562; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[16 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20032373; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04500s; TotalTimePerSample = 0.17999ms; SamplesPerSecond = 5555 + Epoch[16 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14822192; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04490s; TotalTimePerSample = 0.17961ms; SamplesPerSecond = 5567 + Epoch[16 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10779248; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[16 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16034375; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 + Epoch[16 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19777490; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 + Epoch[16 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15483081; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[16 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14559521; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[16 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14460498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04517s; TotalTimePerSample = 0.18068ms; SamplesPerSecond = 5534 + Epoch[16 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16182080; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04541s; TotalTimePerSample = 0.18164ms; SamplesPerSecond = 5505 + Epoch[16 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17307129; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04521s; TotalTimePerSample = 0.18083ms; SamplesPerSecond = 5529 + Epoch[16 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16362549; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[16 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09776416; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13648877; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 + Epoch[16 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16567480; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04506s; TotalTimePerSample = 0.18025ms; SamplesPerSecond = 5547 + Epoch[16 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17059229; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[16 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13688623; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[16 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17496973; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5557 + Epoch[16 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18197900; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[16 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16353174; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[16 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16290332; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 +Finished Epoch[16 of 50]: [Training Set] TrainLossPerSample = 0.15612552; EvalErrPerSample = 0.072099999; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796983 +Starting Epoch 17: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 16 at record count 160000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2037 retries for 10000 elements (20.4%) to ensure window condition +randomordering: recached sequence for seed 16: 4884, 4383, ... + Epoch[17 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13465163; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04819s; TotalTimePerSample = 0.19278ms; SamplesPerSecond = 5187 + Epoch[17 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17920305; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 + Epoch[17 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15974200; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04434s; TotalTimePerSample = 0.17737ms; SamplesPerSecond = 5637 + Epoch[17 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16869012; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04431s; TotalTimePerSample = 0.17725ms; SamplesPerSecond = 5641 + Epoch[17 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17422345; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 + Epoch[17 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16351520; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04427s; TotalTimePerSample = 0.17708ms; SamplesPerSecond = 5647 + Epoch[17 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11277435; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04432s; TotalTimePerSample = 0.17726ms; SamplesPerSecond = 5641 + Epoch[17 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14457776; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04417s; TotalTimePerSample = 0.17669ms; SamplesPerSecond = 5659 + Epoch[17 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14491663; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04428s; TotalTimePerSample = 0.17712ms; SamplesPerSecond = 5645 + Epoch[17 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14099316; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[17 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12237012; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04408s; TotalTimePerSample = 0.17632ms; SamplesPerSecond = 5671 + Epoch[17 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17041846; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04424s; TotalTimePerSample = 0.17695ms; SamplesPerSecond = 5651 + Epoch[17 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16491626; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04407s; TotalTimePerSample = 0.17630ms; SamplesPerSecond = 5672 + Epoch[17 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19155933; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04426s; TotalTimePerSample = 0.17705ms; SamplesPerSecond = 5648 + Epoch[17 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904541; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[17 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16747656; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04440s; TotalTimePerSample = 0.17762ms; SamplesPerSecond = 5630 + Epoch[17 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14390161; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04422s; TotalTimePerSample = 0.17690ms; SamplesPerSecond = 5653 + Epoch[17 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13106763; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04423s; TotalTimePerSample = 0.17694ms; SamplesPerSecond = 5651 + Epoch[17 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15458960; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04426s; TotalTimePerSample = 0.17706ms; SamplesPerSecond = 5647 + Epoch[17 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15445386; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17674ms; SamplesPerSecond = 5658 + Epoch[17 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17567090; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04413s; TotalTimePerSample = 0.17652ms; SamplesPerSecond = 5665 + Epoch[17 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14774487; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04442s; TotalTimePerSample = 0.17767ms; SamplesPerSecond = 5628 + Epoch[17 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14936938; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04425s; TotalTimePerSample = 0.17701ms; SamplesPerSecond = 5649 + Epoch[17 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18822534; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04406s; TotalTimePerSample = 0.17625ms; SamplesPerSecond = 5673 + Epoch[17 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13277808; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[17 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16678833; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17780ms; SamplesPerSecond = 5624 + Epoch[17 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12372900; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 + Epoch[17 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904199; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04414s; TotalTimePerSample = 0.17655ms; SamplesPerSecond = 5664 + Epoch[17 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16823633; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04416s; TotalTimePerSample = 0.17665ms; SamplesPerSecond = 5660 + Epoch[17 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16629639; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04421s; TotalTimePerSample = 0.17684ms; SamplesPerSecond = 5654 + Epoch[17 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19804639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04413s; TotalTimePerSample = 0.17651ms; SamplesPerSecond = 5665 + Epoch[17 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13714258; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04430s; TotalTimePerSample = 0.17719ms; SamplesPerSecond = 5643 + Epoch[17 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14821777; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04396s; TotalTimePerSample = 0.17583ms; SamplesPerSecond = 5687 + Epoch[17 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12661865; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04417s; TotalTimePerSample = 0.17667ms; SamplesPerSecond = 5660 + Epoch[17 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15863330; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04421s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 + Epoch[17 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16433936; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04413s; TotalTimePerSample = 0.17653ms; SamplesPerSecond = 5664 + Epoch[17 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18692627; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04443s; TotalTimePerSample = 0.17772ms; SamplesPerSecond = 5626 + Epoch[17 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11682324; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04425s; TotalTimePerSample = 0.17699ms; SamplesPerSecond = 5650 + Epoch[17 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16239307; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04408s; TotalTimePerSample = 0.17632ms; SamplesPerSecond = 5671 + Epoch[17 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19032471; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04413s; TotalTimePerSample = 0.17650ms; SamplesPerSecond = 5665 +Finished Epoch[17 of 50]: [Training Set] TrainLossPerSample = 0.15601081; EvalErrPerSample = 0.075300001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.776697 +Starting Epoch 18: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 17 at record count 170000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1944 retries for 10000 elements (19.4%) to ensure window condition +randomordering: recached sequence for seed 17: 1700, 4307, ... + Epoch[18 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17588332; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04867s; TotalTimePerSample = 0.19470ms; SamplesPerSecond = 5136 + Epoch[18 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12923604; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5575 + Epoch[18 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15792795; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[18 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16389697; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[18 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14437134; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[18 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12547186; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[18 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16366309; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 + Epoch[18 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15109570; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 + Epoch[18 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10915527; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[18 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14117944; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[18 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20950757; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[18 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13919482; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[18 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16089600; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[18 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512463; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 + Epoch[18 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13849780; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[18 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14411523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5593 + Epoch[18 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17097339; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[18 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21734570; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[18 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12918726; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[18 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643921; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[18 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588525; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[18 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17428369; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[18 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13475342; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[18 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11663159; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[18 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13888208; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[18 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22669946; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[18 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13757300; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[18 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12249902; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[18 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20050586; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 + Epoch[18 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18216162; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04489s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 + Epoch[18 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16056787; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[18 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16622314; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04525s; TotalTimePerSample = 0.18102ms; SamplesPerSecond = 5524 + Epoch[18 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17283984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04501s; TotalTimePerSample = 0.18006ms; SamplesPerSecond = 5553 + Epoch[18 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13685938; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[18 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21995117; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[18 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13811914; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[18 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14204834; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[18 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18141699; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[18 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16959912; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[18 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15641895; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 +Finished Epoch[18 of 50]: [Training Set] TrainLossPerSample = 0.15692703; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795097 +Starting Epoch 19: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 18 at record count 180000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2073 retries for 10000 elements (20.7%) to ensure window condition +randomordering: recached sequence for seed 18: 3974, 797, ... + Epoch[19 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13207307; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04877s; TotalTimePerSample = 0.19509ms; SamplesPerSecond = 5125 + Epoch[19 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13981007; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 + Epoch[19 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15596564; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[19 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14441849; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[19 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13712158; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[19 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15629230; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[19 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15038147; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 + Epoch[19 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15722290; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5573 + Epoch[19 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16579480; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[19 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15973206; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[19 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12839282; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 + Epoch[19 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15963623; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[19 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13392346; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04508s; TotalTimePerSample = 0.18034ms; SamplesPerSecond = 5545 + Epoch[19 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19707300; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[19 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16536011; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[19 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15737549; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[19 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17403125; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[19 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17994727; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[19 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19541064; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 + Epoch[19 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17745801; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 + Epoch[19 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20086279; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[19 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12403540; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[19 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14028516; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 + Epoch[19 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19981592; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[19 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16951782; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17819ms; SamplesPerSecond = 5611 + Epoch[19 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15190747; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[19 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13389893; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[19 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12309375; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[19 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19979102; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[19 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322949; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[19 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11870898; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[19 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17556445; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[19 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18256982; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[19 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11174219; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04503s; TotalTimePerSample = 0.18010ms; SamplesPerSecond = 5552 + Epoch[19 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13577148; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04495s; TotalTimePerSample = 0.17982ms; SamplesPerSecond = 5561 + Epoch[19 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292432; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[19 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16210498; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[19 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12245996; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[19 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14956982; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5585 + Epoch[19 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16419238; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 +Finished Epoch[19 of 50]: [Training Set] TrainLossPerSample = 0.15573667; EvalErrPerSample = 0.074900001; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.796807 +Starting Epoch 20: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 19 at record count 190000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1918 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 19: 549, 1755, ... + Epoch[20 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18164697; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04850s; TotalTimePerSample = 0.19399ms; SamplesPerSecond = 5154 + Epoch[20 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14521094; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04441s; TotalTimePerSample = 0.17763ms; SamplesPerSecond = 5629 + Epoch[20 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15597580; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 + Epoch[20 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12063333; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[20 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13853162; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 + Epoch[20 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621198; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[20 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18415680; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[20 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13171667; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[20 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13916663; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[20 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16387109; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[20 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19112891; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[20 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12275732; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 + Epoch[20 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15608887; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 + Epoch[20 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16431812; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[20 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15825244; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[20 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16326318; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[20 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15132129; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 + Epoch[20 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13621021; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[20 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17509082; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[20 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14556982; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[20 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14166553; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[20 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16108887; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17774ms; SamplesPerSecond = 5626 + Epoch[20 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21493335; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[20 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13326514; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 + Epoch[20 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15445020; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[20 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15669336; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[20 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16875928; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[20 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007568; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[20 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16125879; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[20 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14758447; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[20 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16430664; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[20 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12669043; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[20 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553271; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[20 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18063574; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[20 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14528320; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[20 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18753271; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[20 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19194580; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[20 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14930615; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[20 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14721973; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[20 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13791357; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 +Finished Epoch[20 of 50]: [Training Set] TrainLossPerSample = 0.15693159; EvalErrPerSample = 0.074999996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.793192 +Starting Epoch 21: learning rate per sample = 0.008000 effective momentum = 0.900000 +starting epoch 20 at record count 200000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1970 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 20: 1080, 44, ... + Epoch[21 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12496954; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04870s; TotalTimePerSample = 0.19481ms; SamplesPerSecond = 5133 + Epoch[21 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17533447; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[21 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19186441; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[21 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12946396; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[21 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13574353; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[21 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139471; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 + Epoch[21 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10968701; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[21 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14205054; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 + Epoch[21 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11600293; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 + Epoch[21 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16565967; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[21 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16167261; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04510s; TotalTimePerSample = 0.18038ms; SamplesPerSecond = 5543 + Epoch[21 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12315039; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[21 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19873743; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 + Epoch[21 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15707446; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[21 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12886548; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[21 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19557129; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[21 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16244556; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[21 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16032031; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[21 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18584741; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17807ms; SamplesPerSecond = 5615 + Epoch[21 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14336938; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[21 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12707104; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04507s; TotalTimePerSample = 0.18030ms; SamplesPerSecond = 5546 + Epoch[21 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13008472; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5563 + Epoch[21 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16317065; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[21 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19089893; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 + Epoch[21 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17855615; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[21 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15948315; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 + Epoch[21 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16497705; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[21 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13283838; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[21 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19128271; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[21 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17139648; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[21 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20447852; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[21 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15682861; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[21 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17994922; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[21 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15734082; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[21 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11994775; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 + Epoch[21 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12581787; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[21 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14635156; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[21 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21252393; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 + Epoch[21 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13149316; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[21 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13085107; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04538s; TotalTimePerSample = 0.18152ms; SamplesPerSecond = 5508 +Finished Epoch[21 of 50]: [Training Set] TrainLossPerSample = 0.15636417; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00800000038; EpochTime=1.795501 +Starting Epoch 22: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 21 at record count 210000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2012 retries for 10000 elements (20.1%) to ensure window condition +randomordering: recached sequence for seed 21: 549, 612, ... + Epoch[22 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17249321; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04856s; TotalTimePerSample = 0.19426ms; SamplesPerSecond = 5147 + Epoch[22 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13486766; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 + Epoch[22 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23927286; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 + Epoch[22 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14058728; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[22 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13351758; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[22 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12863477; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[22 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13745990; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[22 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18005945; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[22 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14101172; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[22 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12557007; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[22 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22041040; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[22 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17405029; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17815ms; SamplesPerSecond = 5613 + Epoch[22 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16253601; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5578 + Epoch[22 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14346411; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 + Epoch[22 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12405127; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[22 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12869604; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[22 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17248706; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[22 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15263428; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[22 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14513379; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[22 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18100293; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[22 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13791406; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[22 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17094653; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[22 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14585889; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 + Epoch[22 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15929150; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[22 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17747388; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[22 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13812695; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[22 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18408325; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[22 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14613623; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[22 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11093994; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[22 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18320898; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[22 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17237891; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[22 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14933740; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 + Epoch[22 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13352490; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[22 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13781689; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[22 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16293164; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[22 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19111816; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[22 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12595020; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[22 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13964209; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[22 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08325732; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[22 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21109717; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 +Finished Epoch[22 of 50]: [Training Set] TrainLossPerSample = 0.15497439; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794121 +Starting Epoch 23: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 22 at record count 220000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1950 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 22: 43, 802, ... + Epoch[23 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13861447; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04862s; TotalTimePerSample = 0.19449ms; SamplesPerSecond = 5141 + Epoch[23 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15250851; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[23 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15022876; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 + Epoch[23 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13989343; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 + Epoch[23 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15462238; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[23 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13589734; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[23 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17117334; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04495s; TotalTimePerSample = 0.17980ms; SamplesPerSecond = 5561 + Epoch[23 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15149866; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 + Epoch[23 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14791724; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[23 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15872766; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[23 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19306750; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[23 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18024060; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[23 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11721790; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[23 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08968396; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[23 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17251343; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[23 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18874536; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[23 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15459204; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[23 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15327686; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[23 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12174170; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[23 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14656714; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 + Epoch[23 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20618359; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[23 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17090063; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[23 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15778906; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[23 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16741211; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[23 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12689673; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[23 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14608740; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[23 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15718481; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[23 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13117432; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[23 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19989893; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[23 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16584961; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17975ms; SamplesPerSecond = 5563 + Epoch[23 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17821826; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[23 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20771924; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 + Epoch[23 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14987598; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[23 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15761572; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 + Epoch[23 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13198340; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[23 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16465576; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[23 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11607617; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[23 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14489258; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 + Epoch[23 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12753369; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[23 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14904980; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 +Finished Epoch[23 of 50]: [Training Set] TrainLossPerSample = 0.15439315; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795236 +Starting Epoch 24: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 23 at record count 230000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1952 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 23: 3227, 3145, ... + Epoch[24 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14031882; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04848s; TotalTimePerSample = 0.19392ms; SamplesPerSecond = 5156 + Epoch[24 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11842618; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[24 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13751770; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[24 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17792755; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[24 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15292828; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[24 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17554633; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[24 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19090387; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[24 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14506067; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[24 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17882153; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 + Epoch[24 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13650439; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[24 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14354358; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 + Epoch[24 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14098999; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 + Epoch[24 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15522107; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[24 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19907764; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[24 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14186401; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[24 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18555957; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[24 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14737354; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[24 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16094092; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[24 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13224023; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[24 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16364185; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 + Epoch[24 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12108496; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[24 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16725464; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[24 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14817480; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[24 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15528418; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[24 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15678467; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[24 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13416504; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[24 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17107593; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[24 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14259326; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[24 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15677246; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[24 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13764355; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[24 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12931787; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[24 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18501465; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 + Epoch[24 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11815723; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[24 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19544434; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[24 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13329053; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5575 + Epoch[24 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18037500; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[24 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16110352; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[24 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18099561; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 + Epoch[24 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13044922; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5617 + Epoch[24 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14814795; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 +Finished Epoch[24 of 50]: [Training Set] TrainLossPerSample = 0.15443842; EvalErrPerSample = 0.073600002; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793236 +Starting Epoch 25: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 24 at record count 240000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1932 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 24: 3407, 2083, ... + Epoch[25 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10401460; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04858s; TotalTimePerSample = 0.19434ms; SamplesPerSecond = 5145 + Epoch[25 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15403728; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[25 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14292963; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[25 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16843939; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[25 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17557404; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 + Epoch[25 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11396613; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 + Epoch[25 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14637885; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5581 + Epoch[25 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17022577; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[25 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16505737; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[25 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18194116; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[25 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16375818; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[25 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10269714; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[25 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12660144; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[25 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12848438; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[25 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15899890; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[25 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15867456; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 + Epoch[25 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10445361; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17967ms; SamplesPerSecond = 5565 + Epoch[25 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15862817; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[25 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15271533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 + Epoch[25 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18500146; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[25 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19119116; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[25 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17116187; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 + Epoch[25 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16713135; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04530s; TotalTimePerSample = 0.18121ms; SamplesPerSecond = 5518 + Epoch[25 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16805591; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04537s; TotalTimePerSample = 0.18148ms; SamplesPerSecond = 5510 + Epoch[25 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19619067; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04521s; TotalTimePerSample = 0.18086ms; SamplesPerSecond = 5529 + Epoch[25 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18263086; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04437s; TotalTimePerSample = 0.17748ms; SamplesPerSecond = 5634 + Epoch[25 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14543237; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[25 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13985742; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[25 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15661035; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[25 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15545068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[25 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16540723; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[25 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15906396; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[25 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15388477; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[25 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14379590; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[25 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15475977; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[25 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16222705; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04495s; TotalTimePerSample = 0.17980ms; SamplesPerSecond = 5561 + Epoch[25 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14356641; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[25 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17399707; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[25 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12595068; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[25 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16378369; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04495s; TotalTimePerSample = 0.17979ms; SamplesPerSecond = 5562 +Finished Epoch[25 of 50]: [Training Set] TrainLossPerSample = 0.15456817; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798305 +Starting Epoch 26: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 25 at record count 250000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1924 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 25: 1325, 1664, ... + Epoch[26 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14272597; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04866s; TotalTimePerSample = 0.19465ms; SamplesPerSecond = 5137 + Epoch[26 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21971495; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[26 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13571994; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 + Epoch[26 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16259573; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[26 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13755927; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[26 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18914001; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[26 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11552826; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[26 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14476648; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[26 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18686255; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[26 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13507581; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17790ms; SamplesPerSecond = 5621 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[26 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15479773; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[26 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12926501; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[26 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12030884; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[26 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14794409; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[26 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15031055; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 + Epoch[26 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11519922; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[26 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10703442; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[26 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11749365; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[26 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17174731; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[26 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17957764; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17933ms; SamplesPerSecond = 5576 + Epoch[26 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13146729; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04504s; TotalTimePerSample = 0.18017ms; SamplesPerSecond = 5550 + Epoch[26 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16610107; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5572 + Epoch[26 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10538916; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[26 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13408008; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[26 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16431982; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[26 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18921313; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[26 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14537109; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[26 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17432764; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04448s; TotalTimePerSample = 0.17793ms; SamplesPerSecond = 5620 + Epoch[26 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19397510; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[26 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950000; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[26 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13004541; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[26 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17084473; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[26 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16149268; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18015ms; SamplesPerSecond = 5550 + Epoch[26 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20250391; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5607 + Epoch[26 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15491113; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[26 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22479639; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[26 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20011865; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[26 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17540625; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[26 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12219189; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 + Epoch[26 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12968066; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 +Finished Epoch[26 of 50]: [Training Set] TrainLossPerSample = 0.15397759; EvalErrPerSample = 0.072999999; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79425 +Starting Epoch 27: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 26 at record count 260000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1927 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 26: 375, 3629, ... + Epoch[27 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12588452; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04864s; TotalTimePerSample = 0.19455ms; SamplesPerSecond = 5140 + Epoch[27 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14300961; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[27 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14642679; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[27 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12520630; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[27 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13292584; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[27 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14024603; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[27 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10274500; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[27 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13299634; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[27 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17985303; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[27 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16833691; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[27 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21882690; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17911ms; SamplesPerSecond = 5583 + Epoch[27 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18278040; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[27 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17143176; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[27 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14857495; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[27 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14733496; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 + Epoch[27 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17670459; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[27 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14757007; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[27 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12504517; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[27 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13788232; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[27 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14063794; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[27 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13357983; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[27 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20383594; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[27 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18813232; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[27 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23009766; EvalErr[0]PerSample = 0.13200000; TotalTime = 0.04492s; TotalTimePerSample = 0.17970ms; SamplesPerSecond = 5564 + Epoch[27 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13490405; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17944ms; SamplesPerSecond = 5572 + Epoch[27 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20232202; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 + Epoch[27 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15602759; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[27 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10659521; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[27 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19164111; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[27 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13914014; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[27 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12599121; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[27 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11994922; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[27 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22676465; EvalErr[0]PerSample = 0.12000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[27 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10160986; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[27 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12502100; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[27 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13096729; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[27 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18643994; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[27 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14990186; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 + Epoch[27 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17400000; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 + Epoch[27 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15188770; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 +Finished Epoch[27 of 50]: [Training Set] TrainLossPerSample = 0.1543307; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79564 +Starting Epoch 28: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 27 at record count 270000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2091 retries for 10000 elements (20.9%) to ensure window condition +randomordering: recached sequence for seed 27: 1656, 2946, ... + Epoch[28 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14384726; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04867s; TotalTimePerSample = 0.19468ms; SamplesPerSecond = 5136 + Epoch[28 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17758655; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[28 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15442941; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[28 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907935; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[28 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12490643; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[28 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14148981; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[28 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13621857; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[28 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14112085; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[28 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14918579; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[28 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14690503; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[28 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17772888; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[28 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19665735; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[28 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14749402; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[28 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17928772; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[28 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12745068; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[28 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18881909; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[28 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12280054; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[28 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19135083; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[28 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13609644; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5617 + Epoch[28 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16096045; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 + Epoch[28 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15061768; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[28 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15546216; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[28 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15687354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04507s; TotalTimePerSample = 0.18029ms; SamplesPerSecond = 5546 + Epoch[28 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13221436; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[28 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15861523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17883ms; SamplesPerSecond = 5591 + Epoch[28 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20283716; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[28 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12571924; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[28 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17611768; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[28 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15847119; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[28 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13257910; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5610 + Epoch[28 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12166602; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04494s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 + Epoch[28 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16247754; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[28 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16935352; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 + Epoch[28 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11980615; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[28 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14580957; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[28 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16940039; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 + Epoch[28 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13439014; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5601 + Epoch[28 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15725049; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[28 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18272949; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[28 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16753027; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 +Finished Epoch[28 of 50]: [Training Set] TrainLossPerSample = 0.1543334; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794768 +Starting Epoch 29: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 28 at record count 280000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2057 retries for 10000 elements (20.6%) to ensure window condition +randomordering: recached sequence for seed 28: 1080, 2027, ... + Epoch[29 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643460; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04866s; TotalTimePerSample = 0.19463ms; SamplesPerSecond = 5137 + Epoch[29 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17673239; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04516s; TotalTimePerSample = 0.18062ms; SamplesPerSecond = 5536 + Epoch[29 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17501965; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[29 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18176697; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[29 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16094989; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[29 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13162311; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[29 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16178186; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[29 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14593005; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[29 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16553857; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[29 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20095337; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[29 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17819421; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17954ms; SamplesPerSecond = 5569 + Epoch[29 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11219897; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[29 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13306409; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04498s; TotalTimePerSample = 0.17990ms; SamplesPerSecond = 5558 + Epoch[29 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13262695; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[29 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17499731; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[29 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11501489; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[29 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293115; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[29 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13474609; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[29 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16392187; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[29 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15163599; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[29 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10707788; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[29 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12640332; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[29 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19808716; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[29 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17678784; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[29 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14488403; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[29 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14671704; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[29 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15546655; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[29 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15554980; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[29 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16030908; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[29 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16830664; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[29 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18280859; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[29 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12016406; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[29 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18406348; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 + Epoch[29 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11061035; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[29 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17913623; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[29 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14679883; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[29 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17387402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[29 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14572705; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[29 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16189697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 + Epoch[29 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13214697; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 +Finished Epoch[29 of 50]: [Training Set] TrainLossPerSample = 0.15407194; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795556 +Starting Epoch 30: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 29 at record count 290000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1974 retries for 10000 elements (19.7%) to ensure window condition +randomordering: recached sequence for seed 29: 914, 4227, ... + Epoch[30 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14465047; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04847s; TotalTimePerSample = 0.19386ms; SamplesPerSecond = 5158 + Epoch[30 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16279860; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[30 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12415610; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[30 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15766827; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[30 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15625421; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[30 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12182874; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[30 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11108282; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[30 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13562219; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[30 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17527539; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[30 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11627612; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[30 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14553870; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[30 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22418823; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[30 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20641150; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[30 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13691345; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[30 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15052173; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[30 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558325; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[30 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16657935; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 + Epoch[30 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15470361; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[30 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16345532; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[30 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13692334; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[30 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10039355; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[30 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14082397; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[30 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16516626; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 + Epoch[30 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13545752; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04445s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 + Epoch[30 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15586499; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[30 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13069263; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 + Epoch[30 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13733008; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[30 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14450171; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 + Epoch[30 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14557178; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[30 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13832080; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[30 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21320020; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[30 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16202344; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[30 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20276807; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[30 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16053955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 + Epoch[30 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21381396; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[30 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16618750; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 + Epoch[30 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16696973; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 + Epoch[30 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14461035; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[30 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17023926; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[30 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13199170; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 +Finished Epoch[30 of 50]: [Training Set] TrainLossPerSample = 0.15457246; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793778 +Starting Epoch 31: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 30 at record count 300000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1851 retries for 10000 elements (18.5%) to ensure window condition +randomordering: recached sequence for seed 30: 3803, 3991, ... + Epoch[31 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14464336; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04872s; TotalTimePerSample = 0.19488ms; SamplesPerSecond = 5131 + Epoch[31 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16304344; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[31 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14846210; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 + Epoch[31 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17292856; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[31 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17017865; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[31 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18828259; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[31 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14660876; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[31 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18798572; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[31 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16629407; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[31 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14552612; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[31 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15519067; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 + Epoch[31 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11363782; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[31 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11201221; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[31 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18323889; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 + Epoch[31 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14004224; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[31 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18273169; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17976ms; SamplesPerSecond = 5562 + Epoch[31 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17291748; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[31 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11845776; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[31 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11312305; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[31 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12602563; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5579 + Epoch[31 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12005713; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18017ms; SamplesPerSecond = 5550 + Epoch[31 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14552344; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 + Epoch[31 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19756104; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04542s; TotalTimePerSample = 0.18168ms; SamplesPerSecond = 5504 + Epoch[31 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12402295; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04513s; TotalTimePerSample = 0.18052ms; SamplesPerSecond = 5539 + Epoch[31 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17038525; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[31 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12756250; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[31 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12024561; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[31 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13515845; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04450s; TotalTimePerSample = 0.17802ms; SamplesPerSecond = 5617 + Epoch[31 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21380908; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[31 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20564746; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[31 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15676807; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[31 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17230371; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[31 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16065967; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 + Epoch[31 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13149805; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[31 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15634863; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04454s; TotalTimePerSample = 0.17815ms; SamplesPerSecond = 5613 + Epoch[31 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12047559; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[31 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18569824; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04450s; TotalTimePerSample = 0.17800ms; SamplesPerSecond = 5618 + Epoch[31 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18437402; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[31 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16043848; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[31 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15113330; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 +Finished Epoch[31 of 50]: [Training Set] TrainLossPerSample = 0.15477504; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795784 +Starting Epoch 32: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 31 at record count 310000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 31: 2853, 507, ... + Epoch[32 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16708208; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04899s; TotalTimePerSample = 0.19597ms; SamplesPerSecond = 5102 + Epoch[32 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19161845; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[32 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13780560; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 + Epoch[32 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13825052; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[32 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13485834; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[32 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15949799; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[32 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11792560; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[32 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12071558; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[32 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131982; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[32 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18264221; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5623 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[32 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14340527; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[32 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17570520; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[32 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13826721; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 + Epoch[32 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15120520; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[32 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12938940; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 + Epoch[32 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16178320; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[32 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16653662; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[32 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17045508; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04490s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[32 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907202; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 + Epoch[32 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15496533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[32 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14055078; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[32 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13834326; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[32 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12971484; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[32 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16714795; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[32 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21098340; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[32 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16618188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 + Epoch[32 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11618066; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[32 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16812061; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[32 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17626123; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[32 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14793311; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[32 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14779639; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 + Epoch[32 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19174854; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[32 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14862061; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[32 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18168115; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17799ms; SamplesPerSecond = 5618 + Epoch[32 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13130322; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 + Epoch[32 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13535596; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17803ms; SamplesPerSecond = 5616 + Epoch[32 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15619971; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[32 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12708936; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[32 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18303760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[32 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904688; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 +Finished Epoch[32 of 50]: [Training Set] TrainLossPerSample = 0.15414494; EvalErrPerSample = 0.074199997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793314 +Starting Epoch 33: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 32 at record count 320000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1906 retries for 10000 elements (19.1%) to ensure window condition +randomordering: recached sequence for seed 32: 88, 89, ... + Epoch[33 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16716551; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04884s; TotalTimePerSample = 0.19535ms; SamplesPerSecond = 5118 + Epoch[33 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19286800; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17899ms; SamplesPerSecond = 5586 + Epoch[33 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13189536; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[33 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12907028; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[33 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16408899; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[33 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19647198; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04493s; TotalTimePerSample = 0.17972ms; SamplesPerSecond = 5564 + Epoch[33 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18189166; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[33 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11359680; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 + Epoch[33 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13805994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 + Epoch[33 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19713013; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[33 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14238354; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[33 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12312415; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[33 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14386133; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[33 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.22228809; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04515s; TotalTimePerSample = 0.18059ms; SamplesPerSecond = 5537 + Epoch[33 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19910229; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[33 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13059497; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[33 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12431787; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04435s; TotalTimePerSample = 0.17738ms; SamplesPerSecond = 5637 + Epoch[33 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15514819; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04431s; TotalTimePerSample = 0.17725ms; SamplesPerSecond = 5641 + Epoch[33 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14358105; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[33 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14937158; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[33 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16569067; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[33 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12747168; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[33 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16297607; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[33 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15803052; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[33 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.07712256; EvalErr[0]PerSample = 0.03200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[33 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14247266; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[33 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16709619; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[33 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19128711; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5589 + Epoch[33 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14422900; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[33 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11404785; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 + Epoch[33 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14415771; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04479s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[33 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11419043; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 + Epoch[33 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.23558252; EvalErr[0]PerSample = 0.13600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[33 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13658057; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[33 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14604687; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[33 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19530811; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 + Epoch[33 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12498535; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[33 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16718457; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[33 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468311; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[33 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14720410; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17937ms; SamplesPerSecond = 5575 +Finished Epoch[33 of 50]: [Training Set] TrainLossPerSample = 0.15405898; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795919 +Starting Epoch 34: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 33 at record count 330000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1929 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 33: 3804, 1788, ... + Epoch[34 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13529294; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04900s; TotalTimePerSample = 0.19601ms; SamplesPerSecond = 5101 + Epoch[34 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13338748; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04513s; TotalTimePerSample = 0.18052ms; SamplesPerSecond = 5539 + Epoch[34 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14988202; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[34 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18168158; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[34 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15277631; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[34 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11326105; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[34 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14692413; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[34 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13512140; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[34 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15688416; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[34 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15452649; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[34 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13762524; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[34 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20037817; EvalErr[0]PerSample = 0.11600000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 + Epoch[34 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12037317; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 + Epoch[34 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14686340; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[34 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14223120; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[34 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16736865; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[34 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15634155; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[34 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16114111; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[34 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14548047; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 + Epoch[34 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18879370; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04450s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[34 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15783008; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[34 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14752661; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[34 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09680249; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[34 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13845166; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 + Epoch[34 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16419336; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[34 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14899731; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[34 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14379956; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[34 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19261401; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 + Epoch[34 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16730078; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[34 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15251025; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[34 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16484766; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04452s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[34 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13353027; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[34 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11693164; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 + Epoch[34 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18941309; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[34 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19238916; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[34 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16366406; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[34 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11366504; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[34 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17076514; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[34 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21054834; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[34 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18187646; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 +Finished Epoch[34 of 50]: [Training Set] TrainLossPerSample = 0.15434977; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794518 +Starting Epoch 35: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 34 at record count 340000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1977 retries for 10000 elements (19.8%) to ensure window condition +randomordering: recached sequence for seed 34: 0, 1369, ... + Epoch[35 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17786343; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04858s; TotalTimePerSample = 0.19432ms; SamplesPerSecond = 5146 + Epoch[35 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10489035; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[35 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15705469; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[35 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14206171; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 + Epoch[35 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09796198; EvalErr[0]PerSample = 0.02800000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[35 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13728088; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[35 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11030792; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 + Epoch[35 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17046454; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[35 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17805920; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[35 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14502661; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[35 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14964685; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[35 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442212; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[35 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15535950; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[35 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12269214; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 + Epoch[35 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12346741; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[35 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19613354; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[35 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16324268; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[35 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13881152; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 + Epoch[35 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17424585; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[35 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14777832; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[35 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13090381; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[35 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17800317; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 + Epoch[35 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17693579; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[35 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12922388; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[35 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15329004; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04496s; TotalTimePerSample = 0.17982ms; SamplesPerSecond = 5560 + Epoch[35 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15193213; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[35 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15441504; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[35 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19106250; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04503s; TotalTimePerSample = 0.18013ms; SamplesPerSecond = 5551 + Epoch[35 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16050244; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04533s; TotalTimePerSample = 0.18132ms; SamplesPerSecond = 5515 + Epoch[35 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18208252; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[35 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16271045; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 + Epoch[35 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14521729; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[35 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10702100; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[35 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16960010; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 + Epoch[35 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14514160; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[35 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19403418; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 + Epoch[35 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18302832; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[35 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16520020; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 + Epoch[35 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16275586; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[35 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18107520; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 +Finished Epoch[35 of 50]: [Training Set] TrainLossPerSample = 0.15452266; EvalErrPerSample = 0.074499995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.796423 +Starting Epoch 36: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 35 at record count 350000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 35: 3511, 991, ... + Epoch[36 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13047495; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04854s; TotalTimePerSample = 0.19418ms; SamplesPerSecond = 5149 + Epoch[36 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16482227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[36 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18663434; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[36 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19181180; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 + Epoch[36 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13587238; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[36 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14003510; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17841ms; SamplesPerSecond = 5605 + Epoch[36 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18628754; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[36 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11480872; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17814ms; SamplesPerSecond = 5613 + Epoch[36 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17090662; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 + Epoch[36 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16116858; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[36 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21025952; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04497s; TotalTimePerSample = 0.17988ms; SamplesPerSecond = 5559 + Epoch[36 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558655; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[36 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16749841; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 + Epoch[36 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11105835; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 + Epoch[36 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11523584; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[36 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12071509; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 + Epoch[36 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13111694; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[36 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12550391; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[36 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17317896; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 + Epoch[36 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12554370; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[36 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17823950; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[36 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14454175; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17933ms; SamplesPerSecond = 5576 + Epoch[36 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16279858; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[36 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14005908; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[36 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14763037; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[36 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11815405; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[36 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14461572; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[36 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18522266; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[36 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14727051; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[36 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21325684; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[36 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14022363; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[36 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18725537; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17821ms; SamplesPerSecond = 5611 + Epoch[36 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13104980; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5557 + Epoch[36 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13527734; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[36 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13485742; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[36 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14839697; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04548s; TotalTimePerSample = 0.18190ms; SamplesPerSecond = 5497 + Epoch[36 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17245850; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[36 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16834473; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04448s; TotalTimePerSample = 0.17793ms; SamplesPerSecond = 5620 + Epoch[36 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17312891; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04449s; TotalTimePerSample = 0.17796ms; SamplesPerSecond = 5619 + Epoch[36 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15522168; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17929ms; SamplesPerSecond = 5577 +Finished Epoch[36 of 50]: [Training Set] TrainLossPerSample = 0.15391307; EvalErrPerSample = 0.0735; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794126 +Starting Epoch 37: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 36 at record count 360000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1919 retries for 10000 elements (19.2%) to ensure window condition +randomordering: recached sequence for seed 36: 87, 1672, ... + Epoch[37 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13406784; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04857s; TotalTimePerSample = 0.19426ms; SamplesPerSecond = 5147 + Epoch[37 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12213620; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17955ms; SamplesPerSecond = 5569 + Epoch[37 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16642133; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04475s; TotalTimePerSample = 0.17901ms; SamplesPerSecond = 5586 + Epoch[37 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13588062; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 + Epoch[37 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13375146; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[37 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17796021; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[37 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11213745; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[37 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17547949; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[37 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13824939; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 + Epoch[37 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14724207; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[37 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18361682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[37 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14241943; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[37 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15306934; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[37 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18684570; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[37 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15043994; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 + Epoch[37 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10936426; EvalErr[0]PerSample = 0.02400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[37 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16943872; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[37 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270752; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 + Epoch[37 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177393; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[37 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17062476; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[37 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16197144; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[37 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18620679; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[37 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14755078; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17995ms; SamplesPerSecond = 5557 + Epoch[37 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13411182; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[37 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17147534; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[37 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17776978; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17985ms; SamplesPerSecond = 5560 + Epoch[37 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16226855; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[37 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15357520; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[37 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15677686; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[37 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12218408; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04511s; TotalTimePerSample = 0.18042ms; SamplesPerSecond = 5542 + Epoch[37 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16878760; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04530s; TotalTimePerSample = 0.18118ms; SamplesPerSecond = 5519 + Epoch[37 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10114111; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04509s; TotalTimePerSample = 0.18036ms; SamplesPerSecond = 5544 + Epoch[37 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14782568; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[37 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16620752; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04455s; TotalTimePerSample = 0.17819ms; SamplesPerSecond = 5612 + Epoch[37 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15330420; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04437s; TotalTimePerSample = 0.17747ms; SamplesPerSecond = 5634 + Epoch[37 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16823730; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[37 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21951025; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5585 + Epoch[37 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12842187; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[37 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11264844; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[37 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16844629; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 +Finished Epoch[37 of 50]: [Training Set] TrainLossPerSample = 0.15355118; EvalErrPerSample = 0.071999997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.797248 +Starting Epoch 38: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 37 at record count 370000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2090 retries for 10000 elements (20.9%) to ensure window condition +randomordering: recached sequence for seed 37: 2147, 1618, ... + Epoch[38 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15777213; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04858s; TotalTimePerSample = 0.19433ms; SamplesPerSecond = 5145 + Epoch[38 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13953687; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[38 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13222168; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[38 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14788760; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[38 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16613861; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[38 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14783533; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[38 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18742883; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[38 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17585291; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04484s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[38 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17658301; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[38 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15941370; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[38 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15363379; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04460s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[38 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11999939; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[38 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15738574; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17963ms; SamplesPerSecond = 5566 + Epoch[38 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12428113; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[38 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14158398; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[38 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19533032; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[38 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16717017; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[38 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15142261; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[38 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12683936; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5571 + Epoch[38 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14768604; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04492s; TotalTimePerSample = 0.17966ms; SamplesPerSecond = 5565 + Epoch[38 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12246387; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[38 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14735449; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5579 + Epoch[38 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16589087; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[38 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13883545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[38 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17940845; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04540s; TotalTimePerSample = 0.18161ms; SamplesPerSecond = 5506 + Epoch[38 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18145972; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[38 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12568311; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04492s; TotalTimePerSample = 0.17968ms; SamplesPerSecond = 5565 + Epoch[38 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18243408; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[38 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13476904; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[38 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16763135; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17839ms; SamplesPerSecond = 5605 + Epoch[38 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14108545; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[38 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10751416; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[38 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12097266; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[38 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20723926; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[38 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20658691; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[38 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15319434; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[38 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14894971; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[38 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18262549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[38 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15305811; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[38 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12360889; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 +Finished Epoch[38 of 50]: [Training Set] TrainLossPerSample = 0.15416922; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.796034 +Starting Epoch 39: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 38 at record count 380000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1947 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 38: 1196, 3933, ... + Epoch[39 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17274179; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04880s; TotalTimePerSample = 0.19518ms; SamplesPerSecond = 5123 + Epoch[39 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15039322; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04509s; TotalTimePerSample = 0.18035ms; SamplesPerSecond = 5544 + Epoch[39 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15331415; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17825ms; SamplesPerSecond = 5610 + Epoch[39 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16131561; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17914ms; SamplesPerSecond = 5582 + Epoch[39 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16033606; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[39 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14100433; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17947ms; SamplesPerSecond = 5572 + Epoch[39 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12815009; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[39 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18430042; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04502s; TotalTimePerSample = 0.18008ms; SamplesPerSecond = 5553 + Epoch[39 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15005725; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[39 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17220642; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[39 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19733789; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17880ms; SamplesPerSecond = 5592 + Epoch[39 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14495154; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[39 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15419104; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17996ms; SamplesPerSecond = 5556 + Epoch[39 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20454956; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[39 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14535596; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17854ms; SamplesPerSecond = 5600 + Epoch[39 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11774902; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[39 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14718091; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[39 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14335718; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[39 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15269067; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04478s; TotalTimePerSample = 0.17913ms; SamplesPerSecond = 5582 + Epoch[39 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14877075; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[39 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10336499; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[39 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14907422; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17849ms; SamplesPerSecond = 5602 + Epoch[39 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14744800; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[39 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12295947; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04506s; TotalTimePerSample = 0.18026ms; SamplesPerSecond = 5547 + Epoch[39 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14030884; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[39 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13191162; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 + Epoch[39 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12906909; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[39 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14715698; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04488s; TotalTimePerSample = 0.17952ms; SamplesPerSecond = 5570 + Epoch[39 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13055420; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5580 + Epoch[39 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21046387; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[39 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17694287; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 + Epoch[39 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13921973; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[39 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15824512; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[39 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18433203; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04470s; TotalTimePerSample = 0.17881ms; SamplesPerSecond = 5592 + Epoch[39 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337988; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5559 + Epoch[39 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16194678; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[39 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15131396; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[39 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18984082; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[39 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16435498; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[39 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13071533; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 +Finished Epoch[39 of 50]: [Training Set] TrainLossPerSample = 0.15406391; EvalErrPerSample = 0.074099995; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798211 +Starting Epoch 40: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 39 at record count 390000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1956 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 39: 2661, 3152, ... + Epoch[40 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14158043; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04863s; TotalTimePerSample = 0.19453ms; SamplesPerSecond = 5140 + Epoch[40 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14605721; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[40 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18199774; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[40 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17594177; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[40 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17060614; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[40 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17618237; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[40 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13822772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17833ms; SamplesPerSecond = 5607 + Epoch[40 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16948486; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 + Epoch[40 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16614709; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 + Epoch[40 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14237219; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[40 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10226660; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[40 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15575549; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[40 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15612415; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04455s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5611 + Epoch[40 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20174915; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[40 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12652759; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[40 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12409521; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[40 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14512671; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04497s; TotalTimePerSample = 0.17987ms; SamplesPerSecond = 5559 + Epoch[40 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18385840; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[40 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18300562; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[40 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16562012; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17831ms; SamplesPerSecond = 5608 + Epoch[40 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10861694; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[40 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15586011; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[40 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18680933; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[40 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20423071; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17828ms; SamplesPerSecond = 5609 + Epoch[40 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16040210; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5613 + Epoch[40 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14412622; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[40 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14596484; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04457s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[40 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12723340; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17953ms; SamplesPerSecond = 5570 + Epoch[40 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14322510; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04502s; TotalTimePerSample = 0.18009ms; SamplesPerSecond = 5552 + Epoch[40 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14650244; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5590 + Epoch[40 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12752783; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 + Epoch[40 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13561084; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 + Epoch[40 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13040381; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[40 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14084570; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[40 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15090723; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[40 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14863672; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 + Epoch[40 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15431055; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[40 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20134863; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[40 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14854785; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[40 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12673096; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04487s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5571 +Finished Epoch[40 of 50]: [Training Set] TrainLossPerSample = 0.15351419; EvalErrPerSample = 0.0713; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79461 +Starting Epoch 41: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 40 at record count 400000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1997 retries for 10000 elements (20.0%) to ensure window condition +randomordering: recached sequence for seed 40: 4294, 1646, ... + Epoch[41 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16006821; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04815s; TotalTimePerSample = 0.19261ms; SamplesPerSecond = 5191 + Epoch[41 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16724875; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04422s; TotalTimePerSample = 0.17690ms; SamplesPerSecond = 5652 + Epoch[41 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10595062; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04419s; TotalTimePerSample = 0.17676ms; SamplesPerSecond = 5657 + Epoch[41 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18145435; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[41 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19507477; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 + Epoch[41 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16439099; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04427s; TotalTimePerSample = 0.17710ms; SamplesPerSecond = 5646 + Epoch[41 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13010004; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04424s; TotalTimePerSample = 0.17696ms; SamplesPerSecond = 5650 + Epoch[41 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19075085; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04426s; TotalTimePerSample = 0.17706ms; SamplesPerSecond = 5647 + Epoch[41 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15007227; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04412s; TotalTimePerSample = 0.17649ms; SamplesPerSecond = 5666 + Epoch[41 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12810120; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04416s; TotalTimePerSample = 0.17662ms; SamplesPerSecond = 5661 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[41 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15982971; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04438s; TotalTimePerSample = 0.17751ms; SamplesPerSecond = 5633 + Epoch[41 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13172498; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04421s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 + Epoch[41 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13100256; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04436s; TotalTimePerSample = 0.17744ms; SamplesPerSecond = 5635 + Epoch[41 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21527686; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04423s; TotalTimePerSample = 0.17692ms; SamplesPerSecond = 5652 + Epoch[41 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11033472; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04434s; TotalTimePerSample = 0.17736ms; SamplesPerSecond = 5638 + Epoch[41 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14036792; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04419s; TotalTimePerSample = 0.17677ms; SamplesPerSecond = 5657 + Epoch[41 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13896533; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04416s; TotalTimePerSample = 0.17664ms; SamplesPerSecond = 5661 + Epoch[41 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15246411; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04412s; TotalTimePerSample = 0.17648ms; SamplesPerSecond = 5666 + Epoch[41 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14996655; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04429s; TotalTimePerSample = 0.17717ms; SamplesPerSecond = 5644 + Epoch[41 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11589673; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17811ms; SamplesPerSecond = 5614 + Epoch[41 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20237646; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 + Epoch[41 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12496899; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[41 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14686450; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04420s; TotalTimePerSample = 0.17682ms; SamplesPerSecond = 5655 + Epoch[41 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15260010; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04432s; TotalTimePerSample = 0.17728ms; SamplesPerSecond = 5640 + Epoch[41 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12362109; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04421s; TotalTimePerSample = 0.17683ms; SamplesPerSecond = 5655 + Epoch[41 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15063843; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04419s; TotalTimePerSample = 0.17677ms; SamplesPerSecond = 5657 + Epoch[41 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16341724; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[41 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13300439; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04422s; TotalTimePerSample = 0.17689ms; SamplesPerSecond = 5653 + Epoch[41 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17211768; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04434s; TotalTimePerSample = 0.17738ms; SamplesPerSecond = 5637 + Epoch[41 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17051416; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04418s; TotalTimePerSample = 0.17672ms; SamplesPerSecond = 5658 + Epoch[41 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19277441; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04427s; TotalTimePerSample = 0.17709ms; SamplesPerSecond = 5646 + Epoch[41 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14424658; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04415s; TotalTimePerSample = 0.17660ms; SamplesPerSecond = 5662 + Epoch[41 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20588477; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04447s; TotalTimePerSample = 0.17787ms; SamplesPerSecond = 5622 + Epoch[41 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12091211; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04427s; TotalTimePerSample = 0.17708ms; SamplesPerSecond = 5647 + Epoch[41 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18827930; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04431s; TotalTimePerSample = 0.17724ms; SamplesPerSecond = 5642 + Epoch[41 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13721533; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04425s; TotalTimePerSample = 0.17698ms; SamplesPerSecond = 5650 + Epoch[41 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12533447; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04425s; TotalTimePerSample = 0.17700ms; SamplesPerSecond = 5649 + Epoch[41 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15904785; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04424s; TotalTimePerSample = 0.17695ms; SamplesPerSecond = 5651 + Epoch[41 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15600879; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04426s; TotalTimePerSample = 0.17704ms; SamplesPerSecond = 5648 + Epoch[41 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17057568; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04429s; TotalTimePerSample = 0.17716ms; SamplesPerSecond = 5644 +Finished Epoch[41 of 50]: [Training Set] TrainLossPerSample = 0.1539861; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.778478 +Starting Epoch 42: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 41 at record count 410000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1963 retries for 10000 elements (19.6%) to ensure window condition +randomordering: recached sequence for seed 41: 3344, 3717, ... + Epoch[42 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15670647; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04866s; TotalTimePerSample = 0.19462ms; SamplesPerSecond = 5138 + Epoch[42 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16601924; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[42 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15016824; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5603 + Epoch[42 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09896414; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04507s; TotalTimePerSample = 0.18028ms; SamplesPerSecond = 5546 + Epoch[42 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15355798; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[42 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14959656; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[42 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14912543; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[42 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15205188; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[42 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17921887; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 + Epoch[42 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16415247; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04510s; TotalTimePerSample = 0.18040ms; SamplesPerSecond = 5543 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[42 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12882190; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[42 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12317432; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[42 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10102087; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[42 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18998999; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[42 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20682227; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04494s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 + Epoch[42 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16399731; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[42 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14118408; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[42 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18587280; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04464s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[42 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14129785; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[42 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14985107; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17919ms; SamplesPerSecond = 5580 + Epoch[42 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16006787; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[42 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11417261; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[42 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16649072; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[42 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15156982; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[42 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11802979; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17834ms; SamplesPerSecond = 5607 + Epoch[42 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16400879; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[42 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13871289; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17957ms; SamplesPerSecond = 5568 + Epoch[42 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17601343; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17896ms; SamplesPerSecond = 5587 + Epoch[42 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15601025; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04456s; TotalTimePerSample = 0.17823ms; SamplesPerSecond = 5610 + Epoch[42 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15830273; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[42 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21906494; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[42 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12150439; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17810ms; SamplesPerSecond = 5614 + Epoch[42 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16681006; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17884ms; SamplesPerSecond = 5591 + Epoch[42 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12314014; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[42 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19674365; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[42 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18909473; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[42 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14651758; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[42 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14916064; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[42 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13985498; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[42 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14332861; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5571 +Finished Epoch[42 of 50]: [Training Set] TrainLossPerSample = 0.1537548; EvalErrPerSample = 0.075099997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795267 +Starting Epoch 43: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 42 at record count 420000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 2041 retries for 10000 elements (20.4%) to ensure window condition +randomordering: recached sequence for seed 42: 4625, 1494, ... + Epoch[43 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16337048; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04868s; TotalTimePerSample = 0.19472ms; SamplesPerSecond = 5135 + Epoch[43 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13375079; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04478s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[43 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12735876; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[43 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16123383; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 + Epoch[43 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12667511; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17923ms; SamplesPerSecond = 5579 + Epoch[43 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14843439; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17905ms; SamplesPerSecond = 5584 + Epoch[43 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20357190; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[43 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15245459; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17934ms; SamplesPerSecond = 5576 + Epoch[43 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15251233; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 + Epoch[43 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14170276; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17855ms; SamplesPerSecond = 5600 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[43 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13866748; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 + Epoch[43 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13282947; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04518s; TotalTimePerSample = 0.18070ms; SamplesPerSecond = 5533 + Epoch[43 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16564197; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04535s; TotalTimePerSample = 0.18140ms; SamplesPerSecond = 5512 + Epoch[43 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15745947; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04536s; TotalTimePerSample = 0.18144ms; SamplesPerSecond = 5511 + Epoch[43 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13340820; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04481s; TotalTimePerSample = 0.17925ms; SamplesPerSecond = 5578 + Epoch[43 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11160010; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[43 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16105322; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[43 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11706250; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[43 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16442944; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17845ms; SamplesPerSecond = 5603 + Epoch[43 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17853076; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[43 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15478223; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04470s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[43 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12719238; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17942ms; SamplesPerSecond = 5573 + Epoch[43 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13923169; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[43 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16462354; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[43 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19056055; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[43 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13770239; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[43 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892383; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17906ms; SamplesPerSecond = 5584 + Epoch[43 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18419531; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17902ms; SamplesPerSecond = 5586 + Epoch[43 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17152490; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04467s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5596 + Epoch[43 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17453027; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 + Epoch[43 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13219531; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[43 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17414697; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[43 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15467627; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[43 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11950830; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[43 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15333545; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5606 + Epoch[43 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12053564; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[43 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18559570; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[43 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17155273; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[43 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19393018; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[43 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14711523; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 +Finished Epoch[43 of 50]: [Training Set] TrainLossPerSample = 0.15369016; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.798963 +Starting Epoch 44: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 43 at record count 430000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition +randomordering: recached sequence for seed 43: 1442, 760, ... + Epoch[44 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19626430; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04860s; TotalTimePerSample = 0.19441ms; SamplesPerSecond = 5143 + Epoch[44 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17290984; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04499s; TotalTimePerSample = 0.17994ms; SamplesPerSecond = 5557 + Epoch[44 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17088531; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[44 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13643585; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[44 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13539447; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[44 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13834625; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[44 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15468256; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04462s; TotalTimePerSample = 0.17847ms; SamplesPerSecond = 5603 + Epoch[44 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808887; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17888ms; SamplesPerSecond = 5590 + Epoch[44 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14468994; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04477s; TotalTimePerSample = 0.17910ms; SamplesPerSecond = 5583 + Epoch[44 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15306775; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04475s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[44 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15897144; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[44 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13882825; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[44 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18498499; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04504s; TotalTimePerSample = 0.18016ms; SamplesPerSecond = 5550 + Epoch[44 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16007202; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04468s; TotalTimePerSample = 0.17870ms; SamplesPerSecond = 5595 + Epoch[44 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20337402; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04497s; TotalTimePerSample = 0.17987ms; SamplesPerSecond = 5559 + Epoch[44 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13722144; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[44 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10227490; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 + Epoch[44 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13890625; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[44 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14858911; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 + Epoch[44 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17651758; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[44 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14888232; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[44 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14692041; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 + Epoch[44 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13211938; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[44 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17383594; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04535s; TotalTimePerSample = 0.18142ms; SamplesPerSecond = 5512 + Epoch[44 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13997656; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[44 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17595068; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04476s; TotalTimePerSample = 0.17903ms; SamplesPerSecond = 5585 + Epoch[44 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12542529; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[44 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12775000; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04437s; TotalTimePerSample = 0.17749ms; SamplesPerSecond = 5634 + Epoch[44 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18241943; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17835ms; SamplesPerSecond = 5607 + Epoch[44 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16874072; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04501s; TotalTimePerSample = 0.18005ms; SamplesPerSecond = 5553 + Epoch[44 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12801904; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04502s; TotalTimePerSample = 0.18008ms; SamplesPerSecond = 5553 + Epoch[44 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18607813; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04516s; TotalTimePerSample = 0.18064ms; SamplesPerSecond = 5535 + Epoch[44 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11408203; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04513s; TotalTimePerSample = 0.18051ms; SamplesPerSecond = 5539 + Epoch[44 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16484082; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04513s; TotalTimePerSample = 0.18051ms; SamplesPerSecond = 5539 + Epoch[44 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13667822; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04530s; TotalTimePerSample = 0.18120ms; SamplesPerSecond = 5518 + Epoch[44 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18358496; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04543s; TotalTimePerSample = 0.18173ms; SamplesPerSecond = 5502 + Epoch[44 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18765576; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04509s; TotalTimePerSample = 0.18038ms; SamplesPerSecond = 5543 + Epoch[44 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12407764; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04487s; TotalTimePerSample = 0.17949ms; SamplesPerSecond = 5571 + Epoch[44 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16974805; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04484s; TotalTimePerSample = 0.17936ms; SamplesPerSecond = 5575 + Epoch[44 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13159570; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 +Finished Epoch[44 of 50]: [Training Set] TrainLossPerSample = 0.15347216; EvalErrPerSample = 0.0748; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.800242 +Starting Epoch 45: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 44 at record count 440000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1862 retries for 10000 elements (18.6%) to ensure window condition +randomordering: recached sequence for seed 44: 491, 13, ... + Epoch[45 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15387369; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04887s; TotalTimePerSample = 0.19549ms; SamplesPerSecond = 5115 + Epoch[45 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13984473; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[45 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16473340; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[45 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15177023; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04461s; TotalTimePerSample = 0.17842ms; SamplesPerSecond = 5604 + Epoch[45 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20702142; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04487s; TotalTimePerSample = 0.17946ms; SamplesPerSecond = 5572 + Epoch[45 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14098303; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17964ms; SamplesPerSecond = 5566 + Epoch[45 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13669562; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17875ms; SamplesPerSecond = 5594 + Epoch[45 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14183423; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[45 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13592822; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17868ms; SamplesPerSecond = 5596 + Epoch[45 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16768311; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04466s; TotalTimePerSample = 0.17862ms; SamplesPerSecond = 5598 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[45 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12566309; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04453s; TotalTimePerSample = 0.17813ms; SamplesPerSecond = 5613 + Epoch[45 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12210388; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[45 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16080310; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04493s; TotalTimePerSample = 0.17971ms; SamplesPerSecond = 5564 + Epoch[45 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16150610; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[45 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17048022; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[45 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16558862; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[45 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12026343; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[45 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14211206; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[45 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20260547; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[45 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16693872; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[45 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14628687; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04340s; TotalTimePerSample = 0.17360ms; SamplesPerSecond = 5760 + Epoch[45 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21542749; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04214s; TotalTimePerSample = 0.16854ms; SamplesPerSecond = 5933 + Epoch[45 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14195264; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04224s; TotalTimePerSample = 0.16897ms; SamplesPerSecond = 5918 + Epoch[45 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13079199; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04243s; TotalTimePerSample = 0.16971ms; SamplesPerSecond = 5892 + Epoch[45 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13407056; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04223s; TotalTimePerSample = 0.16892ms; SamplesPerSecond = 5920 + Epoch[45 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16342114; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04232s; TotalTimePerSample = 0.16929ms; SamplesPerSecond = 5906 + Epoch[45 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16815112; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04236s; TotalTimePerSample = 0.16945ms; SamplesPerSecond = 5901 + Epoch[45 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14529932; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04237s; TotalTimePerSample = 0.16948ms; SamplesPerSecond = 5900 + Epoch[45 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15902930; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04235s; TotalTimePerSample = 0.16939ms; SamplesPerSecond = 5903 + Epoch[45 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11276465; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04243s; TotalTimePerSample = 0.16971ms; SamplesPerSecond = 5892 + Epoch[45 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13555859; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04236s; TotalTimePerSample = 0.16945ms; SamplesPerSecond = 5901 + Epoch[45 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12780225; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04217s; TotalTimePerSample = 0.16866ms; SamplesPerSecond = 5928 + Epoch[45 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17016504; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04227s; TotalTimePerSample = 0.16908ms; SamplesPerSecond = 5914 + Epoch[45 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18041113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04233s; TotalTimePerSample = 0.16931ms; SamplesPerSecond = 5906 + Epoch[45 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21134766; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04237s; TotalTimePerSample = 0.16948ms; SamplesPerSecond = 5900 + Epoch[45 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16988965; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04232s; TotalTimePerSample = 0.16928ms; SamplesPerSecond = 5907 + Epoch[45 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11136670; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04210s; TotalTimePerSample = 0.16841ms; SamplesPerSecond = 5937 + Epoch[45 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13335352; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04230s; TotalTimePerSample = 0.16918ms; SamplesPerSecond = 5910 + Epoch[45 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15975391; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04226s; TotalTimePerSample = 0.16904ms; SamplesPerSecond = 5915 + Epoch[45 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15928564; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04238s; TotalTimePerSample = 0.16953ms; SamplesPerSecond = 5898 +Finished Epoch[45 of 50]: [Training Set] TrainLossPerSample = 0.15386404; EvalErrPerSample = 0.074000001; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.748834 +Starting Epoch 46: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 45 at record count 450000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1934 retries for 10000 elements (19.3%) to ensure window condition +randomordering: recached sequence for seed 45: 1579, 1557, ... + Epoch[46 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16873199; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04903s; TotalTimePerSample = 0.19613ms; SamplesPerSecond = 5098 + Epoch[46 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16506183; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[46 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17666888; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[46 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13984259; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5598 + Epoch[46 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15266638; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[46 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.09864636; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17852ms; SamplesPerSecond = 5601 + Epoch[46 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17270251; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 + Epoch[46 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14790149; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17824ms; SamplesPerSecond = 5610 + Epoch[46 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15422278; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[46 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18104761; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5624 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[46 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15919177; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[46 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15662683; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04498s; TotalTimePerSample = 0.17992ms; SamplesPerSecond = 5558 + Epoch[46 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16385181; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17940ms; SamplesPerSecond = 5574 + Epoch[46 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15194409; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[46 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18345337; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17859ms; SamplesPerSecond = 5599 + Epoch[46 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13968359; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[46 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14504272; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[46 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15621167; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[46 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15997559; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04492s; TotalTimePerSample = 0.17967ms; SamplesPerSecond = 5565 + Epoch[46 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15241724; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17809ms; SamplesPerSecond = 5615 + Epoch[46 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17079468; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04480s; TotalTimePerSample = 0.17918ms; SamplesPerSecond = 5580 + Epoch[46 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18360156; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04505s; TotalTimePerSample = 0.18019ms; SamplesPerSecond = 5549 + Epoch[46 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12528809; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04469s; TotalTimePerSample = 0.17877ms; SamplesPerSecond = 5593 + Epoch[46 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15011523; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[46 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13309790; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5590 + Epoch[46 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15234912; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04514s; TotalTimePerSample = 0.18054ms; SamplesPerSecond = 5538 + Epoch[46 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14155371; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04585s; TotalTimePerSample = 0.18341ms; SamplesPerSecond = 5452 + Epoch[46 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16554199; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04535s; TotalTimePerSample = 0.18140ms; SamplesPerSecond = 5512 + Epoch[46 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16656445; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04512s; TotalTimePerSample = 0.18050ms; SamplesPerSecond = 5540 + Epoch[46 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17517920; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[46 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11573779; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[46 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13670752; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[46 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13808984; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[46 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11996289; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[46 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14716162; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[46 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19882568; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04472s; TotalTimePerSample = 0.17889ms; SamplesPerSecond = 5589 + Epoch[46 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16697510; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04457s; TotalTimePerSample = 0.17829ms; SamplesPerSecond = 5608 + Epoch[46 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17219482; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17959ms; SamplesPerSecond = 5568 + Epoch[46 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15390625; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04433s; TotalTimePerSample = 0.17731ms; SamplesPerSecond = 5639 + Epoch[46 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11087256; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04454s; TotalTimePerSample = 0.17817ms; SamplesPerSecond = 5612 +Finished Epoch[46 of 50]: [Training Set] TrainLossPerSample = 0.15376027; EvalErrPerSample = 0.0744; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79819 +Starting Epoch 47: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 46 at record count 460000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1896 retries for 10000 elements (19.0%) to ensure window condition +randomordering: recached sequence for seed 46: 375, 4056, ... + Epoch[47 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17008383; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04862s; TotalTimePerSample = 0.19448ms; SamplesPerSecond = 5141 + Epoch[47 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14077335; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04462s; TotalTimePerSample = 0.17848ms; SamplesPerSecond = 5602 + Epoch[47 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13972415; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[47 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14504892; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04495s; TotalTimePerSample = 0.17978ms; SamplesPerSecond = 5562 + Epoch[47 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12919092; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17956ms; SamplesPerSecond = 5569 + Epoch[47 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16585364; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[47 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16475818; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04452s; TotalTimePerSample = 0.17808ms; SamplesPerSecond = 5615 + Epoch[47 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17667603; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04475s; TotalTimePerSample = 0.17900ms; SamplesPerSecond = 5586 + Epoch[47 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12736060; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17871ms; SamplesPerSecond = 5595 + Epoch[47 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15712415; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04456s; TotalTimePerSample = 0.17822ms; SamplesPerSecond = 5610 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[47 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17211584; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04468s; TotalTimePerSample = 0.17874ms; SamplesPerSecond = 5594 + Epoch[47 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11364697; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[47 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17509961; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17894ms; SamplesPerSecond = 5588 + Epoch[47 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12182776; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04474s; TotalTimePerSample = 0.17897ms; SamplesPerSecond = 5587 + Epoch[47 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16530200; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04493s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 + Epoch[47 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12411670; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04515s; TotalTimePerSample = 0.18062ms; SamplesPerSecond = 5536 + Epoch[47 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14344165; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[47 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11813501; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[47 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15428955; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04458s; TotalTimePerSample = 0.17830ms; SamplesPerSecond = 5608 + Epoch[47 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18582031; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 + Epoch[47 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16063818; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17786ms; SamplesPerSecond = 5622 + Epoch[47 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18036328; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04464s; TotalTimePerSample = 0.17856ms; SamplesPerSecond = 5600 + Epoch[47 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12445923; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17928ms; SamplesPerSecond = 5577 + Epoch[47 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14665308; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04496s; TotalTimePerSample = 0.17984ms; SamplesPerSecond = 5560 + Epoch[47 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16736230; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5589 + Epoch[47 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13780688; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 + Epoch[47 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17362231; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04491s; TotalTimePerSample = 0.17965ms; SamplesPerSecond = 5566 + Epoch[47 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15593262; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 + Epoch[47 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14730029; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04463s; TotalTimePerSample = 0.17851ms; SamplesPerSecond = 5601 + Epoch[47 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13614795; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04447s; TotalTimePerSample = 0.17789ms; SamplesPerSecond = 5621 + Epoch[47 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10945850; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04447s; TotalTimePerSample = 0.17788ms; SamplesPerSecond = 5621 + Epoch[47 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13528076; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[47 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20589160; EvalErr[0]PerSample = 0.11200000; TotalTime = 0.04465s; TotalTimePerSample = 0.17858ms; SamplesPerSecond = 5599 + Epoch[47 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13290430; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[47 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15068262; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17869ms; SamplesPerSecond = 5596 + Epoch[47 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17109180; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5600 + Epoch[47 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19824805; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04483s; TotalTimePerSample = 0.17931ms; SamplesPerSecond = 5576 + Epoch[47 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20577832; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04466s; TotalTimePerSample = 0.17865ms; SamplesPerSecond = 5597 + Epoch[47 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13286084; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04480s; TotalTimePerSample = 0.17922ms; SamplesPerSecond = 5579 + Epoch[47 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17738281; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17924ms; SamplesPerSecond = 5579 +Finished Epoch[47 of 50]: [Training Set] TrainLossPerSample = 0.15350637; EvalErrPerSample = 0.073399998; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.79638 +Starting Epoch 48: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 47 at record count 470000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1949 retries for 10000 elements (19.5%) to ensure window condition +randomordering: recached sequence for seed 47: 2638, 2515, ... + Epoch[48 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15027005; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04850s; TotalTimePerSample = 0.19398ms; SamplesPerSecond = 5155 + Epoch[48 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13481610; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04470s; TotalTimePerSample = 0.17879ms; SamplesPerSecond = 5593 + Epoch[48 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13959433; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04445s; TotalTimePerSample = 0.17781ms; SamplesPerSecond = 5623 + Epoch[48 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19054303; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[48 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15588031; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04458s; TotalTimePerSample = 0.17832ms; SamplesPerSecond = 5608 + Epoch[48 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17125250; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[48 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12560059; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04476s; TotalTimePerSample = 0.17904ms; SamplesPerSecond = 5585 + Epoch[48 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11920215; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[48 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13620642; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[48 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13336023; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04501s; TotalTimePerSample = 0.18004ms; SamplesPerSecond = 5554 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[48 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16068066; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04478s; TotalTimePerSample = 0.17912ms; SamplesPerSecond = 5582 + Epoch[48 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16086951; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04442s; TotalTimePerSample = 0.17770ms; SamplesPerSecond = 5627 + Epoch[48 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15739978; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04443s; TotalTimePerSample = 0.17773ms; SamplesPerSecond = 5626 + Epoch[48 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13238123; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04446s; TotalTimePerSample = 0.17782ms; SamplesPerSecond = 5623 + Epoch[48 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18616211; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04469s; TotalTimePerSample = 0.17878ms; SamplesPerSecond = 5593 + Epoch[48 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19020459; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04471s; TotalTimePerSample = 0.17882ms; SamplesPerSecond = 5592 + Epoch[48 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13482642; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[48 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15368921; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 + Epoch[48 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14498755; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[48 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17892627; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04465s; TotalTimePerSample = 0.17861ms; SamplesPerSecond = 5598 + Epoch[48 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15293530; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[48 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16804004; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04479s; TotalTimePerSample = 0.17917ms; SamplesPerSecond = 5581 + Epoch[48 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11757837; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[48 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215381; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[48 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13225903; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04468s; TotalTimePerSample = 0.17872ms; SamplesPerSecond = 5595 + Epoch[48 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15199707; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04465s; TotalTimePerSample = 0.17860ms; SamplesPerSecond = 5599 + Epoch[48 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13713770; EvalErr[0]PerSample = 0.04000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17907ms; SamplesPerSecond = 5584 + Epoch[48 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12650757; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04485s; TotalTimePerSample = 0.17939ms; SamplesPerSecond = 5574 + Epoch[48 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14910400; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5595 + Epoch[48 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13762646; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04445s; TotalTimePerSample = 0.17780ms; SamplesPerSecond = 5624 + Epoch[48 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17600146; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04461s; TotalTimePerSample = 0.17844ms; SamplesPerSecond = 5604 + Epoch[48 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15713623; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17930ms; SamplesPerSecond = 5577 + Epoch[48 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17898389; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17776ms; SamplesPerSecond = 5625 + Epoch[48 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13861963; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17886ms; SamplesPerSecond = 5591 + Epoch[48 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12689941; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[48 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16532910; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04454s; TotalTimePerSample = 0.17818ms; SamplesPerSecond = 5612 + Epoch[48 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17739404; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04456s; TotalTimePerSample = 0.17826ms; SamplesPerSecond = 5609 + Epoch[48 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19358691; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[48 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17704639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04461s; TotalTimePerSample = 0.17843ms; SamplesPerSecond = 5604 + Epoch[48 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17228711; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04471s; TotalTimePerSample = 0.17885ms; SamplesPerSecond = 5591 +Finished Epoch[48 of 50]: [Training Set] TrainLossPerSample = 0.15388691; EvalErrPerSample = 0.074599996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.793014 +Starting Epoch 49: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 48 at record count 480000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1888 retries for 10000 elements (18.9%) to ensure window condition +randomordering: recached sequence for seed 48: 1688, 1039, ... + Epoch[49 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17310571; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04871s; TotalTimePerSample = 0.19484ms; SamplesPerSecond = 5132 + Epoch[49 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14044775; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 + Epoch[49 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16788113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04490s; TotalTimePerSample = 0.17958ms; SamplesPerSecond = 5568 + Epoch[49 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19724570; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 + Epoch[49 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15709772; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04455s; TotalTimePerSample = 0.17820ms; SamplesPerSecond = 5611 + Epoch[49 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14763385; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17867ms; SamplesPerSecond = 5596 + Epoch[49 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17850745; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17891ms; SamplesPerSecond = 5589 + Epoch[49 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17535193; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5606 + Epoch[49 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.10281067; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04462s; TotalTimePerSample = 0.17846ms; SamplesPerSecond = 5603 + Epoch[49 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12199316; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04483s; TotalTimePerSample = 0.17932ms; SamplesPerSecond = 5576 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[49 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16238562; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[49 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14362817; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04444s; TotalTimePerSample = 0.17777ms; SamplesPerSecond = 5625 + Epoch[49 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12313818; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04466s; TotalTimePerSample = 0.17863ms; SamplesPerSecond = 5598 + Epoch[49 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18512012; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04496s; TotalTimePerSample = 0.17983ms; SamplesPerSecond = 5560 + Epoch[49 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13569580; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17811ms; SamplesPerSecond = 5614 + Epoch[49 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18304980; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04464s; TotalTimePerSample = 0.17857ms; SamplesPerSecond = 5599 + Epoch[49 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14132764; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04453s; TotalTimePerSample = 0.17812ms; SamplesPerSecond = 5614 + Epoch[49 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12797217; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04462s; TotalTimePerSample = 0.17850ms; SamplesPerSecond = 5602 + Epoch[49 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14221387; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04466s; TotalTimePerSample = 0.17864ms; SamplesPerSecond = 5597 + Epoch[49 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14380859; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5583 + Epoch[49 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14911719; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04439s; TotalTimePerSample = 0.17757ms; SamplesPerSecond = 5631 + Epoch[49 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14431519; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04477s; TotalTimePerSample = 0.17908ms; SamplesPerSecond = 5584 + Epoch[49 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16214160; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04463s; TotalTimePerSample = 0.17853ms; SamplesPerSecond = 5601 + Epoch[49 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17706299; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04460s; TotalTimePerSample = 0.17840ms; SamplesPerSecond = 5605 + Epoch[49 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12700317; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04485s; TotalTimePerSample = 0.17938ms; SamplesPerSecond = 5574 + Epoch[49 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15265112; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04489s; TotalTimePerSample = 0.17954ms; SamplesPerSecond = 5569 + Epoch[49 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11090063; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17921ms; SamplesPerSecond = 5580 + Epoch[49 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13091309; EvalErr[0]PerSample = 0.04400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17945ms; SamplesPerSecond = 5572 + Epoch[49 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19095850; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[49 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14681836; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04448s; TotalTimePerSample = 0.17791ms; SamplesPerSecond = 5620 + Epoch[49 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13930273; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04481s; TotalTimePerSample = 0.17926ms; SamplesPerSecond = 5578 + Epoch[49 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13441406; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[49 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18254639; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04459s; TotalTimePerSample = 0.17837ms; SamplesPerSecond = 5606 + Epoch[49 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15285645; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04468s; TotalTimePerSample = 0.17873ms; SamplesPerSecond = 5594 + Epoch[49 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16258301; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[49 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18678027; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04491s; TotalTimePerSample = 0.17962ms; SamplesPerSecond = 5567 + Epoch[49 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15848584; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17916ms; SamplesPerSecond = 5581 + Epoch[49 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19601270; EvalErr[0]PerSample = 0.10400000; TotalTime = 0.04485s; TotalTimePerSample = 0.17941ms; SamplesPerSecond = 5573 + Epoch[49 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12490137; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04446s; TotalTimePerSample = 0.17784ms; SamplesPerSecond = 5622 + Epoch[49 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14639160; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 +Finished Epoch[49 of 50]: [Training Set] TrainLossPerSample = 0.15316428; EvalErrPerSample = 0.073299997; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.795359 +Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting epoch 49 at record count 490000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 49: 954, 533, ... + Epoch[50 of 50]-Minibatch[ 1- 10 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16215799; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04846s; TotalTimePerSample = 0.19383ms; SamplesPerSecond = 5159 + Epoch[50 of 50]-Minibatch[ 11- 20 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15890320; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[50 of 50]-Minibatch[ 21- 30 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13722400; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04449s; TotalTimePerSample = 0.17798ms; SamplesPerSecond = 5618 + Epoch[50 of 50]-Minibatch[ 31- 40 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14856100; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[50 of 50]-Minibatch[ 41- 50 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.21979169; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04477s; TotalTimePerSample = 0.17909ms; SamplesPerSecond = 5583 + Epoch[50 of 50]-Minibatch[ 51- 60 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14950995; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[50 of 50]-Minibatch[ 61- 70 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13682397; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04500s; TotalTimePerSample = 0.18002ms; SamplesPerSecond = 5554 + Epoch[50 of 50]-Minibatch[ 71- 80 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18404150; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04479s; TotalTimePerSample = 0.17915ms; SamplesPerSecond = 5581 + Epoch[50 of 50]-Minibatch[ 81- 90 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13718176; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04474s; TotalTimePerSample = 0.17895ms; SamplesPerSecond = 5588 + Epoch[50 of 50]-Minibatch[ 91- 100 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15286511; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04452s; TotalTimePerSample = 0.17807ms; SamplesPerSecond = 5615 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[50 of 50]-Minibatch[ 101- 110 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13951636; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04484s; TotalTimePerSample = 0.17935ms; SamplesPerSecond = 5575 + Epoch[50 of 50]-Minibatch[ 111- 120 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12581262; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04444s; TotalTimePerSample = 0.17778ms; SamplesPerSecond = 5624 + Epoch[50 of 50]-Minibatch[ 121- 130 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15118884; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04459s; TotalTimePerSample = 0.17836ms; SamplesPerSecond = 5606 + Epoch[50 of 50]-Minibatch[ 131- 140 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13054846; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04473s; TotalTimePerSample = 0.17890ms; SamplesPerSecond = 5589 + Epoch[50 of 50]-Minibatch[ 141- 150 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12965796; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04460s; TotalTimePerSample = 0.17838ms; SamplesPerSecond = 5605 + Epoch[50 of 50]-Minibatch[ 151- 160 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15022876; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04474s; TotalTimePerSample = 0.17898ms; SamplesPerSecond = 5587 + Epoch[50 of 50]-Minibatch[ 161- 170 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14381396; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04486s; TotalTimePerSample = 0.17943ms; SamplesPerSecond = 5573 + Epoch[50 of 50]-Minibatch[ 171- 180 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.08574292; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04457s; TotalTimePerSample = 0.17827ms; SamplesPerSecond = 5609 + Epoch[50 of 50]-Minibatch[ 181- 190 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17754126; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17892ms; SamplesPerSecond = 5588 + Epoch[50 of 50]-Minibatch[ 191- 200 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20406201; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04496s; TotalTimePerSample = 0.17986ms; SamplesPerSecond = 5560 + Epoch[50 of 50]-Minibatch[ 201- 210 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17242920; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04447s; TotalTimePerSample = 0.17789ms; SamplesPerSecond = 5621 + Epoch[50 of 50]-Minibatch[ 211- 220 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12447583; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04445s; TotalTimePerSample = 0.17778ms; SamplesPerSecond = 5624 + Epoch[50 of 50]-Minibatch[ 221- 230 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14477441; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[50 of 50]-Minibatch[ 231- 240 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16081299; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17806ms; SamplesPerSecond = 5616 + Epoch[50 of 50]-Minibatch[ 241- 250 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.15686450; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04473s; TotalTimePerSample = 0.17893ms; SamplesPerSecond = 5588 + Epoch[50 of 50]-Minibatch[ 251- 260 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.11660156; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04472s; TotalTimePerSample = 0.17887ms; SamplesPerSecond = 5590 + Epoch[50 of 50]-Minibatch[ 261- 270 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17683594; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 + Epoch[50 of 50]-Minibatch[ 271- 280 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12256201; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04451s; TotalTimePerSample = 0.17805ms; SamplesPerSecond = 5616 + Epoch[50 of 50]-Minibatch[ 281- 290 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.20207324; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04451s; TotalTimePerSample = 0.17804ms; SamplesPerSecond = 5616 + Epoch[50 of 50]-Minibatch[ 291- 300 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14701660; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04482s; TotalTimePerSample = 0.17927ms; SamplesPerSecond = 5578 + Epoch[50 of 50]-Minibatch[ 301- 310 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14405566; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04487s; TotalTimePerSample = 0.17948ms; SamplesPerSecond = 5571 + Epoch[50 of 50]-Minibatch[ 311- 320 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13429590; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04454s; TotalTimePerSample = 0.17816ms; SamplesPerSecond = 5612 + Epoch[50 of 50]-Minibatch[ 321- 330 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18196924; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04467s; TotalTimePerSample = 0.17866ms; SamplesPerSecond = 5597 + Epoch[50 of 50]-Minibatch[ 331- 340 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.16981592; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04445s; TotalTimePerSample = 0.17779ms; SamplesPerSecond = 5624 + Epoch[50 of 50]-Minibatch[ 341- 350 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.18866113; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04488s; TotalTimePerSample = 0.17950ms; SamplesPerSecond = 5570 + Epoch[50 of 50]-Minibatch[ 351- 360 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.14027246; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04494s; TotalTimePerSample = 0.17974ms; SamplesPerSecond = 5563 + Epoch[50 of 50]-Minibatch[ 361- 370 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.17351709; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04480s; TotalTimePerSample = 0.17920ms; SamplesPerSecond = 5580 + Epoch[50 of 50]-Minibatch[ 371- 380 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.13018750; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04449s; TotalTimePerSample = 0.17797ms; SamplesPerSecond = 5618 + Epoch[50 of 50]-Minibatch[ 381- 390 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.19874121; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04449s; TotalTimePerSample = 0.17794ms; SamplesPerSecond = 5619 + Epoch[50 of 50]-Minibatch[ 391- 400 of 400]: SamplesSeen = 250; TrainLossPerSample = 0.12178613; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04469s; TotalTimePerSample = 0.17876ms; SamplesPerSecond = 5594 +Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15332304; EvalErrPerSample = 0.073699996; AvgLearningRatePerSample = 0.00400000019; EpochTime=1.794151 +CNTKCommandTrainEnd: Simple_Demo +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + + +Allocating matrices for forward propagation. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) +B2[2, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) +B1[50, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) +B0[50, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) +InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) +MeanOfFeatures[2, 1] = Mean(features[2, 0]) +features[2, 0] = InputValue +W0[50, 2] = LearnableParameter +W1[50, 50] = LearnableParameter +W2[2, 50] = LearnableParameter +labels[2, 0] = InputValue + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +starting at epoch 0 counting lines to determine record count + + 603 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch +Minibatch[1]: ActualMBSize = 603 +Total Samples Evaluated = 603 +COMPLETED +=== Deleting last epoch data +==== Re-running from checkpoint +=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/debug/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 makeMode=true +------------------------------------------------------------------- +Build info: + + Built time: Nov 19 2015 12:01:19 + Last modified date: Thu Nov 19 08:15:43 2015 + Built by svcphil on liana-08-w + Build Path: c:\jenkins\workspace\CNTK-Build-Windows\MachineLearning\CNTK\ + CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0 + Build Branch: HEAD + Build SHA1: 70db50387b8998cf0f444e0164d4e4473a027bc9 +------------------------------------------------------------------- +running on DPHAIM-25 at 2015/11/19 12:09:08 +command line: +C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\debug\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple/cntk.config RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple DeviceId=0 makeMode=true + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=$RunDir$/models/simple.dnn +deviceId=$DeviceNumber$ +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=$DataDir$/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +outputPath=$RunDir$/SimpleOutput +] +RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=0 +makeMode=true + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +command=Simple_Demo:Simple_Demo_Output +DeviceNumber=-1 +precision=float +modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +deviceId=-1 +outputNodeNames=ScaledLogLikelihood +traceLevel=1 +Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] +Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput +] +RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +DeviceId=0 +makeMode=true + +<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< + +>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>> +configparameters: cntk.config:command=Simple_Demo:Simple_Demo_Output +configparameters: cntk.config:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Simple +configparameters: cntk.config:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data +configparameters: cntk.config:deviceId=0 +configparameters: cntk.config:DeviceNumber=-1 +configparameters: cntk.config:makeMode=true +configparameters: cntk.config:modelPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +configparameters: cntk.config:outputNodeNames=ScaledLogLikelihood +configparameters: cntk.config:precision=float +configparameters: cntk.config:RunDir=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu +configparameters: cntk.config:Simple_Demo=[ + action=train + SimpleNetworkBuilder=[ + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + SGD=[ + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt + miniBatchMode=Partial + randomize=Auto + verbosity=1 + features=[ +dim=2 +start=0 + ] + labels=[ +start=2 +dim=1 +labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +] + +configparameters: cntk.config:Simple_Demo_Output=[ + action=write + reader=[ + readerType=UCIFastReader + file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleMapping.txt + ] + ] +outputPath=C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/SimpleOutput +] + +configparameters: cntk.config:traceLevel=1 +<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<< +command: Simple_Demo Simple_Demo_Output +precision = float +CNTKModelPath: C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn +CNTKCommandTrainInfo: Simple_Demo : 50 +CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 50 +CNTKCommandTrainBegin: Simple_Demo +SimpleNetworkBuilder Using GPU 0 +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTrain.txt +Starting from checkpoint. Load Network From File C:\Windows\TEMP\cntk-test-20151119120636.543625\Speech_Simple@debug_gpu/models/simple.dnn.49. + + +Allocating matrices for forward propagation. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) +B2[2, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) +B1[50, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) +B0[50, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) +InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) +MeanOfFeatures[2, 1] = Mean(features[2, 0]) +features[2, 0] = InputValue +W0[50, 2] = LearnableParameter +W1[50, 50] = LearnableParameter +W2[2, 50] = LearnableParameter +labels[2, 0] = InputValue + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +GetTrainCriterionNodes ... +GetEvalCriterionNodes ... + + +Allocating matrices for gradient computing +No PreCompute nodes found, skipping PreCompute step +Warning: checkpoint file is missing. learning parameters will be initialized from 0 +Set Max Temp Mem Size For Convolution Nodes to 0 samples. +Starting Epoch 50: learning rate per sample = 0.004000 effective momentum = 0.900000 +starting at epoch 49 counting lines to determine record count + + 10000 records found +starting epoch 49 at record count 490000, and file position 0 +already there from last epoch + +Starting minibatch loop. +randomordering: 1995 retries for 10000 elements (19.9%) to ensure window condition +randomordering: recached sequence for seed 49: 954, 533, ... + Epoch[50 of 50]-Minibatch[ 1- 10]: SamplesSeen = 250; TrainLossPerSample = 0.16219635; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.35556s; TotalTimePerSample = 1.42223ms; SamplesPerSecond = 703 + Epoch[50 of 50]-Minibatch[ 11- 20]: SamplesSeen = 250; TrainLossPerSample = 0.15900116; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04740s; TotalTimePerSample = 0.18962ms; SamplesPerSecond = 5273 + Epoch[50 of 50]-Minibatch[ 21- 30]: SamplesSeen = 250; TrainLossPerSample = 0.13724194; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04737s; TotalTimePerSample = 0.18949ms; SamplesPerSecond = 5277 + Epoch[50 of 50]-Minibatch[ 31- 40]: SamplesSeen = 250; TrainLossPerSample = 0.14847681; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04733s; TotalTimePerSample = 0.18931ms; SamplesPerSecond = 5282 + Epoch[50 of 50]-Minibatch[ 41- 50]: SamplesSeen = 250; TrainLossPerSample = 0.21968488; EvalErr[0]PerSample = 0.12400000; TotalTime = 0.04727s; TotalTimePerSample = 0.18908ms; SamplesPerSecond = 5288 + Epoch[50 of 50]-Minibatch[ 51- 60]: SamplesSeen = 250; TrainLossPerSample = 0.14950464; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04719s; TotalTimePerSample = 0.18875ms; SamplesPerSecond = 5298 + Epoch[50 of 50]-Minibatch[ 61- 70]: SamplesSeen = 250; TrainLossPerSample = 0.13677826; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04733s; TotalTimePerSample = 0.18931ms; SamplesPerSecond = 5282 + Epoch[50 of 50]-Minibatch[ 71- 80]: SamplesSeen = 250; TrainLossPerSample = 0.18400269; EvalErr[0]PerSample = 0.10000000; TotalTime = 0.04721s; TotalTimePerSample = 0.18884ms; SamplesPerSecond = 5295 + Epoch[50 of 50]-Minibatch[ 81- 90]: SamplesSeen = 250; TrainLossPerSample = 0.13719153; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04712s; TotalTimePerSample = 0.18848ms; SamplesPerSecond = 5305 + Epoch[50 of 50]-Minibatch[ 91- 100]: SamplesSeen = 250; TrainLossPerSample = 0.15287048; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04767s; TotalTimePerSample = 0.19069ms; SamplesPerSecond = 5244 +WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times. + Epoch[50 of 50]-Minibatch[ 101- 110]: SamplesSeen = 250; TrainLossPerSample = 0.13952893; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04729s; TotalTimePerSample = 0.18916ms; SamplesPerSecond = 5286 + Epoch[50 of 50]-Minibatch[ 111- 120]: SamplesSeen = 250; TrainLossPerSample = 0.12584436; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04724s; TotalTimePerSample = 0.18896ms; SamplesPerSecond = 5292 + Epoch[50 of 50]-Minibatch[ 121- 130]: SamplesSeen = 250; TrainLossPerSample = 0.15119482; EvalErr[0]PerSample = 0.08000000; TotalTime = 0.04701s; TotalTimePerSample = 0.18803ms; SamplesPerSecond = 5318 + Epoch[50 of 50]-Minibatch[ 131- 140]: SamplesSeen = 250; TrainLossPerSample = 0.13055371; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04721s; TotalTimePerSample = 0.18884ms; SamplesPerSecond = 5295 + Epoch[50 of 50]-Minibatch[ 141- 150]: SamplesSeen = 250; TrainLossPerSample = 0.12967017; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04719s; TotalTimePerSample = 0.18876ms; SamplesPerSecond = 5297 + Epoch[50 of 50]-Minibatch[ 151- 160]: SamplesSeen = 250; TrainLossPerSample = 0.15023340; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04749s; TotalTimePerSample = 0.18996ms; SamplesPerSecond = 5264 + Epoch[50 of 50]-Minibatch[ 161- 170]: SamplesSeen = 250; TrainLossPerSample = 0.14382349; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04699s; TotalTimePerSample = 0.18797ms; SamplesPerSecond = 5320 + Epoch[50 of 50]-Minibatch[ 171- 180]: SamplesSeen = 250; TrainLossPerSample = 0.08575903; EvalErr[0]PerSample = 0.03600000; TotalTime = 0.04714s; TotalTimePerSample = 0.18858ms; SamplesPerSecond = 5302 + Epoch[50 of 50]-Minibatch[ 181- 190]: SamplesSeen = 250; TrainLossPerSample = 0.17751733; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04711s; TotalTimePerSample = 0.18845ms; SamplesPerSecond = 5306 + Epoch[50 of 50]-Minibatch[ 191- 200]: SamplesSeen = 250; TrainLossPerSample = 0.20404810; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04745s; TotalTimePerSample = 0.18980ms; SamplesPerSecond = 5268 + Epoch[50 of 50]-Minibatch[ 201- 210]: SamplesSeen = 250; TrainLossPerSample = 0.17242993; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04755s; TotalTimePerSample = 0.19020ms; SamplesPerSecond = 5257 + Epoch[50 of 50]-Minibatch[ 211- 220]: SamplesSeen = 250; TrainLossPerSample = 0.12448657; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04743s; TotalTimePerSample = 0.18970ms; SamplesPerSecond = 5271 + Epoch[50 of 50]-Minibatch[ 221- 230]: SamplesSeen = 250; TrainLossPerSample = 0.14477075; EvalErr[0]PerSample = 0.06800000; TotalTime = 0.04730s; TotalTimePerSample = 0.18921ms; SamplesPerSecond = 5285 + Epoch[50 of 50]-Minibatch[ 231- 240]: SamplesSeen = 250; TrainLossPerSample = 0.16081714; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04736s; TotalTimePerSample = 0.18945ms; SamplesPerSecond = 5278 + Epoch[50 of 50]-Minibatch[ 241- 250]: SamplesSeen = 250; TrainLossPerSample = 0.15686157; EvalErr[0]PerSample = 0.07200000; TotalTime = 0.04742s; TotalTimePerSample = 0.18970ms; SamplesPerSecond = 5271 + Epoch[50 of 50]-Minibatch[ 251- 260]: SamplesSeen = 250; TrainLossPerSample = 0.11661328; EvalErr[0]PerSample = 0.05600000; TotalTime = 0.04693s; TotalTimePerSample = 0.18772ms; SamplesPerSecond = 5327 + Epoch[50 of 50]-Minibatch[ 261- 270]: SamplesSeen = 250; TrainLossPerSample = 0.17682983; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04731s; TotalTimePerSample = 0.18925ms; SamplesPerSecond = 5283 + Epoch[50 of 50]-Minibatch[ 271- 280]: SamplesSeen = 250; TrainLossPerSample = 0.12256445; EvalErr[0]PerSample = 0.05200000; TotalTime = 0.04750s; TotalTimePerSample = 0.18999ms; SamplesPerSecond = 5263 + Epoch[50 of 50]-Minibatch[ 281- 290]: SamplesSeen = 250; TrainLossPerSample = 0.20207422; EvalErr[0]PerSample = 0.10800000; TotalTime = 0.04712s; TotalTimePerSample = 0.18847ms; SamplesPerSecond = 5305 + Epoch[50 of 50]-Minibatch[ 291- 300]: SamplesSeen = 250; TrainLossPerSample = 0.14701465; EvalErr[0]PerSample = 0.07600000; TotalTime = 0.04783s; TotalTimePerSample = 0.19133ms; SamplesPerSecond = 5226 + Epoch[50 of 50]-Minibatch[ 301- 310]: SamplesSeen = 250; TrainLossPerSample = 0.14405762; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04732s; TotalTimePerSample = 0.18928ms; SamplesPerSecond = 5283 + Epoch[50 of 50]-Minibatch[ 311- 320]: SamplesSeen = 250; TrainLossPerSample = 0.13430029; EvalErr[0]PerSample = 0.06400000; TotalTime = 0.04707s; TotalTimePerSample = 0.18826ms; SamplesPerSecond = 5311 + Epoch[50 of 50]-Minibatch[ 321- 330]: SamplesSeen = 250; TrainLossPerSample = 0.18196143; EvalErr[0]PerSample = 0.09200000; TotalTime = 0.04719s; TotalTimePerSample = 0.18876ms; SamplesPerSecond = 5297 + Epoch[50 of 50]-Minibatch[ 331- 340]: SamplesSeen = 250; TrainLossPerSample = 0.16981787; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04731s; TotalTimePerSample = 0.18924ms; SamplesPerSecond = 5284 + Epoch[50 of 50]-Minibatch[ 341- 350]: SamplesSeen = 250; TrainLossPerSample = 0.18865869; EvalErr[0]PerSample = 0.08400000; TotalTime = 0.04708s; TotalTimePerSample = 0.18832ms; SamplesPerSecond = 5310 + Epoch[50 of 50]-Minibatch[ 351- 360]: SamplesSeen = 250; TrainLossPerSample = 0.14027637; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04707s; TotalTimePerSample = 0.18828ms; SamplesPerSecond = 5311 + Epoch[50 of 50]-Minibatch[ 361- 370]: SamplesSeen = 250; TrainLossPerSample = 0.17351953; EvalErr[0]PerSample = 0.08800000; TotalTime = 0.04706s; TotalTimePerSample = 0.18825ms; SamplesPerSecond = 5312 + Epoch[50 of 50]-Minibatch[ 371- 380]: SamplesSeen = 250; TrainLossPerSample = 0.13019482; EvalErr[0]PerSample = 0.06000000; TotalTime = 0.04705s; TotalTimePerSample = 0.18820ms; SamplesPerSecond = 5313 + Epoch[50 of 50]-Minibatch[ 381- 390]: SamplesSeen = 250; TrainLossPerSample = 0.19873682; EvalErr[0]PerSample = 0.09600000; TotalTime = 0.04735s; TotalTimePerSample = 0.18940ms; SamplesPerSecond = 5279 + Epoch[50 of 50]-Minibatch[ 391- 400]: SamplesSeen = 250; TrainLossPerSample = 0.12178662; EvalErr[0]PerSample = 0.04800000; TotalTime = 0.04755s; TotalTimePerSample = 0.19019ms; SamplesPerSecond = 5257 +Finished Epoch[50 of 50]: [Training Set] TrainLossPerSample = 0.15332235; EvalErrPerSample = 0.073799998; AvgLearningRatePerSample = 0.00400000019; EpochTime=2.209268 +CNTKCommandTrainEnd: Simple_Demo +reading uci file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\Speech\Data/SimpleDataTest.txt + + +Allocating matrices for forward propagation. + + +Printing Gradient Computation Node Order ... + +CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[2, 0], HLast[0, 0]) +HLast[0, 0] = Plus(W2*H1[0, 0], B2[2, 1]) +B2[2, 1] = LearnableParameter +W2*H1[0, 0] = Times(W2[2, 50], H2[0, 0]) +H2[0, 0] = Sigmoid(W1*H1+B1[0, 0]) +W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[50, 1]) +B1[50, 1] = LearnableParameter +W1*H1[0, 0] = Times(W1[50, 50], H1[0, 0]) +H1[0, 0] = Sigmoid(W0*features+B0[0, 0]) +W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[50, 1]) +B0[50, 1] = LearnableParameter +W0*features[0, 0] = Times(W0[50, 2], MVNormalizedFeatures[0, 0]) +MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[2, 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) +InvStdOfFeatures[2, 1] = InvStdDev(features[2, 0]) +MeanOfFeatures[2, 1] = Mean(features[2, 0]) +features[2, 0] = InputValue +W0[50, 2] = LearnableParameter +W1[50, 50] = LearnableParameter +W2[2, 50] = LearnableParameter +labels[2, 0] = InputValue + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 12 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node CrossEntropyWithSoftmax. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node CrossEntropyWithSoftmax, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 11 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node ScaledLogLikelihood. 22 nodes to process in pass 1. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood. 9 nodes to process in pass 2. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +Validating for node ScaledLogLikelihood, final verification. + +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> Prior = Mean(labels[2, MBSize 0]) -> [2, 1] +Validating --> LogOfPrior = Log(Prior[2, 1]) -> [2, 1] +Validating --> ScaledLogLikelihood = Minus(HLast[2, MBSize 0], LogOfPrior[2, 1]) -> [2, MBSize 0] + +10 out of 22 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + + + +Validating for node EvalErrorPrediction. 20 nodes to process in pass 1. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction. 9 nodes to process in pass 2. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +Validating for node EvalErrorPrediction, final verification. + +Validating --> labels = InputValue -> [2, MBSize 0] +Validating --> W2 = LearnableParameter -> [2, 50] +Validating --> W1 = LearnableParameter -> [50, 50] +Validating --> W0 = LearnableParameter -> [50, 2] +Validating --> features = InputValue -> [2, MBSize 0] +Validating --> MeanOfFeatures = Mean(features[2, MBSize 0]) -> [2, 1] +Validating --> InvStdOfFeatures = InvStdDev(features[2, MBSize 0]) -> [2, 1] +Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[2, MBSize 0], MeanOfFeatures[2, 1], InvStdOfFeatures[2, 1]) -> [2, MBSize 0] +Validating --> W0*features = Times(W0[50, 2], MVNormalizedFeatures[2, MBSize 0]) -> [50, MBSize 0] +Validating --> B0 = LearnableParameter -> [50, 1] +Validating --> W0*features+B0 = Plus(W0*features[50, MBSize 0], B0[50, 1]) -> [50, MBSize 0] +Validating --> H1 = Sigmoid(W0*features+B0[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W1*H1 = Times(W1[50, 50], H1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> B1 = LearnableParameter -> [50, 1] +Validating --> W1*H1+B1 = Plus(W1*H1[50, MBSize 0], B1[50, 1]) -> [50, MBSize 0] +Validating --> H2 = Sigmoid(W1*H1+B1[50, MBSize 0]) -> [50, MBSize 0] +Validating --> W2*H1 = Times(W2[2, 50], H2[50, MBSize 0]) -> [2, MBSize 0] +Validating --> B2 = LearnableParameter -> [2, 1] +Validating --> HLast = Plus(W2*H1[2, MBSize 0], B2[2, 1]) -> [2, MBSize 0] +Validating --> EvalErrorPrediction = ErrorPrediction(labels[2, MBSize 0], HLast[2, MBSize 0]) -> [1, 1] + +9 out of 20 nodes do not share the minibatch layout with the input data. + +starting at epoch 0 counting lines to determine record count + + 603 records found +starting epoch 0 at record count 0, and file position 0 +already there from last epoch +Minibatch[1]: ActualMBSize = 603 +Total Samples Evaluated = 603 +COMPLETED diff --git a/Tests/EndToEndTests/Speech/Simple/cntk.config b/Tests/EndToEndTests/Speech/Simple/cntk.config index 1f3d22202d2e..195e197c1d5c 100644 --- a/Tests/EndToEndTests/Speech/Simple/cntk.config +++ b/Tests/EndToEndTests/Speech/Simple/cntk.config @@ -1,97 +1,97 @@ -command=Simple_Demo:Simple_Demo_Output - -# deviceId=-1 for CPU, >=0 for GPU devices -DeviceNumber=-1 - -#stderr=Demo - -precision=float - -modelPath=$RunDir$/models/simple.dnn -deviceId=$DeviceNumber$ - -outputNodeNames=ScaledLogLikelihood -traceLevel=1 - -####################################### -# TRAINING CONFIG (Simple, Fixed LR) # -####################################### - -Simple_Demo=[ - action=train - - # Notation xxx:yyy*n:zzz is equivalent to xxx, - # then yyy repeated n times, then zzz - # example: 10:20*3:5 is equivalent to 10:20:20:20:5 - SimpleNetworkBuilder=[ - # 2 input, 2 50-element hidden, 2 output - layerSizes=2:50*2:2 - trainingCriterion=CrossEntropyWithSoftmax - evalCriterion=ErrorPrediction - layerTypes=Sigmoid - initValueScale=1.0 - applyMeanVarNorm=true - uniformInit=true - needPrior=true - ] - - SGD=[ - # epochSize=0 means epochSize is the size of - # the training set. Must be evenly divisible - # into number of data frames. - epochSize=0 - minibatchSize=25 - learningRatesPerMB=0.5:0.2*20:0.1 - momentumPerMB=0.9 - dropoutRate=0.0 - maxEpochs=50 - ] - - # Parameter values for the reader - reader=[ - # reader to use - readerType=UCIFastReader - file=$DataDir$/SimpleDataTrain.txt - - miniBatchMode=Partial - randomize=Auto - verbosity=1 - - features=[ - dim=2 # two-dimensional input data - start=0 # Start with first element on line - ] - - labels=[ - start=2 # Skip two elements - dim=1 # One label dimension - labelDim=2 # Two labels possible - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] -] - -####################################### -# OUTPUT RESUTLS (Simple) # -####################################### -Simple_Demo_Output=[ - action=write - - # Parameter values for the reader - reader=[ - # reader to use - readerType=UCIFastReader - file=$DataDir$/SimpleDataTest.txt - features=[ - dim=2 - start=0 - ] - labels=[ - start=2 - dim=1 - labelDim=2 - labelMappingFile=$DataDir$/SimpleMapping.txt - ] - ] - outputPath=$RunDir$/SimpleOutput # Dump output as text -] +command=Simple_Demo:Simple_Demo_Output + +# deviceId=-1 for CPU, >=0 for GPU devices +DeviceNumber=-1 + +#stderr=Demo + +precision=float + +modelPath=$RunDir$/models/simple.dnn +deviceId=$DeviceNumber$ + +outputNodeNames=ScaledLogLikelihood +traceLevel=1 + +####################################### +# TRAINING CONFIG (Simple, Fixed LR) # +####################################### + +Simple_Demo=[ + action=train + + # Notation xxx:yyy*n:zzz is equivalent to xxx, + # then yyy repeated n times, then zzz + # example: 10:20*3:5 is equivalent to 10:20:20:20:5 + SimpleNetworkBuilder=[ + # 2 input, 2 50-element hidden, 2 output + layerSizes=2:50*2:2 + trainingCriterion=CrossEntropyWithSoftmax + evalCriterion=ErrorPrediction + layerTypes=Sigmoid + initValueScale=1.0 + applyMeanVarNorm=true + uniformInit=true + needPrior=true + ] + + SGD=[ + # epochSize=0 means epochSize is the size of + # the training set. Must be evenly divisible + # into number of data frames. + epochSize=0 + minibatchSize=25 + learningRatesPerMB=0.5:0.2*20:0.1 + momentumPerMB=0.9 + dropoutRate=0.0 + maxEpochs=50 + ] + + # Parameter values for the reader + reader=[ + # reader to use + readerType=UCIFastReader + file=$DataDir$/SimpleDataTrain.txt + + miniBatchMode=Partial + randomize=Auto + verbosity=1 + + features=[ + dim=2 # two-dimensional input data + start=0 # Start with first element on line + ] + + labels=[ + start=2 # Skip two elements + dim=1 # One label dimension + labelDim=2 # Two labels possible + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] +] + +####################################### +# OUTPUT RESUTLS (Simple) # +####################################### +Simple_Demo_Output=[ + action=write + + # Parameter values for the reader + reader=[ + # reader to use + readerType=UCIFastReader + file=$DataDir$/SimpleDataTest.txt + features=[ + dim=2 + start=0 + ] + labels=[ + start=2 + dim=1 + labelDim=2 + labelMappingFile=$DataDir$/SimpleMapping.txt + ] + ] + outputPath=$RunDir$/SimpleOutput # Dump output as text +] diff --git a/Tests/UnitTests/EvalTest/EvalTest.vcxproj b/Tests/UnitTests/EvalTest/EvalTest.vcxproj index dfad654c6466..712f8130b5af 100644 --- a/Tests/UnitTests/EvalTest/EvalTest.vcxproj +++ b/Tests/UnitTests/EvalTest/EvalTest.vcxproj @@ -1,120 +1,120 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {731312A8-6DA3-4841-AFCD-57520BA1BF8E} - Win32Proj - EvalTest - - - - Application - true - v120 - Unicode - - - Application - false - v120 - true - Unicode - - - - - - - - - - - - - true - ..\..\..\Source\Math;..\..\..\Source\Common\include;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\..\Source\Math;..\..\..\Source\Common\include;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - - - Console - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - - - Console - true - true - true - Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) - - - - - - - - - NotUsing - NotUsing - - - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - NotUsing - NotUsing - - - - Create - Create - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {731312A8-6DA3-4841-AFCD-57520BA1BF8E} + Win32Proj + EvalTest + + + + Application + true + v120 + Unicode + + + Application + false + v120 + true + Unicode + + + + + + + + + + + + + true + ..\..\..\Source\Math;..\..\..\Source\Common\include;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\..\Source\Math;..\..\..\Source\Common\include;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(SolutionDir)$(Platform)\$(Configuration);$(VCInstallDir)lib\amd64;$(VCInstallDir)atlmfc\lib\amd64;$(WindowsSDK_LibraryPath_x64); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + + + Console + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + + + Console + true + true + true + Math.lib; kernel32.lib; user32.lib; shell32.lib; %(AdditionalDependencies) + + + + + + + + + NotUsing + NotUsing + + + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + NotUsing + NotUsing + + + + Create + Create + + + + + \ No newline at end of file diff --git a/Tests/UnitTests/MathPerformanceTests/MathPerformanceTests.vcxproj b/Tests/UnitTests/MathPerformanceTests/MathPerformanceTests.vcxproj index 053cffc99aec..f6756949cf74 100644 --- a/Tests/UnitTests/MathPerformanceTests/MathPerformanceTests.vcxproj +++ b/Tests/UnitTests/MathPerformanceTests/MathPerformanceTests.vcxproj @@ -1,118 +1,118 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {668BEED5-AC07-4F35-B3AE-EE65A7F9C976} - - - - - - - - - Win32Proj - MathPerformanceTests - - - - Application - true - v120 - Unicode - - - Application - false - v120 - true - Unicode - - - - - - - - - - - - - - true - ..\..\..\Source\Common\include\;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - Use - Level4 - Disabled - WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - ..\..\..\Source\Math; ..\..\..\Source\Common\Include; $(CudaToolkitIncludeDir); %(AdditionalIncludeDirectories) - - - Console - true - Math.lib;%(AdditionalDependencies) - $(OutDir);%(AdditionalLibraryDirectories) - - - 64 - compute_20,sm_20;compute_30,sm_30;%(CodeGeneration) - - - - - Level4 - Use - MaxSpeed - true - true - WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) - true - true - ..\..\..\Source\Math; ..\..\..\Source\Common\Include; $(CudaToolkitIncludeDir); %(AdditionalIncludeDirectories) - true - - - Console - true - true - true - $(OutDir);%(AdditionalLibraryDirectories) - Math.lib;%(AdditionalDependencies) - - - - - - - - - - Create - Create - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {668BEED5-AC07-4F35-B3AE-EE65A7F9C976} + + + + + + + + + Win32Proj + MathPerformanceTests + + + + Application + true + v120 + Unicode + + + Application + false + v120 + true + Unicode + + + + + + + + + + + + + + true + ..\..\..\Source\Common\include\;$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + Use + Level4 + Disabled + WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + ..\..\..\Source\Math; ..\..\..\Source\Common\Include; $(CudaToolkitIncludeDir); %(AdditionalIncludeDirectories) + + + Console + true + Math.lib;%(AdditionalDependencies) + $(OutDir);%(AdditionalLibraryDirectories) + + + 64 + compute_20,sm_20;compute_30,sm_30;%(CodeGeneration) + + + + + Level4 + Use + MaxSpeed + true + true + WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions) + true + true + ..\..\..\Source\Math; ..\..\..\Source\Common\Include; $(CudaToolkitIncludeDir); %(AdditionalIncludeDirectories) + true + + + Console + true + true + true + $(OutDir);%(AdditionalLibraryDirectories) + Math.lib;%(AdditionalDependencies) + + + + + + + + + + Create + Create + + + + + + \ No newline at end of file diff --git a/Tests/UnitTests/MathTests/GPUMatrixTests.cpp b/Tests/UnitTests/MathTests/GPUMatrixTests.cpp index 4eb5ffc8c603..87788d671e7c 100644 --- a/Tests/UnitTests/MathTests/GPUMatrixTests.cpp +++ b/Tests/UnitTests/MathTests/GPUMatrixTests.cpp @@ -1,555 +1,555 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -// GPUMatrix unit tests should go here -// -#include "stdafx.h" -#include "../../../Source/Math/GPUMatrix.h" - -using namespace Microsoft::MSR::CNTK; - -namespace Microsoft -{ - namespace MSR - { - namespace CNTK - { - namespace Test - { - BOOST_AUTO_TEST_SUITE(GPUMatrixSuite) - - BOOST_FIXTURE_TEST_CASE(GPUMatrixConstructorNoFlag, RandomSeedFixture) - { - // TODO: consider splitting into several tests - GPUMatrix m0(c_deviceIdZero); - BOOST_CHECK(m0.IsEmpty()); - - GPUMatrix m1(12, 53, c_deviceIdZero); - BOOST_CHECK_EQUAL(12, m1.GetNumRows()); - BOOST_CHECK_EQUAL(53, m1.GetNumCols()); - BOOST_CHECK_EQUAL(12 * 53, m1.GetNumElements()); - - std::array array = { 1, 14 }; - m1.SetValue(1, 2, c_deviceIdZero, array.data()); - - unique_ptr result(m1.CopyToArray()); - BOOST_CHECK_EQUAL_COLLECTIONS(result.get(), result.get() + 2, array.begin(), array.end()); - - GPUMatrix m1Copy(m1); - BOOST_CHECK(m1.IsEqualTo(m1Copy)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixConstructorFlagNormal, RandomSeedFixture) - { - std::array array = { 1, 2, 3, 4, 5, 6 }; - GPUMatrix m(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - - unique_ptr result(m.CopyToArray()); - BOOST_CHECK_EQUAL_COLLECTIONS(result.get(), result.get() + 6, array.begin(), array.end()); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixIdentityAndZero, RandomSeedFixture) - { - // TODO: consider splitting into two separate tests? - const int size = 60; - GPUMatrix m0(GPUMatrix::Eye(size, c_deviceIdZero)); - unique_ptr result0(m0.CopyToArray()); - - for (int i = 0; i < size; i++) { - for (int j = 0; j < size; j++) { - BOOST_CHECK_CLOSE(result0[i * size + j], i == j, 0.01); - } - } - - GPUMatrix m1(GPUMatrix::Zeros(size, size, c_deviceIdZero)); - unique_ptr result1(m1.CopyToArray()); - for (int i = 0; i < size; i++) { - for (int j = 0; j < size; j++) { - BOOST_CHECK_CLOSE(result1[i * size + j], 0.0f, 0.01); - } - } - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixElementWiseOperations, RandomSeedFixture) - { - const float val = 3.0; - const int rows = 16; - const int cols = 23; - - GPUMatrix m0(rows, cols, c_deviceIdZero); - m0.SetValue(val); - GPUMatrix m1(rows, cols, c_deviceIdZero); - GPUMatrix mr(rows, cols, c_deviceIdZero); - - // test element wise power - float alpha = 2.0f; - GPUMatrix::ElementWisePower(alpha, m0, m1); - mr.SetValue(std::pow(val, alpha)); - BOOST_CHECK(mr.IsEqualTo(m1, c_epsilonFloatE4)); - - alpha = 0.234f; - GPUMatrix::ElementWisePower(alpha, m0, m1); - mr.SetValue(std::pow(val, alpha)); - BOOST_CHECK(mr.IsEqualTo(m1, c_epsilonFloatE4)); - - // test element wise absolute value - m0.SetValue(-val); - m1.AssignAbsOf(m0); - mr.SetValue(val); - BOOST_CHECK(mr.IsEqualTo(m1)); - - // TODO: add other element wise operations? - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixInplaceOperations, RandomSeedFixture) - { - const float val = 0.42f; - const int rows = 16; - const int cols = 23; - - GPUMatrix m(rows, cols, c_deviceIdZero); - GPUMatrix mr(rows, cols, c_deviceIdZero); - - m.SetValue(val); - m.InplaceExp(); - mr.SetValue(std::exp(val)); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - m.SetValue(val); - m.InplaceLog(); - mr.SetValue(std::log(val)); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - m.SetValue(val); - m.InplaceTanh(); - mr.SetValue(std::tanh(val)); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - m.SetValue(-val); - m.InplaceAbs(); - mr.SetValue(val); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - m.SetValue(val); - m.InplaceSqrt(); - mr.SetValue(std::sqrt(val)); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - m.SetValue(val); - m.InplaceSigmoid(); - mr.SetValue(1 / (std::exp(-val) + 1)); - BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); - - // TODO: there are two more inplace operations. Test these? compare to CPU results? - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixAddAndSub, RandomSeedFixture) - { - std::array array0 = { 1, 2, 3, 4, 5, 6 }; - GPUMatrix m0(2, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); - - std::array array1 = { 11, 12, 13, 14, 15, 16 }; - GPUMatrix m1(2, 3, c_deviceIdZero, array1.data(), matrixFlagNormal); - - std::array array2 = { 12, 14, 16, 18, 20, 22 }; - GPUMatrix m2(2, 3, c_deviceIdZero, array2.data(), matrixFlagNormal); - - std::array arrayCRS = { 10, 10, 10 }; - GPUMatrix mc(2, 1, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); - GPUMatrix mr(1, 3, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); - GPUMatrix ms(1, 1, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); - - GPUMatrix m3 = m2 - m0; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3 += m0; - BOOST_CHECK(m3.IsEqualTo(m2)); - - m3 = m0 + 10; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3 -= 10; - BOOST_CHECK(m3.IsEqualTo(m0)); - - m3 = m1 + m0; - BOOST_CHECK(m3.IsEqualTo(m2)); - - m3 -= m0; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3 = m1 - 10; - BOOST_CHECK(m3.IsEqualTo(m0)); - - m3 += 10; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3 -= mc; - BOOST_CHECK(m3.IsEqualTo(m0)); - - m3 += mc; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3 -= mr; - BOOST_CHECK(m3.IsEqualTo(m0)); - - m3 += mr; - BOOST_CHECK(m3.IsEqualTo(m1)); - - m3.AssignDifferenceOf(m3, ms); - BOOST_CHECK(m3.IsEqualTo(m0)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixNorms, RandomSeedFixture) - { - std::array array = { - 1, 4, 2, - 5, 3, 6 - }; - GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - - GPUMatrix m3(c_deviceIdZero); - m0.VectorNorm1(m3, true); - array[0] = 5; array[1] = 7; array[2] = 9; - GPUMatrix m2(1, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - m0.VectorNorm1(m3, false); - m2.Resize(2, 1); - array[0] = 6; array[1] = 15; - m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - m0.VectorNorm2(m3, true); - m2.Resize(1, 3); - array[0] = 4.1231f; array[1] = 5.3852f; array[2] = 6.7082f; - m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2, c_epsilonFloat5E4)); - - m0.VectorNorm2(m3, false); - m2.Resize(2, 1); - array[0] = 3.7417f; array[1] = 8.7750f; - m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2, c_epsilonFloat5E4)); - - array[0] = 1; array[2] = 2; array[4] = 3; - array[1] = 4; array[3] = 5; array[5] = 6; - GPUMatrix m00(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - - GPUMatrix m1(c_deviceIdZero); - m00.VectorMax(m1, m3, true); - m2.Resize(1, 3); - array[0] = 4; array[1] = 5; array[2] = 6; - m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - m00.VectorMax(m1, m3, false); - m2.Resize(2, 1); - array[0] = 3.; array[1] = 6; - m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - m0.VectorNormInf(m3, true); - m2.Resize(1, 3); - array[0] = 4; array[1] = 5; array[2] = 6; - m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - m0.VectorNormInf(m3, false); - m2.Resize(2, 1); - array[0] = 3.; array[1] = 6; - m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK(m3.IsEqualTo(m2)); - - array[0] = 1; array[2] = 2; array[4] = 3; - array[1] = 4; array[3] = 5; array[5] = 6; - m00.SetValue(2, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); - BOOST_CHECK_EQUAL(6, m00.MatrixNormInf()); - - BOOST_CHECK(abs(m0.FrobeniusNorm() - 9.5394) < c_epsilonFloatE4); - BOOST_CHECK(abs(m0.MatrixNormInf() - 6) < c_epsilonFloatE4); - BOOST_CHECK_EQUAL(21, m00.MatrixNorm1()); - - GPUMatrix a = GPUMatrix::Eye(4096, c_deviceIdZero); - BOOST_CHECK_EQUAL(4096, a.MatrixNorm0()); - - GPUMatrix b = GPUMatrix::Eye(5, c_deviceIdZero); - BOOST_CHECK_EQUAL(5, b.MatrixNorm0()); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixRandomUniform, RandomSeedFixture) - { - const float low = -0.035f; - const float high = 0.035f; - auto m = GPUMatrix::RandomUniform(768, 50, c_deviceIdZero, low, high, IncrementCounter()); - unique_ptr result(m.CopyToArray()); - - for (int i = 0; i < 768 * 50; ++i) - { - BOOST_CHECK_LE(result[i], high); - BOOST_CHECK_GT(result[i], low); - } - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixColumnSlice, RandomSeedFixture) - { - std::array array = { - 1, 4, 2, - 5, 3, 6 - }; - GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - GPUMatrix m1(2, 2, c_deviceIdZero, array.data(), matrixFlagNormal); - - GPUMatrix m2 = m0.ColumnSlice(0, 2); - BOOST_CHECK(m2.IsEqualTo(m1)); - - std::array array3 = { array[2], array[3], array[4], array[5] }; - GPUMatrix m3(2, 2, c_deviceIdZero, array3.data(), matrixFlagNormal); - - m2 = m0.ColumnSlice(1, 2); - BOOST_CHECK(m2.IsEqualTo(m3)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixRowSlice, RandomSeedFixture) - { - std::array array0 = { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9, - 10, 11, 12, - 13, 14, 15 - }; - GPUMatrix m0(5, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); - - std::array array1 = { - 3, 4, 8, - 9, 13, 14 - }; - GPUMatrix m1(2, 3, c_deviceIdZero, array1.data(), matrixFlagNormal); - - GPUMatrix m2(c_deviceIdZero); - m2.AssignRowSliceValuesOf(m0, 2, 2); - BOOST_CHECK(m2.IsEqualTo(m1)); - - std::array array3 = { - 0, 0, 3, - 4, 0, 0, - 0, 8, 9, - 0, 0, 0, - 13, 14, 0 - }; - GPUMatrix m3(5, 3, c_deviceIdZero, array3.data(), matrixFlagNormal); - - m3 += m0; - m0.AddToRowSliceValuesOf(m1, 2, 2); - BOOST_CHECK(m3.IsEqualTo(m0)); - - m2.AddWithRowSliceValuesOf(m1, 0, 2); - std::array array4 = { - 6, 8, 16, - 18, 26, 28 - }; - GPUMatrix m4(2, 3, c_deviceIdZero, array4.data(), matrixFlagNormal); - BOOST_CHECK(m2.IsEqualTo(m4)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixKhatriRaoProduct, RandomSeedFixture) - { - std::array arrayA = { - 0.8147f, 0.9058f, 0.1270f, 0.9134f, - 0.6324f, 0.0975f, 0.2785f, 0.5469f, - 0.9575f, 0.9649f, 0.1576f, 0.9706f - }; - GPUMatrix a(3, 4, c_deviceIdZero, arrayA.data()); - - std::array arrayB = { - 0.9572f, 0.4854f, 0.8003f, 0.1419f, - 0.4218f, 0.9157f, 0.7922f, 0.9595f - }; - GPUMatrix b(2, 4, c_deviceIdZero, arrayB.data()); - - std::array arrayD = { - 0.7798f, 0.8670f, 0.1215f, 0.3954f, - 0.4396f, 0.0616f, 0.7310f, 0.5061f, - 0.0781f, 0.1296f, 0.0897f, 0.0138f, - 0.1175f, 0.2307f, 0.4038f, 0.2550f, - 0.5008f, 0.8768f, 0.7644f, 0.1249f, - 0.7689f, 0.9258f, 0.1512f, 0.9313f - }; - GPUMatrix d(6, 4, c_deviceIdZero, arrayD.data()); - - GPUMatrix c(c_deviceIdZero); - c.AssignKhatriRaoProductOf(a, b); - BOOST_CHECK(c.IsEqualTo(d, c_epsilonFloatE4)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixAddColumnReshapeProductOf, RandomSeedFixture) - { - // tests column-wise reshaped product. Used to compute KhatriRaoProduct Gradient - std::array arrayA = { - 0.6557f, 0.0357f, - 0.8491f, 0.9340f, - 0.6787f, 0.7577f, - 0.7431f, 0.3922f, - 0.6555f, 0.1712f, - 0.7060f, 0.0318f, - }; - GPUMatrix a(6, 2, c_deviceIdZero, arrayA.data()); - - std::array arrayB = { - 0.2769f, 0.0462f, - 0.0971f, 0.8235f, - 0.6948f, 0.3171f - }; - GPUMatrix b(3, 2, c_deviceIdZero, arrayB.data()); - - std::array arrayD0 = { - 0.2867f, 0.1266f, - 1.2913f, 0.4520f - }; - GPUMatrix d0(2, 2, c_deviceIdZero, arrayD0.data()); - - std::array arrayD1 = { - 0.2657f, 0.3636f, - 1.0923f, 0.6416f - }; - GPUMatrix d1(2, 2, c_deviceIdZero, arrayD1.data()); - - GPUMatrix c(2, 2, c_deviceIdZero); - c.SetValue(0.0f); - c.AddColumnReshapeProductOf(a, b, false); - BOOST_CHECK(c.IsEqualTo(d0, c_epsilonFloatE4)); - - c.SetValue(0.0f); - c.AddColumnReshapeProductOf(a, b, true); - BOOST_CHECK(c.IsEqualTo(d1, c_epsilonFloatE4)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixInnerProduct, RandomSeedFixture) - { - std::array array = { - 1, 4, 2, - 5, 3, 6 - }; - GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); - - GPUMatrix m1(c_deviceIdZero), m2(c_deviceIdZero); - m1.AssignInnerProductOf(m0, m0, true); - m2.AssignVectorNorm2Of(m0, true); - m1.InplaceSqrt(); - BOOST_CHECK(m1.IsEqualTo(m2)); - - m1.AssignInnerProductOf(m0, m0, false); - m2.AssignVectorNorm2Of(m0, false); - m1.InplaceSqrt(); - BOOST_CHECK(m1.IsEqualTo(m2)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixAssignRepeatOf, RandomSeedFixture) - { - std::array array0 = { - 1, 2, - 6, 7, - 11, 12 - }; - GPUMatrix m0(2, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); - - GPUMatrix m1(c_deviceIdZero); - m1.AssignRepeatOf(m0, 1, 1); - BOOST_CHECK(m1.IsEqualTo(m0)); - - std::array array2 = { - 1, 2, 1, 2, 1, 2, - 6, 7, 6, 7, 6, 7, - 11, 12, 11, 12, 11, 12, - 1, 2, 1, 2, 1, 2, - 6, 7, 6, 7, 6, 7, - 11, 12, 11, 12, 11, 12 - }; - GPUMatrix m2(6, 6, c_deviceIdZero, array2.data(), matrixFlagNormal); - - m1.AssignRepeatOf(m0, 3, 2); - BOOST_CHECK(m1.IsEqualTo(m2)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixRowElementOperations, RandomSeedFixture) - { - GPUMatrix m0 = GPUMatrix::RandomUniform(20, 28, c_deviceIdZero, -1, 1, IncrementCounter()); - GPUMatrix m1 = GPUMatrix::RandomUniform(1, 28, c_deviceIdZero, 1, 2, IncrementCounter()); - - GPUMatrix m2(c_deviceIdZero); - m2.SetValue(m0); - m2.RowElementMultiplyWith(m1); - m2.RowElementDivideBy(m1); - - BOOST_CHECK(m0.IsEqualTo(m2, c_epsilonFloatE4)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixColumnElementOperations, RandomSeedFixture) - { - GPUMatrix m0 = GPUMatrix::RandomUniform(20, 28, c_deviceIdZero, -1, 1, IncrementCounter()); - GPUMatrix m1 = GPUMatrix::RandomUniform(20, 1, c_deviceIdZero, 1, 2, IncrementCounter()); - - GPUMatrix m2(c_deviceIdZero); - m2.SetValue(m0); - m2.ColumnElementMultiplyWith(m1); - m2.ColumnElementDivideBy(m1); - - BOOST_CHECK(m0.IsEqualTo(m2, c_epsilonFloatE4)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixCurandSeedingFloat, RandomSeedFixture) - { - const float low = 0; - const float high = 1; - const unsigned long seedUsed = 1; - const unsigned long seedIgnored = 4711; - - // The current GPUMatrix implementation uses a static RNG. - - GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); - auto m1 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); - - GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); - auto m2 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); - - BOOST_CHECK(m1.IsEqualTo(m2)); - } - - BOOST_FIXTURE_TEST_CASE(GPUMatrixCurandSeedingDouble, RandomSeedFixture) - { - const double low = 0; - const double high = 1; - const unsigned long seedUsed = 1; - const unsigned long seedIgnored = 4711; - - // The current GPUMatrix implementation uses a static RNG. - - GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); - auto m1 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); - - GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); - auto m2 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); - - BOOST_CHECK(m1.IsEqualTo(m2)); - } - -#if 0 // Temporarily disabling - BOOST_FIXTURE_TEST_CASE(GPUMatrixLargeInequality, RandomSeedFixture) - { - const int rows = 33553921; - const int cols = 1; - - auto m0 = GPUMatrix::Zeros(rows, cols, c_deviceIdZero); - auto m1 = GPUMatrix::Ones(rows, cols, c_deviceIdZero); - - BOOST_CHECK(!m1.IsEqualTo(m0, c_epsilonFloatE5)); - } -#endif - - BOOST_AUTO_TEST_SUITE_END() - } - } - } -} +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +// GPUMatrix unit tests should go here +// +#include "stdafx.h" +#include "../../../Source/Math/GPUMatrix.h" + +using namespace Microsoft::MSR::CNTK; + +namespace Microsoft +{ + namespace MSR + { + namespace CNTK + { + namespace Test + { + BOOST_AUTO_TEST_SUITE(GPUMatrixSuite) + + BOOST_FIXTURE_TEST_CASE(GPUMatrixConstructorNoFlag, RandomSeedFixture) + { + // TODO: consider splitting into several tests + GPUMatrix m0(c_deviceIdZero); + BOOST_CHECK(m0.IsEmpty()); + + GPUMatrix m1(12, 53, c_deviceIdZero); + BOOST_CHECK_EQUAL(12, m1.GetNumRows()); + BOOST_CHECK_EQUAL(53, m1.GetNumCols()); + BOOST_CHECK_EQUAL(12 * 53, m1.GetNumElements()); + + std::array array = { 1, 14 }; + m1.SetValue(1, 2, c_deviceIdZero, array.data()); + + unique_ptr result(m1.CopyToArray()); + BOOST_CHECK_EQUAL_COLLECTIONS(result.get(), result.get() + 2, array.begin(), array.end()); + + GPUMatrix m1Copy(m1); + BOOST_CHECK(m1.IsEqualTo(m1Copy)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixConstructorFlagNormal, RandomSeedFixture) + { + std::array array = { 1, 2, 3, 4, 5, 6 }; + GPUMatrix m(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + + unique_ptr result(m.CopyToArray()); + BOOST_CHECK_EQUAL_COLLECTIONS(result.get(), result.get() + 6, array.begin(), array.end()); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixIdentityAndZero, RandomSeedFixture) + { + // TODO: consider splitting into two separate tests? + const int size = 60; + GPUMatrix m0(GPUMatrix::Eye(size, c_deviceIdZero)); + unique_ptr result0(m0.CopyToArray()); + + for (int i = 0; i < size; i++) { + for (int j = 0; j < size; j++) { + BOOST_CHECK_CLOSE(result0[i * size + j], i == j, 0.01); + } + } + + GPUMatrix m1(GPUMatrix::Zeros(size, size, c_deviceIdZero)); + unique_ptr result1(m1.CopyToArray()); + for (int i = 0; i < size; i++) { + for (int j = 0; j < size; j++) { + BOOST_CHECK_CLOSE(result1[i * size + j], 0.0f, 0.01); + } + } + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixElementWiseOperations, RandomSeedFixture) + { + const float val = 3.0; + const int rows = 16; + const int cols = 23; + + GPUMatrix m0(rows, cols, c_deviceIdZero); + m0.SetValue(val); + GPUMatrix m1(rows, cols, c_deviceIdZero); + GPUMatrix mr(rows, cols, c_deviceIdZero); + + // test element wise power + float alpha = 2.0f; + GPUMatrix::ElementWisePower(alpha, m0, m1); + mr.SetValue(std::pow(val, alpha)); + BOOST_CHECK(mr.IsEqualTo(m1, c_epsilonFloatE4)); + + alpha = 0.234f; + GPUMatrix::ElementWisePower(alpha, m0, m1); + mr.SetValue(std::pow(val, alpha)); + BOOST_CHECK(mr.IsEqualTo(m1, c_epsilonFloatE4)); + + // test element wise absolute value + m0.SetValue(-val); + m1.AssignAbsOf(m0); + mr.SetValue(val); + BOOST_CHECK(mr.IsEqualTo(m1)); + + // TODO: add other element wise operations? + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixInplaceOperations, RandomSeedFixture) + { + const float val = 0.42f; + const int rows = 16; + const int cols = 23; + + GPUMatrix m(rows, cols, c_deviceIdZero); + GPUMatrix mr(rows, cols, c_deviceIdZero); + + m.SetValue(val); + m.InplaceExp(); + mr.SetValue(std::exp(val)); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + m.SetValue(val); + m.InplaceLog(); + mr.SetValue(std::log(val)); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + m.SetValue(val); + m.InplaceTanh(); + mr.SetValue(std::tanh(val)); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + m.SetValue(-val); + m.InplaceAbs(); + mr.SetValue(val); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + m.SetValue(val); + m.InplaceSqrt(); + mr.SetValue(std::sqrt(val)); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + m.SetValue(val); + m.InplaceSigmoid(); + mr.SetValue(1 / (std::exp(-val) + 1)); + BOOST_CHECK(mr.IsEqualTo(m, c_epsilonFloatE4)); + + // TODO: there are two more inplace operations. Test these? compare to CPU results? + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixAddAndSub, RandomSeedFixture) + { + std::array array0 = { 1, 2, 3, 4, 5, 6 }; + GPUMatrix m0(2, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); + + std::array array1 = { 11, 12, 13, 14, 15, 16 }; + GPUMatrix m1(2, 3, c_deviceIdZero, array1.data(), matrixFlagNormal); + + std::array array2 = { 12, 14, 16, 18, 20, 22 }; + GPUMatrix m2(2, 3, c_deviceIdZero, array2.data(), matrixFlagNormal); + + std::array arrayCRS = { 10, 10, 10 }; + GPUMatrix mc(2, 1, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); + GPUMatrix mr(1, 3, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); + GPUMatrix ms(1, 1, c_deviceIdZero, arrayCRS.data(), matrixFlagNormal); + + GPUMatrix m3 = m2 - m0; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3 += m0; + BOOST_CHECK(m3.IsEqualTo(m2)); + + m3 = m0 + 10; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3 -= 10; + BOOST_CHECK(m3.IsEqualTo(m0)); + + m3 = m1 + m0; + BOOST_CHECK(m3.IsEqualTo(m2)); + + m3 -= m0; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3 = m1 - 10; + BOOST_CHECK(m3.IsEqualTo(m0)); + + m3 += 10; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3 -= mc; + BOOST_CHECK(m3.IsEqualTo(m0)); + + m3 += mc; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3 -= mr; + BOOST_CHECK(m3.IsEqualTo(m0)); + + m3 += mr; + BOOST_CHECK(m3.IsEqualTo(m1)); + + m3.AssignDifferenceOf(m3, ms); + BOOST_CHECK(m3.IsEqualTo(m0)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixNorms, RandomSeedFixture) + { + std::array array = { + 1, 4, 2, + 5, 3, 6 + }; + GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + + GPUMatrix m3(c_deviceIdZero); + m0.VectorNorm1(m3, true); + array[0] = 5; array[1] = 7; array[2] = 9; + GPUMatrix m2(1, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + m0.VectorNorm1(m3, false); + m2.Resize(2, 1); + array[0] = 6; array[1] = 15; + m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + m0.VectorNorm2(m3, true); + m2.Resize(1, 3); + array[0] = 4.1231f; array[1] = 5.3852f; array[2] = 6.7082f; + m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2, c_epsilonFloat5E4)); + + m0.VectorNorm2(m3, false); + m2.Resize(2, 1); + array[0] = 3.7417f; array[1] = 8.7750f; + m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2, c_epsilonFloat5E4)); + + array[0] = 1; array[2] = 2; array[4] = 3; + array[1] = 4; array[3] = 5; array[5] = 6; + GPUMatrix m00(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + + GPUMatrix m1(c_deviceIdZero); + m00.VectorMax(m1, m3, true); + m2.Resize(1, 3); + array[0] = 4; array[1] = 5; array[2] = 6; + m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + m00.VectorMax(m1, m3, false); + m2.Resize(2, 1); + array[0] = 3.; array[1] = 6; + m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + m0.VectorNormInf(m3, true); + m2.Resize(1, 3); + array[0] = 4; array[1] = 5; array[2] = 6; + m2.SetValue(1, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + m0.VectorNormInf(m3, false); + m2.Resize(2, 1); + array[0] = 3.; array[1] = 6; + m2.SetValue(2, 1, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK(m3.IsEqualTo(m2)); + + array[0] = 1; array[2] = 2; array[4] = 3; + array[1] = 4; array[3] = 5; array[5] = 6; + m00.SetValue(2, 3, m2.GetComputeDeviceId(), array.data(), matrixFlagNormal); + BOOST_CHECK_EQUAL(6, m00.MatrixNormInf()); + + BOOST_CHECK(abs(m0.FrobeniusNorm() - 9.5394) < c_epsilonFloatE4); + BOOST_CHECK(abs(m0.MatrixNormInf() - 6) < c_epsilonFloatE4); + BOOST_CHECK_EQUAL(21, m00.MatrixNorm1()); + + GPUMatrix a = GPUMatrix::Eye(4096, c_deviceIdZero); + BOOST_CHECK_EQUAL(4096, a.MatrixNorm0()); + + GPUMatrix b = GPUMatrix::Eye(5, c_deviceIdZero); + BOOST_CHECK_EQUAL(5, b.MatrixNorm0()); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixRandomUniform, RandomSeedFixture) + { + const float low = -0.035f; + const float high = 0.035f; + auto m = GPUMatrix::RandomUniform(768, 50, c_deviceIdZero, low, high, IncrementCounter()); + unique_ptr result(m.CopyToArray()); + + for (int i = 0; i < 768 * 50; ++i) + { + BOOST_CHECK_LE(result[i], high); + BOOST_CHECK_GT(result[i], low); + } + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixColumnSlice, RandomSeedFixture) + { + std::array array = { + 1, 4, 2, + 5, 3, 6 + }; + GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + GPUMatrix m1(2, 2, c_deviceIdZero, array.data(), matrixFlagNormal); + + GPUMatrix m2 = m0.ColumnSlice(0, 2); + BOOST_CHECK(m2.IsEqualTo(m1)); + + std::array array3 = { array[2], array[3], array[4], array[5] }; + GPUMatrix m3(2, 2, c_deviceIdZero, array3.data(), matrixFlagNormal); + + m2 = m0.ColumnSlice(1, 2); + BOOST_CHECK(m2.IsEqualTo(m3)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixRowSlice, RandomSeedFixture) + { + std::array array0 = { + 1, 2, 3, + 4, 5, 6, + 7, 8, 9, + 10, 11, 12, + 13, 14, 15 + }; + GPUMatrix m0(5, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); + + std::array array1 = { + 3, 4, 8, + 9, 13, 14 + }; + GPUMatrix m1(2, 3, c_deviceIdZero, array1.data(), matrixFlagNormal); + + GPUMatrix m2(c_deviceIdZero); + m2.AssignRowSliceValuesOf(m0, 2, 2); + BOOST_CHECK(m2.IsEqualTo(m1)); + + std::array array3 = { + 0, 0, 3, + 4, 0, 0, + 0, 8, 9, + 0, 0, 0, + 13, 14, 0 + }; + GPUMatrix m3(5, 3, c_deviceIdZero, array3.data(), matrixFlagNormal); + + m3 += m0; + m0.AddToRowSliceValuesOf(m1, 2, 2); + BOOST_CHECK(m3.IsEqualTo(m0)); + + m2.AddWithRowSliceValuesOf(m1, 0, 2); + std::array array4 = { + 6, 8, 16, + 18, 26, 28 + }; + GPUMatrix m4(2, 3, c_deviceIdZero, array4.data(), matrixFlagNormal); + BOOST_CHECK(m2.IsEqualTo(m4)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixKhatriRaoProduct, RandomSeedFixture) + { + std::array arrayA = { + 0.8147f, 0.9058f, 0.1270f, 0.9134f, + 0.6324f, 0.0975f, 0.2785f, 0.5469f, + 0.9575f, 0.9649f, 0.1576f, 0.9706f + }; + GPUMatrix a(3, 4, c_deviceIdZero, arrayA.data()); + + std::array arrayB = { + 0.9572f, 0.4854f, 0.8003f, 0.1419f, + 0.4218f, 0.9157f, 0.7922f, 0.9595f + }; + GPUMatrix b(2, 4, c_deviceIdZero, arrayB.data()); + + std::array arrayD = { + 0.7798f, 0.8670f, 0.1215f, 0.3954f, + 0.4396f, 0.0616f, 0.7310f, 0.5061f, + 0.0781f, 0.1296f, 0.0897f, 0.0138f, + 0.1175f, 0.2307f, 0.4038f, 0.2550f, + 0.5008f, 0.8768f, 0.7644f, 0.1249f, + 0.7689f, 0.9258f, 0.1512f, 0.9313f + }; + GPUMatrix d(6, 4, c_deviceIdZero, arrayD.data()); + + GPUMatrix c(c_deviceIdZero); + c.AssignKhatriRaoProductOf(a, b); + BOOST_CHECK(c.IsEqualTo(d, c_epsilonFloatE4)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixAddColumnReshapeProductOf, RandomSeedFixture) + { + // tests column-wise reshaped product. Used to compute KhatriRaoProduct Gradient + std::array arrayA = { + 0.6557f, 0.0357f, + 0.8491f, 0.9340f, + 0.6787f, 0.7577f, + 0.7431f, 0.3922f, + 0.6555f, 0.1712f, + 0.7060f, 0.0318f, + }; + GPUMatrix a(6, 2, c_deviceIdZero, arrayA.data()); + + std::array arrayB = { + 0.2769f, 0.0462f, + 0.0971f, 0.8235f, + 0.6948f, 0.3171f + }; + GPUMatrix b(3, 2, c_deviceIdZero, arrayB.data()); + + std::array arrayD0 = { + 0.2867f, 0.1266f, + 1.2913f, 0.4520f + }; + GPUMatrix d0(2, 2, c_deviceIdZero, arrayD0.data()); + + std::array arrayD1 = { + 0.2657f, 0.3636f, + 1.0923f, 0.6416f + }; + GPUMatrix d1(2, 2, c_deviceIdZero, arrayD1.data()); + + GPUMatrix c(2, 2, c_deviceIdZero); + c.SetValue(0.0f); + c.AddColumnReshapeProductOf(a, b, false); + BOOST_CHECK(c.IsEqualTo(d0, c_epsilonFloatE4)); + + c.SetValue(0.0f); + c.AddColumnReshapeProductOf(a, b, true); + BOOST_CHECK(c.IsEqualTo(d1, c_epsilonFloatE4)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixInnerProduct, RandomSeedFixture) + { + std::array array = { + 1, 4, 2, + 5, 3, 6 + }; + GPUMatrix m0(2, 3, c_deviceIdZero, array.data(), matrixFlagNormal); + + GPUMatrix m1(c_deviceIdZero), m2(c_deviceIdZero); + m1.AssignInnerProductOf(m0, m0, true); + m2.AssignVectorNorm2Of(m0, true); + m1.InplaceSqrt(); + BOOST_CHECK(m1.IsEqualTo(m2)); + + m1.AssignInnerProductOf(m0, m0, false); + m2.AssignVectorNorm2Of(m0, false); + m1.InplaceSqrt(); + BOOST_CHECK(m1.IsEqualTo(m2)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixAssignRepeatOf, RandomSeedFixture) + { + std::array array0 = { + 1, 2, + 6, 7, + 11, 12 + }; + GPUMatrix m0(2, 3, c_deviceIdZero, array0.data(), matrixFlagNormal); + + GPUMatrix m1(c_deviceIdZero); + m1.AssignRepeatOf(m0, 1, 1); + BOOST_CHECK(m1.IsEqualTo(m0)); + + std::array array2 = { + 1, 2, 1, 2, 1, 2, + 6, 7, 6, 7, 6, 7, + 11, 12, 11, 12, 11, 12, + 1, 2, 1, 2, 1, 2, + 6, 7, 6, 7, 6, 7, + 11, 12, 11, 12, 11, 12 + }; + GPUMatrix m2(6, 6, c_deviceIdZero, array2.data(), matrixFlagNormal); + + m1.AssignRepeatOf(m0, 3, 2); + BOOST_CHECK(m1.IsEqualTo(m2)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixRowElementOperations, RandomSeedFixture) + { + GPUMatrix m0 = GPUMatrix::RandomUniform(20, 28, c_deviceIdZero, -1, 1, IncrementCounter()); + GPUMatrix m1 = GPUMatrix::RandomUniform(1, 28, c_deviceIdZero, 1, 2, IncrementCounter()); + + GPUMatrix m2(c_deviceIdZero); + m2.SetValue(m0); + m2.RowElementMultiplyWith(m1); + m2.RowElementDivideBy(m1); + + BOOST_CHECK(m0.IsEqualTo(m2, c_epsilonFloatE4)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixColumnElementOperations, RandomSeedFixture) + { + GPUMatrix m0 = GPUMatrix::RandomUniform(20, 28, c_deviceIdZero, -1, 1, IncrementCounter()); + GPUMatrix m1 = GPUMatrix::RandomUniform(20, 1, c_deviceIdZero, 1, 2, IncrementCounter()); + + GPUMatrix m2(c_deviceIdZero); + m2.SetValue(m0); + m2.ColumnElementMultiplyWith(m1); + m2.ColumnElementDivideBy(m1); + + BOOST_CHECK(m0.IsEqualTo(m2, c_epsilonFloatE4)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixCurandSeedingFloat, RandomSeedFixture) + { + const float low = 0; + const float high = 1; + const unsigned long seedUsed = 1; + const unsigned long seedIgnored = 4711; + + // The current GPUMatrix implementation uses a static RNG. + + GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); + auto m1 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); + + GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); + auto m2 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); + + BOOST_CHECK(m1.IsEqualTo(m2)); + } + + BOOST_FIXTURE_TEST_CASE(GPUMatrixCurandSeedingDouble, RandomSeedFixture) + { + const double low = 0; + const double high = 1; + const unsigned long seedUsed = 1; + const unsigned long seedIgnored = 4711; + + // The current GPUMatrix implementation uses a static RNG. + + GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); + auto m1 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); + + GPUMatrix::ResetCurandObject(seedUsed, __FUNCTION__); + auto m2 = GPUMatrix::RandomUniform(16, 16, c_deviceIdZero, low, high, seedIgnored); + + BOOST_CHECK(m1.IsEqualTo(m2)); + } + +#if 0 // Temporarily disabling + BOOST_FIXTURE_TEST_CASE(GPUMatrixLargeInequality, RandomSeedFixture) + { + const int rows = 33553921; + const int cols = 1; + + auto m0 = GPUMatrix::Zeros(rows, cols, c_deviceIdZero); + auto m1 = GPUMatrix::Ones(rows, cols, c_deviceIdZero); + + BOOST_CHECK(!m1.IsEqualTo(m0, c_epsilonFloatE5)); + } +#endif + + BOOST_AUTO_TEST_SUITE_END() + } + } + } +} diff --git a/Tests/UnitTests/MathTests/MathTests.vcxproj b/Tests/UnitTests/MathTests/MathTests.vcxproj index 74f1a807a48b..a7a19536bdc2 100644 --- a/Tests/UnitTests/MathTests/MathTests.vcxproj +++ b/Tests/UnitTests/MathTests/MathTests.vcxproj @@ -1,161 +1,161 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {4701E678-5E6F-470D-B348-9CD1A2C095D1} - Win32Proj - MathTests - - - - Application - true - v120 - Unicode - false - - - - - true - - - - - false - - - - - Application - false - v120 - true - Unicode - false - - - - - - - - - - - - - - true - $(IncludePath) - $(Platform)\$(Configuration)\$(ProjectName)\ - $(LibraryPath) - $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ - - - true - $(IncludePath);$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); - $(Platform)\$(Configuration)\$(ProjectName)\ - $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ - - - - NotUsing - Level4 - Disabled - $(BOOST_INCLUDE_PATH);..\..\..\Source\Common\include\;%(AdditionalIncludeDirectories) - WIN32;_DEBUG;%(PreprocessorDefinitions) - true - true - true - - - Console - true - $(OutDir)..\;$(BOOST_LIB_PATH);%(AdditionalLibraryDirectories) - Math.lib;%(AdditionalDependencies) - - - 64 - compute_20,sm_20;compute_30,sm_30;%(CodeGeneration) - - - - - Level4 - Use - MaxSpeed - true - true - $(BOOST_INCLUDE_PATH);..\..\..\Source\Common\include;%(AdditionalIncludeDirectories) - WIN32;NDEBUG;%(PreprocessorDefinitions) - true - true - true - false - /d2Zi+ %(AdditionalOptions) - - - Console - true - true - true - $(OutDir)..\;$(BOOST_LIB_PATH);%(AdditionalLibraryDirectories) - Math.lib;%(AdditionalDependencies) - - - - - - - - - - - - - - - - - - - - - - - - - Create - - - - - - - - - - - - - - $(OutDir)..\cudnn64_4.dll - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {4701E678-5E6F-470D-B348-9CD1A2C095D1} + Win32Proj + MathTests + + + + Application + true + v120 + Unicode + false + + + + + true + + + + + false + + + + + Application + false + v120 + true + Unicode + false + + + + + + + + + + + + + + true + $(IncludePath) + $(Platform)\$(Configuration)\$(ProjectName)\ + $(LibraryPath) + $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ + + + true + $(IncludePath);$(VCInstallDir)include;$(VCInstallDir)atlmfc\include;$(WindowsSDK_IncludePath); + $(Platform)\$(Configuration)\$(ProjectName)\ + $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ + + + + NotUsing + Level4 + Disabled + $(BOOST_INCLUDE_PATH);..\..\..\Source\Common\include\;%(AdditionalIncludeDirectories) + WIN32;_DEBUG;%(PreprocessorDefinitions) + true + true + true + + + Console + true + $(OutDir)..\;$(BOOST_LIB_PATH);%(AdditionalLibraryDirectories) + Math.lib;%(AdditionalDependencies) + + + 64 + compute_20,sm_20;compute_30,sm_30;%(CodeGeneration) + + + + + Level4 + Use + MaxSpeed + true + true + $(BOOST_INCLUDE_PATH);..\..\..\Source\Common\include;%(AdditionalIncludeDirectories) + WIN32;NDEBUG;%(PreprocessorDefinitions) + true + true + true + false + /d2Zi+ %(AdditionalOptions) + + + Console + true + true + true + $(OutDir)..\;$(BOOST_LIB_PATH);%(AdditionalLibraryDirectories) + Math.lib;%(AdditionalDependencies) + + + + + + + + + + + + + + + + + + + + + + + + + Create + + + + + + + + + + + + + + $(OutDir)..\cudnn64_4.dll + + + + + + + + \ No newline at end of file diff --git a/Tests/UnitTests/MathTests/MatrixDataSynchronizationTests.cpp b/Tests/UnitTests/MathTests/MatrixDataSynchronizationTests.cpp index 04ebdf2170cc..b5e6eb94c937 100644 --- a/Tests/UnitTests/MathTests/MatrixDataSynchronizationTests.cpp +++ b/Tests/UnitTests/MathTests/MatrixDataSynchronizationTests.cpp @@ -1,121 +1,121 @@ -// -// -// Copyright (c) Microsoft Corporation. All rights reserved. -// -// -#include "stdafx.h" -#include "../../../Source/Math/Matrix.h" -#include "../../../Source/Math/Helpers.h" - -using namespace Microsoft::MSR::CNTK; - -namespace Microsoft -{ - namespace MSR - { - namespace CNTK - { - namespace Test - { +// +// +// Copyright (c) Microsoft Corporation. All rights reserved. +// +// +#include "stdafx.h" +#include "../../../Source/Math/Matrix.h" +#include "../../../Source/Math/Helpers.h" + +using namespace Microsoft::MSR::CNTK; + +namespace Microsoft +{ + namespace MSR + { + namespace CNTK + { + namespace Test + { BOOST_AUTO_TEST_SUITE(GPUMatrixSuite) - - // Requires GPU - BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_DefaultBehaviorTestForConstructors, RandomSeedFixture) - { - const SingleMatrix matrixA; - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(0, matrixA.GetNumCols()); - BOOST_CHECK_EQUAL(0, matrixA.GetNumRows()); - - const SingleMatrix matrixA1(AUTOPLACEMATRIX); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA1.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(0, matrixA1.GetNumCols()); - BOOST_CHECK_EQUAL(0, matrixA1.GetNumRows()); - - const SingleMatrix matrixA2(static_cast(13), static_cast(12)); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA2.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(12, matrixA2.GetNumCols()); - BOOST_CHECK_EQUAL(13, matrixA2.GetNumRows()); - - float arr[5 * 45]; - const SingleMatrix matrixA3(5, 45, arr, matrixFlagNormal); - BOOST_CHECK(CurrentDataLocation::GPU == matrixA3.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(45, matrixA3.GetNumCols()); - BOOST_CHECK_EQUAL(5, matrixA3.GetNumRows()); - } - - // Requires GPU - BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_AccessPatternAndTransferTest, RandomSeedFixture) - { - float arr[5 * 45]; - const SingleMatrix matrixA(5, 45, arr, matrixFlagNormal); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(45, matrixA.GetNumCols()); - BOOST_CHECK_EQUAL(5, matrixA.GetNumRows()); - - float x = matrixA(1, 1); - BOOST_CHECK(matrixA.GetCurrentMatrixLocation() != CurrentDataLocation::GPU); - foreach_coord(i, j, matrixA) - { - x = matrixA(i, j); - BOOST_CHECK(matrixA.GetCurrentMatrixLocation() != CurrentDataLocation::GPU); - } - - SingleMatrix matrixB(15, 15, arr, matrixFlagNormal); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(15, matrixB.GetNumCols()); - BOOST_CHECK_EQUAL(15, matrixB.GetNumRows()); - - x = matrixB(1, 1); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); - matrixB(4, 2) = x; - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); - foreach_coord(i, j, matrixB) - { - x = matrixB(i, j); - matrixB(j, i) = x; - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); - } - - matrixB.TransferFromDeviceToDevice(-1, 0, false); - BOOST_CHECK_EQUAL(CurrentDataLocation::BOTH, matrixB.GetCurrentMatrixLocation()); - matrixB.TransferFromDeviceToDevice(0, -1, false); - BOOST_CHECK_EQUAL(CurrentDataLocation::BOTH, matrixB.GetCurrentMatrixLocation()); - matrixB.TransferFromDeviceToDevice(-1, 0, true); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); - matrixB.TransferFromDeviceToDevice(0, -1, true); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); - - const SingleMatrix matrixC(-1); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixC.GetCurrentMatrixLocation()); - - const SingleMatrix matrixD(15, 15, arr, matrixFlagNormal, -1); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixD.GetCurrentMatrixLocation()); - } - - // Requires GPU - BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_GravitatingTowardsPreferredDevice, RandomSeedFixture) - { - SingleMatrix matrixA = SingleMatrix::RandomGaussian(64, 23, 0, 2, IncrementCounter()); - SingleMatrix matrixB = SingleMatrix::Eye(23); - - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); - - // Set the current matrix location by reading a value of the matrix - float x = matrixA(1, 1); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixA.GetCurrentMatrixLocation()); - x = matrixB(1, 1); - BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); - - const SingleMatrix matrixC = matrixA * matrixB; - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); - BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixC.GetCurrentMatrixLocation()); - } - - BOOST_AUTO_TEST_SUITE_END() - } - } - } -} + + // Requires GPU + BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_DefaultBehaviorTestForConstructors, RandomSeedFixture) + { + const SingleMatrix matrixA; + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(0, matrixA.GetNumCols()); + BOOST_CHECK_EQUAL(0, matrixA.GetNumRows()); + + const SingleMatrix matrixA1(AUTOPLACEMATRIX); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA1.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(0, matrixA1.GetNumCols()); + BOOST_CHECK_EQUAL(0, matrixA1.GetNumRows()); + + const SingleMatrix matrixA2(static_cast(13), static_cast(12)); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA2.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(12, matrixA2.GetNumCols()); + BOOST_CHECK_EQUAL(13, matrixA2.GetNumRows()); + + float arr[5 * 45]; + const SingleMatrix matrixA3(5, 45, arr, matrixFlagNormal); + BOOST_CHECK(CurrentDataLocation::GPU == matrixA3.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(45, matrixA3.GetNumCols()); + BOOST_CHECK_EQUAL(5, matrixA3.GetNumRows()); + } + + // Requires GPU + BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_AccessPatternAndTransferTest, RandomSeedFixture) + { + float arr[5 * 45]; + const SingleMatrix matrixA(5, 45, arr, matrixFlagNormal); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(45, matrixA.GetNumCols()); + BOOST_CHECK_EQUAL(5, matrixA.GetNumRows()); + + float x = matrixA(1, 1); + BOOST_CHECK(matrixA.GetCurrentMatrixLocation() != CurrentDataLocation::GPU); + foreach_coord(i, j, matrixA) + { + x = matrixA(i, j); + BOOST_CHECK(matrixA.GetCurrentMatrixLocation() != CurrentDataLocation::GPU); + } + + SingleMatrix matrixB(15, 15, arr, matrixFlagNormal); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(15, matrixB.GetNumCols()); + BOOST_CHECK_EQUAL(15, matrixB.GetNumRows()); + + x = matrixB(1, 1); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); + matrixB(4, 2) = x; + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); + foreach_coord(i, j, matrixB) + { + x = matrixB(i, j); + matrixB(j, i) = x; + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); + } + + matrixB.TransferFromDeviceToDevice(-1, 0, false); + BOOST_CHECK_EQUAL(CurrentDataLocation::BOTH, matrixB.GetCurrentMatrixLocation()); + matrixB.TransferFromDeviceToDevice(0, -1, false); + BOOST_CHECK_EQUAL(CurrentDataLocation::BOTH, matrixB.GetCurrentMatrixLocation()); + matrixB.TransferFromDeviceToDevice(-1, 0, true); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); + matrixB.TransferFromDeviceToDevice(0, -1, true); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); + + const SingleMatrix matrixC(-1); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixC.GetCurrentMatrixLocation()); + + const SingleMatrix matrixD(15, 15, arr, matrixFlagNormal, -1); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixD.GetCurrentMatrixLocation()); + } + + // Requires GPU + BOOST_FIXTURE_TEST_CASE(MatrixDataSynchronization_GravitatingTowardsPreferredDevice, RandomSeedFixture) + { + SingleMatrix matrixA = SingleMatrix::RandomGaussian(64, 23, 0, 2, IncrementCounter()); + SingleMatrix matrixB = SingleMatrix::Eye(23); + + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); + + // Set the current matrix location by reading a value of the matrix + float x = matrixA(1, 1); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixA.GetCurrentMatrixLocation()); + x = matrixB(1, 1); + BOOST_CHECK_EQUAL(CurrentDataLocation::CPU, matrixB.GetCurrentMatrixLocation()); + + const SingleMatrix matrixC = matrixA * matrixB; + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixA.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixB.GetCurrentMatrixLocation()); + BOOST_CHECK_EQUAL(CurrentDataLocation::GPU, matrixC.GetCurrentMatrixLocation()); + } + + BOOST_AUTO_TEST_SUITE_END() + } + } + } +} diff --git a/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj b/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj index c23630ac3bd0..65a818a0ca23 100644 --- a/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj +++ b/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj @@ -1,184 +1,184 @@ - - - - - Debug - x64 - - - Release - x64 - - - - {A4FC3467-4787-43E8-BBC0-D79AE56B468D} - Win32Proj - HTKLMFReaderTests - ReaderTests - - - - Application - true - v120 - Unicode - false - - - - - true - - - - - false - - - - - Application - false - v120 - true - Unicode - false - - - - - - - - - - - - false - ..\..\..\Source\Common\include;..\..\..\Source\Math;$(IncludePath) - $(OutDir);$(LibraryPath) - $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ - $(Platform)\$(Configuration)\$(ProjectName)\ - - - false - ..\..\..\Source\Common\include;..\..\..\Source\Math;$(IncludePath) - $(OutDir);$(LibraryPath) - $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ - $(Platform)\$(Configuration)\$(ProjectName)\ - - - - NotUsing - Level4 - Disabled - WIN32;_DEBUG;%(PreprocessorDefinitions) - $(BOOST_INCLUDE_PATH);%(AdditionalIncludeDirectories) - true - true - true - - - Console - true - $(BOOST_LIB_PATH);$(OutDir)..\; - htkmlfreader.lib;Math.lib;%(AdditionalDependencies) - true - - - - - Level4 - NotUsing - MaxSpeed - true - true - WIN32;NDEBUG;%(PreprocessorDefinitions) - $(BOOST_INCLUDE_PATH);%(AdditionalIncludeDirectories) - true - true - true - true - /d2Zi+ %(AdditionalOptions) - - - Console - true - true - true - $(BOOST_LIB_PATH);$(OutDir)..\; - htkmlfreader.lib;Math.lib;%(AdditionalDependencies) - - - - - - - - - - - - - - - - - - Create - Create - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + Debug + x64 + + + Release + x64 + + + + {A4FC3467-4787-43E8-BBC0-D79AE56B468D} + Win32Proj + HTKLMFReaderTests + ReaderTests + + + + Application + true + v120 + Unicode + false + + + + + true + + + + + false + + + + + Application + false + v120 + true + Unicode + false + + + + + + + + + + + + false + ..\..\..\Source\Common\include;..\..\..\Source\Math;$(IncludePath) + $(OutDir);$(LibraryPath) + $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ + $(Platform)\$(Configuration)\$(ProjectName)\ + + + false + ..\..\..\Source\Common\include;..\..\..\Source\Math;$(IncludePath) + $(OutDir);$(LibraryPath) + $(SolutionDir)$(Platform)\$(Configuration)\UnitTests\ + $(Platform)\$(Configuration)\$(ProjectName)\ + + + + NotUsing + Level4 + Disabled + WIN32;_DEBUG;%(PreprocessorDefinitions) + $(BOOST_INCLUDE_PATH);%(AdditionalIncludeDirectories) + true + true + true + + + Console + true + $(BOOST_LIB_PATH);$(OutDir)..\; + htkmlfreader.lib;Math.lib;%(AdditionalDependencies) + true + + + + + Level4 + NotUsing + MaxSpeed + true + true + WIN32;NDEBUG;%(PreprocessorDefinitions) + $(BOOST_INCLUDE_PATH);%(AdditionalIncludeDirectories) + true + true + true + true + /d2Zi+ %(AdditionalOptions) + + + Console + true + true + true + $(BOOST_LIB_PATH);$(OutDir)..\; + htkmlfreader.lib;Math.lib;%(AdditionalDependencies) + + + + + + + + + + + + + + + + + + Create + Create + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj.filters b/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj.filters index 33b865ee43df..f50e08c2193d 100644 --- a/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj.filters +++ b/Tests/UnitTests/ReaderTests/ReaderTests.vcxproj.filters @@ -1,157 +1,157 @@ - - - - - - - Common - - - - - - - Common - - - Common - - - Common - - - Common - - - Common - - - Common - - - - Common - - - - - {c448395c-93e6-46ab-adda-9e09683d7098} - - - {73a7917d-7dae-4d52-89ab-9aa5ab0632d8} - - - {2803cda6-39ec-416a-9054-e5fc7e442626} - - - {024ba6c7-74fe-4a89-a5e9-394e46c13f82} - - - - - Data - - - Data - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Config - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - - Control - - + + + + + + + Common + + + + + + + Common + + + Common + + + Common + + + Common + + + Common + + + Common + + + + Common + + + + + {c448395c-93e6-46ab-adda-9e09683d7098} + + + {73a7917d-7dae-4d52-89ab-9aa5ab0632d8} + + + {2803cda6-39ec-416a-9054-e5fc7e442626} + + + {024ba6c7-74fe-4a89-a5e9-394e46c13f82} + + + + + Data + + + Data + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Config + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + + Control + + \ No newline at end of file