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Examples/Speech/Miscellaneous}/TIMIT/lib/scp/TIMIT.train.scp.mfcc.fullpath.100 (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/README.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/scripts/util.py (100%) mode change 100755 => 100644 rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/global.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/global.s2s.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/lstmlm.gpu.classlm.2stream.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/lstmlm.gpu.classlm.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/lstmlm.gpu.classlm.config.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/lstmlm.gpu.nce.config.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/setups/s2s.class.alignment.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/steps/s1.newscomments.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/steps/s2.kn.lm.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/steps/s3.class.lstm.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/steps/s4.2stream.class.lstm.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/News/steps/s5.s2s.log (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/README.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/conlleval.pl (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/feat.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/globals.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/lbl.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/lstmNDL.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/rnnlu.config (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/rnnluModelEditor.txt (100%) rename {ExampleSetups => Examples/Text/Miscellaneous}/SLU/score.sh (100%) rename {Demos/Text => Examples/Text/PennTreebank}/AdditionalFiles/ExpectedResults.txt (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/CPU/ExpectedOutputs.txt (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/CPU/fnnlm.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/CPU/global.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/CPU/rnnlm.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/GPU/ExpectedOutputs.txt (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/GPU/fnnlm.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/GPU/global.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/GPU/rnnlm.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/GPU/rnnlm.gpu.config (100%) rename {ExampleSetups/LM => Examples/Text/PennTreebank/AdditionalFiles}/RNNLM/rnnlm.ndl (100%) rename {Demos/Text => Examples/Text/PennTreebank}/AdditionalFiles/perplexity.txt (100%) rename {Demos/Text => Examples/Text/PennTreebank}/Config/rnn.config (100%) rename {Demos/Text => Examples/Text/PennTreebank}/README.md (100%) diff --git a/CNTK.sln b/CNTK.sln index 47e76c8fb5cb..421e6310af38 100644 --- a/CNTK.sln +++ b/CNTK.sln @@ -10,9 +10,6 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CNTK", "Source\CNTK\CNTK.vc EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tests", "Tests", "{D45DF403-6781-444E-B654-A96868C5BE68}" - ProjectSection(SolutionItems) = preProject - Tests\TestDriver.py = Tests\TestDriver.py - EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Reader Plugins", "Reader Plugins", "{33EBFE78-A1A8-4961-8938-92A271941F94}" EndProject @@ -386,82 +383,19 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple", "Simple", "{81AE01 Tests\EndToEndTests\Speech\Simple\testcases.yml = Tests\EndToEndTests\Speech\Simple\testcases.yml EndProjectSection EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Demos", "Demos", "{47755F2E-D674-4175-9E38-8EA053455072}" +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Examples", "Examples", "{47755F2E-D674-4175-9E38-8EA053455072}" ProjectSection(SolutionItems) = preProject - Demos\README.md = Demos\README.md + Examples\README.md = Examples\README.md EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{9BDFA4BE-790E-408F-915B-5979BB5078C6}" ProjectSection(SolutionItems) = preProject - Demos\Image\README.md = Demos\Image\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{C0677F34-0267-44C9-8460-6D3A9A277DF6}" - ProjectSection(SolutionItems) = preProject - Demos\Simple2d\README.md = Demos\Simple2d\README.md + Examples\Image\README.md = Examples\Image\README.md EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", "{3CE841C0-02E5-46DB-B401-6F8784880173}" - ProjectSection(SolutionItems) = preProject - Demos\Speech\README.md = Demos\Speech\README.md - EndProjectSection EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Text", "Text", "{97AAB0C8-D553-49CB-A539-004FCD7FD59F}" - ProjectSection(SolutionItems) = preProject - Demos\Text\README.md = Demos\Text\README.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{B478F99A-D37F-4169-9493-5A312019B1EF}" - ProjectSection(SolutionItems) = preProject - Demos\Simple2d\Data\SimpleDataTest.txt = Demos\Simple2d\Data\SimpleDataTest.txt - Demos\Simple2d\Data\SimpleDataTrain.txt = Demos\Simple2d\Data\SimpleDataTrain.txt - Demos\Simple2d\Data\SimpleMapping.txt = Demos\Simple2d\Data\SimpleMapping.txt - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{4258EAB1-C16E-4EE7-9640-A41F53F5072D}" - ProjectSection(SolutionItems) = preProject - Demos\Simple2d\Config\Multigpu.config = Demos\Simple2d\Config\Multigpu.config - Demos\Simple2d\Config\Simple.config = Demos\Simple2d\Config\Simple.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{88A27DB7-181B-4823-BEB3-B9A7E57024F9}" - ProjectSection(SolutionItems) = preProject - Demos\Simple2d\AdditionalFiles\MakeData.m = Demos\Simple2d\AdditionalFiles\MakeData.m - Demos\Simple2d\AdditionalFiles\SimpleDemoDataReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoDataReference.png - Demos\Simple2d\AdditionalFiles\SimpleDemoErrorRateReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoErrorRateReference.png - Demos\Simple2d\AdditionalFiles\SimpleDemoOutputReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoOutputReference.png - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{8BBD0355-B450-406B-814B-E58792C0F7FE}" - ProjectSection(SolutionItems) = preProject - Demos\Speech\Data\000000000.chunk = Demos\Speech\Data\000000000.chunk - Demos\Speech\Data\glob_0000.mlf = Demos\Speech\Data\glob_0000.mlf - Demos\Speech\Data\glob_0000.scp = Demos\Speech\Data\glob_0000.scp - Demos\Speech\Data\state.list = Demos\Speech\Data\state.list - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{C613F7AC-B729-43B9-BA31-9A943F96DA55}" - ProjectSection(SolutionItems) = preProject - Demos\Speech\Config\FeedForward.config = Demos\Speech\Config\FeedForward.config - Demos\Speech\Config\LSTM-NDL.config = Demos\Speech\Config\LSTM-NDL.config - Demos\Speech\Config\lstmp-3layer-opt.ndl = Demos\Speech\Config\lstmp-3layer-opt.ndl - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{EDE295D1-37F6-48C2-A5AF-8FC66BF20E68}" - ProjectSection(SolutionItems) = preProject - Demos\Speech\AdditionalFiles\AN4.LICENSE.html = Demos\Speech\AdditionalFiles\AN4.LICENSE.html - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{A07A002C-A05C-477C-9DED-F01272724C6E}" - ProjectSection(SolutionItems) = preProject - Demos\Text\Config\rnn.config = Demos\Text\Config\rnn.config - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{8EBD7E6F-415C-4B6A-927C-1AF82905B16C}" - ProjectSection(SolutionItems) = preProject - Demos\Text\AdditionalFiles\ExpectedResults.txt = Demos\Text\AdditionalFiles\ExpectedResults.txt - Demos\Text\AdditionalFiles\perplexity.txt = Demos\Text\AdditionalFiles\perplexity.txt - EndProjectSection EndProject Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ReaderTests", "Tests\UnitTests\ReaderTests\ReaderTests.vcxproj", "{A4FC3467-4787-43E8-BBC0-D79AE56B468D}" ProjectSection(ProjectDependencies) = postProject @@ -572,6 +506,248 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{BFBC6BE1-C33 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\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 + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{CCD56F12-BA17-4753-B5EE-4995FE682995}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{D2A060F1-128E-42A1-A0D0-3E3E1DFBC427}" + ProjectSection(SolutionItems) = preProject + Examples\Miscellaneous\Simple2d\README.md = Examples\Miscellaneous\Simple2d\README.md + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NdlExamples", "NdlExamples", "{FC573A62-6DAE-40A4-8153-520C8571A007}" + ProjectSection(SolutionItems) = preProject + Examples\Miscellaneous\NdlExamples\NDLExamples.ndl = Examples\Miscellaneous\NdlExamples\NDLExamples.ndl + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{1E37CE40-556D-4693-B58C-F8D4CE349BB7}" + ProjectSection(SolutionItems) = preProject + Examples\Miscellaneous\Simple2d\Config\Multigpu.config = Examples\Miscellaneous\Simple2d\Config\Multigpu.config + Examples\Miscellaneous\Simple2d\Config\Simple.config = Examples\Miscellaneous\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}") = "G2P", "G2P", "{7FC6C635-D664-4244-9804-962165C3B037}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\G2P\README.txt = Examples\Speech\Miscellaneous\G2P\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}") = "setups", "setups", "{654756BA-9A00-4225-A2BF-BA4547801848}" + ProjectSection(SolutionItems) = preProject + Examples\Speech\Miscellaneous\G2P\setups\bilstm.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.config + Examples\Speech\Miscellaneous\G2P\setups\bilstm.indstreams.joint.conditional.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.indstreams.joint.conditional.config + Examples\Speech\Miscellaneous\G2P\setups\bilstm.ndl.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.ndl.config + Examples\Speech\Miscellaneous\G2P\setups\global.lstm.config = Examples\Speech\Miscellaneous\G2P\setups\global.lstm.config + Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.lw7.conditional.mb100.fw6.config = Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.lw7.conditional.mb100.fw6.config + Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.mb100.noemb.config = Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.mb100.noemb.config + Examples\Speech\Miscellaneous\G2P\setups\lstm.ndl = Examples\Speech\Miscellaneous\G2P\setups\lstm.ndl + Examples\Speech\Miscellaneous\G2P\setups\s2s.mpd.rnd.hiddenstate.2nets.500.100mb.2layers.config = Examples\Speech\Miscellaneous\G2P\setups\s2s.mpd.rnd.hiddenstate.2nets.500.100mb.2layers.config + 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}") = "News", "News", "{52556781-2778-4BA0-9A0C-1FA0C92B3896}" + ProjectSection(SolutionItems) = preProject + Examples\Text\Miscellaneous\News\README.txt = Examples\Text\Miscellaneous\News\README.txt + 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}") = "SMT", "SMT", "{AED49E07-AC6B-4EE0-81AC-A572ABCA0518}" + ProjectSection(SolutionItems) = preProject + Examples\Text\Miscellaneous\SMT\README.txt = Examples\Text\Miscellaneous\SMT\README.txt + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "setups", "setups", "{906E41CB-F603-4E83-98D8-4F77350124BC}" + ProjectSection(SolutionItems) = preProject + Examples\Text\Miscellaneous\News\setups\global.config = Examples\Text\Miscellaneous\News\setups\global.config + Examples\Text\Miscellaneous\News\setups\global.s2s.config = Examples\Text\Miscellaneous\News\setups\global.s2s.config + Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.2stream.config = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.2stream.config + Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config + Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config.txt = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config.txt + Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.nce.config.txt = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.nce.config.txt + Examples\Text\Miscellaneous\News\setups\s2s.class.alignment.config = Examples\Text\Miscellaneous\News\setups\s2s.class.alignment.config + EndProjectSection +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "setups", "setups", "{7734DDA6-5997-4F43-BC99-DF0495DA5D1D}" + ProjectSection(SolutionItems) = preProject + Examples\Text\Miscellaneous\SMT\setups\global.config = Examples\Text\Miscellaneous\SMT\setups\global.config + Examples\Text\Miscellaneous\SMT\setups\global.cs-en.config = Examples\Text\Miscellaneous\SMT\setups\global.cs-en.config + Examples\Text\Miscellaneous\SMT\setups\global.local.config = Examples\Text\Miscellaneous\SMT\setups\global.local.config + Examples\Text\Miscellaneous\SMT\setups\s2s.2layers.config = Examples\Text\Miscellaneous\SMT\setups\s2s.2layers.config + Examples\Text\Miscellaneous\SMT\setups\s2s.class.alignment.config = Examples\Text\Miscellaneous\SMT\setups\s2s.class.alignment.config + Examples\Text\Miscellaneous\SMT\setups\s2s.class.config = Examples\Text\Miscellaneous\SMT\setups\s2s.class.config + Examples\Text\Miscellaneous\SMT\setups\s2sfb.class.alignment.config = Examples\Text\Miscellaneous\SMT\setups\s2sfb.class.alignment.config + EndProjectSection +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|x64 = Debug|x64 @@ -707,17 +883,8 @@ Global {81AE014F-DD63-47C7-B6E2-DB1D2833DCD1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8} {47755F2E-D674-4175-9E38-8EA053455072} = {39E42C4B-A078-4CA4-9D92-B883D8129601} {9BDFA4BE-790E-408F-915B-5979BB5078C6} = {47755F2E-D674-4175-9E38-8EA053455072} - {C0677F34-0267-44C9-8460-6D3A9A277DF6} = {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} - {B478F99A-D37F-4169-9493-5A312019B1EF} = {C0677F34-0267-44C9-8460-6D3A9A277DF6} - {4258EAB1-C16E-4EE7-9640-A41F53F5072D} = {C0677F34-0267-44C9-8460-6D3A9A277DF6} - {88A27DB7-181B-4823-BEB3-B9A7E57024F9} = {C0677F34-0267-44C9-8460-6D3A9A277DF6} - {8BBD0355-B450-406B-814B-E58792C0F7FE} = {3CE841C0-02E5-46DB-B401-6F8784880173} - {C613F7AC-B729-43B9-BA31-9A943F96DA55} = {3CE841C0-02E5-46DB-B401-6F8784880173} - {EDE295D1-37F6-48C2-A5AF-8FC66BF20E68} = {3CE841C0-02E5-46DB-B401-6F8784880173} - {A07A002C-A05C-477C-9DED-F01272724C6E} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} - {8EBD7E6F-415C-4B6A-927C-1AF82905B16C} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F} {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} @@ -739,5 +906,37 @@ Global {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} + {7FC6C635-D664-4244-9804-962165C3B037} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830} + {439F8840-309E-4B7F-BCEC-196E0EADE949} = {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} + {654756BA-9A00-4225-A2BF-BA4547801848} = {7FC6C635-D664-4244-9804-962165C3B037} + {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} + {52556781-2778-4BA0-9A0C-1FA0C92B3896} = {8C128B1D-87E0-4643-AB93-2581589AE425} + {E6DC3B7D-303D-4A54-B040-D8DCF8C56E17} = {8C128B1D-87E0-4643-AB93-2581589AE425} + {AED49E07-AC6B-4EE0-81AC-A572ABCA0518} = {8C128B1D-87E0-4643-AB93-2581589AE425} + {906E41CB-F603-4E83-98D8-4F77350124BC} = {52556781-2778-4BA0-9A0C-1FA0C92B3896} + {7734DDA6-5997-4F43-BC99-DF0495DA5D1D} = {AED49E07-AC6B-4EE0-81AC-A572ABCA0518} EndGlobalSection EndGlobal diff --git a/ExampleSetups/LM/LSTMLM/ExpectedResults.log b/ExampleSetups/LM/LSTMLM/ExpectedResults.log deleted file mode 100644 index cb7cf8f66b2a..000000000000 --- a/ExampleSetups/LM/LSTMLM/ExpectedResults.log +++ /dev/null @@ -1,56 +0,0 @@ -$ grep -a --text Finish gpulstmlm_train_test.log | grep Train -Finished Epoch[1]: [Training Set] Train Loss Per Sample = 6.0689292 EvalErr Per Sample = 6.0689292 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.42099 -Finished Epoch[1]: [Validation Set] Train Loss Per Sample = 5.7789721 EvalErr Per Sample = 5.7789721 -Finished Epoch[2]: [Training Set] Train Loss Per Sample = 5.6369767 EvalErr Per Sample = 5.6369767 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.62399 -Finished Epoch[2]: [Validation Set] Train Loss Per Sample = 5.5511756 EvalErr Per Sample = 5.5511756 -Finished Epoch[3]: [Training Set] Train Loss Per Sample = 5.4327216 EvalErr Per Sample = 5.4327216 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.52399 -Finished Epoch[3]: [Validation Set] Train Loss Per Sample = 5.4396544 EvalErr Per Sample = 5.4396544 -Finished Epoch[4]: [Training Set] Train Loss Per Sample = 5.2914305 EvalErr Per Sample = 5.2914305 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=503.44199 -Finished Epoch[4]: [Validation Set] Train Loss Per Sample = 5.345387 EvalErr Per Sample = 5.345387 -Finished Epoch[5]: [Training Set] Train Loss Per Sample = 5.1821184 EvalErr Per Sample = 5.1821184 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=508.905 -Finished Epoch[5]: [Validation Set] Train Loss Per Sample = 5.2927375 EvalErr Per Sample = 5.2927375 -Finished Epoch[6]: [Training Set] Train Loss Per Sample = 5.0916734 EvalErr Per Sample = 5.0916734 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=522.10303 -Finished Epoch[6]: [Validation Set] Train Loss Per Sample = 5.2429872 EvalErr Per Sample = 5.2429872 -Finished Epoch[7]: [Training Set] Train Loss Per Sample = 5.0149188 EvalErr Per Sample = 5.0149188 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=526.94702 -Finished Epoch[7]: [Validation Set] Train Loss Per Sample = 5.1904435 EvalErr Per Sample = 5.1904435 -Finished Epoch[8]: [Training Set] Train Loss Per Sample = 4.9472914 EvalErr Per Sample = 4.9472914 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=544.46198 -Finished Epoch[8]: [Validation Set] Train Loss Per Sample = 5.1749911 EvalErr Per Sample = 5.1749911 -Finished Epoch[9]: [Training Set] Train Loss Per Sample = 4.8864255 EvalErr Per Sample = 4.8864255 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=531.33002 -Finished Epoch[9]: [Validation Set] Train Loss Per Sample = 5.1553888 EvalErr Per Sample = 5.1553888 -Finished Epoch[10]: [Training Set] Train Loss Per Sample = 4.831315 EvalErr Per Sample = 4.831315 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=523.29999 -Finished Epoch[10]: [Validation Set] Train Loss Per Sample = 5.1265602 EvalErr Per Sample = 5.1265602 -Finished Epoch[11]: [Training Set] Train Loss Per Sample = 4.7812014 EvalErr Per Sample = 4.7812014 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=521.64697 -Finished Epoch[11]: [Validation Set] Train Loss Per Sample = 5.1217532 EvalErr Per Sample = 5.1217532 -Finished Epoch[12]: [Training Set] Train Loss Per Sample = 4.6650786 EvalErr Per Sample = 4.6650786 Ave Learn Rate Per Sample = 0.004999999888 Epoch Time=523.90198 -Finished Epoch[12]: [Validation Set] Train Loss Per Sample = 5.0993018 EvalErr Per Sample = 5.0993018 -Finished Epoch[13]: [Training Set] Train Loss Per Sample = 4.5911136 EvalErr Per Sample = 4.5911136 Ave Learn Rate Per Sample = 0.002499999944 Epoch Time=520.66101 -Finished Epoch[13]: [Validation Set] Train Loss Per Sample = 5.0787692 EvalErr Per Sample = 5.0787692 -Finished Epoch[14]: [Training Set] Train Loss Per Sample = 4.5472517 EvalErr Per Sample = 4.5472517 Ave Learn Rate Per Sample = 0.001249999972 Epoch Time=517.742 -Finished Epoch[14]: [Validation Set] Train Loss Per Sample = 5.0703268 EvalErr Per Sample = 5.0703268 -Finished Epoch[15]: [Training Set] Train Loss Per Sample = 4.5230498 EvalErr Per Sample = 4.5230498 Ave Learn Rate Per Sample = 0.000624999986 Epoch Time=516.28998 -Finished Epoch[15]: [Validation Set] Train Loss Per Sample = 5.0566416 EvalErr Per Sample = 5.0566416 -Finished Epoch[16]: [Training Set] Train Loss Per Sample = 4.510385 EvalErr Per Sample = 4.510385 Ave Learn Rate Per Sample = 0.000312499993 Epoch Time=513.30499 -Finished Epoch[16]: [Validation Set] Train Loss Per Sample = 5.0524426 EvalErr Per Sample = 5.0524426 - -$ grep -a --text Finish gpulstmlm_train_test.log | grep Validation -Finished Epoch[1]: [Validation Set] Train Loss Per Sample = 5.7789721 EvalErr Per Sample = 5.7789721 -Finished Epoch[2]: [Validation Set] Train Loss Per Sample = 5.5511756 EvalErr Per Sample = 5.5511756 -Finished Epoch[3]: [Validation Set] Train Loss Per Sample = 5.4396544 EvalErr Per Sample = 5.4396544 -Finished Epoch[4]: [Validation Set] Train Loss Per Sample = 5.345387 EvalErr Per Sample = 5.345387 -Finished Epoch[5]: [Validation Set] Train Loss Per Sample = 5.2927375 EvalErr Per Sample = 5.2927375 -Finished Epoch[6]: [Validation Set] Train Loss Per Sample = 5.2429872 EvalErr Per Sample = 5.2429872 -Finished Epoch[7]: [Validation Set] Train Loss Per Sample = 5.1904435 EvalErr Per Sample = 5.1904435 -Finished Epoch[8]: [Validation Set] Train Loss Per Sample = 5.1749911 EvalErr Per Sample = 5.1749911 -Finished Epoch[9]: [Validation Set] Train Loss Per Sample = 5.1553888 EvalErr Per Sample = 5.1553888 -Finished Epoch[10]: [Validation Set] Train Loss Per Sample = 5.1265602 EvalErr Per Sample = 5.1265602 -Finished Epoch[11]: [Validation Set] Train Loss Per Sample = 5.1217532 EvalErr Per Sample = 5.1217532 -Finished Epoch[12]: [Validation Set] Train Loss Per Sample = 5.0993018 EvalErr Per Sample = 5.0993018 -Finished Epoch[13]: [Validation Set] Train Loss Per Sample = 5.0787692 EvalErr Per Sample = 5.0787692 -Finished Epoch[14]: [Validation Set] Train Loss Per Sample = 5.0703268 EvalErr Per Sample = 5.0703268 -Finished Epoch[15]: [Validation Set] Train Loss Per Sample = 5.0566416 EvalErr Per Sample = 5.0566416 -Finished Epoch[16]: [Validation Set] Train Loss Per Sample = 5.0524426 EvalErr Per Sample = 5.0524426 - - -# test -Final Results: Minibatch[1-82430]: Samples Seen = 82430 TrainNodeClassBasedCrossEntropy/Sample = 4.9702353 TrainNodeClassBasedCrossEntropy/Sample = 4.9702353 -corresponding PPL = 144 \ No newline at end of file diff --git a/ExampleSetups/LM/LSTMLM/global.config b/ExampleSetups/LM/LSTMLM/global.config deleted file mode 100644 index fba0cc54053c..000000000000 --- a/ExampleSetups/LM/LSTMLM/global.config +++ /dev/null @@ -1,7 +0,0 @@ -ExpDir=d:\temp\lstm -ConfigDir=D:\zhaoyg\2015-5-6\cntk\ExampleSetups\LM\LSTMLM\ -DataDir=\\speechstore5\transient\kaishengy\data\lm\PennTreeBank - - -# to run this -# ..\..\..\x64\debug\cntk.exe configFile=global.config+lstmlm.gpu.config ExpDir=d:\temp\lm\ptb \ No newline at end of file diff --git a/ExampleSetups/LM/LSTMLM/lstmlm.cpu.nce.config b/ExampleSetups/LM/LSTMLM/lstmlm.cpu.nce.config deleted file mode 100644 index 94bed3141918..000000000000 --- a/ExampleSetups/LM/LSTMLM/lstmlm.cpu.nce.config +++ /dev/null @@ -1,432 +0,0 @@ -# configuration file for class based RNN training -# final test PPL=122.54 -ExpFolder=$ExpDir$ -ConfigFolder=$ConfigDir$ -DataFolder=$DataDir$ - -stderr=$ExpFolder$ -numCPUThreads=4 -# command=dumpNodeInfo -#command=train -#command=test -command=writeWordAndClassInfo:train:test -command=train:test -type=double - -DEVICEID=-1 - -NOISE=100 -RATE=0.1 -VOCABSIZE=10000 -CLASSSIZE=50 -makeMode=true -TRAINFILE=ptb.train.cntk.txt -VALIDFILE=ptb.valid.cntk.txt -TESTFILE=ptb.test.cntk.txt - -#number of threads -nthreads=4 - -writeWordAndClassInfo=[ - action=writeWordAndClass - inputFile=$DataFolder$\$TRAINFILE$ - outputVocabFile=$DataFolder$\vocab.txt - outputWord2Cls=$ExpFolder$\word2cls.txt - outputCls2Index=$ExpFolder$\cls2idx.txt - vocabSize=$VOCABSIZE$ - cutoff=0 - printValues=true -] - -dumpNodeInfo=[ - action=dumpnode - modelPath=$ExpFolder$\modelRnnCNTK - #nodeName=W0 - printValues=true -] - -devtest=[action=devtest] - -train=[ - action=train - minibatchSize=10 - traceLevel=1 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=NCELSTM - #CLASSLSTM - - # uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM - # NDLNetworkBuilder=[ - # networkDescription=$ConfigFolder$\rnnlm.ndl - # ] - - SimpleNetworkBuilder=[ - trainingCriterion=NoiseContrastiveEstimationNode - evalCriterion=NoiseContrastiveEstimationNode - nodeType=Sigmoid - initValueScale=6.0 - layerSizes=$VOCABSIZE$:200:$VOCABSIZE$ - addPrior=false - addDropoutNodes=false - applyMeanVarNorm=false - uniformInit=true; - - # these are for the class information for class-based language modeling - vocabSize=$VOCABSIZE$ - #nbrClass=$CLASSSIZE$ - noise_number=$NOISE$ - ] - - # configuration file, base parameters - SGD=[ - makeMode=true - learningRatesPerSample=$RATE$ - momentumPerMB=0 - gradientClippingWithTruncation=true - clippingThresholdPerSample=15.0 - maxEpochs=40 - unroll=false - numMBsToShowResult=2000 - # gradUpdateType=AdaGrad - gradUpdateType=None - - modelPath=$ExpFolder$\modelRnnCNTK - loadBestModel=true - - # settings for Auto Adjust Learning Rate - AutoAdjust=[ - # auto learning rate adjustment - 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=$DataFolder$\vocab.txt - noise_number=$NOISE$ - mode=nce - # 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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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 - # vocabulary size - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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 - mode=softmax - # word class info - wordclass=$DataFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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" - - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=[ - 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 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=NCELSTM - - modelPath=$ExpFolder$\modelRnnCNTK - - reader=[ - # reader to use - readerType=LMSequenceReader - randomize=None - mode=softmax - # word class info - wordclass=$DataFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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/ExampleSetups/LM/LSTMLM/lstmlm.gpu.config b/ExampleSetups/LM/LSTMLM/lstmlm.gpu.config deleted file mode 100644 index c100e3991a90..000000000000 --- a/ExampleSetups/LM/LSTMLM/lstmlm.gpu.config +++ /dev/null @@ -1,425 +0,0 @@ -# configuration file for class based RNN training -# ppl=133.35 -ExpFolder=$ExpDir$ -ConfigFolder=$ConfigDir$ -DataFolder=$DataDir$ - -stderr=$ExpFolder$ - -# command=dumpNodeInfo -#command=train -#command=test -command=writeWordAndClassInfo:train:test -#command=writeWordAndClassInfo -type=double - -DEVICEID=Auto -numCPUThreads=4 - -VOCABSIZE=10000 -CLASSSIZE=50 - -TRAINFILE=ptb.train.cntk.txt -VALIDFILE=ptb.valid.cntk.txt -TESTFILE=ptb.test.cntk.txt - -writeWordAndClassInfo=[ - action=writeWordAndClass - inputFile=$DataFolder$\$TRAINFILE$ - outputVocabFile=$ExpFolder$\vocab.txt - outputWord2Cls=$ExpFolder$\word2cls.txt - outputCls2Index=$ExpFolder$\cls2idx.txt - vocabSize=$VOCABSIZE$ - nbrClass=$CLASSSIZE$ - cutoff=0 - printValues=true -] - -dumpNodeInfo=[ - action=dumpnode - modelPath=$ExpFolder$\modelRnnCNTK - #nodeName=W0 - printValues=true -] - -devtest=[action=devtest] - -train=[ - action=train - minibatchSize=10 - traceLevel=1 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=CLASSLSTM - - # uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM - # NDLNetworkBuilder=[ - # networkDescription=$ConfigFolder$\rnnlm.ndl - # ] - - SimpleNetworkBuilder=[ - trainingCriterion=classcrossentropywithsoftmax - evalCriterion=classcrossentropywithsoftmax - nodeType=Sigmoid - initValueScale=6.0 - layerSizes=$VOCABSIZE$:150:200:$VOCABSIZE$ - addPrior=false - addDropoutNodes=false - applyMeanVarNorm=false - uniformInit=true; - lookupTableOrder=1 - # these are for the class information for class-based language modeling - vocabSize=$VOCABSIZE$ - nbrClass=$CLASSSIZE$ - ] - - # configuration file, base parameters - SGD=[ - learningRatesPerSample=0.1 - momentumPerMB=0 - gradientClippingWithTruncation=true - clippingThresholdPerSample=15.0 - maxEpochs=40 - unroll=false - numMBsToShowResult=2000 - # gradUpdateType=AdaGrad - gradUpdateType=None - - modelPath=$ExpFolder$\modelRnnCNTK - loadBestModel=true - - # settings for Auto Adjust Learning Rate - AutoAdjust=[ - # auto learning rate adjustment - 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=$ExpFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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 - # vocabulary size - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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=$ExpFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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" - - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=[ - 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 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=CLASSLM - - modelPath=$ExpFolder$\modelRnnCNTK - - reader=[ - # reader to use - readerType=LMSequenceReader - randomize=None - - # word class info - wordclass=$ExpFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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/ExampleSetups/LM/LSTMLM/lstmlm.gpu.nce.config b/ExampleSetups/LM/LSTMLM/lstmlm.gpu.nce.config deleted file mode 100644 index 94bed3141918..000000000000 --- a/ExampleSetups/LM/LSTMLM/lstmlm.gpu.nce.config +++ /dev/null @@ -1,432 +0,0 @@ -# configuration file for class based RNN training -# final test PPL=122.54 -ExpFolder=$ExpDir$ -ConfigFolder=$ConfigDir$ -DataFolder=$DataDir$ - -stderr=$ExpFolder$ -numCPUThreads=4 -# command=dumpNodeInfo -#command=train -#command=test -command=writeWordAndClassInfo:train:test -command=train:test -type=double - -DEVICEID=-1 - -NOISE=100 -RATE=0.1 -VOCABSIZE=10000 -CLASSSIZE=50 -makeMode=true -TRAINFILE=ptb.train.cntk.txt -VALIDFILE=ptb.valid.cntk.txt -TESTFILE=ptb.test.cntk.txt - -#number of threads -nthreads=4 - -writeWordAndClassInfo=[ - action=writeWordAndClass - inputFile=$DataFolder$\$TRAINFILE$ - outputVocabFile=$DataFolder$\vocab.txt - outputWord2Cls=$ExpFolder$\word2cls.txt - outputCls2Index=$ExpFolder$\cls2idx.txt - vocabSize=$VOCABSIZE$ - cutoff=0 - printValues=true -] - -dumpNodeInfo=[ - action=dumpnode - modelPath=$ExpFolder$\modelRnnCNTK - #nodeName=W0 - printValues=true -] - -devtest=[action=devtest] - -train=[ - action=train - minibatchSize=10 - traceLevel=1 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=NCELSTM - #CLASSLSTM - - # uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM - # NDLNetworkBuilder=[ - # networkDescription=$ConfigFolder$\rnnlm.ndl - # ] - - SimpleNetworkBuilder=[ - trainingCriterion=NoiseContrastiveEstimationNode - evalCriterion=NoiseContrastiveEstimationNode - nodeType=Sigmoid - initValueScale=6.0 - layerSizes=$VOCABSIZE$:200:$VOCABSIZE$ - addPrior=false - addDropoutNodes=false - applyMeanVarNorm=false - uniformInit=true; - - # these are for the class information for class-based language modeling - vocabSize=$VOCABSIZE$ - #nbrClass=$CLASSSIZE$ - noise_number=$NOISE$ - ] - - # configuration file, base parameters - SGD=[ - makeMode=true - learningRatesPerSample=$RATE$ - momentumPerMB=0 - gradientClippingWithTruncation=true - clippingThresholdPerSample=15.0 - maxEpochs=40 - unroll=false - numMBsToShowResult=2000 - # gradUpdateType=AdaGrad - gradUpdateType=None - - modelPath=$ExpFolder$\modelRnnCNTK - loadBestModel=true - - # settings for Auto Adjust Learning Rate - AutoAdjust=[ - # auto learning rate adjustment - 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=$DataFolder$\vocab.txt - noise_number=$NOISE$ - mode=nce - # 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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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 - # vocabulary size - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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 - mode=softmax - # word class info - wordclass=$DataFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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" - - labelDim=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=[ - 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 - deviceId=$DEVICEID$ - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=NCELSTM - - modelPath=$ExpFolder$\modelRnnCNTK - - reader=[ - # reader to use - readerType=LMSequenceReader - randomize=None - mode=softmax - # word class info - wordclass=$DataFolder$\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=$ExpFolder$\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=$VOCABSIZE$ - - file=$DataFolder$\$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=$VOCABSIZE$ - labelMappingFile=$ExpFolder$\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=$VOCABSIZE$ - - labelMappingFile=$ExpFolder$\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/ExampleSetups/LM/LSTMLM/lstmlmconfig.txt b/ExampleSetups/LM/LSTMLM/lstmlmconfig.txt deleted file mode 100644 index baa8aaff57db..000000000000 --- a/ExampleSetups/LM/LSTMLM/lstmlmconfig.txt +++ /dev/null @@ -1,415 +0,0 @@ -# configuration file for CNTK MNIST Before Check-In Tests - -stderr=c:\temp\\penntreebank\lstmpenntreebank.log -command=penntreebanklstmrndmb10lr01:penntreebanktestlstmrndmb10lr01 - -# -# Class-based cross entropy LSTM experiments -# -# learning rate: 0.15 -# minibatch size 10 -# number of sentences in each minibatch nbruttsineachrecurrentiter=10 -# -# iter 30: PPL = exp(4.9226532) = 137.37 -# iter 20: PPL = exp(4.9226532) = 137.37 -# iter 10: PPL = exp(4.9226532) = 137.37 -# iter 1: PPL = exp(5.087323) = 161.96 - -# -# baseline from Thomas Mikolov's paper -# http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf -# test PPL : 136 -# -# LSTM results from Alex Graves LSTM paper -# http://arxiv.org/pdf/1308.0850v2.pdf -# test PPL : 138 -# notice that Alex's paper doesn't use class for speed-up. -# - - -penntreebanklstmrndmb10lr01=[ - # this is the maximum size for the minibatch, since sequence minibatches are really just a single sequence - # can be considered as the maximum length of a sentence - action=train - -# correspond to the number of words/characteres to train in a minibatch - minibatchSize=6 - # need to be small since models are updated for each minibatch - traceLevel=1 - deviceId=-1 - epochSize=4430000 - # which is 886 * 5000 - SimpleNetworkBuilder=[ - recurrentLayer=1 - defaultHiddenActivity=0.1 - rnnType=CLASSLSTM - - trainingCriterion=classcrossentropywithsoftmax - evalCriterion=classcrossentropywithsoftmax - nodeType=Sigmoid - initValueScale=6.0 - layerSizes=10000:200:10000 - addPrior=false - addDropoutNodes=false - applyMeanVarNorm=false - uniformInit=true; - vocabSize=10000 - nbrClass=50 - ] - - # configuration file, base parameters - SGD=[ - learningRatesPerSample=0.15 - momentumPerMB=0.90 -# momentumPerMB=0.0 -# gradientClippingWithTruncation=false -# clippingThresholdPerSample=15.0 - - maxEpochs=150 - unroll=false - numMBsToShowResult=1000 - useAdagrad=true - - modelPath=c:\temp\penntreebank\cntkdebug.dnn - loadBestModel=true - - # settings for Auto Adjust Learning Rate - AutoAdjust=[ - # auto learning rate adjustment - autoAdjustLR=adjustafterepoch - reduceLearnRateIfImproveLessThan=0 - increaseLearnRateIfImproveMoreThan=1000000000 - learnRateDecreaseFactor=0.618 - learnRateIncreaseFactor=1.382 - numMiniBatch4LRSearch=100 - numPrevLearnRates=5 - numBestSearchEpoch=1 - ] - - - dropoutRate=0.0 - ] - - reader=[ - # reader to use - readerType=LMSequenceReader - randomize=None - - # word class info - wordclass=c:\exp\penntreebank\data\wordclass.txt - - # number of utterances to be allocated for each minibatch - nbruttsineachrecurrentiter=10 - - # 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=c:\temp\penntreebank\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=10000 - -# file=c:\exp\penntreebank\data\ptb.train.cntk.100.txt - file=c:\exp\penntreebank\data\ptb.train.cntk.txt - - #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=10000 - labelMappingFile=c:\temp\penntreebank\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=10000 - - labelMappingFile=c:\temp\penntreebank\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=c:\exp\penntreebank\data\wordclass.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=c:\temp\penntreebank\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=10000 - -# file=c:\exp\penntreebank\data\ptb.valid.cntk.100.txt - file=c:\exp\penntreebank\data\ptb.valid.cntk.txt - - #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=10000 - labelMappingFile=c:\temp\penntreebank\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" - - labelDim=10000 - labelMappingFile=c:\temp\penntreebank\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 - ] - ] - ] -] - -penntreebanktestlstmrndmb10lr01=[ - # this is the maximum size for the minibatch, since sequence minibatches are really just a single sequence - # can be considered as the maximum length of a sentence - action=eval - -# correspond to the number of words/characteres to train in a minibatch - minibatchSize=6 - # need to be small since models are updated for each minibatch - traceLevel=1 - deviceId=-1 - epochSize=4430000 - # which is 886 * 5000 - recurrentLayer=1 - defaultHiddenActivity=0.1 - useValidation=true - rnnType=CLASSLM - - modelPath=c:\temp\penntreebank\cntkdebug.dnn - - reader=[ - # reader to use - readerType=LMSequenceReader - randomize=None - - # word class info - wordclass=c:\exp\penntreebank\data\wordclass.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=c:\temp\penntreebank\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=10000 - -# file=c:\exp\penntreebank\data\ptb.test.cntk.100.txt - file=c:\exp\penntreebank\data\ptb.test.cntk.txt - - #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=10000 - labelMappingFile=c:\temp\penntreebank\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=10000 - - labelMappingFile=c:\temp\penntreebank\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/ExampleSetups/LM/LSTMLM/perplexity.class50.lr0.1.txt b/ExampleSetups/LM/LSTMLM/perplexity.class50.lr0.1.txt deleted file mode 100644 index e88d1eceff40a60a8504f42cfaaef7cfa91607a3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 23172 zcmeI4%We}v5JhW^#6RQ*2(dkWA+Z6$4ul923&es)5JIeI*dCWhuQ{A5FU61Ux{5e7tP>vAKE8-t+97C z_l4$uwL9(YnV!9?QJ>o9?rG$C`{=X%Xg~3g@lW+h=Q{FJz1thz`=C$wZa4Kf9(6d< zwH;d{eS014S{fG@NBl^~_~nnn@9q2j&}*n$>cYI8^f5Ee`9D-%w`=AML4ITiCiX26 zu-{QX%+Nw(H)3cjk~X@(yqGg*hRUgQhPvn(8CtY4Xr-vK6-zk`LC>+q6N6To-OZUZ zL*-ODL;o@a3*;rPV_q7ILmQDp99n1=d94P9%BggQQVeay0+&UPhcSlM;_XPYVkv*R60W`h5`$8v~n5RilLqOAw$&S zhf1k*hEfcLEVdUDdHo@LG_^e*ioR0+4iP1{u0K&6VzqwPR&sSPR7#~YlxB$E zC|N01>EoUnhNyDrG1fv*360KAil4A^V=s3cz3~`7jksaY2fIY*Ce=YuDV5I95JRk1 zunV^1A%`Kl)719eoOyd87%HLC8M@36^*O8Vp*NK4r|Ac=)=Gb|20vX^r8AUf2wwva zxw#C67#((>%{>lv43$yo45b*t72}Qjd1*YCvDV7^H+!$^AgGK+=O@h%`}*iLM_+R_ zKVhA9FNaYJL1k1rLurQS+|qYDgdrjjxvUF$EY`_t8|8j z8R9p3h?i#9D+I-TYV(gm{7R;(bcTi*V!i7SFAYPitJM^RrfPJ4hWKG^gB8e*h8!<_ zd8h3BBhYk>&d)GE!6O{TPffKv=iN@#==_ZFlfSkad^F?x^?wZ!h_2#c)#l$SouM&? zI4||E7{m^bdRA9M6q>5i85(0KcfV^_&xfMN#G$DwouP4tVt*&kOS4BUW`ck&sE`L7m+rm3955bJZS^5*>a;IQ_{=+0lv`TJ$>D*yYHeIC)>j8Q}u R3p*CsaaKn*E2k2Qegn4a#LoZ# diff --git a/ExampleSetups/LM/LSTMLM/perplexity.nce100.lr0.1.txt b/ExampleSetups/LM/LSTMLM/perplexity.nce100.lr0.1.txt deleted file mode 100644 index 149f0c642f56..000000000000 --- a/ExampleSetups/LM/LSTMLM/perplexity.nce100.lr0.1.txt +++ /dev/null @@ -1,40 +0,0 @@ -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.4415899 Perplexity = 230.80885 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.4415899 Perplexity = 230.80885 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.2513086 Perplexity = 190.8158 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.2513086 Perplexity = 190.8158 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.1372039 Perplexity = 170.23909 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.1372039 Perplexity = 170.23909 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0720036 Perplexity = 159.49358 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0720036 Perplexity = 159.49358 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0618825 Perplexity = 157.88746 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0618825 Perplexity = 157.88746 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0321352 Perplexity = 153.25991 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0321352 Perplexity = 153.25991 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0083887 Perplexity = 149.66339 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0083887 Perplexity = 149.66339 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0220441 Perplexity = 151.72111 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0220441 Perplexity = 151.72111 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9666183 Perplexity = 143.54066 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9666183 Perplexity = 143.54066 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9719939 Perplexity = 144.31436 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9719939 Perplexity = 144.31436 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9397468 Perplexity = 139.73487 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9397468 Perplexity = 139.73487 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9442405 Perplexity = 140.36421 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9442405 Perplexity = 140.36421 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9287878 Perplexity = 138.21187 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9287878 Perplexity = 138.21187 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9292672 Perplexity = 138.27814 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9292672 Perplexity = 138.27814 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9187232 Perplexity = 136.82781 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9187232 Perplexity = 136.82781 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9224714 Perplexity = 137.34162 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9224714 Perplexity = 137.34162 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9124989 Perplexity = 135.97878 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9124989 Perplexity = 135.97878 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9146056 Perplexity = 136.26556 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9146056 Perplexity = 136.26556 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9083939 Perplexity = 135.42174 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9083939 Perplexity = 135.42174 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9042288 Perplexity = 134.85886 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9042288 Perplexity = 134.85886 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9007954 Perplexity = 134.39664 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9007954 Perplexity = 134.39664 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8977358 Perplexity = 133.98607 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8977358 Perplexity = 133.98607 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8960381 Perplexity = 133.75879 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8960381 Perplexity = 133.75879 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8950807 Perplexity = 133.63079 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8950807 Perplexity = 133.63079 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8947338 Perplexity = 133.58444 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8947338 Perplexity = 133.58444 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8945776 Perplexity = 133.56358 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8945776 Perplexity = 133.56358 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944772 Perplexity = 133.55017 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944772 Perplexity = 133.55017 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944128 Perplexity = 133.54157 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944128 Perplexity = 133.54157 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943936 Perplexity = 133.53901 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943936 Perplexity = 133.53901 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.89438 Perplexity = 133.53718 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.89438 Perplexity = 133.53718 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943731 Perplexity = 133.53627 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943731 Perplexity = 133.53627 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943699 Perplexity = 133.53584 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943699 Perplexity = 133.53584 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894368 Perplexity = 133.53558 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894368 Perplexity = 133.53558 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894367 Perplexity = 133.53545 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894367 Perplexity = 133.53545 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943667 Perplexity = 133.53541 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943667 Perplexity = 133.53541 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943665 Perplexity = 133.53538 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943665 Perplexity = 133.53538 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 -Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943663 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943663 Perplexity = 133.53537 -Final Results: Minibatch[1-82430]: Samples Seen = 82430 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8084526 Perplexity = 122.54185 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8084526 Perplexity = 122.54185 diff --git a/ExampleSetups/LM/LSTMLM/readme.txt b/ExampleSetups/LM/LSTMLM/readme.txt deleted file mode 100644 index ec5c5d6bf5bd..000000000000 --- a/ExampleSetups/LM/LSTMLM/readme.txt +++ /dev/null @@ -1,4 +0,0 @@ -# LSTM LM setup on PennTreebank. - -to use it, call -cntk.exe configFile=global.config+lstmlm.gpu.config ExpDir= diff --git a/ExampleSetups/Readme.md b/ExampleSetups/Readme.md deleted file mode 100644 index edbb78100d33..000000000000 --- a/ExampleSetups/Readme.md +++ /dev/null @@ -1,14 +0,0 @@ -# CNTK Demos and Example Setups - -This folder contains examples that correspond to popular data sets and tasks. -These data sets often require a license and are therefore not included in the repository. -The 'Demos' folder contains a few self-contained and documented demos to get started with CNTK. - -The four examples shown in the table below provide a good introduction to CNTK. - -|Folder | Domain | Network types | -|:------------------------|:-------------------------------------------------|:----------------| -Demos/Simple2d | Synthetic 2d data | FF (CPU and GPU) -Demos/Speech | Speech data (CMU AN4) | FF and LSTM -Demos/Text | Text data (penn treebank) | RNN -ExampleSetups/Image/MNIST | Image data (MNIST handwritten digit recognition) | CNN diff --git a/ExampleSetups/Image/MNIST/AdditionalFiles/mnist_convert.py b/Examples/Image/MNIST/AdditionalFiles/mnist_convert.py old mode 100755 new mode 100644 similarity index 100% rename from ExampleSetups/Image/MNIST/AdditionalFiles/mnist_convert.py rename to Examples/Image/MNIST/AdditionalFiles/mnist_convert.py diff --git a/ExampleSetups/Image/MNIST/Config/01_OneHidden.config b/Examples/Image/MNIST/Config/01_OneHidden.config similarity index 100% rename from ExampleSetups/Image/MNIST/Config/01_OneHidden.config rename to Examples/Image/MNIST/Config/01_OneHidden.config diff --git a/ExampleSetups/Image/MNIST/Config/01_OneHidden.ndl b/Examples/Image/MNIST/Config/01_OneHidden.ndl similarity index 100% rename from ExampleSetups/Image/MNIST/Config/01_OneHidden.ndl rename to Examples/Image/MNIST/Config/01_OneHidden.ndl diff --git a/ExampleSetups/Image/MNIST/Config/02_Convolution.config b/Examples/Image/MNIST/Config/02_Convolution.config similarity index 100% rename from ExampleSetups/Image/MNIST/Config/02_Convolution.config rename to Examples/Image/MNIST/Config/02_Convolution.config diff --git a/ExampleSetups/Image/MNIST/Config/02_Convolution.ndl b/Examples/Image/MNIST/Config/02_Convolution.ndl similarity index 100% rename from ExampleSetups/Image/MNIST/Config/02_Convolution.ndl rename to Examples/Image/MNIST/Config/02_Convolution.ndl diff --git a/ExampleSetups/Image/MNIST/Config/03_ConvBatchNorm.config b/Examples/Image/MNIST/Config/03_ConvBatchNorm.config similarity index 100% rename from ExampleSetups/Image/MNIST/Config/03_ConvBatchNorm.config rename to Examples/Image/MNIST/Config/03_ConvBatchNorm.config diff --git a/ExampleSetups/Image/MNIST/Config/03_ConvBatchNorm.ndl b/Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl similarity index 100% rename from ExampleSetups/Image/MNIST/Config/03_ConvBatchNorm.ndl rename to Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl diff --git a/ExampleSetups/Image/MNIST/Config/Macros.ndl b/Examples/Image/MNIST/Config/Macros.ndl similarity index 100% rename from ExampleSetups/Image/MNIST/Config/Macros.ndl rename to Examples/Image/MNIST/Config/Macros.ndl diff --git a/ExampleSetups/Image/MNIST/Data/labelsmap.txt b/Examples/Image/MNIST/Data/labelsmap.txt similarity index 100% rename from ExampleSetups/Image/MNIST/Data/labelsmap.txt rename to Examples/Image/MNIST/Data/labelsmap.txt diff --git a/ExampleSetups/Image/MNIST/README.md b/Examples/Image/MNIST/README.md similarity index 100% rename from ExampleSetups/Image/MNIST/README.md rename to Examples/Image/MNIST/README.md diff --git a/ExampleSetups/Image/CIFAR-10/01_Conv.config b/Examples/Image/Miscellaneous/CIFAR-10/01_Conv.config similarity index 100% rename from ExampleSetups/Image/CIFAR-10/01_Conv.config rename to Examples/Image/Miscellaneous/CIFAR-10/01_Conv.config diff --git a/ExampleSetups/Image/CIFAR-10/01_Convolution.ndl b/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl similarity index 100% rename from ExampleSetups/Image/CIFAR-10/01_Convolution.ndl rename to Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl diff --git a/ExampleSetups/Image/CIFAR-10/02_BatchNormConv.config b/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.config similarity index 100% rename from ExampleSetups/Image/CIFAR-10/02_BatchNormConv.config rename to Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.config diff --git a/ExampleSetups/Image/CIFAR-10/02_BatchNormConv.mel b/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.mel similarity index 100% rename from ExampleSetups/Image/CIFAR-10/02_BatchNormConv.mel rename to Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.mel diff --git a/ExampleSetups/Image/CIFAR-10/02_BatchNormConv.ndl b/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl similarity index 100% rename from ExampleSetups/Image/CIFAR-10/02_BatchNormConv.ndl rename to Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl diff --git a/ExampleSetups/Image/CIFAR-10/CIFAR_convert.py b/Examples/Image/Miscellaneous/CIFAR-10/CIFAR_convert.py old mode 100755 new mode 100644 similarity index 100% rename from ExampleSetups/Image/CIFAR-10/CIFAR_convert.py rename to Examples/Image/Miscellaneous/CIFAR-10/CIFAR_convert.py diff --git a/ExampleSetups/Image/CIFAR-10/Macros.ndl b/Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl similarity index 100% rename from ExampleSetups/Image/CIFAR-10/Macros.ndl rename to Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl diff --git a/ExampleSetups/Image/CIFAR-10/labelsmap.txt b/Examples/Image/Miscellaneous/CIFAR-10/labelsmap.txt similarity index 100% rename from ExampleSetups/Image/CIFAR-10/labelsmap.txt rename to Examples/Image/Miscellaneous/CIFAR-10/labelsmap.txt diff --git a/ExampleSetups/Image/CIFAR-10/readme.txt b/Examples/Image/Miscellaneous/CIFAR-10/readme.txt similarity index 100% rename from ExampleSetups/Image/CIFAR-10/readme.txt rename to Examples/Image/Miscellaneous/CIFAR-10/readme.txt diff --git a/ExampleSetups/Image/ImageNet/AlexNet/AlexNet.config b/Examples/Image/Miscellaneous/ImageNet/AlexNet/AlexNet.config similarity index 100% rename from ExampleSetups/Image/ImageNet/AlexNet/AlexNet.config rename to Examples/Image/Miscellaneous/ImageNet/AlexNet/AlexNet.config diff --git a/ExampleSetups/Image/ImageNet/AlexNet/AlexNet.ndl b/Examples/Image/Miscellaneous/ImageNet/AlexNet/AlexNet.ndl similarity index 100% rename from ExampleSetups/Image/ImageNet/AlexNet/AlexNet.ndl rename to Examples/Image/Miscellaneous/ImageNet/AlexNet/AlexNet.ndl diff --git 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