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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,16 +1,6 @@ | ||
| m=LoadModel($CurModel$, format=cntk) | ||
| SetDefaultModel(m) | ||
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| conv1.bn_e = BatchNormalization(conv1.c, conv1.sc, conv1.b, conv1.m, conv1.isd, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(conv1.y, 0, conv1.bn_e) | ||
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| conv2.bn_e = BatchNormalization(conv2.c, conv2.sc, conv2.b, conv2.m, conv2.isd, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(conv2.y, 0, conv2.bn_e) | ||
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| conv3.bn_e = BatchNormalization(conv3.c, conv3.sc, conv3.b, conv3.m, conv3.isd, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(conv3.y, 0, conv3.bn_e) | ||
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| h1.bn_e = BatchNormalization(h1.t, h1.sc, h1.b, h1.m, h1.isd, eval = true, spatial = false) | ||
| SetNodeInput(h1.y, 0, h1.bn_e) | ||
| SetPropertyForSubTree(CE, batchNormEvalMode, true) | ||
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| SaveModel(m, $NewModel$, format=cntk) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,52 +1,6 @@ | ||
| m=LoadModel($CurModel$, format=cntk) | ||
| SetDefaultModel(m) | ||
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| conv1.bn_e = BatchNormalization(conv1.c, conv1.sc, conv1.b, conv1.m, conv1.isd, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(conv1.y, 0, conv1.bn_e) | ||
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| rn1_1.bn1_e = BatchNormalization(rn1_1.c1, rn1_1.sc1, rn1_1.b1, rn1_1.m1, rn1_1.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_1.y1, 0, rn1_1.bn1_e) | ||
| rn1_1.bn2_e = BatchNormalization(rn1_1.c2, rn1_1.sc2, rn1_1.b2, rn1_1.m2, rn1_1.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_1.p, 0, rn1_1.bn2_e) | ||
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| rn1_2.bn1_e = BatchNormalization(rn1_2.c1, rn1_2.sc1, rn1_2.b1, rn1_2.m1, rn1_2.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_2.y1, 0, rn1_2.bn1_e) | ||
| rn1_2.bn2_e = BatchNormalization(rn1_2.c2, rn1_2.sc2, rn1_2.b2, rn1_2.m2, rn1_2.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_2.p, 0, rn1_2.bn2_e) | ||
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| rn1_3.bn1_e = BatchNormalization(rn1_3.c1, rn1_3.sc1, rn1_3.b1, rn1_3.m1, rn1_3.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_3.y1, 0, rn1_3.bn1_e) | ||
| rn1_3.bn2_e = BatchNormalization(rn1_3.c2, rn1_3.sc2, rn1_3.b2, rn1_3.m2, rn1_3.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn1_3.p, 0, rn1_3.bn2_e) | ||
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| rn2_1.bn1_e = BatchNormalization(rn2_1.c1, rn2_1.sc1, rn2_1.b1, rn2_1.m1, rn2_1.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_1.y1, 0, rn2_1.bn1_e) | ||
| rn2_1.bn2_e = BatchNormalization(rn2_1.c2, rn2_1.sc2, rn2_1.b2, rn2_1.m2, rn2_1.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_1.p, 0, rn2_1.bn2_e) | ||
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| rn2_2.bn1_e = BatchNormalization(rn2_2.c1, rn2_2.sc1, rn2_2.b1, rn2_2.m1, rn2_2.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_2.y1, 0, rn2_2.bn1_e) | ||
| rn2_2.bn2_e = BatchNormalization(rn2_2.c2, rn2_2.sc2, rn2_2.b2, rn2_2.m2, rn2_2.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_2.p, 0, rn2_2.bn2_e) | ||
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| rn2_3.bn1_e = BatchNormalization(rn2_3.c1, rn2_3.sc1, rn2_3.b1, rn2_3.m1, rn2_3.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_3.y1, 0, rn2_3.bn1_e) | ||
| rn2_3.bn2_e = BatchNormalization(rn2_3.c2, rn2_3.sc2, rn2_3.b2, rn2_3.m2, rn2_3.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn2_3.p, 0, rn2_3.bn2_e) | ||
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| rn3_1.bn1_e = BatchNormalization(rn3_1.c1, rn3_1.sc1, rn3_1.b1, rn3_1.m1, rn3_1.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_1.y1, 0, rn3_1.bn1_e) | ||
| rn3_1.bn2_e = BatchNormalization(rn3_1.c2, rn3_1.sc2, rn3_1.b2, rn3_1.m2, rn3_1.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_1.p, 0, rn3_1.bn2_e) | ||
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| rn3_2.bn1_e = BatchNormalization(rn3_2.c1, rn3_2.sc1, rn3_2.b1, rn3_2.m1, rn3_2.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_2.y1, 0, rn3_2.bn1_e) | ||
| rn3_2.bn2_e = BatchNormalization(rn3_2.c2, rn3_2.sc2, rn3_2.b2, rn3_2.m2, rn3_2.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_2.p, 0, rn3_2.bn2_e) | ||
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| rn3_3.bn1_e = BatchNormalization(rn3_3.c1, rn3_3.sc1, rn3_3.b1, rn3_3.m1, rn3_3.isd1, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_3.y1, 0, rn3_3.bn1_e) | ||
| rn3_3.bn2_e = BatchNormalization(rn3_3.c2, rn3_3.sc2, rn3_3.b2, rn3_3.m2, rn3_3.isd2, eval = true, spatial = true, imageLayout = "cudnn") | ||
| SetNodeInput(rn3_3.p, 0, rn3_3.bn2_e) | ||
| SetPropertyForSubTree(CE, batchNormEvalMode, true) | ||
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| SaveModel(m, $NewModel$, format=cntk) |
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106 changes: 106 additions & 0 deletions
106
Examples/Image/Miscellaneous/CIFAR-10/04_ResNet_56.config
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| RootDir = "." | ||
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| ConfigDir = "$RootDir$" | ||
| DataDir = "$RootDir$" | ||
| OutputDir = "$RootDir$/Output" | ||
| ModelDir = "$OutputDir$/Models" | ||
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| ndlMacros=$ConfigDir$/Macros.ndl | ||
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| precision=float | ||
| deviceId=Auto | ||
| prefetch=true | ||
| parallelTrain=false | ||
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| command=Train:AddBNEval:Test | ||
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| stderr=$OutputDir$/04_ResNet_56 | ||
| traceLevel=1 | ||
| numMBsToShowResult=200 | ||
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| Proj16to32Filename = $ConfigDir$/16to32.txt | ||
| Proj32to64Filename = $ConfigDir$/32to64.txt | ||
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| Train=[ | ||
| action=train | ||
| modelPath=$ModelDir$/04_ResNet_56 | ||
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| NDLNetworkBuilder=[ | ||
| networkDescription=$ConfigDir$/04_ResNet_56.ndl | ||
| ] | ||
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| SGD=[ | ||
| epochSize=0 | ||
| minibatchSize=128 | ||
| learningRatesPerMB=0.1*80:0.01*40:0.001 | ||
| momentumPerMB=0.9 | ||
| maxEpochs=1 | ||
| L2RegWeight=0.0001 | ||
| dropoutRate=0 | ||
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| ParallelTrain=[ | ||
| parallelizationMethod=DataParallelSGD | ||
| distributedMBReading=true | ||
| parallelizationStartEpoch=1 | ||
| DataParallelSGD=[ | ||
| gradientBits=1 | ||
| ] | ||
| ] | ||
| ] | ||
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| reader=[ | ||
| readerType=ImageReader | ||
| file=$DataDir$/train_map.txt | ||
| randomize=Auto | ||
| features=[ | ||
| width=32 | ||
| height=32 | ||
| channels=3 | ||
| cropType=Random | ||
| cropRatio=0.8 | ||
| jitterType=UniRatio | ||
| interpolations=Linear | ||
| meanFile=$ConfigDir$/CIFAR-10_mean.xml | ||
| ] | ||
| labels=[ | ||
| labelDim=10 | ||
| ] | ||
| ] | ||
| ] | ||
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| AddBNEval=[ | ||
| action=edit | ||
| CurModel=$ModelDir$/04_ResNet_56 | ||
| NewModel=$ModelDir$/04_ResNet_56.Eval | ||
| editPath=$ConfigDir$/03_ResNet.mel | ||
| ] | ||
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| Test=[ | ||
| action=test | ||
| modelPath=$ModelDir$/04_ResNet_56 | ||
| # Set minibatch size for testing. | ||
| minibatchSize=512 | ||
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| NDLNetworkBuilder=[ | ||
| networkDescription=$ConfigDir$/04_ResNet_56.ndl | ||
| ] | ||
|
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| reader=[ | ||
| readerType=ImageReader | ||
| file=$DataDir$/test_map.txt | ||
| randomize=Auto | ||
| features=[ | ||
| width=32 | ||
| height=32 | ||
| channels=3 | ||
| cropType=Center | ||
| cropRatio=1 | ||
| jitterType=UniRatio | ||
| interpolations=Linear | ||
| meanFile=$ConfigDir$/CIFAR-10_mean.xml | ||
| ] | ||
| labels=[ | ||
| labelDim=10 | ||
| ] | ||
| ] | ||
| ] |
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