Merge remote-tracking branch 'origin/master' into thilow/Implement_Times_Sparse_Dense_on_CPU
# Conflicts: # Source/ComputationNetworkLib/LinearAlgebraNodes.h
This commit is contained in:
Thilo Will
Коммит
fd13a8b0eb
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|
@ -40,6 +40,7 @@ generate_build_info text eol=lf
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|||
run-test text eol=lf
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||||
run-test-common text eol=lf
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||||
run-timit-test-common text eol=lf
|
||||
run-boost-test-common text eol=lf
|
||||
make_binary_drop_linux text eol=lf
|
||||
|
||||
# Used from Unix / Cygwin 'md5sum -c', needs to have LF line endings:
|
||||
|
|
|
|||
|
|
@ -191,6 +191,10 @@ Source/CNTK/buildinfo.h$$
|
|||
# Unit test output
|
||||
Tests/UnitTests/ReaderTests/Control/**/*_Output.txt
|
||||
Tests/UnitTests/NetworkTests/Output/
|
||||
Tests/EndToEndTests/UnitTests/MathTests/M.txt
|
||||
Tests/EndToEndTests/UnitTests/MathTests/MCPU.txt
|
||||
Tests/EndToEndTests/UnitTests/MathTests/MGPU.txt
|
||||
Tests/EndToEndTests/UnitTests/MathTests/MS.txt
|
||||
|
||||
Dependencies/CNTKCustomMKL/Publish
|
||||
Dependencies/CNTKCustomMKL/CNTKCustomMKL-Linux-*.tgz
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@
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|||
<NvmlLibPath>"c:\Program Files\NVIDIA Corporation\GDK\gdk_win7_amd64_release\nvml\lib"</NvmlLibPath>
|
||||
|
||||
<CudaVersion/>
|
||||
<CudaVersion Condition="Exists('$(CUDA_PATH_V8_0)') And '$(CudaVersion)' == ''">8.0</CudaVersion>
|
||||
<CudaVersion Condition="Exists('$(CUDA_PATH_V7_5)') And '$(CudaVersion)' == ''">7.5</CudaVersion>
|
||||
<CudaVersion Condition="Exists('$(CUDA_PATH_V7_0)') And '$(CudaVersion)' == ''">7.0</CudaVersion>
|
||||
|
||||
|
|
@ -87,6 +88,12 @@
|
|||
<OpenCvBinPath>$(OpenCvPath)\x64\vc12\bin</OpenCvBinPath>
|
||||
</PropertyGroup>
|
||||
|
||||
<PropertyGroup Condition="'$(CudaVersion)' == '8.0'">
|
||||
<CudaPath>$(CUDA_PATH_V8_0)</CudaPath>
|
||||
<CudaRuntimeDll>cudart64_80.dll</CudaRuntimeDll>
|
||||
<CudaDlls>cublas64_80.dll;cusparse64_80.dll;curand64_80.dll;$(CudaRuntimeDll)</CudaDlls>
|
||||
</PropertyGroup>
|
||||
|
||||
<PropertyGroup Condition="'$(CudaVersion)' == '7.5'">
|
||||
<CudaPath>$(CUDA_PATH_V7_5)</CudaPath>
|
||||
<CudaRuntimeDll>cudart64_75.dll</CudaRuntimeDll>
|
||||
|
|
|
|||
168
CNTK.sln
168
CNTK.sln
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@ -463,32 +463,34 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "Addition
|
|||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{6E5A252C-ACCE-42E0-9819-FF4DEF6D739E}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Image\MNIST\Config\01_OneHidden.cntk = Examples\Image\MNIST\Config\01_OneHidden.cntk
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||||
Examples\Image\MNIST\Config\01_OneHidden.ndl = Examples\Image\MNIST\Config\01_OneHidden.ndl
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||||
Examples\Image\MNIST\Config\02_Convolution.cntk = Examples\Image\MNIST\Config\02_Convolution.cntk
|
||||
Examples\Image\MNIST\Config\01_OneHidden_ndl_deprecated.cntk = Examples\Image\MNIST\Config\01_OneHidden_ndl_deprecated.cntk
|
||||
Examples\Image\MNIST\Config\02_Convolution.ndl = Examples\Image\MNIST\Config\02_Convolution.ndl
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||||
Examples\Image\MNIST\Config\03_ConvBatchNorm.cntk = Examples\Image\MNIST\Config\03_ConvBatchNorm.cntk
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||||
Examples\Image\MNIST\Config\02_Convolution_ndl_deprecated.cntk = Examples\Image\MNIST\Config\02_Convolution_ndl_deprecated.cntk
|
||||
Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl = Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl
|
||||
Examples\Image\MNIST\Config\03_ConvBatchNorm_ndl_deprecated.cntk = Examples\Image\MNIST\Config\03_ConvBatchNorm_ndl_deprecated.cntk
|
||||
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.cntk = Examples\Image\Miscellaneous\CIFAR-10\01_Conv.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\01_Conv_ndl_deprecated.cntk = Examples\Image\Miscellaneous\CIFAR-10\01_Conv_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl = Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl
|
||||
Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.cntk = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.cntk
|
||||
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\02_BatchNormConv_ndl_deprecated.cntk = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py = Examples\Image\Miscellaneous\CIFAR-10\03_ProjWeightsGen.py
|
||||
Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.cntk = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.mel
|
||||
Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet.ndl
|
||||
Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56.cntk = Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\03_ResNet_ndl_deprecated.cntk = Examples\Image\Miscellaneous\CIFAR-10\03_ResNet_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56.ndl = Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56.ndl
|
||||
Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56_ndl_deprecated.cntk = Examples\Image\Miscellaneous\CIFAR-10\04_ResNet_56_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\CIFAR-10\16to32.txt = Examples\Image\Miscellaneous\CIFAR-10\16to32.txt
|
||||
Examples\Image\Miscellaneous\CIFAR-10\32to64.txt = Examples\Image\Miscellaneous\CIFAR-10\32to64.txt
|
||||
Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py = Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py
|
||||
Examples\Image\Miscellaneous\CIFAR-10\CifarConverter.py = Examples\Image\Miscellaneous\CIFAR-10\CifarConverter.py
|
||||
Examples\Image\Miscellaneous\CIFAR-10\CifarConverter_py3.py = Examples\Image\Miscellaneous\CIFAR-10\CifarConverter_py3.py
|
||||
Examples\Image\Miscellaneous\CIFAR-10\CifarDownload.py = Examples\Image\Miscellaneous\CIFAR-10\CifarDownload.py
|
||||
Examples\Image\Miscellaneous\CIFAR-10\CifarDownload_py3.py = Examples\Image\Miscellaneous\CIFAR-10\CifarDownload_py3.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
|
||||
|
|
@ -499,8 +501,8 @@ 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.cntk = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl
|
||||
EndProjectSection
|
||||
EndProject
|
||||
|
|
@ -509,12 +511,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "VGG", "VGG", "{BC0D6DFF-80C
|
|||
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.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_BN_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E_ndl_deprecated.cntk
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Other", "Other", "{CCD56F12-BA17-4753-B5EE-4995FE682995}"
|
||||
|
|
@ -533,8 +535,8 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NdlExamples", "NdlExamples"
|
|||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{1E37CE40-556D-4693-B58C-F8D4CE349BB7}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Other\Simple2d\Config\AddOperatorConstant.cntk = Examples\Other\Simple2d\Config\AddOperatorConstant.cntk
|
||||
Examples\Other\Simple2d\Config\AddOperatorConstantNoInput.cntk = Examples\Other\Simple2d\Config\AddOperatorConstantNoInput.cntk
|
||||
Examples\Other\Simple2d\Config\AddOperatorConstant_ndl_deprecated.cntk = Examples\Other\Simple2d\Config\AddOperatorConstant_ndl_deprecated.cntk
|
||||
Examples\Other\Simple2d\Config\AddOperatorConstantNoInput_ndl_deprecated.cntk = Examples\Other\Simple2d\Config\AddOperatorConstantNoInput_ndl_deprecated.cntk
|
||||
Examples\Other\Simple2d\Config\Multigpu.cntk = Examples\Other\Simple2d\Config\Multigpu.cntk
|
||||
Examples\Other\Simple2d\Config\Simple.cntk = Examples\Other\Simple2d\Config\Simple.cntk
|
||||
EndProjectSection
|
||||
|
|
@ -551,7 +553,7 @@ EndProject
|
|||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\AN4\Config\FeedForward.cntk = Examples\Speech\AN4\Config\FeedForward.cntk
|
||||
Examples\Speech\AN4\Config\LSTM-NDL.cntk = Examples\Speech\AN4\Config\LSTM-NDL.cntk
|
||||
Examples\Speech\AN4\Config\LSTM-NDL_ndl_deprecated.cntk = Examples\Speech\AN4\Config\LSTM-NDL_ndl_deprecated.cntk
|
||||
Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl = Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl
|
||||
EndProjectSection
|
||||
EndProject
|
||||
|
|
@ -576,12 +578,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "cntk_config", "cntk_config"
|
|||
Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\Align.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\Align.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_ndl_deprecated.cntk
|
||||
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.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.cntk = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.cntk
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl
|
||||
|
|
@ -616,27 +618,27 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "config", "config", "{1C7D22
|
|||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.cntk = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.cntk
|
||||
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.cntk = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.cntk = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM_ndl_deprecated.cntk
|
||||
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.cntk = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.cntk = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN_ndl_deprecated.cntk
|
||||
Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM_ndl_deprecated.cntk = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM_ndl_deprecated.cntk
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "PennTreebank", "PennTreebank", "{6F4125B5-220F-4FB7-B6C4-85A966A0268C}"
|
||||
|
|
@ -670,12 +672,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ResNet", "ResNet", "{06D2C6
|
|||
Examples\Image\Miscellaneous\ImageNet\ResNet\create_eval_model.mel = Examples\Image\Miscellaneous\ImageNet\ResNet\create_eval_model.mel
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\Macros.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py = Examples\Image\Miscellaneous\ImageNet\ResNet\ProjWeightsGen.py
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_152_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_34_ndl_deprecated.cntk
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50.ndl = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50.ndl
|
||||
Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50_ndl_deprecated.cntk = Examples\Image\Miscellaneous\ImageNet\ResNet\ResNet_50_ndl_deprecated.cntk
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Extensibility", "Extensibility", "{60F87E25-BC87-4782-8E20-1621AAEBB113}"
|
||||
|
|
@ -1139,21 +1141,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Scripts", "Scripts", "{6826
|
|||
EndProject
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ManagedEvalTests", "Tests\UnitTests\ManagedEvalTests\ManagedEvalTests.csproj", "{CC8DDDCB-D53A-4B30-8596-AEF1C493DB31}"
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Evaluation", "Evaluation", "{3385EBEA-5F97-4B2B-9F30-0E6D7F91B9CA}"
|
||||
EndProject
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CSEvalClient", "Examples\Evaluation\CSEvalClient\CSEvalClient.csproj", "{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}"
|
||||
EndProject
|
||||
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CPPEvalClient", "Examples\Evaluation\CPPEvalClient\CPPEvalClient.vcxproj", "{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}"
|
||||
ProjectSection(ProjectDependencies) = postProject
|
||||
{482999D1-B7E2-466E-9F8D-2119F93EAFD9} = {482999D1-B7E2-466E-9F8D-2119F93EAFD9}
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "BrainScriptTests", "Tests\UnitTests\BrainScriptTests\BrainScriptTests.vcxproj", "{9F999212-AFC5-4EAC-AA78-F7247D46C456}"
|
||||
ProjectSection(ProjectDependencies) = postProject
|
||||
{60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}
|
||||
{928ABD1B-4D3B-4017-AEF1-0FA1B4467513} = {928ABD1B-4D3B-4017-AEF1-0FA1B4467513}
|
||||
{EAD17188-072C-4726-B840-A769C36DAD1B} = {EAD17188-072C-4726-B840-A769C36DAD1B}
|
||||
{60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {60BDB847-D0C4-4FD3-A947-0C15C08BCDB5}
|
||||
{86883653-8A61-4038-81A0-2379FAE4200A} = {86883653-8A61-4038-81A0-2379FAE4200A}
|
||||
{EAD17188-072C-4726-B840-A769C36DAD1B} = {EAD17188-072C-4726-B840-A769C36DAD1B}
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tutorials", "Tutorials", "{8BE0642A-A3AA-4A64-95D0-C78FB285B2A4}"
|
||||
|
|
@ -1222,6 +1215,51 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TrainSimpleNetwork", "Train
|
|||
Tests\EndToEndTests\Examples\Speech\TIMIT\TrainSimpleNetwork\testcases.yml = Tests\EndToEndTests\Examples\Speech\TIMIT\TrainSimpleNetwork\testcases.yml
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "FullUtteranceLayers", "FullUtteranceLayers", "{4DDFD21D-E82C-4321-B380-8A9382E18107}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.cpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.windows.cpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\baseline.windows.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\run-test = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\run-test
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\testcases.yml = Tests\EndToEndTests\Speech\LSTM\FullUtteranceLayers\testcases.yml
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "FullUtteranceCuDNN5", "FullUtteranceCuDNN5", "{D293C7C7-B7D7-47CE-A796-45262C16F980}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\baseline.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\baseline.windows.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\run-test = Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\run-test
|
||||
Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\testcases.yml = Tests\EndToEndTests\Speech\LSTM\FullUtteranceCuDNN5\testcases.yml
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TruncatedLayers", "TruncatedLayers", "{4F534076-39A5-40E2-BF87-64EC464C52B2}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.cpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.windows.cpu.txt = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.windows.cpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.windows.gpu.txt = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\baseline.windows.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\run-test = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\run-test
|
||||
Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\testcases.yml = Tests\EndToEndTests\Speech\LSTM\TruncatedLayers\testcases.yml
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Truncated-Kaldi", "Truncated-Kaldi", "{31263D7F-F590-475E-B4F0-7DFA4E4FF4B8}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\baseline.cpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\baseline.cpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\baseline.gpu.txt = Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\baseline.gpu.txt
|
||||
Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\run-test = Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\run-test
|
||||
Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\testcases.yml = Tests\EndToEndTests\Speech\LSTM\Truncated-Kaldi\testcases.yml
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "EvalClientTests", "EvalClientTests", "{05E45AF7-C069-4057-BC16-0A532D068CE4}"
|
||||
EndProject
|
||||
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CPPEvalClientTest", "Tests\EndToEndTests\EvalClientTests\CPPEvalClientTest\CPPEvalClientTest.vcxproj", "{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}"
|
||||
ProjectSection(ProjectDependencies) = postProject
|
||||
{482999D1-B7E2-466E-9F8D-2119F93EAFD9} = {482999D1-B7E2-466E-9F8D-2119F93EAFD9}
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CSEvalClientTest", "Tests\EndToEndTests\EvalClientTests\CSEvalClientTest\CSEvalClientTest.csproj", "{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}"
|
||||
EndProject
|
||||
Global
|
||||
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||
Debug_CpuOnly|x64 = Debug_CpuOnly|x64
|
||||
|
|
@ -1484,22 +1522,6 @@ Global
|
|||
{CC8DDDCB-D53A-4B30-8596-AEF1C493DB31}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{CC8DDDCB-D53A-4B30-8596-AEF1C493DB31}.Release|x64.ActiveCfg = Release|x64
|
||||
{CC8DDDCB-D53A-4B30-8596-AEF1C493DB31}.Release|x64.Build.0 = Release|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug|x64.Build.0 = Debug|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release_CpuOnly|x64.ActiveCfg = Release_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release|x64.ActiveCfg = Release|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release|x64.Build.0 = Release|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug|x64.Build.0 = Debug|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release_CpuOnly|x64.ActiveCfg = Release_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release|x64.ActiveCfg = Release|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release|x64.Build.0 = Release|x64
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Debug|x64.ActiveCfg = Debug|x64
|
||||
|
|
@ -1508,6 +1530,22 @@ Global
|
|||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Release|x64.ActiveCfg = Release|x64
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456}.Release|x64.Build.0 = Release|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Debug|x64.Build.0 = Debug|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release_CpuOnly|x64.ActiveCfg = Release_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release|x64.ActiveCfg = Release|x64
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}.Release|x64.Build.0 = Release|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug_CpuOnly|x64.ActiveCfg = Debug_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug_CpuOnly|x64.Build.0 = Debug_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Debug|x64.Build.0 = Debug|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release_CpuOnly|x64.ActiveCfg = Release_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release_CpuOnly|x64.Build.0 = Release_CpuOnly|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release|x64.ActiveCfg = Release|x64
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}.Release|x64.Build.0 = Release|x64
|
||||
EndGlobalSection
|
||||
GlobalSection(SolutionProperties) = preSolution
|
||||
HideSolutionNode = FALSE
|
||||
|
|
@ -1663,9 +1701,6 @@ Global
|
|||
{E5606ECE-48CA-4464-BB12-09D81D02B9EF} = {DD043083-71A4-409A-AA91-F9C548DCF7EC}
|
||||
{F4CC3AB2-0DB2-4281-929A-2E68E30F0F6E} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
|
||||
{CC8DDDCB-D53A-4B30-8596-AEF1C493DB31} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
|
||||
{3385EBEA-5F97-4B2B-9F30-0E6D7F91B9CA} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF} = {3385EBEA-5F97-4B2B-9F30-0E6D7F91B9CA}
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E} = {3385EBEA-5F97-4B2B-9F30-0E6D7F91B9CA}
|
||||
{9F999212-AFC5-4EAC-AA78-F7247D46C456} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
|
||||
{2230BF3D-4317-4A3F-A743-DDD6160503F8} = {8BE0642A-A3AA-4A64-95D0-C78FB285B2A4}
|
||||
{CC143D08-567D-4DAC-9E14-264749C19039} = {8BE0642A-A3AA-4A64-95D0-C78FB285B2A4}
|
||||
|
|
@ -1677,5 +1712,12 @@ Global
|
|||
{4A59163B-1EDE-4439-9E7D-40A30B82A3A0} = {2230BF3D-4317-4A3F-A743-DDD6160503F8}
|
||||
{B586AA4C-0BB9-4629-9EDA-25FF2618AC9F} = {FB7AF7B9-6BEA-459F-94D9-94D53916D2B6}
|
||||
{C2102C39-BF5F-4B12-9C41-849D1ED35EE8} = {B586AA4C-0BB9-4629-9EDA-25FF2618AC9F}
|
||||
{4DDFD21D-E82C-4321-B380-8A9382E18107} = {19EE975B-232D-49F0-94C7-6F1C6424FB53}
|
||||
{D293C7C7-B7D7-47CE-A796-45262C16F980} = {19EE975B-232D-49F0-94C7-6F1C6424FB53}
|
||||
{4F534076-39A5-40E2-BF87-64EC464C52B2} = {19EE975B-232D-49F0-94C7-6F1C6424FB53}
|
||||
{31263D7F-F590-475E-B4F0-7DFA4E4FF4B8} = {19EE975B-232D-49F0-94C7-6F1C6424FB53}
|
||||
{05E45AF7-C069-4057-BC16-0A532D068CE4} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5}
|
||||
{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E} = {05E45AF7-C069-4057-BC16-0A532D068CE4}
|
||||
{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF} = {05E45AF7-C069-4057-BC16-0A532D068CE4}
|
||||
EndGlobalSection
|
||||
EndGlobal
|
||||
|
|
|
|||
|
|
@ -0,0 +1,28 @@
|
|||
|
||||
Microsoft Visual Studio Solution File, Format Version 12.00
|
||||
# Visual Studio 2013
|
||||
VisualStudioVersion = 12.0.40629.0
|
||||
MinimumVisualStudioVersion = 10.0.40219.1
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CNTKAzureTutorial01", "CNTKAzureTutorial01\CNTKAzureTutorial01.csproj", "{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}"
|
||||
EndProject
|
||||
Global
|
||||
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||
Debug|Any CPU = Debug|Any CPU
|
||||
Debug|x64 = Debug|x64
|
||||
Release|Any CPU = Release|Any CPU
|
||||
Release|x64 = Release|x64
|
||||
EndGlobalSection
|
||||
GlobalSection(ProjectConfigurationPlatforms) = postSolution
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Debug|Any CPU.Build.0 = Debug|Any CPU
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Debug|x64.Build.0 = Debug|x64
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Release|Any CPU.ActiveCfg = Release|Any CPU
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Release|Any CPU.Build.0 = Release|Any CPU
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Release|x64.ActiveCfg = Release|x64
|
||||
{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}.Release|x64.Build.0 = Release|x64
|
||||
EndGlobalSection
|
||||
GlobalSection(SolutionProperties) = preSolution
|
||||
HideSolutionNode = FALSE
|
||||
EndGlobalSection
|
||||
EndGlobal
|
||||
|
|
@ -0,0 +1,271 @@
|
|||
using System.Globalization;
|
||||
using System.Linq;
|
||||
using System.Web.Http;
|
||||
using System.Web.Http.Description;
|
||||
using Swashbuckle.Application;
|
||||
using Swashbuckle.Swagger;
|
||||
using WebActivatorEx;
|
||||
using CNTKAzureTutorial01;
|
||||
|
||||
[assembly: PreApplicationStartMethod(typeof(SwaggerConfig), "Register")]
|
||||
|
||||
namespace CNTKAzureTutorial01
|
||||
{
|
||||
public class SwaggerConfig
|
||||
{
|
||||
public static void Register()
|
||||
{
|
||||
var thisAssembly = typeof(SwaggerConfig).Assembly;
|
||||
|
||||
GlobalConfiguration.Configuration
|
||||
.EnableSwagger(c =>
|
||||
{
|
||||
// By default, the service root url is inferred from the request used to access the docs.
|
||||
// However, there may be situations (e.g. proxy and load-balanced environments) where this does not
|
||||
// resolve correctly. You can workaround this by providing your own code to determine the root URL.
|
||||
//
|
||||
//c.RootUrl(req => GetRootUrlFromAppConfig());
|
||||
|
||||
// If schemes are not explicitly provided in a Swagger 2.0 document, then the scheme used to access
|
||||
// the docs is taken as the default. If your API supports multiple schemes and you want to be explicit
|
||||
// about them, you can use the "Schemes" option as shown below.
|
||||
//
|
||||
//c.Schemes(new[] { "http", "https" });
|
||||
|
||||
// Use "SingleApiVersion" to describe a single version API. Swagger 2.0 includes an "Info" object to
|
||||
// hold additional metadata for an API. Version and title are required but you can also provide
|
||||
// additional fields by chaining methods off SingleApiVersion.
|
||||
//
|
||||
c.SingleApiVersion("v1", "CNTKAzureTutorial01");
|
||||
|
||||
// If your API has multiple versions, use "MultipleApiVersions" instead of "SingleApiVersion".
|
||||
// In this case, you must provide a lambda that tells Swashbuckle which actions should be
|
||||
// included in the docs for a given API version. Like "SingleApiVersion", each call to "Version"
|
||||
// returns an "Info" builder so you can provide additional metadata per API version.
|
||||
//
|
||||
//c.MultipleApiVersions(
|
||||
// (apiDesc, targetApiVersion) => ResolveVersionSupportByRouteConstraint(apiDesc, targetApiVersion),
|
||||
// (vc) =>
|
||||
// {
|
||||
// vc.Version("v2", "Swashbuckle Dummy API V2");
|
||||
// vc.Version("v1", "Swashbuckle Dummy API V1");
|
||||
// });
|
||||
|
||||
// You can use "BasicAuth", "ApiKey" or "OAuth2" options to describe security schemes for the API.
|
||||
// See https://github.com/swagger-api/swagger-spec/blob/master/versions/2.0.md for more details.
|
||||
// NOTE: These only define the schemes and need to be coupled with a corresponding "security" property
|
||||
// at the document or operation level to indicate which schemes are required for an operation. To do this,
|
||||
// you'll need to implement a custom IDocumentFilter and/or IOperationFilter to set these properties
|
||||
// according to your specific authorization implementation
|
||||
//
|
||||
//c.BasicAuth("basic")
|
||||
// .Description("Basic HTTP Authentication");
|
||||
//
|
||||
//c.ApiKey("apiKey")
|
||||
// .Description("API Key Authentication")
|
||||
// .Name("apiKey")
|
||||
// .In("header");
|
||||
//
|
||||
//c.OAuth2("oauth2")
|
||||
// .Description("OAuth2 Implicit Grant")
|
||||
// .Flow("implicit")
|
||||
// .AuthorizationUrl("http://petstore.swagger.wordnik.com/api/oauth/dialog")
|
||||
// //.TokenUrl("https://tempuri.org/token")
|
||||
// .Scopes(scopes =>
|
||||
// {
|
||||
// scopes.Add("read", "Read access to protected resources");
|
||||
// scopes.Add("write", "Write access to protected resources");
|
||||
// });
|
||||
|
||||
// Set this flag to omit descriptions for any actions decorated with the Obsolete attribute
|
||||
//c.IgnoreObsoleteActions();
|
||||
|
||||
// Each operation be assigned one or more tags which are then used by consumers for various reasons.
|
||||
// For example, the swagger-ui groups operations according to the first tag of each operation.
|
||||
// By default, this will be controller name but you can use the "GroupActionsBy" option to
|
||||
// override with any value.
|
||||
//
|
||||
//c.GroupActionsBy(apiDesc => apiDesc.HttpMethod.ToString());
|
||||
|
||||
// You can also specify a custom sort order for groups (as defined by "GroupActionsBy") to dictate
|
||||
// the order in which operations are listed. For example, if the default grouping is in place
|
||||
// (controller name) and you specify a descending alphabetic sort order, then actions from a
|
||||
// ProductsController will be listed before those from a CustomersController. This is typically
|
||||
// used to customize the order of groupings in the swagger-ui.
|
||||
//
|
||||
//c.OrderActionGroupsBy(new DescendingAlphabeticComparer());
|
||||
|
||||
// If you annotate Controllers and API Types with
|
||||
// Xml comments (http://msdn.microsoft.com/en-us/library/b2s063f7(v=vs.110).aspx), you can incorporate
|
||||
// those comments into the generated docs and UI. You can enable this by providing the path to one or
|
||||
// more Xml comment files.
|
||||
//
|
||||
//c.IncludeXmlComments(GetXmlCommentsPath());
|
||||
|
||||
// Swashbuckle makes a best attempt at generating Swagger compliant JSON schemas for the various types
|
||||
// exposed in your API. However, there may be occasions when more control of the output is needed.
|
||||
// This is supported through the "MapType" and "SchemaFilter" options:
|
||||
//
|
||||
// Use the "MapType" option to override the Schema generation for a specific type.
|
||||
// It should be noted that the resulting Schema will be placed "inline" for any applicable Operations.
|
||||
// While Swagger 2.0 supports inline definitions for "all" Schema types, the swagger-ui tool does not.
|
||||
// It expects "complex" Schemas to be defined separately and referenced. For this reason, you should only
|
||||
// use the "MapType" option when the resulting Schema is a primitive or array type. If you need to alter a
|
||||
// complex Schema, use a Schema filter.
|
||||
//
|
||||
//c.MapType<ProductType>(() => new Schema { type = "integer", format = "int32" });
|
||||
|
||||
// If you want to post-modify "complex" Schemas once they've been generated, across the board or for a
|
||||
// specific type, you can wire up one or more Schema filters.
|
||||
//
|
||||
//c.SchemaFilter<ApplySchemaVendorExtensions>();
|
||||
|
||||
// In a Swagger 2.0 document, complex types are typically declared globally and referenced by unique
|
||||
// Schema Id. By default, Swashbuckle does NOT use the full type name in Schema Ids. In most cases, this
|
||||
// works well because it prevents the "implementation detail" of type namespaces from leaking into your
|
||||
// Swagger docs and UI. However, if you have multiple types in your API with the same class name, you'll
|
||||
// need to opt out of this behavior to avoid Schema Id conflicts.
|
||||
//
|
||||
//c.UseFullTypeNameInSchemaIds();
|
||||
|
||||
// Alternatively, you can provide your own custom strategy for inferring SchemaId's for
|
||||
// describing "complex" types in your API.
|
||||
//
|
||||
//c.SchemaId(t => t.FullName.Contains('`') ? t.FullName.Substring(0, t.FullName.IndexOf('`')) : t.FullName);
|
||||
|
||||
// Set this flag to omit schema property descriptions for any type properties decorated with the
|
||||
// Obsolete attribute
|
||||
//c.IgnoreObsoleteProperties();
|
||||
|
||||
// In accordance with the built in JsonSerializer, Swashbuckle will, by default, describe enums as integers.
|
||||
// You can change the serializer behavior by configuring the StringToEnumConverter globally or for a given
|
||||
// enum type. Swashbuckle will honor this change out-of-the-box. However, if you use a different
|
||||
// approach to serialize enums as strings, you can also force Swashbuckle to describe them as strings.
|
||||
//
|
||||
//c.DescribeAllEnumsAsStrings();
|
||||
|
||||
// Similar to Schema filters, Swashbuckle also supports Operation and Document filters:
|
||||
//
|
||||
// Post-modify Operation descriptions once they've been generated by wiring up one or more
|
||||
// Operation filters.
|
||||
//
|
||||
//c.OperationFilter<AddDefaultResponse>();
|
||||
//
|
||||
// If you've defined an OAuth2 flow as described above, you could use a custom filter
|
||||
// to inspect some attribute on each action and infer which (if any) OAuth2 scopes are required
|
||||
// to execute the operation
|
||||
//
|
||||
//c.OperationFilter<AssignOAuth2SecurityRequirements>();
|
||||
//
|
||||
|
||||
// Set filter to eliminate duplicate operation ids from being generated
|
||||
// when there are multiple operations with the same verb in the API.
|
||||
// ***
|
||||
// If you would prefer to globally impact the Swagger operation id's rather
|
||||
// than control them on a per-action method basis, uncomment the next line
|
||||
// and the IncludeParameterNamesInOperationIdFilter class below.
|
||||
// ***
|
||||
//c.OperationFilter<IncludeParameterNamesInOperationIdFilter>();
|
||||
//
|
||||
|
||||
// Post-modify the entire Swagger document by wiring up one or more Document filters.
|
||||
// This gives full control to modify the final SwaggerDocument. You should have a good understanding of
|
||||
// the Swagger 2.0 spec. - https://github.com/swagger-api/swagger-spec/blob/master/versions/2.0.md
|
||||
// before using this option.
|
||||
//
|
||||
//c.DocumentFilter<ApplyDocumentVendorExtensions>();
|
||||
|
||||
// In contrast to WebApi, Swagger 2.0 does not include the query string component when mapping a URL
|
||||
// to an action. As a result, Swashbuckle will raise an exception if it encounters multiple actions
|
||||
// with the same path (sans query string) and HTTP method. You can workaround this by providing a
|
||||
// custom strategy to pick a winner or merge the descriptions for the purposes of the Swagger docs
|
||||
//
|
||||
//c.ResolveConflictingActions(apiDescriptions => apiDescriptions.First());
|
||||
|
||||
// Wrap the default SwaggerGenerator with additional behavior (e.g. caching) or provide an
|
||||
// alternative implementation for ISwaggerProvider with the CustomProvider option.
|
||||
//
|
||||
//c.CustomProvider((defaultProvider) => new CachingSwaggerProvider(defaultProvider));
|
||||
// ***** Uncomment the following to enable the swagger UI *****
|
||||
/*
|
||||
})
|
||||
.EnableSwaggerUi(c =>
|
||||
{
|
||||
*/
|
||||
// Use the "InjectStylesheet" option to enrich the UI with one or more additional CSS stylesheets.
|
||||
// The file must be included in your project as an "Embedded Resource", and then the resource's
|
||||
// "Logical Name" is passed to the method as shown below.
|
||||
//
|
||||
//c.InjectStylesheet(containingAssembly, "Swashbuckle.Dummy.SwaggerExtensions.testStyles1.css");
|
||||
|
||||
// Use the "InjectJavaScript" option to invoke one or more custom JavaScripts after the swagger-ui
|
||||
// has loaded. The file must be included in your project as an "Embedded Resource", and then the resource's
|
||||
// "Logical Name" is passed to the method as shown above.
|
||||
//
|
||||
//c.InjectJavaScript(thisAssembly, "Swashbuckle.Dummy.SwaggerExtensions.testScript1.js");
|
||||
|
||||
// The swagger-ui renders boolean data types as a dropdown. By default, it provides "true" and "false"
|
||||
// strings as the possible choices. You can use this option to change these to something else,
|
||||
// for example 0 and 1.
|
||||
//
|
||||
//c.BooleanValues(new[] { "0", "1" });
|
||||
|
||||
// By default, swagger-ui will validate specs against swagger.io's online validator and display the result
|
||||
// in a badge at the bottom of the page. Use these options to set a different validator URL or to disable the
|
||||
// feature entirely.
|
||||
//c.SetValidatorUrl("http://localhost/validator");
|
||||
//c.DisableValidator();
|
||||
|
||||
// Use this option to control how the Operation listing is displayed.
|
||||
// It can be set to "None" (default), "List" (shows operations for each resource),
|
||||
// or "Full" (fully expanded: shows operations and their details).
|
||||
//
|
||||
//c.DocExpansion(DocExpansion.List);
|
||||
|
||||
// Use the CustomAsset option to provide your own version of assets used in the swagger-ui.
|
||||
// It's typically used to instruct Swashbuckle to return your version instead of the default
|
||||
// when a request is made for "index.html". As with all custom content, the file must be included
|
||||
// in your project as an "Embedded Resource", and then the resource's "Logical Name" is passed to
|
||||
// the method as shown below.
|
||||
//
|
||||
//c.CustomAsset("index", containingAssembly, "YourWebApiProject.SwaggerExtensions.index.html");
|
||||
|
||||
// If your API has multiple versions and you've applied the MultipleApiVersions setting
|
||||
// as described above, you can also enable a select box in the swagger-ui, that displays
|
||||
// a discovery URL for each version. This provides a convenient way for users to browse documentation
|
||||
// for different API versions.
|
||||
//
|
||||
//c.EnableDiscoveryUrlSelector();
|
||||
|
||||
// If your API supports the OAuth2 Implicit flow, and you've described it correctly, according to
|
||||
// the Swagger 2.0 specification, you can enable UI support as shown below.
|
||||
//
|
||||
//c.EnableOAuth2Support("test-client-id", "test-realm", "Swagger UI");
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// If you would prefer to control the Swagger Operation ID
|
||||
/// values globally, uncomment this class, as well as the
|
||||
/// call above that wires this Operation Filter into
|
||||
/// the pipeline.
|
||||
/// </summary>
|
||||
/*
|
||||
internal class IncludeParameterNamesInOperationIdFilter : IOperationFilter
|
||||
{
|
||||
public void Apply(Operation operation, SchemaRegistry schemaRegistry, ApiDescription apiDescription)
|
||||
{
|
||||
if (operation.parameters != null)
|
||||
{
|
||||
// Select the capitalized parameter names
|
||||
var parameters = operation.parameters.Select(
|
||||
p => CultureInfo.InvariantCulture.TextInfo.ToTitleCase(p.name));
|
||||
|
||||
// Set the operation id to match the format "OperationByParam1AndParam2"
|
||||
operation.operationId = $"{operation.operationId}By{string.Join("And", parameters)}";
|
||||
}
|
||||
}
|
||||
}
|
||||
*/
|
||||
}
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using System.Web.Http;
|
||||
|
||||
namespace CNTKAzureTutorial01
|
||||
{
|
||||
public static class WebApiConfig
|
||||
{
|
||||
public static void Register(HttpConfiguration config)
|
||||
{
|
||||
// Web API configuration and services
|
||||
|
||||
// Web API routes
|
||||
config.MapHttpAttributeRoutes();
|
||||
|
||||
config.Routes.MapHttpRoute(
|
||||
name: "DefaultApi",
|
||||
routeTemplate: "api/{controller}/{id}",
|
||||
defaults: new { id = RouteParameter.Optional }
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,166 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<Project ToolsVersion="12.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||
<Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
|
||||
<PropertyGroup>
|
||||
<Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
|
||||
<Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
|
||||
<ProductVersion />
|
||||
<SchemaVersion>2.0</SchemaVersion>
|
||||
<ProjectGuid>{2B7D7FFE-EC2B-4D20-8EB8-56FBDDF9EBE3}</ProjectGuid>
|
||||
<ProjectTypeGuids>{349c5851-65df-11da-9384-00065b846f21};{fae04ec0-301f-11d3-bf4b-00c04f79efbc}</ProjectTypeGuids>
|
||||
<OutputType>Library</OutputType>
|
||||
<AppDesignerFolder>Properties</AppDesignerFolder>
|
||||
<RootNamespace>CNTKAzureTutorial01</RootNamespace>
|
||||
<AssemblyName>CNTKAzureTutorial01</AssemblyName>
|
||||
<TargetFrameworkVersion>v4.5</TargetFrameworkVersion>
|
||||
<UseIISExpress>true</UseIISExpress>
|
||||
<IISExpressSSLPort />
|
||||
<IISExpressAnonymousAuthentication />
|
||||
<IISExpressWindowsAuthentication />
|
||||
<IISExpressUseClassicPipelineMode />
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
|
||||
<DebugSymbols>true</DebugSymbols>
|
||||
<DebugType>full</DebugType>
|
||||
<Optimize>false</Optimize>
|
||||
<OutputPath>bin\</OutputPath>
|
||||
<DefineConstants>DEBUG;TRACE</DefineConstants>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<WarningLevel>4</WarningLevel>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
|
||||
<DebugType>pdbonly</DebugType>
|
||||
<Optimize>true</Optimize>
|
||||
<OutputPath>bin\</OutputPath>
|
||||
<DefineConstants>TRACE</DefineConstants>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<WarningLevel>4</WarningLevel>
|
||||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="Microsoft.CSharp" />
|
||||
<Reference Include="Microsoft.Web.Infrastructure, Version=1.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35, processorArchitecture=MSIL">
|
||||
<Private>True</Private>
|
||||
<HintPath>..\packages\Microsoft.Web.Infrastructure.1.0.0.0\lib\net40\Microsoft.Web.Infrastructure.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="Swashbuckle.Core">
|
||||
<HintPath>..\packages\Swashbuckle.Core.5.3.1\lib\net40\Swashbuckle.Core.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="System.IdentityModel.Tokens.Jwt">
|
||||
<HintPath>..\packages\System.IdentityModel.Tokens.Jwt.4.0.0\lib\net45\System.IdentityModel.Tokens.Jwt.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="System.Net.Http" />
|
||||
<Reference Include="System.Web.DynamicData" />
|
||||
<Reference Include="System.Web.Entity" />
|
||||
<Reference Include="System.Web.ApplicationServices" />
|
||||
<Reference Include="System.ComponentModel.DataAnnotations" />
|
||||
<Reference Include="System" />
|
||||
<Reference Include="System.Data" />
|
||||
<Reference Include="System.Core" />
|
||||
<Reference Include="System.Data.DataSetExtensions" />
|
||||
<Reference Include="System.Web.Extensions" />
|
||||
<Reference Include="System.Xml.Linq" />
|
||||
<Reference Include="System.Drawing" />
|
||||
<Reference Include="System.Web" />
|
||||
<Reference Include="System.Xml" />
|
||||
<Reference Include="System.Configuration" />
|
||||
<Reference Include="System.Web.Services" />
|
||||
<Reference Include="System.EnterpriseServices" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Folder Include="App_Data\" />
|
||||
<Folder Include="CNTK\Models\" />
|
||||
<Folder Include="Models\" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="WebActivatorEx">
|
||||
<HintPath>..\packages\WebActivatorEx.2.0.6\lib\net40\WebActivatorEx.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="Newtonsoft.Json">
|
||||
<HintPath>..\packages\Newtonsoft.Json.6.0.8\lib\net45\Newtonsoft.Json.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="System.Net.Http.Formatting">
|
||||
<HintPath>..\packages\Microsoft.AspNet.WebApi.Client.5.2.3\lib\net45\System.Net.Http.Formatting.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="System.Web.Http">
|
||||
<HintPath>..\packages\Microsoft.AspNet.WebApi.Core.5.2.3\lib\net45\System.Web.Http.dll</HintPath>
|
||||
</Reference>
|
||||
<Reference Include="System.Web.Http.WebHost">
|
||||
<HintPath>..\packages\Microsoft.AspNet.WebApi.WebHost.5.2.3\lib\net45\System.Web.Http.WebHost.dll</HintPath>
|
||||
</Reference>
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Content Include="Global.asax" />
|
||||
<Content Include="Web.config" />
|
||||
<Content Include="packages.config" />
|
||||
<None Include="Project_Readme.html" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<Compile Include="App_Start\SwaggerConfig.cs" />
|
||||
<Compile Include="App_Start\WebApiConfig.cs" />
|
||||
<Compile Include="CNTKImageProcessing.cs" />
|
||||
<Compile Include="Controllers\ValuesController.cs" />
|
||||
<Compile Include="Global.asax.cs">
|
||||
<DependentUpon>Global.asax</DependentUpon>
|
||||
</Compile>
|
||||
<Compile Include="Properties\AssemblyInfo.cs" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<None Include="Web.Debug.config">
|
||||
<DependentUpon>Web.config</DependentUpon>
|
||||
</None>
|
||||
<None Include="Web.Release.config">
|
||||
<DependentUpon>Web.config</DependentUpon>
|
||||
</None>
|
||||
</ItemGroup>
|
||||
<ItemGroup />
|
||||
<PropertyGroup>
|
||||
<VisualStudioVersion Condition="'$(VisualStudioVersion)' == ''">10.0</VisualStudioVersion>
|
||||
<VSToolsPath Condition="'$(VSToolsPath)' == ''">$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)</VSToolsPath>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug|x64'">
|
||||
<DebugSymbols>true</DebugSymbols>
|
||||
<OutputPath>bin\</OutputPath>
|
||||
<DefineConstants>DEBUG;TRACE</DefineConstants>
|
||||
<DebugType>full</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Release|x64'">
|
||||
<OutputPath>bin\</OutputPath>
|
||||
<DefineConstants>TRACE</DefineConstants>
|
||||
<Optimize>true</Optimize>
|
||||
<DebugType>pdbonly</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
</PropertyGroup>
|
||||
<Import Project="$(MSBuildBinPath)\Microsoft.CSharp.targets" />
|
||||
<Import Project="$(VSToolsPath)\WebApplications\Microsoft.WebApplication.targets" Condition="'$(VSToolsPath)' != ''" />
|
||||
<Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v10.0\WebApplications\Microsoft.WebApplication.targets" Condition="false" />
|
||||
<ProjectExtensions>
|
||||
<VisualStudio>
|
||||
<FlavorProperties GUID="{349c5851-65df-11da-9384-00065b846f21}">
|
||||
<WebProjectProperties>
|
||||
<UseIIS>True</UseIIS>
|
||||
<AutoAssignPort>True</AutoAssignPort>
|
||||
<DevelopmentServerPort>3469</DevelopmentServerPort>
|
||||
<DevelopmentServerVPath>/</DevelopmentServerVPath>
|
||||
<IISUrl>http://localhost:3469/</IISUrl>
|
||||
<NTLMAuthentication>False</NTLMAuthentication>
|
||||
<UseCustomServer>False</UseCustomServer>
|
||||
<CustomServerUrl>
|
||||
</CustomServerUrl>
|
||||
<SaveServerSettingsInUserFile>False</SaveServerSettingsInUserFile>
|
||||
</WebProjectProperties>
|
||||
</FlavorProperties>
|
||||
</VisualStudio>
|
||||
</ProjectExtensions>
|
||||
<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
|
||||
Other similar extension points exist, see Microsoft.Common.targets.
|
||||
<Target Name="BeforeBuild">
|
||||
</Target>
|
||||
<Target Name="AfterBuild">
|
||||
</Target>
|
||||
-->
|
||||
</Project>
|
||||
|
|
@ -0,0 +1,114 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Drawing;
|
||||
using System.Drawing.Imaging;
|
||||
using System.Linq;
|
||||
using System.Threading.Tasks;
|
||||
using System.Web;
|
||||
|
||||
namespace CNTKAzureTutorial01
|
||||
{
|
||||
static class ImageProcessing
|
||||
{
|
||||
/// <summary>
|
||||
/// Extracts image pixels in CHW using parallelization
|
||||
/// </summary>
|
||||
/// <param name="image">The bitmap image to extract features from</param>
|
||||
/// <returns>A list of pixels in CHW order</returns>
|
||||
public static List<float> ParallelExtractCHW(this Bitmap image)
|
||||
{
|
||||
// We use local variables to avoid contention on the image object through the multiple threads.
|
||||
int channelStride = image.Width * image.Height;
|
||||
int imageWidth = image.Width;
|
||||
int imageHeight = image.Height;
|
||||
|
||||
var features = new byte[imageWidth * imageHeight * 3];
|
||||
var bitmapData = image.LockBits(new System.Drawing.Rectangle(0, 0, imageWidth, imageHeight), ImageLockMode.ReadOnly, image.PixelFormat);
|
||||
IntPtr ptr = bitmapData.Scan0;
|
||||
int bytes = Math.Abs(bitmapData.Stride) * bitmapData.Height;
|
||||
byte[] rgbValues = new byte[bytes];
|
||||
|
||||
int stride = bitmapData.Stride;
|
||||
|
||||
// Copy the RGB values into the array.
|
||||
System.Runtime.InteropServices.Marshal.Copy(ptr, rgbValues, 0, bytes);
|
||||
|
||||
// The mapping depends on the pixel format
|
||||
// The mapPixel lambda will return the right color channel for the desired pixel
|
||||
Func<int, int, int, int> mapPixel = GetPixelMapper(image.PixelFormat, stride);
|
||||
|
||||
Parallel.For(0, imageHeight, (int h) =>
|
||||
{
|
||||
Parallel.For(0, imageWidth, (int w) =>
|
||||
{
|
||||
Parallel.For(0, 3, (int c) =>
|
||||
{
|
||||
features[channelStride * c + imageWidth * h + w] = rgbValues[mapPixel(h, w, c)];
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
image.UnlockBits(bitmapData);
|
||||
|
||||
return features.Select(b => (float)b).ToList();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Returns a function for extracting the R-G-B values properly from an image based on its pixel format
|
||||
/// </summary>
|
||||
/// <param name="pixelFormat">The image's pixel format</param>
|
||||
/// <param name="heightStride">The stride (row byte count)</param>
|
||||
/// <returns>A function with signature (height, width, channel) returning the corresponding color value</returns>
|
||||
private static Func<int, int, int, int> GetPixelMapper(PixelFormat pixelFormat, int heightStride)
|
||||
{
|
||||
switch (pixelFormat)
|
||||
{
|
||||
case PixelFormat.Format32bppArgb:
|
||||
return (h, w, c) => h * heightStride + w * 4 + c; // bytes are B-G-R-A
|
||||
case PixelFormat.Format24bppRgb:
|
||||
default:
|
||||
return (h, w, c) => h * heightStride + w * 3 + c; // bytes are B-G-R
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Resizes an image
|
||||
/// </summary>
|
||||
/// <param name="image">The image to resize</param>
|
||||
/// <param name="width">New width in pixels</param>
|
||||
/// <param name="height">New height in pixesl</param>
|
||||
/// <param name="useHighQuality">Resize quality</param>
|
||||
/// <returns>The resized image</returns>
|
||||
public static Bitmap Resize(this Bitmap image, int width, int height, bool useHighQuality)
|
||||
{
|
||||
var newImg = new Bitmap(width, height);
|
||||
|
||||
newImg.SetResolution(image.HorizontalResolution, image.VerticalResolution);
|
||||
|
||||
using (var g = Graphics.FromImage(newImg))
|
||||
{
|
||||
g.CompositingMode = System.Drawing.Drawing2D.CompositingMode.SourceCopy;
|
||||
if (useHighQuality)
|
||||
{
|
||||
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
|
||||
g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.HighQuality;
|
||||
g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
|
||||
g.PixelOffsetMode = System.Drawing.Drawing2D.PixelOffsetMode.HighQuality;
|
||||
}
|
||||
else
|
||||
{
|
||||
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.Default;
|
||||
g.CompositingQuality = System.Drawing.Drawing2D.CompositingQuality.Default;
|
||||
g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.Default;
|
||||
g.PixelOffsetMode = System.Drawing.Drawing2D.PixelOffsetMode.Default;
|
||||
}
|
||||
|
||||
var attributes = new ImageAttributes();
|
||||
attributes.SetWrapMode(System.Drawing.Drawing2D.WrapMode.TileFlipXY);
|
||||
g.DrawImage(image, new System.Drawing.Rectangle(0, 0, width, height), 0, 0, image.Width, image.Height, GraphicsUnit.Pixel, attributes);
|
||||
}
|
||||
|
||||
return newImg;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using System.Net;
|
||||
using System.Net.Http;
|
||||
using System.Web.Http;
|
||||
using Swashbuckle.Swagger.Annotations;
|
||||
|
||||
using Microsoft.MSR.CNTK.Extensibility.Managed;
|
||||
using System.Threading.Tasks;
|
||||
using System.IO;
|
||||
using System.Drawing;
|
||||
|
||||
|
||||
namespace CNTKAzureTutorial01.Controllers
|
||||
{
|
||||
public class ValuesController : ApiController
|
||||
{
|
||||
// GET api/values
|
||||
[SwaggerOperation("GetAll")]
|
||||
public async Task<IEnumerable<string>> Get()
|
||||
{
|
||||
return await this.EvaluateCustomDNN("http://3.bp.blogspot.com/-Mwr4UZALiA0/TWBt-3vFR8I/AAAAAAAAA4Y/0tXjI-NhVPM/s1600/j0262568.jpg");
|
||||
}
|
||||
|
||||
// GET api/values/5
|
||||
[SwaggerOperation("GetById")]
|
||||
[SwaggerResponse(HttpStatusCode.OK)]
|
||||
[SwaggerResponse(HttpStatusCode.NotFound)]
|
||||
public string Get(int id)
|
||||
{
|
||||
return "value";
|
||||
}
|
||||
|
||||
// POST api/values
|
||||
[SwaggerOperation("Create")]
|
||||
[SwaggerResponse(HttpStatusCode.Created)]
|
||||
public void Post([FromBody]string value)
|
||||
{
|
||||
}
|
||||
|
||||
// PUT api/values/5
|
||||
[SwaggerOperation("Update")]
|
||||
[SwaggerResponse(HttpStatusCode.OK)]
|
||||
[SwaggerResponse(HttpStatusCode.NotFound)]
|
||||
public void Put(int id, [FromBody]string value)
|
||||
{
|
||||
}
|
||||
|
||||
// DELETE api/values/5
|
||||
[SwaggerOperation("Delete")]
|
||||
[SwaggerResponse(HttpStatusCode.OK)]
|
||||
[SwaggerResponse(HttpStatusCode.NotFound)]
|
||||
public void Delete(int id)
|
||||
{
|
||||
}
|
||||
|
||||
|
||||
|
||||
public async Task<string[]> EvaluateCustomDNN(string imageUrl)
|
||||
{
|
||||
string domainBaseDirectory = AppDomain.CurrentDomain.BaseDirectory;
|
||||
string workingDirectory = Environment.CurrentDirectory;
|
||||
|
||||
try
|
||||
{
|
||||
// This example requires the RestNet_18 model.
|
||||
// The model can be downloaded from <see cref="https://www.cntk.ai/resnet/ResNet_18.model"/>
|
||||
int numThreads = 1;
|
||||
List<float> outputs;
|
||||
|
||||
using (var model = new IEvaluateModelManagedF())
|
||||
{
|
||||
// initialize the evaluation engine
|
||||
// TODO: initializing the evaluation engine should be one only once at the beginning
|
||||
var initParams = string.Format("numCPUThreads={0}", numThreads);
|
||||
model.Init(initParams);
|
||||
|
||||
// load the model
|
||||
string modelFilePath = Path.Combine(domainBaseDirectory, @"CNTK\Models\ResNet_18.model");
|
||||
var modelOption = string.Format("modelPath=\"{0}\"", modelFilePath);
|
||||
model.CreateNetwork(modelOption, deviceId: -1);
|
||||
|
||||
// Prepare input value in the appropriate structure and size
|
||||
var inDims = model.GetNodeDimensions(NodeGroup.Input);
|
||||
if (inDims.First().Value != 224 * 224 * 3)
|
||||
{
|
||||
throw new CNTKRuntimeException(string.Format("The input dimension for {0} is {1} which is not the expected size of {2}.", inDims.First(), inDims.First().Value, 224 * 224 * 3), string.Empty);
|
||||
}
|
||||
|
||||
// Transform the image
|
||||
System.Net.Http.HttpClient httpClient = new HttpClient();
|
||||
Stream imageStream = await httpClient.GetStreamAsync(imageUrl);
|
||||
Bitmap bmp = new Bitmap(Bitmap.FromStream(imageStream));
|
||||
|
||||
var resized = bmp.Resize(224, 224, true);
|
||||
var resizedCHW = resized.ParallelExtractCHW();
|
||||
var inputs = new Dictionary<string, List<float>>() { { inDims.First().Key, resizedCHW } };
|
||||
|
||||
// We can call the evaluate method and get back the results (single layer output)...
|
||||
var outDims = model.GetNodeDimensions(NodeGroup.Output);
|
||||
outputs = model.Evaluate(inputs, outDims.First().Key);
|
||||
}
|
||||
|
||||
List<string> o = new List<string>();
|
||||
|
||||
foreach (float f in outputs)
|
||||
{
|
||||
o.Add(f.ToString());
|
||||
}
|
||||
|
||||
return o.ToArray<string>();
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
return new string[] { string.Format("domainBase directory {0}, workingDirectory {1}, exception details: {2}.", domainBaseDirectory, workingDirectory, ex.ToString()) };
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
<%@ Application Codebehind="Global.asax.cs" Inherits="CNTKAzureTutorial01.WebApiApplication" Language="C#" %>
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using System.Web;
|
||||
using System.Web.Http;
|
||||
using System.Web.Routing;
|
||||
|
||||
namespace CNTKAzureTutorial01
|
||||
{
|
||||
public class WebApiApplication : System.Web.HttpApplication
|
||||
{
|
||||
protected void Application_Start()
|
||||
{
|
||||
GlobalConfiguration.Configure(WebApiConfig.Register);
|
||||
|
||||
string pathValue = Environment.GetEnvironmentVariable("PATH");
|
||||
string domainBaseDir = AppDomain.CurrentDomain.BaseDirectory;
|
||||
string cntkPath = domainBaseDir + @"bin\";
|
||||
pathValue += ";" + cntkPath;
|
||||
Environment.SetEnvironmentVariable("PATH", pathValue);
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<title>Azure API Apps</title>
|
||||
<style>
|
||||
body {
|
||||
background: #fff;
|
||||
color: #505050;
|
||||
font: 14px 'Segoe UI', tahoma, arial, helvetica, sans-serif;
|
||||
margin: 20px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
#header {
|
||||
background: #efefef;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
h1 {
|
||||
font-size: 48px;
|
||||
font-weight: normal;
|
||||
margin: 0;
|
||||
padding: 0 30px;
|
||||
line-height: 150px;
|
||||
}
|
||||
|
||||
p {
|
||||
font-size: 20px;
|
||||
color: #fff;
|
||||
background: #969696;
|
||||
padding: 0 30px;
|
||||
line-height: 50px;
|
||||
}
|
||||
|
||||
#main {
|
||||
padding: 5px 30px;
|
||||
}
|
||||
|
||||
.section {
|
||||
width: 21.7%;
|
||||
float: left;
|
||||
margin: 0 0 0 4%;
|
||||
}
|
||||
|
||||
.section h2 {
|
||||
font-size: 13px;
|
||||
text-transform: uppercase;
|
||||
margin: 0;
|
||||
border-bottom: 1px solid silver;
|
||||
padding-bottom: 12px;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.section.first {
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
.section.first h2 {
|
||||
font-size: 24px;
|
||||
text-transform: none;
|
||||
margin-bottom: 25px;
|
||||
border: none;
|
||||
}
|
||||
|
||||
.section.first li {
|
||||
border-top: 1px solid silver;
|
||||
padding: 8px 0;
|
||||
}
|
||||
|
||||
.section.last {
|
||||
margin-right: 0;
|
||||
}
|
||||
|
||||
ul {
|
||||
list-style: none;
|
||||
padding: 0;
|
||||
margin: 0;
|
||||
line-height: 20px;
|
||||
}
|
||||
|
||||
li {
|
||||
padding: 4px 0;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #267cb2;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
text-decoration: underline;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<div id="header">
|
||||
<h1>Your Azure API App</h1>
|
||||
<p>Congratulations! You've created a project</p>
|
||||
</div>
|
||||
|
||||
<div id="main">
|
||||
|
||||
<div class="section first">
|
||||
<h2>This application consists of:</h2>
|
||||
<ul>
|
||||
<li>NuGet packages used to create an <a href="http://www.asp.net/web-api">ASP.NET Web API</a></li>
|
||||
<li>Swagger metadata generation by <a href="http://www.nuget.org/packages/Swashbuckle">Swashbuckle</a></li>
|
||||
<li>Metadata files used by the Azure Marketplace during API App Marketplace packaging</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="section">
|
||||
<h2>Learn More</h2>
|
||||
<ul>
|
||||
<li><a href="http://go.microsoft.com/fwlink/?LinkID=529090">What is an Azure API App?</a></li>
|
||||
<li><a href="http://go.microsoft.com/fwlink/?LinkID=529086">What is Azure App Service?</a></li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="section">
|
||||
<h2>Create, Debug, Deploy</h2>
|
||||
<ul>
|
||||
<li><a href="http://go.microsoft.com/fwlink/?LinkID=529089">Create an Azure API App</a></li>
|
||||
<li><a href="http://go.microsoft.com/fwlink/?LinkID=529088">Deploy an API App</a></li>
|
||||
<li><a href="http://go.microsoft.com/fwlink/?LinkID=529087">Debug an Azure API App</a></li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="section last">
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
using System.Reflection;
|
||||
using System.Runtime.CompilerServices;
|
||||
using System.Runtime.InteropServices;
|
||||
|
||||
// General Information about an assembly is controlled through the following
|
||||
// set of attributes. Change these attribute values to modify the information
|
||||
// associated with an assembly.
|
||||
[assembly: AssemblyTitle("CNTKAzureTutorial01")]
|
||||
[assembly: AssemblyDescription("")]
|
||||
[assembly: AssemblyConfiguration("")]
|
||||
[assembly: AssemblyCompany("")]
|
||||
[assembly: AssemblyProduct("CNTKAzureTutorial01")]
|
||||
[assembly: AssemblyCopyright("Copyright © 2016")]
|
||||
[assembly: AssemblyTrademark("")]
|
||||
[assembly: AssemblyCulture("")]
|
||||
|
||||
// Setting ComVisible to false makes the types in this assembly not visible
|
||||
// to COM components. If you need to access a type in this assembly from
|
||||
// COM, set the ComVisible attribute to true on that type.
|
||||
[assembly: ComVisible(false)]
|
||||
|
||||
// The following GUID is for the ID of the typelib if this project is exposed to COM
|
||||
[assembly: Guid("a36eded2-a8bc-4995-89d7-10860a870268")]
|
||||
|
||||
// Version information for an assembly consists of the following four values:
|
||||
//
|
||||
// Major Version
|
||||
// Minor Version
|
||||
// Build Number
|
||||
// Revision
|
||||
//
|
||||
// You can specify all the values or you can default the Revision and Build Numbers
|
||||
// by using the '*' as shown below:
|
||||
[assembly: AssemblyVersion("1.0.0.0")]
|
||||
[assembly: AssemblyFileVersion("1.0.0.0")]
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<!-- For more information on using web.config transformation visit http://go.microsoft.com/fwlink/?LinkId=125889 -->
|
||||
|
||||
<configuration xmlns:xdt="http://schemas.microsoft.com/XML-Document-Transform">
|
||||
<!--
|
||||
In the example below, the "SetAttributes" transform will change the value of
|
||||
"connectionString" to use "ReleaseSQLServer" only when the "Match" locator
|
||||
finds an attribute "name" that has a value of "MyDB".
|
||||
|
||||
<connectionStrings>
|
||||
<add name="MyDB"
|
||||
connectionString="Data Source=ReleaseSQLServer;Initial Catalog=MyReleaseDB;Integrated Security=True"
|
||||
xdt:Transform="SetAttributes" xdt:Locator="Match(name)"/>
|
||||
</connectionStrings>
|
||||
-->
|
||||
<system.web>
|
||||
<!--
|
||||
In the example below, the "Replace" transform will replace the entire
|
||||
<customErrors> section of your web.config file.
|
||||
Note that because there is only one customErrors section under the
|
||||
<system.web> node, there is no need to use the "xdt:Locator" attribute.
|
||||
|
||||
<customErrors defaultRedirect="GenericError.htm"
|
||||
mode="RemoteOnly" xdt:Transform="Replace">
|
||||
<error statusCode="500" redirect="InternalError.htm"/>
|
||||
</customErrors>
|
||||
-->
|
||||
</system.web>
|
||||
</configuration>
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
|
||||
<!-- For more information on using web.config transformation visit http://go.microsoft.com/fwlink/?LinkId=125889 -->
|
||||
|
||||
<configuration xmlns:xdt="http://schemas.microsoft.com/XML-Document-Transform">
|
||||
<!--
|
||||
In the example below, the "SetAttributes" transform will change the value of
|
||||
"connectionString" to use "ReleaseSQLServer" only when the "Match" locator
|
||||
finds an attribute "name" that has a value of "MyDB".
|
||||
|
||||
<connectionStrings>
|
||||
<add name="MyDB"
|
||||
connectionString="Data Source=ReleaseSQLServer;Initial Catalog=MyReleaseDB;Integrated Security=True"
|
||||
xdt:Transform="SetAttributes" xdt:Locator="Match(name)"/>
|
||||
</connectionStrings>
|
||||
-->
|
||||
<system.web>
|
||||
<compilation xdt:Transform="RemoveAttributes(debug)" />
|
||||
<!--
|
||||
In the example below, the "Replace" transform will replace the entire
|
||||
<customErrors> section of your web.config file.
|
||||
Note that because there is only one customErrors section under the
|
||||
<system.web> node, there is no need to use the "xdt:Locator" attribute.
|
||||
|
||||
<customErrors defaultRedirect="GenericError.htm"
|
||||
mode="RemoteOnly" xdt:Transform="Replace">
|
||||
<error statusCode="500" redirect="InternalError.htm"/>
|
||||
</customErrors>
|
||||
-->
|
||||
</system.web>
|
||||
</configuration>
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<!--
|
||||
For more information on how to configure your ASP.NET application, please visit
|
||||
http://go.microsoft.com/fwlink/?LinkId=301879
|
||||
-->
|
||||
<configuration>
|
||||
<appSettings>
|
||||
</appSettings>
|
||||
<system.web>
|
||||
<compilation debug="true" targetFramework="4.5" />
|
||||
<httpRuntime targetFramework="4.5" />
|
||||
</system.web>
|
||||
<system.webServer>
|
||||
<handlers>
|
||||
<remove name="ExtensionlessUrlHandler-Integrated-4.0" />
|
||||
<remove name="OPTIONSVerbHandler" />
|
||||
<remove name="TRACEVerbHandler" />
|
||||
<add name="ExtensionlessUrlHandler-Integrated-4.0" path="*." verb="*" type="System.Web.Handlers.TransferRequestHandler" preCondition="integratedMode,runtimeVersionv4.0" />
|
||||
</handlers>
|
||||
</system.webServer>
|
||||
<runtime>
|
||||
<assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1">
|
||||
<dependentAssembly>
|
||||
<assemblyIdentity name="Newtonsoft.Json" culture="neutral" publicKeyToken="30ad4fe6b2a6aeed" />
|
||||
<bindingRedirect oldVersion="0.0.0.0-6.0.0.0" newVersion="6.0.0.0" />
|
||||
</dependentAssembly>
|
||||
<dependentAssembly>
|
||||
<assemblyIdentity name="System.Net.Http.Formatting" publicKeyToken="31bf3856ad364e35" culture="neutral" />
|
||||
<bindingRedirect oldVersion="0.0.0.0-5.2.3.0" newVersion="5.2.3.0" />
|
||||
</dependentAssembly>
|
||||
<dependentAssembly>
|
||||
<assemblyIdentity name="System.Web.Http" publicKeyToken="31bf3856ad364e35" culture="neutral" />
|
||||
<bindingRedirect oldVersion="0.0.0.0-5.2.3.0" newVersion="5.2.3.0" />
|
||||
</dependentAssembly>
|
||||
</assemblyBinding>
|
||||
</runtime>
|
||||
</configuration>
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="Microsoft.AspNet.WebApi" version="5.2.3" targetFramework="net45" />
|
||||
<package id="Microsoft.AspNet.WebApi.Client" version="5.2.3" targetFramework="net45" />
|
||||
<package id="Microsoft.AspNet.WebApi.Core" version="5.2.3" targetFramework="net45" />
|
||||
<package id="Microsoft.AspNet.WebApi.WebHost" version="5.2.3" targetFramework="net45" />
|
||||
<package id="Microsoft.Web.Infrastructure" version="1.0.0.0" targetFramework="net45" />
|
||||
<package id="Newtonsoft.Json" version="6.0.8" targetFramework="net45" />
|
||||
<package id="Swashbuckle" version="5.3.1" targetFramework="net45" />
|
||||
<package id="Swashbuckle.Core" version="5.3.1" targetFramework="net45" />
|
||||
<package id="System.IdentityModel.Tokens.Jwt" version="4.0.0" targetFramework="net45" />
|
||||
<package id="WebActivatorEx" version="2.0.6" targetFramework="net45" />
|
||||
</packages>
|
||||
|
|
@ -4,6 +4,7 @@
|
|||
//
|
||||
// CPPEvalClient.cpp : Sample application using the evaluation interface from C++
|
||||
//
|
||||
#include <sys/stat.h>
|
||||
#include "Eval.h"
|
||||
#ifdef _WIN32
|
||||
#include "Windows.h"
|
||||
|
|
@ -52,11 +53,17 @@ int main(int argc, char* argv[])
|
|||
// This relative path assumes launching from CNTK's binary folder, e.g. build/release/bin/
|
||||
const std::string modelWorkingDirectory = path + "/../../../Examples/Image/MNIST/Data/";
|
||||
#endif
|
||||
|
||||
GetEvalF(&model);
|
||||
|
||||
const std::string modelFilePath = modelWorkingDirectory + "../Output/Models/01_OneHidden";
|
||||
|
||||
struct stat statBuf;
|
||||
if (stat(modelFilePath.c_str(), &statBuf) != 0)
|
||||
{
|
||||
fprintf(stderr, "Error: The model %s does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.\n", modelFilePath.c_str());
|
||||
return(1);
|
||||
}
|
||||
|
||||
GetEvalF(&model);
|
||||
|
||||
// Load model with desired outputs
|
||||
std::string networkConfiguration;
|
||||
// Uncomment the following line to re-define the outputs (include h1.z AND the output ol.z)
|
||||
|
|
|
|||
|
|
@ -1,112 +1,66 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<Project DefaultTargets="Build" ToolsVersion="12.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
|
||||
<ItemGroup Label="ProjectConfigurations">
|
||||
<ProjectConfiguration Include="Debug|x64">
|
||||
<Configuration>Debug</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release|x64">
|
||||
<Configuration>Release</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Debug_CpuOnly|x64">
|
||||
<Configuration>Debug_CpuOnly</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release_CpuOnly|x64">
|
||||
<Configuration>Release_CpuOnly</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
</ItemGroup>
|
||||
<PropertyGroup Label="Globals">
|
||||
<ProjectGuid>{CCC07E8E-F33A-4AF7-9F60-93E2AA61C75E}</ProjectGuid>
|
||||
<ProjectGuid>{C81CE839-184C-42C7-BB1C-9D0ABA17078D}</ProjectGuid>
|
||||
<Keyword>Win32Proj</Keyword>
|
||||
<RootNamespace>CPPEvalClient</RootNamespace>
|
||||
<ProjectName>CPPEvalClient</ProjectName>
|
||||
</PropertyGroup>
|
||||
<Import Project="$(SolutionDir)\CNTK.Cpp.props" />
|
||||
<PropertyGroup Condition="$(DebugBuild)" Label="Configuration">
|
||||
<ConfigurationType>Application</ConfigurationType>
|
||||
<UseDebugLibraries>true</UseDebugLibraries>
|
||||
<PlatformToolset>v120</PlatformToolset>
|
||||
<CharacterSet>Unicode</CharacterSet>
|
||||
<UseIntelMKL>No</UseIntelMKL>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="$(ReleaseBuild)" Label="Configuration">
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
|
||||
<ConfigurationType>Application</ConfigurationType>
|
||||
<UseDebugLibraries>false</UseDebugLibraries>
|
||||
<PlatformToolset>v120</PlatformToolset>
|
||||
<WholeProgramOptimization>true</WholeProgramOptimization>
|
||||
<CharacterSet>Unicode</CharacterSet>
|
||||
<UseIntelMKL>No</UseIntelMKL>
|
||||
<UseIntelIPP>false</UseIntelIPP>
|
||||
</PropertyGroup>
|
||||
<!--Importing CPP defaults must occur after declaring the desired toolset above
|
||||
Otherwise, the build may default back to an previous toolset -->
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
|
||||
<ImportGroup Label="ExtensionSettings" />
|
||||
<ImportGroup Label="PropertySheets">
|
||||
<ImportGroup Label="ExtensionSettings">
|
||||
</ImportGroup>
|
||||
<ImportGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="PropertySheets">
|
||||
<Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
|
||||
</ImportGroup>
|
||||
<PropertyGroup>
|
||||
<!-- TODO intentional for all? -->
|
||||
<PropertyGroup Label="UserMacros" />
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
|
||||
<LinkIncremental>false</LinkIncremental>
|
||||
<TargetName>CPPEvalClient</TargetName>
|
||||
<OutDir>$(SolutionDir)..\..\$(Platform)\$(ProjectName).$(Configuration)\</OutDir>
|
||||
</PropertyGroup>
|
||||
<ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
|
||||
<ClCompile>
|
||||
<PrecompiledHeader>NotUsing</PrecompiledHeader>
|
||||
<WarningLevel>Level4</WarningLevel>
|
||||
<AdditionalIncludeDirectories>$(SolutionDir)Source\Common\include;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>
|
||||
<PreprocessorDefinitions>WIN32;UNICODE;_CONSOLE;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<PrecompiledHeader>NotUsing</PrecompiledHeader>
|
||||
<Optimization>MaxSpeed</Optimization>
|
||||
<FunctionLevelLinking>true</FunctionLevelLinking>
|
||||
<IntrinsicFunctions>true</IntrinsicFunctions>
|
||||
<PreprocessorDefinitions>WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<AdditionalIncludeDirectories>$(SolutionDir)..\..\Include</AdditionalIncludeDirectories>
|
||||
<TreatWarningAsError>true</TreatWarningAsError>
|
||||
<SDLCheck>true</SDLCheck>
|
||||
<MultiProcessorCompilation>true</MultiProcessorCompilation>
|
||||
<FloatingPointModel>Fast</FloatingPointModel>
|
||||
<OpenMPSupport>true</OpenMPSupport>
|
||||
<TreatWarningAsError>true</TreatWarningAsError>
|
||||
</ClCompile>
|
||||
<Link>
|
||||
<AdditionalLibraryDirectories>$(OutDir)</AdditionalLibraryDirectories>
|
||||
<SubSystem>Console</SubSystem>
|
||||
<GenerateDebugInformation>true</GenerateDebugInformation>
|
||||
<AdditionalDependencies>EvalDLL.lib;%(AdditionalDependencies)</AdditionalDependencies>
|
||||
<DelayLoadDLLs>%(DelayLoadDLLs)</DelayLoadDLLs>
|
||||
<Profile>true</Profile>
|
||||
</Link>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="$(DebugBuild)">
|
||||
<ClCompile>
|
||||
<PreprocessorDefinitions>_DEBUG;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<Optimization>Disabled</Optimization>
|
||||
<MinimalRebuild>false</MinimalRebuild>
|
||||
</ClCompile>
|
||||
<Link />
|
||||
<ProjectReference>
|
||||
<LinkLibraryDependencies>false</LinkLibraryDependencies>
|
||||
</ProjectReference>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemDefinitionGroup Condition="$(ReleaseBuild)">
|
||||
<ClCompile>
|
||||
<Optimization>MaxSpeed</Optimization>
|
||||
<FunctionLevelLinking>true</FunctionLevelLinking>
|
||||
<IntrinsicFunctions>true</IntrinsicFunctions>
|
||||
<PreprocessorDefinitions>NDEBUG;%(PreprocessorDefinitions)</PreprocessorDefinitions>
|
||||
<EnableParallelCodeGeneration>true</EnableParallelCodeGeneration>
|
||||
<FloatingPointExceptions>false</FloatingPointExceptions>
|
||||
<AdditionalOptions>/d2Zi+ %(AdditionalOptions)</AdditionalOptions>
|
||||
<RuntimeLibrary>MultiThreadedDLL</RuntimeLibrary>
|
||||
</ClCompile>
|
||||
<Link>
|
||||
<EnableCOMDATFolding>true</EnableCOMDATFolding>
|
||||
<OptimizeReferences>true</OptimizeReferences>
|
||||
<AdditionalLibraryDirectories>$(SolutionDir)\..\..\cntk</AdditionalLibraryDirectories>
|
||||
<AdditionalDependencies>EvalDll.lib;%(AdditionalDependencies)</AdditionalDependencies>
|
||||
<OutputFile>$(OutDir)$(TargetName)$(TargetExt)</OutputFile>
|
||||
<Profile>true</Profile>
|
||||
</Link>
|
||||
<ProjectReference>
|
||||
<LinkLibraryDependencies>true</LinkLibraryDependencies>
|
||||
</ProjectReference>
|
||||
</ItemDefinitionGroup>
|
||||
<ItemGroup>
|
||||
<ClCompile Include="CPPEvalClient.cpp" />
|
||||
</ItemGroup>
|
||||
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
|
||||
<ImportGroup Label="ExtensionTargets" />
|
||||
</Project>
|
||||
<ImportGroup Label="ExtensionTargets">
|
||||
</ImportGroup>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -19,4 +19,4 @@
|
|||
<Filter>Source Files</Filter>
|
||||
</ClCompile>
|
||||
</ItemGroup>
|
||||
</Project>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -3,56 +3,55 @@
|
|||
<Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
|
||||
<PropertyGroup>
|
||||
<Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
|
||||
<Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
|
||||
<ProjectGuid>{1C6E6C53-1AA7-4B69-913E-B97BB5A872CF}</ProjectGuid>
|
||||
<Platform Condition=" '$(Platform)' == '' ">x64</Platform>
|
||||
<ProjectGuid>{92CCF4B9-BFED-4914-901A-CF1327B1A02D}</ProjectGuid>
|
||||
<OutputType>Exe</OutputType>
|
||||
<AppDesignerFolder>Properties</AppDesignerFolder>
|
||||
<RootNamespace>Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient</RootNamespace>
|
||||
<AssemblyName>CSEvalClient</AssemblyName>
|
||||
<TargetFrameworkVersion>v4.5</TargetFrameworkVersion>
|
||||
<FileAlignment>512</FileAlignment>
|
||||
<NuGetPackageImportStamp>931e68df</NuGetPackageImportStamp>
|
||||
<PublishUrl>publish\</PublishUrl>
|
||||
<Install>true</Install>
|
||||
<InstallFrom>Disk</InstallFrom>
|
||||
<UpdateEnabled>false</UpdateEnabled>
|
||||
<UpdateMode>Foreground</UpdateMode>
|
||||
<UpdateInterval>7</UpdateInterval>
|
||||
<UpdateIntervalUnits>Days</UpdateIntervalUnits>
|
||||
<UpdatePeriodically>false</UpdatePeriodically>
|
||||
<UpdateRequired>false</UpdateRequired>
|
||||
<MapFileExtensions>true</MapFileExtensions>
|
||||
<ApplicationRevision>0</ApplicationRevision>
|
||||
<ApplicationVersion>1.0.0.%2a</ApplicationVersion>
|
||||
<IsWebBootstrapper>false</IsWebBootstrapper>
|
||||
<UseApplicationTrust>false</UseApplicationTrust>
|
||||
<BootstrapperEnabled>true</BootstrapperEnabled>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug|x64'">
|
||||
<DebugSymbols>true</DebugSymbols>
|
||||
<OutputPath>..\..\..\x64\Debug\</OutputPath>
|
||||
<OutputPath>$(SolutionDir)..\..\$(Platform)\CSEvalClient.$(Configuration)\</OutputPath>
|
||||
<DefineConstants>DEBUG;TRACE</DefineConstants>
|
||||
<TreatWarningsAsErrors>true</TreatWarningsAsErrors>
|
||||
<DebugType>full</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
<Prefer32Bit>true</Prefer32Bit>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug_CpuOnly|x64'">
|
||||
<DebugSymbols>true</DebugSymbols>
|
||||
<OutputPath>..\..\..\x64\Debug_CpuOnly\</OutputPath>
|
||||
<DefineConstants>DEBUG;TRACE</DefineConstants>
|
||||
<DebugType>full</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
<Prefer32Bit>true</Prefer32Bit>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Release|x64'">
|
||||
<OutputPath>..\..\..\x64\Release\</OutputPath>
|
||||
<OutputPath>$(SolutionDir)..\..\$(Platform)\CSEvalClient.$(Configuration)\</OutputPath>
|
||||
<DefineConstants>TRACE</DefineConstants>
|
||||
<Optimize>true</Optimize>
|
||||
<DebugType>pdbonly</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
<Prefer32Bit>true</Prefer32Bit>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Release_CpuOnly|x64'">
|
||||
<OutputPath>..\..\..\x64\Release_CpuOnly\</OutputPath>
|
||||
<DefineConstants>TRACE</DefineConstants>
|
||||
<Optimize>true</Optimize>
|
||||
<DebugType>pdbonly</DebugType>
|
||||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
<Prefer32Bit>true</Prefer32Bit>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="EvalWrapper, Version=0.0.0.0, Culture=neutral, processorArchitecture=AMD64">
|
||||
<HintPath>..\packages\Microsoft.Research.CNTK.CpuEval-mkl.1.7\lib\net45\x64\EvalWrapper.dll</HintPath>
|
||||
<Private>True</Private>
|
||||
</Reference>
|
||||
<Reference Include="System" />
|
||||
<Reference Include="System.Core" />
|
||||
<Reference Include="Microsoft.CSharp" />
|
||||
|
|
@ -66,19 +65,31 @@
|
|||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<None Include="App.config" />
|
||||
<None Include="packages.config" />
|
||||
</ItemGroup>
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\Source\Extensibility\EvalWrapper\EvalWrapper.vcxproj">
|
||||
<Project>{ef766cae-9cb1-494c-9153-0030631a6340}</Project>
|
||||
<Name>EvalWrapper</Name>
|
||||
</ProjectReference>
|
||||
<BootstrapperPackage Include=".NETFramework,Version=v4.5">
|
||||
<Visible>False</Visible>
|
||||
<ProductName>Microsoft .NET Framework 4.5 %28x86 and x64%29</ProductName>
|
||||
<Install>true</Install>
|
||||
</BootstrapperPackage>
|
||||
<BootstrapperPackage Include="Microsoft.Net.Client.3.5">
|
||||
<Visible>False</Visible>
|
||||
<ProductName>.NET Framework 3.5 SP1 Client Profile</ProductName>
|
||||
<Install>false</Install>
|
||||
</BootstrapperPackage>
|
||||
<BootstrapperPackage Include="Microsoft.Net.Framework.3.5.SP1">
|
||||
<Visible>False</Visible>
|
||||
<ProductName>.NET Framework 3.5 SP1</ProductName>
|
||||
<Install>false</Install>
|
||||
</BootstrapperPackage>
|
||||
</ItemGroup>
|
||||
<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
|
||||
<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
|
||||
Other similar extension points exist, see Microsoft.Common.targets.
|
||||
<Target Name="BeforeBuild">
|
||||
</Target>
|
||||
<Target Name="AfterBuild">
|
||||
</Target>
|
||||
-->
|
||||
</Project>
|
||||
<Import Project="..\packages\Microsoft.Research.CNTK.CpuEval-mkl.1.7\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets" Condition="Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.1.7\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" />
|
||||
<Target Name="EnsureNuGetPackageBuildImports" BeforeTargets="PrepareForBuild">
|
||||
<PropertyGroup>
|
||||
<ErrorText>This project references NuGet package(s) that are missing on this computer. Enable NuGet Package Restore to download them. For more information, see http://go.microsoft.com/fwlink/?LinkID=322105. The missing file is {0}.</ErrorText>
|
||||
</PropertyGroup>
|
||||
<Error Condition="!Exists('..\packages\Microsoft.Research.CNTK.CpuEval-mkl.1.7\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets')" Text="$([System.String]::Format('$(ErrorText)', '..\packages\Microsoft.Research.CNTK.CpuEval-mkl.1.7\build\net45\Microsoft.Research.CNTK.CpuEval-mkl.targets'))" />
|
||||
</Target>
|
||||
</Project>
|
||||
|
|
|
|||
|
|
@ -89,6 +89,23 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
Console.ReadLine();
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Checks whether the file exists. If not, write the error message on the console and throw FileNotFoundException.
|
||||
/// </summary>
|
||||
/// <param name="filePath">The file to check.</param>
|
||||
/// <param name="errorMsg">The message to write on console if the file does not exist.</param>
|
||||
private static void ThrowIfFileNotExist(string filePath, string errorMsg)
|
||||
{
|
||||
if (!File.Exists(filePath))
|
||||
{
|
||||
if (!string.IsNullOrEmpty(errorMsg))
|
||||
{
|
||||
Console.WriteLine(errorMsg);
|
||||
}
|
||||
throw new FileNotFoundException(string.Format("File '{0}' not found.", filePath));
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Evaluates a trained model and obtains a single layer output
|
||||
/// </summary>
|
||||
|
|
@ -110,6 +127,9 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
{
|
||||
// Load model
|
||||
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
|
||||
ThrowIfFileNotExist(modelFilePath,
|
||||
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.", modelFilePath));
|
||||
|
||||
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
|
||||
|
||||
// Generate random input values in the appropriate structure and size
|
||||
|
|
@ -159,6 +179,9 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
|
||||
// Load model
|
||||
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\01_OneHidden");
|
||||
ThrowIfFileNotExist(modelFilePath,
|
||||
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.", modelFilePath));
|
||||
|
||||
var desiredOutputLayers = new List<string>() { hiddenLayerName, outputLayerName };
|
||||
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1, outputNodeNames: desiredOutputLayers);
|
||||
|
||||
|
|
@ -208,9 +231,12 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
using (var model = new IEvaluateModelManagedF())
|
||||
{
|
||||
// Create the network
|
||||
// This network (AddOperatorConstant.cntk) is a simple network consisting of a single binary operator (Plus)
|
||||
// This network (AddOperatorConstant_ndl_deprecated.cntk) is a simple network consisting of a single binary operator (Plus)
|
||||
// operating over a single input and a constant
|
||||
string networkDescription = File.ReadAllText(Path.Combine(workingDirectory, @"AddOperatorConstant.cntk"));
|
||||
string networkFilePath = Path.Combine(workingDirectory, @"AddOperatorConstant_ndl_deprecated.cntk");
|
||||
ThrowIfFileNotExist(networkFilePath, string.Format("Error: The network configuration file '{0}' does not exist.", networkFilePath));
|
||||
|
||||
string networkDescription = File.ReadAllText(networkFilePath);
|
||||
model.CreateNetwork(networkDescription, deviceId: -1);
|
||||
|
||||
// Prepare input value in the appropriate structure and size
|
||||
|
|
@ -251,9 +277,12 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
using (var model = new IEvaluateModelManagedF())
|
||||
{
|
||||
// Create the network
|
||||
// This network (AddOperatorConstantNoInput.cntk) is a simple network consisting of a single binary operator (Plus)
|
||||
// This network (AddOperatorConstantNoInput_ndl_deprecated.cntk) is a simple network consisting of a single binary operator (Plus)
|
||||
// operating over a two constants, therefore no input is necessary.
|
||||
string networkDescription = File.ReadAllText(Path.Combine(workingDirectory, @"AddOperatorConstantNoInput.cntk"));
|
||||
string networkFilePath = Path.Combine(workingDirectory, @"AddOperatorConstantNoInput_ndl_deprecated.cntk");
|
||||
ThrowIfFileNotExist(networkFilePath, string.Format("Error: The network configuration file '{0}' does not exist.", networkFilePath));
|
||||
|
||||
string networkDescription = File.ReadAllText(networkFilePath);
|
||||
model.CreateNetwork(networkDescription, deviceId: -1);
|
||||
|
||||
// We can call the evaluate method and get back the results (single layer)...
|
||||
|
|
@ -289,16 +318,9 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
|
||||
try
|
||||
{
|
||||
// The examples assume the executable is running from the data folder
|
||||
// We switch the current directory to the data folder (assuming the executable is in the <CNTK>/x64/Debug|Release folder
|
||||
string workingDirectory = Path.Combine(initialDirectory, @"..\..\Examples\Other\Simple2d\Config");
|
||||
Environment.CurrentDirectory = initialDirectory;
|
||||
|
||||
using (var model = new ModelEvaluationExtendedF())
|
||||
{
|
||||
// Create the network
|
||||
// This network (AddOperatorConstantNoInput.cntk) is a simple network consisting of a single binary operator (Plus)
|
||||
// operating over a two constants, therefore no input is necessary.
|
||||
model.CreateNetwork(modelDefinition);
|
||||
|
||||
VariableSchema outputSchema = model.GetOutputSchema();
|
||||
|
|
@ -355,11 +377,16 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
|
||||
// Load model
|
||||
string modelFilePath = Path.Combine(Environment.CurrentDirectory, @"..\Output\Models\02_Convolution");
|
||||
ThrowIfFileNotExist(modelFilePath,
|
||||
string.Format("Error: The model '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to create the model.", modelFilePath));
|
||||
|
||||
// Initializes the model instances
|
||||
ModelEvaluator.Initialize(numConcurrentModels, modelFilePath);
|
||||
|
||||
string testfile = Path.Combine(Environment.CurrentDirectory, @"Test-28x28_cntk_text.txt");
|
||||
ThrowIfFileNotExist(testfile,
|
||||
string.Format("Error: The test file '{0}' does not exist. Please follow instructions in README.md in <CNTK>/Examples/Image/MNIST to download the data.", testfile));
|
||||
|
||||
Stopwatch sw = new Stopwatch();
|
||||
sw.Start();
|
||||
|
||||
|
|
@ -436,6 +463,9 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
using (var model = new IEvaluateModelManagedF())
|
||||
{
|
||||
string modelFilePath = Path.Combine(workingDirectory, "ResNet_18.model");
|
||||
ThrowIfFileNotExist(modelFilePath,
|
||||
string.Format("Error: The model '{0}' does not exist. Please download the model from https://www.cntk.ai/resnet/ResNet_18.model and save it under ..\\..\\Examples\\Image\\Miscellaneous\\ImageNet\\ResNet.", modelFilePath));
|
||||
|
||||
model.CreateNetwork(string.Format("modelPath=\"{0}\"", modelFilePath), deviceId: -1);
|
||||
|
||||
// Prepare input value in the appropriate structure and size
|
||||
|
|
@ -447,6 +477,8 @@ namespace Microsoft.MSR.CNTK.Extensibility.Managed.CSEvalClient
|
|||
|
||||
// Transform the image
|
||||
string imageFileName = Path.Combine(workingDirectory, "zebra.jpg");
|
||||
ThrowIfFileNotExist(imageFileName, string.Format("Error: The test image file '{0}' does not exist.", imageFileName));
|
||||
|
||||
Bitmap bmp = new Bitmap(Bitmap.FromFile(imageFileName));
|
||||
|
||||
var resized = bmp.Resize(224, 224, true);
|
||||
|
|
|
|||
|
|
@ -1,10 +1,5 @@
|
|||
//
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
|
||||
//
|
||||
// AssemblyInfo.cs -- Assembly information
|
||||
//
|
||||
using System.Reflection;
|
||||
using System.Reflection;
|
||||
using System.Runtime.CompilerServices;
|
||||
using System.Runtime.InteropServices;
|
||||
|
||||
// General Information about an assembly is controlled through the following
|
||||
|
|
@ -15,7 +10,7 @@ using System.Runtime.InteropServices;
|
|||
[assembly: AssemblyConfiguration("")]
|
||||
[assembly: AssemblyCompany("Microsoft")]
|
||||
[assembly: AssemblyProduct("CSEvalClient")]
|
||||
[assembly: AssemblyCopyright("Copyright © 2016 Microsoft. All rights reserved.")]
|
||||
[assembly: AssemblyCopyright("Copyright © 2016")]
|
||||
[assembly: AssemblyTrademark("")]
|
||||
[assembly: AssemblyCulture("")]
|
||||
|
||||
|
|
@ -25,7 +20,7 @@ using System.Runtime.InteropServices;
|
|||
[assembly: ComVisible(false)]
|
||||
|
||||
// The following GUID is for the ID of the typelib if this project is exposed to COM
|
||||
[assembly: Guid("6ec08331-7554-4ebd-b663-b64ab6e719e2")]
|
||||
[assembly: Guid("196bf110-ad4b-46fc-be51-d0329372ebbe")]
|
||||
|
||||
// Version information for an assembly consists of the following four values:
|
||||
//
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<packages>
|
||||
<package id="Microsoft.Research.CNTK.CpuEval-mkl" version="1.7" targetFramework="net45" />
|
||||
</packages>
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
|
||||
Microsoft Visual Studio Solution File, Format Version 12.00
|
||||
# Visual Studio 2013
|
||||
VisualStudioVersion = 12.0.40629.0
|
||||
MinimumVisualStudioVersion = 10.0.40219.1
|
||||
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CPPEvalClient", "CPPEvalClient\CPPEvalClient.vcxproj", "{C81CE839-184C-42C7-BB1C-9D0ABA17078D}"
|
||||
EndProject
|
||||
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "CSEvalClient", "CSEvalClient\CSEvalClient.csproj", "{92CCF4B9-BFED-4914-901A-CF1327B1A02D}"
|
||||
EndProject
|
||||
Global
|
||||
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||
Debug|x64 = Debug|x64
|
||||
Release|x64 = Release|x64
|
||||
EndGlobalSection
|
||||
GlobalSection(ProjectConfigurationPlatforms) = postSolution
|
||||
{C81CE839-184C-42C7-BB1C-9D0ABA17078D}.Debug|x64.ActiveCfg = Release|x64
|
||||
{C81CE839-184C-42C7-BB1C-9D0ABA17078D}.Release|x64.ActiveCfg = Release|x64
|
||||
{C81CE839-184C-42C7-BB1C-9D0ABA17078D}.Release|x64.Build.0 = Release|x64
|
||||
{92CCF4B9-BFED-4914-901A-CF1327B1A02D}.Debug|x64.ActiveCfg = Debug|x64
|
||||
{92CCF4B9-BFED-4914-901A-CF1327B1A02D}.Debug|x64.Build.0 = Debug|x64
|
||||
{92CCF4B9-BFED-4914-901A-CF1327B1A02D}.Release|x64.ActiveCfg = Release|x64
|
||||
{92CCF4B9-BFED-4914-901A-CF1327B1A02D}.Release|x64.Build.0 = Release|x64
|
||||
EndGlobalSection
|
||||
GlobalSection(SolutionProperties) = preSolution
|
||||
HideSolutionNode = FALSE
|
||||
EndGlobalSection
|
||||
EndGlobal
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
#EvalClients
|
||||
|
||||
The folder contains some examples using the CNTK evaluation library. Please note that only the 64-bit target is supported by CNTK evaluation library.
|
||||
-CPPEvalClient: demonstrate the use of the C++ CNTK eval lib. Only the release configuration is supported.
|
||||
-CSEvalClient: demonstrate the use of the C# CNTK eval lib.
|
||||
-EvalClients.sln: the VS2013 solution file to build examples. It creates two binaries in the directory $(SolutionDir)..\..\x64\:
|
||||
* CPPEvalClient.$(Configuration)\CPPEvalClient.exe: the C++ example executable. To run the example, please first include the directory containing CNTK dependent dlls, usually $(SolutionDir)..\..\cntk, in the PATH environment variable.
|
||||
* CSEvalClient.$(Configuration)\CSEvalClient.exe: the C# example executable.
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# Parameters can be overwritten on the command line
|
||||
# for example: cntk configFile=myConfigFile RootDir=../..
|
||||
# For running from Visual Studio add
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# Parameters can be overwritten on the command line
|
||||
# for example: cntk configFile=myConfigFile rootDir=../..
|
||||
# For running from Visual Studio add
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# Parameters can be overwritten on the command line
|
||||
# for example: cntk configFile=myConfigFile RootDir=../..
|
||||
# For running from Visual Studio add
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# Parameters can be overwritten on the command line
|
||||
# for example: cntk configFile=myConfigFile RootDir=../..
|
||||
# For running from Visual Studio add
|
||||
|
|
@ -26,9 +26,9 @@ DnnBNReLULayer(inDim, outDim, x, wScale, bValue, scValue, bnTimeConst) = [
|
|||
b = LearnableParameter(outDim, 1, init = fixedValue, value = bValue)
|
||||
sc = LearnableParameter(outDim, 1, init = fixedValue, value = scValue)
|
||||
m = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
t = Times(W, x)
|
||||
bn = BatchNormalization(t, sc, b, m, isd, eval = false, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
bn = BatchNormalization(t, sc, b, m, v, eval = false, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
y = RectifiedLinear(bn)
|
||||
]
|
||||
|
||||
|
|
@ -72,10 +72,10 @@ ConvBNLayerW(W, inp, outMap, kW, kH, hStride, vStride, bValue, scValue, bnTimeCo
|
|||
b = LearnableParameter(outMap, 1, init=fixedValue, value=bValue)
|
||||
sc = LearnableParameter(outMap, 1, init=fixedValue, value=scValue)
|
||||
m = LearnableParameter(outMap, 1, init=fixedValue, value=0, learningRateMultiplier=0)
|
||||
isd = LearnableParameter(outMap, 1, init=fixedValue, value=0, learningRateMultiplier=0)
|
||||
v = LearnableParameter(outMap, 1, init=fixedValue, value=0, learningRateMultiplier=0)
|
||||
|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding=true, imageLayout=$imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, isd, eval=false, spatial=true, normalizationTimeConstant=bnTimeConst, imageLayout=$imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, v, eval=false, spatial=true, normalizationTimeConstant=bnTimeConst, imageLayout=$imageLayout$)
|
||||
]
|
||||
|
||||
ConvBNLayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scValue, bnTimeConst) = [
|
||||
|
|
|
|||
|
|
@ -22,13 +22,13 @@ DNNImageSigmoidLayer (inW, inH, inC, outDim, x, parmScale) = [
|
|||
# ReLU layer with batch normalization
|
||||
# TODO: rename to DNN-
|
||||
DnnBNReLULayer (inDim, outDim, x, wScale, bValue, scValue, bnTimeConst) = [
|
||||
W = Parameter (outDim, inDim, init = "gaussian", initValueScale = wScale, initOnCPUOnly=true)
|
||||
b = Parameter (outDim, 1, init = "fixedValue", value = bValue)
|
||||
sc = Parameter (outDim, 1, init = "fixedValue", value = scValue)
|
||||
m = Parameter (outDim, 1, init = "fixedValue", value = 0, learningRateMultiplier = 0)
|
||||
isd = Parameter (outDim, 1, init = "fixedValue", value = 0, learningRateMultiplier = 0)
|
||||
W = Parameter (outDim, inDim, init = "gaussian", initValueScale = wScale, initOnCPUOnly=true)
|
||||
b = Parameter (outDim, 1, init = "fixedValue", value = bValue)
|
||||
sc = Parameter (outDim, 1, init = "fixedValue", value = scValue)
|
||||
m = Parameter (outDim, 1, init = "fixedValue", value = 0, learningRateMultiplier = 0)
|
||||
v = Parameter (outDim, 1, init = "fixedValue", value = 0, learningRateMultiplier = 0)
|
||||
t = Times(W, x) # TODO: W * x
|
||||
bn = BatchNormalization(t, sc, b, m, isd, eval = false, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
bn = BatchNormalization(t, sc, b, m, v, eval = false, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
y = RectifiedLinear(bn)
|
||||
].y
|
||||
|
||||
|
|
@ -58,13 +58,13 @@ ConvNDReLULayer (inp, kW, kH, inMap, inWCount, outMap, hStride, vStride, wScale,
|
|||
].out
|
||||
|
||||
ConvBNLayerW (W, inp, outMap, kW, kH, hStride, vStride, bValue, scValue, bnTimeConst) = [ # TODO: delete if not needed
|
||||
b = Parameter(outMap, 1, init="fixedValue", value=bValue)
|
||||
sc = Parameter(outMap, 1, init="fixedValue", value=scValue)
|
||||
m = Parameter(outMap, 1, init="fixedValue", value=0, learningRateMultiplier=0)
|
||||
isd = Parameter(outMap, 1, init="fixedValue", value=0, learningRateMultiplier=0)
|
||||
b = Parameter(outMap, 1, init="fixedValue", value=bValue)
|
||||
sc = Parameter(outMap, 1, init="fixedValue", value=scValue)
|
||||
m = Parameter(outMap, 1, init="fixedValue", value=0, learningRateMultiplier=0)
|
||||
v = Parameter(outMap, 1, init="fixedValue", value=0, learningRateMultiplier=0)
|
||||
|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding=true /* , imageLayout=$imageLayout$*/)
|
||||
y = BatchNormalization(c, sc, b, m, isd, eval=false, spatial=true, normalizationTimeConstant=bnTimeConst /* , imageLayout=$imageLayout$*/)
|
||||
y = BatchNormalization(c, sc, b, m, v, eval=false, spatial=true, normalizationTimeConstant=bnTimeConst /* , imageLayout=$imageLayout$*/)
|
||||
].y
|
||||
|
||||
ConvBNLayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scValue, bnTimeConst) = [
|
||||
|
|
|
|||
|
|
@ -44,12 +44,12 @@ or prefix the call to the cntk executable with the corresponding folder.
|
|||
|
||||
Run the example from the Image/MNIST/Data folder using:
|
||||
|
||||
`cntk configFile=../Config/01_OneHidden.cntk`
|
||||
`cntk configFile=../Config/01_OneHidden_ndl_deprecated.cntk`
|
||||
|
||||
or run from any folder and specify the Data folder as the `currentDirectory`,
|
||||
e.g. running from the Image/MNIST folder using:
|
||||
|
||||
`cntk configFile=Config/01_OneHidden.cntk currentDirectory=Data`
|
||||
`cntk configFile=Config/01_OneHidden_ndl_deprecated.cntk currentDirectory=Data`
|
||||
|
||||
The output folder will be created inside Image/MNIST/.
|
||||
|
||||
|
|
@ -61,22 +61,22 @@ There are four config files and the corresponding network description files in t
|
|||
|
||||
1. 01_OneHidden.ndl is a simple, one hidden layer network that produces 2.3% of error.
|
||||
To run the sample, navigate to the Data folder and run the following command:
|
||||
`cntk configFile=../Config/01_OneHidden.cntk`
|
||||
`cntk configFile=../Config/01_OneHidden_ndl_deprecated.cntk`
|
||||
|
||||
2. 02_Convolution.ndl is more interesting, convolutional network which has 2 convolutional and 2 max pooling layers.
|
||||
The network produces 0.87% of error after training for about 2 minutes on GPU.
|
||||
To run the sample, navigate to the Data folder and run the following command:
|
||||
`cntk configFile=../Config/02_Convolution.cntk`
|
||||
`cntk configFile=../Config/02_Convolution_ndl_deprecated.cntk`
|
||||
|
||||
3. 03_ConvBatchNorm.ndl is almost identical to 02_Convolution.ndl
|
||||
except that it uses batch normalization for the convolutional and fully connected layers.
|
||||
As a result, it achieves around 0.8% of error after training for just 2 epochs (and less than 30 seconds).
|
||||
To run the sample, navigate to the Data folder and run the following command:
|
||||
`cntk configFile=../Config/03_ConvBatchNorm.cntk`
|
||||
`cntk configFile=../Config/03_ConvBatchNorm_ndl_deprecated.cntk`
|
||||
|
||||
4. 04_DeConv.ndl illustrates the usage of Deconvolution and Unpooling. It is a network with one Convolution, one Pooling, one Unpooling and one Deconvolution layer. In fact it is an auto-encoder network where Rectified Linear Unit (ReLU) or Sigmoid layer is now replaced with Convolutional ReLU (for encoding) and Deconvolutional ReLU (for decoding) layers. The network goal is to reconstruct the original signal, with Mean Squared Error (MSE) used to minimize the reconstruction error. Generally such networks are used in semantic segmentation.
|
||||
To run the sample, navigate to the Data folder and run the following command:
|
||||
`cntk configFile=../Config/04_DeConv.cntk`
|
||||
`cntk configFile=../Config/04_DeConv_ndl_deprecated.cntk`
|
||||
|
||||
For more details, refer to .ndl and the corresponding .cntk files.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
# override the above as follows when running on CPU:
|
||||
# deviceId = -1
|
||||
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
command = Train:Test
|
||||
|
||||
stderr = "$OutputDir$/02_BatchNormConv"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
|
||||
Train = [
|
||||
action = "train"
|
||||
modelPath = "$ModelDir$/02_BatchNormConv"
|
||||
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/02_BatchNormConv.ndl"
|
||||
]
|
||||
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
minibatchSize = 64
|
||||
learningRatesPerMB = 0.03*7:0.01
|
||||
momentumPerMB = 0
|
||||
maxEpochs = 10
|
||||
L2RegWeight = 0
|
||||
dropoutRate = 0
|
||||
]
|
||||
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
# See REAMDE.md for details on getting the data (Train_cntk_text.txt).
|
||||
file = "$DataDir$/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
Test = [
|
||||
action = "test"
|
||||
modelPath = "$ModelDir$/02_BatchNormConv"
|
||||
# Set minibatch size for testing.
|
||||
minibatchSize = 16
|
||||
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
makeMode = true
|
||||
RootDir = "."
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
# 06_RegressionSimple.cntk shows how to train a regression model on image data.
|
||||
# It uses a very simple network and a composite reader using both the ImageReader
|
||||
# and CNTKTextFormatReader and defines the RMSE (root mean square error) as the
|
||||
# loss function. The value that the network learns to predict are simply the
|
||||
# average rgb values of an image normalized to [0, 1].
|
||||
# The network consists simply of two linear layers, i.e. two fully connected layers
|
||||
# with no non-linear activation function, simply LinearLayer{...} (v) = W * v + b
|
||||
# See https://github.com/Microsoft/CNTK/wiki/Layers-Reference for details on CNTK layers.
|
||||
# See README.md for instructions on how to generate data and regression labels for this example.
|
||||
|
||||
command = TrainConvNet:Write
|
||||
|
||||
makeMode = false ; traceLevel = 1 ; deviceId = "auto"
|
||||
|
||||
rootDir = "." ; configDir = "$rootDir$" ; dataDir = "$rootDir$" ;
|
||||
outputDir = "$rootDir$/Output" ; modelDir = "$outputDir$/Models"
|
||||
|
||||
modelPath = "$ModelDir$/06_RegressionSimple.cmf"
|
||||
|
||||
# Training action for a convolutional network
|
||||
TrainConvNet = {
|
||||
action = "train"
|
||||
|
||||
BrainScriptNetworkBuilder = [
|
||||
imageShape = 32:32:3
|
||||
featScale = Constant(1/256)
|
||||
labelDim = 3
|
||||
|
||||
model (features) = {
|
||||
featNorm = Scale(features, featScale)
|
||||
h1 = LinearLayer {100, init="gaussian", initValueScale=1.5} (featNorm)
|
||||
ol = LinearLayer {labelDim, init="gaussian", initValueScale=1.5} (h1)
|
||||
}.ol
|
||||
|
||||
# inputs
|
||||
features = Input {imageShape}
|
||||
regrLabels = Input {labelDim}
|
||||
|
||||
# apply model to features
|
||||
ol = model (features)
|
||||
|
||||
# define regression loss
|
||||
# rmse = sqrt(SquareError(regrLabels, ol) / labelDim)
|
||||
sqerr = SquareError (regrLabels, ol)
|
||||
rmse = Sqrt (Constant(1/labelDim).* sqerr)
|
||||
|
||||
featureNodes = (features)
|
||||
labelNodes = (regrLabels)
|
||||
criterionNodes = (rmse)
|
||||
evaluationNodes = (rmse)
|
||||
OutputNodes = (ol)
|
||||
]
|
||||
|
||||
SGD = {
|
||||
epochSize = 0
|
||||
|
||||
maxEpochs = 2
|
||||
minibatchSize = 128
|
||||
learningRatesPerSample = 0.0005
|
||||
momentumAsTimeConstant = 1024
|
||||
|
||||
firstMBsToShowResult = 5 ; numMBsToShowResult = 50
|
||||
}
|
||||
|
||||
# The reader is a composite reader that uses the ImageReader to read images
|
||||
# and the CNTKTextFormatReader to read the regression ground truth labels.
|
||||
# It does so by defining an array of deserializers (using {...} : {...} below)
|
||||
# and assigning the inputs as defined in the network above (cf. features and regrLabels).
|
||||
reader = {
|
||||
verbosity = 0 ; randomize = true
|
||||
deserializers = ({
|
||||
type = "ImageDeserializer" ; module = "ImageReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/train_map.txt"
|
||||
input = {
|
||||
features = { transforms = (
|
||||
{ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } :
|
||||
{ type = "Transpose" }
|
||||
)}
|
||||
ignored = { labelDim = 10 }
|
||||
}
|
||||
} : {
|
||||
type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/train_regrLabels.txt"
|
||||
input = {
|
||||
regrLabels = { dim = 3 ; format = "dense" }
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
# Write action
|
||||
Write = {
|
||||
action = "write"
|
||||
minibatchSize = 1
|
||||
outputNodeNames = (ol, regrLabels, rmse)
|
||||
outputPath = "$OutputDir$/06_RegressionSimple"
|
||||
|
||||
reader = {
|
||||
verbosity = 0 ; randomize = false
|
||||
deserializers = ({
|
||||
type = "ImageDeserializer" ; module = "ImageReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/test_map.txt"
|
||||
input = {
|
||||
features = { transforms = (
|
||||
{ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } :
|
||||
{ type = "Transpose" }
|
||||
)}
|
||||
ignored = { labelDim = 10 }
|
||||
}
|
||||
} : {
|
||||
type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/test_regrLabels.txt"
|
||||
input = {
|
||||
regrLabels = { dim = 3 ; format = "dense" }
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
# Test action
|
||||
Test = {
|
||||
action = "test"
|
||||
minibatchSize = 512
|
||||
outputNodeNames = (ol, regrLabels, rmse)
|
||||
outputPath = "$OutputDir$/06_RegressionSimple"
|
||||
|
||||
reader = {
|
||||
verbosity = 0 ; randomize = false
|
||||
deserializers = ({
|
||||
type = "ImageDeserializer" ; module = "ImageReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/test_map.txt"
|
||||
input = {
|
||||
features = { transforms = (
|
||||
{ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } :
|
||||
{ type = "Transpose" }
|
||||
)}
|
||||
ignored = { labelDim = 10 }
|
||||
}
|
||||
} : {
|
||||
type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
|
||||
file = "$dataDir$/cifar-10-batches-py/test_regrLabels.txt"
|
||||
input = {
|
||||
regrLabels = { dim = 3 ; format = "dense" }
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
|
@ -9,7 +9,7 @@ import xml.dom.minidom
|
|||
|
||||
imgSize = 32
|
||||
|
||||
def saveImage(fname, data, label, mapFile, pad, **key_parms):
|
||||
def saveImage(fname, data, label, mapFile, regrFile, pad, **key_parms):
|
||||
# data in CIFAR-10 dataset is in CHW format.
|
||||
pixData = data.reshape((3, imgSize, imgSize))
|
||||
if ('mean' in key_parms):
|
||||
|
|
@ -25,6 +25,11 @@ def saveImage(fname, data, label, mapFile, pad, **key_parms):
|
|||
pixels[x, y] = (pixData[0][y][x], pixData[1][y][x], pixData[2][y][x])
|
||||
img.save(fname)
|
||||
mapFile.write("%s\t%d\n" % (fname, label))
|
||||
|
||||
# compute per channel mean and store for regression example
|
||||
channelMean = np.mean(pixData, axis=(1,2))
|
||||
regrFile.write("|regrLabels\t%f\t%f\t%f\n" % (channelMean[0]/255.0, channelMean[1]/255.0, channelMean[2]/255.0))
|
||||
|
||||
|
||||
def saveMean(fname, data):
|
||||
root = et.Element('opencv_storage')
|
||||
|
|
@ -57,17 +62,19 @@ if __name__ == "__main__":
|
|||
data = {}
|
||||
dataMean = np.zeros((3, imgSize, imgSize)) # mean is in CHW format.
|
||||
with open(os.path.join(rootDir, 'train_map.txt'), 'w') as mapFile:
|
||||
for ifile in range(1, 6):
|
||||
with open(os.path.join(rootDir, 'data_batch_' + str(ifile)), 'rb') as f:
|
||||
data = cp.load(f)
|
||||
for i in range(10000):
|
||||
fname = os.path.join(trainDir, ('%05d.png' % (i + (ifile - 1) * 10000)))
|
||||
saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, 4, mean=dataMean)
|
||||
with open(os.path.join(rootDir, 'train_regrLabels.txt'), 'w') as regrFile:
|
||||
for ifile in range(1, 6):
|
||||
with open(os.path.join(rootDir, 'data_batch_' + str(ifile)), 'rb') as f:
|
||||
data = cp.load(f)
|
||||
for i in range(10000):
|
||||
fname = os.path.join(trainDir, ('%05d.png' % (i + (ifile - 1) * 10000)))
|
||||
saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, regrFile, 4, mean=dataMean)
|
||||
dataMean = dataMean / (50 * 1000)
|
||||
saveMean(os.path.join(rootDir, 'CIFAR-10_mean.xml'), dataMean)
|
||||
with open(os.path.join(rootDir, 'test_map.txt'), 'w') as mapFile:
|
||||
with open(os.path.join(rootDir, 'test_batch'), 'rb') as f:
|
||||
data = cp.load(f)
|
||||
for i in range(10000):
|
||||
fname = os.path.join(testDir, ('%05d.png' % i))
|
||||
saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, 0)
|
||||
with open(os.path.join(rootDir, 'test_regrLabels.txt'), 'w') as regrFile:
|
||||
with open(os.path.join(rootDir, 'test_batch'), 'rb') as f:
|
||||
data = cp.load(f)
|
||||
for i in range(10000):
|
||||
fname = os.path.join(testDir, ('%05d.png' % i))
|
||||
saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, regrFile, 0)
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ def loadData(src, outFmt):
|
|||
return (trn, tst)
|
||||
|
||||
def usage():
|
||||
print ('Usage: CIFAR_convert.py [-f <format>] \n where format can be either cudnn or legacy. Default is cudnn.')
|
||||
print ('Usage: CifarDownload.py [-f <format>] \n where format can be either cudnn or legacy. Default is cudnn.')
|
||||
|
||||
def parseCmdOpt(argv):
|
||||
if len(argv) == 0:
|
||||
|
|
@ -61,7 +61,7 @@ def loadData(src, outFmt):
|
|||
return (trn, tst)
|
||||
|
||||
def usage():
|
||||
print ('Usage: CIFAR_convert.py [-f <format>] \n where format can be either cudnn or legacy. Default is cudnn.')
|
||||
print ('Usage: CifarDownload_py3.py [-f <format>] \n where format can be either cudnn or legacy. Default is cudnn.')
|
||||
|
||||
def parseCmdOpt(argv):
|
||||
if len(argv) == 0:
|
||||
|
|
@ -1,19 +1,18 @@
|
|||
# Simple CIFAR-10 convnet, without and with BatchNormalization.
|
||||
|
||||
command = TrainConvNet:Eval
|
||||
#command = TrainConvNetWithBN:Eval
|
||||
|
||||
makeMode = false ; traceLevel = 1 ; deviceId = 0
|
||||
|
||||
RootDir = "." ; DataDir = "$RootDir$" ; ModelDir = "$RootDir$/Output/Models"
|
||||
|
||||
modelPath = "$ModelDir$/01_Convolution"
|
||||
modelPath = "$ModelDir$/ConvNet"
|
||||
|
||||
# Training without BN
|
||||
TrainConvNet = [
|
||||
TrainConvNet = {
|
||||
action = "train"
|
||||
|
||||
BrainScriptNetworkBuilder = [
|
||||
BrainScriptNetworkBuilder = {
|
||||
imageShape = 32:32:3
|
||||
labelDim = 10
|
||||
|
||||
|
|
@ -21,15 +20,15 @@ TrainConvNet = [
|
|||
|
||||
model = Sequential (
|
||||
Subtract128 :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, activation = ReLU, init = "gaussian", initValueScale = 0.0043} :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, activation = ReLU, init = 'glorotUniform', initValueScale=0.00390625} :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, activation = ReLU, init = "gaussian", initValueScale = 1.414} :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, activation = ReLU, init = 'glorotUniform'} :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {64, (5:5), pad = true, activation = ReLU, init = "gaussian", initValueScale = 1.414} :
|
||||
ConvolutionalLayer {64, (5:5), pad = true, activation = ReLU, init = 'glorotUniform'} :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
DenseLayer {64, activation = ReLU, init = "gaussian", initValueScale = 12} :
|
||||
DenseLayer {64, activation = ReLU, init = 'glorotUniform', initValueScale=0.1} :
|
||||
Dropout :
|
||||
LinearLayer {labelDim, init = "gaussian", initValueScale = 1.5}
|
||||
LinearLayer {labelDim, init = 'glorotUniform', initValueScale=0.1}
|
||||
)
|
||||
|
||||
# inputs
|
||||
|
|
@ -49,9 +48,9 @@ TrainConvNet = [
|
|||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs) # top5Errs only used in Eval
|
||||
outputNodes = (z)
|
||||
]
|
||||
}
|
||||
|
||||
SGD = [
|
||||
SGD = {
|
||||
epochSize = 49984 ; minibatchSize = 64
|
||||
|
||||
learningRatesPerSample = 0.00015625*10:0.000046875*10:0.000015625
|
||||
|
|
@ -61,97 +60,29 @@ TrainConvNet = [
|
|||
dropoutRate = 0*5:0.5
|
||||
|
||||
firstMBsToShowResult = 10 ; numMBsToShowResult = 500
|
||||
]
|
||||
}
|
||||
|
||||
reader = [
|
||||
reader = {
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [ dim = 3072 ; format = "dense" ]
|
||||
labels = [ dim = 10 ; format = "dense" ]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
# Training with BN
|
||||
# Not working well on this small set. Overtrains.
|
||||
TrainConvNetWithBN = [
|
||||
action = "train"
|
||||
|
||||
BrainScriptNetworkBuilder = [
|
||||
imageShape = 32:32:3
|
||||
labelDim = 10
|
||||
|
||||
Subtract128 (x) = x - Constant (128)
|
||||
|
||||
model = Sequential (
|
||||
Subtract128 :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, bias = false, init = "gaussian", initValueScale = 0.0043} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, bias = false, init = "gaussian", initValueScale = 1.414} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {64, (5:5), pad = true, bias = false, init = "gaussian", initValueScale = 1.414} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
LinearLayer {64, bias = false, init = "gaussian", initValueScale = 12} :
|
||||
BatchNormalizationLayer {normalizationTimeConstant = 4096} : ReLU :
|
||||
LinearLayer {labelDim, init = "gaussian", initValueScale = 1.5}
|
||||
)
|
||||
|
||||
# inputs
|
||||
features = Input {imageShape}
|
||||
labels = Input {labelDim}
|
||||
|
||||
# apply model to features
|
||||
z = model (features)
|
||||
|
||||
# connect to system
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ClassificationError (labels, z)
|
||||
top5Errs = ClassificationError (labels, z, topN=5)
|
||||
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
|
||||
SGD = [
|
||||
epochSize = 49984 ; minibatchSize = 64
|
||||
|
||||
learningRatesPerSample = 0.00046875*7:0.00015625
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 10
|
||||
L2RegWeight = 0
|
||||
dropoutRate = 0
|
||||
|
||||
firstMBsToShowResult = 10 ; numMBsToShowResult = 500
|
||||
]
|
||||
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [ dim = 3072 ; format = "dense" ]
|
||||
labels = [ dim = 10 ; format = "dense" ]
|
||||
]
|
||||
]
|
||||
]
|
||||
input = {
|
||||
features = { dim = 3072 ; format = "dense" }
|
||||
labels = { dim = 10 ; format = "dense" }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Eval action
|
||||
Eval = [
|
||||
Eval = {
|
||||
action = "eval"
|
||||
minibatchSize = 16
|
||||
evalNodeNames = errs:top5Errs # also test top-5 error rate
|
||||
reader = [
|
||||
reader = {
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [ dim = 3072 ; format = "dense" ]
|
||||
labels = [ dim = 10 ; format = "dense" ]
|
||||
]
|
||||
]
|
||||
]
|
||||
input = {
|
||||
features = { dim = 3072 ; format = "dense" }
|
||||
labels = { dim = 10 ; format = "dense" }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,91 @@
|
|||
# Simple CIFAR-10 convnet, without and with BatchNormalization.
|
||||
|
||||
command = TrainConvNetWithBN:Eval
|
||||
|
||||
makeMode = false ; traceLevel = 1 ; deviceId = 0
|
||||
|
||||
RootDir = "." ; DataDir = "$RootDir$" ; ModelDir = "$RootDir$/Output/Models"
|
||||
|
||||
modelPath = "$ModelDir$/ConvNetBN"
|
||||
|
||||
# Training with BN
|
||||
TrainConvNetWithBN = {
|
||||
action = "train"
|
||||
|
||||
BrainScriptNetworkBuilder = {
|
||||
imageShape = 32:32:3
|
||||
labelDim = 10
|
||||
|
||||
Subtract128 (x) = x - Constant (128)
|
||||
|
||||
model = Sequential (
|
||||
Subtract128 :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, bias = false, init = "heNormal", initValueScale=0.00390625} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {32, (5:5), pad = true, bias = false, init = "heNormal"} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
ConvolutionalLayer {64, (5:5), pad = true, bias = false, init = "heNormal"} :
|
||||
BatchNormalizationLayer {spatialRank = 2, normalizationTimeConstant = 4096} : ReLU :
|
||||
MaxPoolingLayer {(3:3), stride = (2:2)} :
|
||||
LinearLayer {64, bias = false, init = "heNormal", initValueScale=0.1} :
|
||||
BatchNormalizationLayer {normalizationTimeConstant = 4096} : ReLU :
|
||||
LinearLayer {labelDim, init = "heNormal", initValueScale=0.1}
|
||||
)
|
||||
|
||||
# inputs
|
||||
features = Input {imageShape}
|
||||
labels = Input {labelDim}
|
||||
|
||||
# apply model to features
|
||||
z = model (features)
|
||||
|
||||
# connect to system
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ClassificationError (labels, z)
|
||||
top5Errs = ClassificationError (labels, z, topN=5)
|
||||
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
}
|
||||
|
||||
SGD = {
|
||||
epochSize = 49984 ; minibatchSize = 64
|
||||
|
||||
learningRatesPerSample = 0.00046875*5:0.00015625
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 10
|
||||
L2RegWeight = 0.003
|
||||
dropoutRate = 0
|
||||
|
||||
firstMBsToShowResult = 10 ; numMBsToShowResult = 500
|
||||
}
|
||||
|
||||
reader = {
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Train_cntk_text.txt"
|
||||
input = {
|
||||
features = { dim = 3072 ; format = "dense" }
|
||||
labels = { dim = 10 ; format = "dense" }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Eval action
|
||||
Eval = {
|
||||
action = "eval"
|
||||
minibatchSize = 16
|
||||
evalNodeNames = errs:top5Errs # also test top-5 error rate
|
||||
reader = {
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test_cntk_text.txt"
|
||||
input = {
|
||||
features = { dim = 3072 ; format = "dense" }
|
||||
labels = { dim = 10 ; format = "dense" }
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -21,10 +21,10 @@ ConvBNLayerW(W, inp, outMap, kW, kH, hStride, vStride, bValue, scValue, bnTimeCo
|
|||
b = LearnableParameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc = LearnableParameter(outMap, 1, init = fixedValue, value = scValue)
|
||||
m = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = $imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, isd, spatial = true, normalizationTimeConstant = bnTimeConst, imageLayout = $imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, v, spatial = true, normalizationTimeConstant = bnTimeConst, imageLayout = $imageLayout$)
|
||||
]
|
||||
|
||||
ConvBNLayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scValue, bnTimeConst)
|
||||
|
|
@ -44,10 +44,10 @@ ProjLayer(W, inp, outMap, hStride, vStride, bValue, scValue, bnTimeConst)
|
|||
b = LearnableParameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc = LearnableParameter(outMap, 1, init = fixedValue, value = scValue)
|
||||
m = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = LearnableParameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
|
||||
c = Convolution(W, inp, 1, 1, outMap, hStride, vStride, zeroPadding = false, imageLayout = $imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, isd, spatial = true, normalizationTimeConstant = bnTimeConst, imageLayout = $imageLayout$)
|
||||
y = BatchNormalization(c, sc, b, m, v, spatial = true, normalizationTimeConstant = bnTimeConst, imageLayout = $imageLayout$)
|
||||
]
|
||||
|
||||
ResNetNode2(inp, outMap, inWCount, kW, kH, wScale, bValue, scValue, bnTimeConst)
|
||||
|
|
@ -113,9 +113,9 @@ DnnBNReLULayer(inDim, outDim, x, wScale, bValue, scValue, bnTimeConst)
|
|||
b = LearnableParameter(outDim, 1, init = fixedValue, value = bValue)
|
||||
sc = LearnableParameter(outDim, 1, init = fixedValue, value = scValue)
|
||||
m = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
t = Times(W, x)
|
||||
bn = BatchNormalization(t, sc, b, m, isd, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
bn = BatchNormalization(t, sc, b, m, v, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
y = RectifiedLinear(bn)
|
||||
]
|
||||
|
||||
|
|
@ -125,9 +125,9 @@ DnnImageBNReLULayer(inW, inH, inC, outDim, x, wScale, bValue, scValue, bnTimeCon
|
|||
b = LearnableParameter(outDim, 1, init = fixedValue, value = bValue)
|
||||
sc = LearnableParameter(outDim, 1, init = fixedValue, value = scValue)
|
||||
m = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = LearnableParameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
t = Times(W, x)
|
||||
bn = BatchNormalization(t, sc, b, m, isd, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
bn = BatchNormalization(t, sc, b, m, v, spatial = false, normalizationTimeConstant = bnTimeConst)
|
||||
y = RectifiedLinear(bn)
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -13,25 +13,20 @@
|
|||
|
||||
### Getting the data
|
||||
|
||||
CIFAR-10 dataset is not included in CNTK distribution but can be easily downloaded and converted by running the following command from this folder:
|
||||
CIFAR-10 dataset is not included in CNTK distribution but can be easily downloaded and converted by running the following commands from this folder:
|
||||
|
||||
`python CIFAR_convert.py [-f <format: cudnn|legacy>]`
|
||||
|
||||
The script will download all required files and convert them to CNTK-supported format.
|
||||
In case you don't have a Python installed, there are 2 options:
|
||||
|
||||
1. Download and install latest version of Python 2.7 from: https://www.python.org/downloads/
|
||||
Then install numpy package by following instruction from: http://www.scipy.org/install.html#individual-packages
|
||||
2. Alternatively install Python Anaconda distribution which contains most of the popular Python packages including numpy:
|
||||
http://continuum.io/downloads
|
||||
|
||||
`-f` parameter is optional and specifies output format of the datasets. `cudnn` option (default) saves dataset in a spatial-major format used by cuDNN, while `legacy` - in CNTK legacy format. Use `cudnn` if CNTK is compiled with cuDNN and `legacy` otherwise.
|
||||
|
||||
ResNet samples require converting CIFAR-10 dataset to actual images. This can be performed by running the following command:
|
||||
```
|
||||
python CifarDownload.py [-f <format: cudnn|legacy>]
|
||||
python CifarConverter.py <path to CIFAR-10 dataset>
|
||||
```
|
||||
The script takes a full path to the original CIFAR-10 dataset (in Python pickle format). The script will create `data` folder inside of provided path where it will store both train and test images (in `train` and `test` folders). It will also create appropriate mapping files for the CNTK ImageReader as well as mean file.
|
||||
|
||||
The scripts will download all required files and convert them to CNTK-supported format.
|
||||
In case you don't have Python installed (you require Python 2.7 and numpy), we recommend to install the Python Anaconda distribution which contains most of the popular Python packages including numpy:
|
||||
http://continuum.io/downloads
|
||||
|
||||
The download script has an optional `-f` parameter which specifies output format of the datasets. `cudnn` option (default) saves dataset in a spatial-major format used by cuDNN, while `legacy` - in CNTK legacy format. Use `cudnn` if CNTK is compiled with cuDNN and `legacy` otherwise.
|
||||
|
||||
The converter script takes a full path to the original CIFAR-10 dataset (in Python pickle format). The script will create `data` folder inside of provided path where it will store both train and test images (in `train` and `test` folders). It will also create appropriate mapping files for the CNTK ImageReader as well as mean file.
|
||||
|
||||
## Details
|
||||
|
||||
|
|
@ -43,18 +38,20 @@ https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers
|
|||
The network produces 20.5% of error after training for about 3 minutes on GPU.
|
||||
To run the sample, navigate to the sample folder and run the following command:
|
||||
```
|
||||
cntk configFile=01_Conv.cntk
|
||||
cntk configFile=01_Conv_ndl_deprecated.cntk
|
||||
```
|
||||
2. 02_BatchNormConv.ndl is a convolutional network which uses batch normalization technique (http://arxiv.org/abs/1502.03167).
|
||||
To run the sample, navigate to the sample folder and run the following command:
|
||||
```
|
||||
cntk configFile=02_BatchNormConv.cntk
|
||||
cntk configFile=02_BatchNormConv_ndl_deprecated.cntk
|
||||
```
|
||||
|
||||
3. 03_ResNet.ndl and 04_ResNet_56.ndl are very deep convolutional networks that use ResNet architecture and have 20 and 56 layers respectively (http://arxiv.org/abs/1512.03385).
|
||||
With 03_ResNet.cntk you should get around 8.2% of error after training for about 50 minutes. 04_ResNet_56.cntk should produce around 6.4% of error after training for about 3 hours (see log files in the Output directory).
|
||||
With 03_ResNet_ndl_deprecated.cntk you should get around 8.2% of error after training for about 50 minutes. 04_ResNet_56_ndl_deprecated.cntk should produce around 6.4% of error after training for about 3 hours (see log files in the Output directory).
|
||||
|
||||
4. 05_ConvLocal.cntk uses locally-connected convolution layers (see `conv_local3` and `conv_local4` in `05_ConvLocal.cntk`) and resembles a network described here: https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-conv-local-11pct.cfg
|
||||
4. 05_ConvLocal_ndl_deprecated.cntk uses locally-connected convolution layers (see `conv_local3` and `conv_local4` in `05_ConvLocal_ndl_deprecated.cntk`) and resembles a network described here: https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-conv-local-11pct.cfg
|
||||
|
||||
5. 06_RegressionSimple.cntk shows how to train a regression model on image data. It uses a very simple network and a composite reader using both the ImageReader and CNTKTextFormatReader and defines a the RMSE (root mean square error) as the loss function. The value that the network learns to predict are simply the average rgb values of an image normalized to [0, 1]. To generate the ground truth labels for regression you need to run the CifarConverter.py script (since this example was added later you might need to rerun it to generate the regression files). See also here: https://github.com/Microsoft/CNTK/wiki/Train-a-regression-model-on-images
|
||||
|
||||
For more details, refer to .ndl and corresponding .cntk files.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
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Разница между файлами не показана из-за своего большого размера
Загрузить разницу
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
|
|
@ -8,9 +8,9 @@ BN(inp, mapCount, bValue, scValue, bnTimeConst)
|
|||
b = Parameter(mapCount, 1, init = fixedValue, value = bValue)
|
||||
sc = Parameter(mapCount, 1, init = fixedValue, value = scValue)
|
||||
m = Parameter(mapCount, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = Parameter(mapCount, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = Parameter(mapCount, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
|
||||
y = BatchNormalization(inp, sc, b, m, isd, spatial = true, normalizationTimeConstant = bnTimeConst, epsilon = 0.000000001, imageLayout = "cudnn")
|
||||
y = BatchNormalization(inp, sc, b, m, v, spatial = true, normalizationTimeConstant = bnTimeConst, epsilon = 0.000000001, imageLayout = "cudnn", engine = "cudnn")
|
||||
]
|
||||
|
||||
ConvBNLayerW(W, inp, outMap, kW, kH, hStride, vStride, bValue, scValue, bnTimeConst)
|
||||
|
|
|
|||
|
|
@ -9,14 +9,14 @@
|
|||
|Training |Stochastic gradient descent with momentum.
|
||||
|
||||
## Details
|
||||
The network configurations and experiment settings in this this folder resemble the ones in the original [ResNet paper](http://arxiv.org/abs/1512.03385) with few minor changes inspired by [this work](https://github.com/facebook/fb.resnet.torch).
|
||||
The network configurations and experiment settings in this this folder resemble the ones in the original [ResNet paper](http://arxiv.org/abs/1512.03385)
|
||||
The following table contains results as well as links to pre-trained models that can be used in various applications.
|
||||
|
||||
| Network | Top-1 error | Top-5 error | Model
|
||||
| ------------- | ----------- | ----------- | ----------
|
||||
| ResNet-18 | 29.57 | 10.41 | [Download](https://www.cntk.ai/resnet/ResNet_18.model)
|
||||
| ResNet-34 | 27.31 | 8.97 | [Download](https://www.cntk.ai/resnet/ResNet_34.model)
|
||||
| ResNet-50 | 24.74 | 7.56 | [Download](https://www.cntk.ai/resnet/ResNet_50.model)
|
||||
| ResNet-50 | 24.56 | 7.47 |
|
||||
| ResNet-152 | 22.57 | 6.44 | [Download](https://www.cntk.ai/resnet/ResNet_152.model)
|
||||
|
||||
## Notes
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -27,7 +27,7 @@ ndlMacros = [
|
|||
scValue = 1
|
||||
|
||||
# Batch normalization time constant.
|
||||
bnTimeConst = 4096
|
||||
bnTimeConst = 0
|
||||
]
|
||||
|
||||
DNN=[
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -10,7 +14,7 @@ ndlMacros="$ConfigDir$/Macros.ndl"
|
|||
precision="float"
|
||||
deviceId="Auto"
|
||||
|
||||
command=Train:CreateEval:Test
|
||||
command=Train:PBN:CreateEval:Test
|
||||
|
||||
parallelTrain="false"
|
||||
|
||||
|
|
@ -19,8 +23,8 @@ traceLevel=1
|
|||
numMBsToShowResult=500
|
||||
|
||||
# Strides for increasing layers. Defaults (paper) are 2 for 1x1 and 1 for 3x3.
|
||||
stride1x1=1
|
||||
stride3x3=2
|
||||
stride1x1=2
|
||||
stride3x3=1
|
||||
|
||||
Train=[
|
||||
action="train"
|
||||
|
|
@ -41,6 +45,8 @@ Train=[
|
|||
gradUpdateType="None"
|
||||
L2RegWeight=0.0001
|
||||
dropoutRate=0
|
||||
|
||||
disableWkInBatchNormal=true
|
||||
|
||||
ParallelTrain=[
|
||||
parallelizationMethod="DataParallelSGD"
|
||||
|
|
@ -54,10 +60,6 @@ Train=[
|
|||
|
||||
reader=[
|
||||
readerType="ImageReader"
|
||||
# Map file which maps images to labels using the following format:
|
||||
# <full path to image><tab><numerical label (0-based class id)>
|
||||
# Example:
|
||||
# C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG<tab>0
|
||||
file="$DataDir$/train_map.txt"
|
||||
# Randomize images before every epoch. Possible values: None, Auto. Default: Auto.
|
||||
randomize="Auto"
|
||||
|
|
@ -70,40 +72,60 @@ Train=[
|
|||
# Possible values: Center, Random. Default: Center
|
||||
cropType="Random"
|
||||
# Horizontal random flip, will be enabled by default if cropType=Random
|
||||
#hflip="true"
|
||||
hflip="true"
|
||||
# Crop scale ratio. Examples: cropRatio=0.9, cropRatio=0.7:0.9. Default: 1.
|
||||
cropRatio=0.46666:0.875
|
||||
# Crop scale ratio jitter type.
|
||||
# Possible values: None, UniRatio, UniLength, UniArea. Default: UniRatio
|
||||
jitterType="UniRatio"
|
||||
# Interpolation to use when scaling image to width x height size.
|
||||
# Possible values: nearest, linear, cubic, lanczos. Default: linear.
|
||||
interpolations="cubic"
|
||||
# Aspect ratio jitter radius. Default is 0 (disabled).
|
||||
aspectRatioRadius=0:0.2
|
||||
# Brightness, contrast and color jittering. Default is 0 (disabled).
|
||||
# Using 0 in the first epoch so the network can process original images.
|
||||
brightnessRadius=0:0.2
|
||||
contrastRadius=0:0.2
|
||||
saturationRadius=0:0.4
|
||||
# Intensity jittering: enabled if file is specified and intensityStdDev > 0.
|
||||
# The file stores 1x3 vector (eigenvalues) and 3x3 matrix (eigenvectors) in OpenCV XML format.
|
||||
intensityFile="$ConfigDir$/ImageNet1K_intensity.xml"
|
||||
# StdDev for intensity jittering. Start from the second epoch.
|
||||
intensityStdDev=0:0.1
|
||||
# Mean subtraction: enabled if file is specified.
|
||||
# The file stores mean values for each pixel in OpenCV matrix XML format.
|
||||
meanFile="$ConfigDir$/ImageNet1K_mean.xml"
|
||||
]
|
||||
labels=[
|
||||
labelDim=1000
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
PBN=[
|
||||
action="pbn"
|
||||
modelPath="$ModelDir$/ResNet_50"
|
||||
# Set minibatch size for testing.
|
||||
minibatchSize=256
|
||||
iters=30
|
||||
|
||||
reader=[
|
||||
readerType="ImageReader"
|
||||
file="$DataDir$/train_map.txt"
|
||||
# Randomize images before every epoch. Possible values: None, Auto. Default: Auto.
|
||||
randomize="Auto"
|
||||
features=[
|
||||
# Below are the required parameters.
|
||||
width=224
|
||||
height=224
|
||||
channels=3
|
||||
# Below are the optional parameters.
|
||||
# Possible values: Center, Random. Default: Center
|
||||
cropType="Random"
|
||||
# Horizontal random flip, will be enabled by default if cropType=Random
|
||||
hflip="true"
|
||||
# Crop scale ratio. Examples: cropRatio=0.9, cropRatio=0.7:0.9. Default: 1.
|
||||
cropRatio=0.46666:0.875
|
||||
# Crop scale ratio jitter type.
|
||||
# Possible values: None, UniRatio, UniLength, UniArea. Default: UniRatio
|
||||
jitterType="UniRatio"
|
||||
# The file stores mean values for each pixel in OpenCV matrix XML format.
|
||||
meanFile="$ConfigDir$/ImageNet1K_mean.xml"
|
||||
]
|
||||
labels=[
|
||||
labelDim=1000
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
CreateEval=[
|
||||
action="edit"
|
||||
CurModel="$ModelDir$/ResNet_50"
|
||||
CurModel="$ModelDir$/ResNet_50.PBN"
|
||||
NewModel="$ModelDir$/ResNet_50.Eval"
|
||||
editPath="$ConfigDir$/CreateEvalModel.mel"
|
||||
]
|
||||
|
|
@ -112,7 +134,7 @@ Test=[
|
|||
action="test"
|
||||
modelPath="$ModelDir$/ResNet_50.Eval"
|
||||
# Set minibatch size for testing.
|
||||
minibatchSize=32
|
||||
minibatchSize=200
|
||||
|
||||
reader=[
|
||||
readerType="ImageReader"
|
||||
|
|
@ -123,6 +145,7 @@ Test=[
|
|||
height=224
|
||||
channels=3
|
||||
cropType="Center"
|
||||
cropRatio=0.875
|
||||
meanFile="$ConfigDir$/ImageNet1K_mean.xml"
|
||||
]
|
||||
labels=[
|
||||
|
|
@ -15,9 +15,9 @@ DnnBNReLULayer(inDim, outDim, x, wScale, bValue)
|
|||
b = Parameter(outDim, 1, init = fixedValue, value = bValue)
|
||||
sc = Parameter(outDim, 1, init = Gaussian, initValueScale = 0.01)
|
||||
m = Parameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = Parameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = Parameter(outDim, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
t = Times(W, x)
|
||||
bn = BatchNormalization(t, sc, b, m, isd, spatial = false)
|
||||
bn = BatchNormalization(t, sc, b, m, v, spatial = false)
|
||||
y = RectifiedLinear(bn)
|
||||
]
|
||||
|
||||
|
|
@ -47,9 +47,9 @@ ConvBNReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue,
|
|||
b = Parameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
|
||||
m = Parameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
isd = Parameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
v = Parameter(outMap, 1, init = fixedValue, value = 0, learningRateMultiplier = 0)
|
||||
|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn = BatchNormalization(c, sc, b, m, isd, spatial = true, imageLayout = "cudnn")
|
||||
bn = BatchNormalization(c, sc, b, m, v, spatial = true, imageLayout = "cudnn")
|
||||
y = RectifiedLinear(bn);
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
RootDir = "."
|
||||
|
||||
ConfigDir = "$RootDir$"
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
# This file is a sample ndl script for a single operator (Plus) adding a constant to an input
|
||||
# It is used by the Extensibility\CSEvalClient program
|
||||
# It is used by the Examples\Evaluation\CSEvalClient program
|
||||
deviceId=0
|
||||
run=AddOne
|
||||
AddOne=[
|
||||
|
|
@ -9,4 +9,4 @@ AddOne=[
|
|||
|
||||
FeatureNodes=(features)
|
||||
OutputNodes=(ol)
|
||||
]
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# deviceId=-1 for CPU, >=0 for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available
|
||||
deviceId = -1
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# deviceId=-1 for CPU, >=0 for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available
|
||||
deviceId = -1
|
||||
|
||||
|
|
@ -193,7 +193,7 @@ BrainScriptNetworkBuilder = (new ComputationNetwork [
|
|||
|
||||
# Note: We reverse our input by running the recurrence from right to left.
|
||||
|
||||
encoderFunction = if useBidirectionalEncoder then BS.RNNs.RecurrentBirectionalLSTMPStack else BS.RNNs.RecurrentLSTMPStack
|
||||
encoderFunction = if useBidirectionalEncoder then BS.RNNs.RecurrentBidirectionalLSTMPStack else BS.RNNs.RecurrentLSTMPStack
|
||||
encoder = encoderFunction (encoderDims, cellDims=encoderDims, S(inputEmbedded), inputDim=inputEmbeddingDim,
|
||||
previousHook=if useBidirectionalEncoder then BS.RNNs.PreviousHC else BS.RNNs.NextHC,
|
||||
enableSelfStabilization=useStabilizer)
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# Parameters can be overwritten on the command line
|
||||
# for example: cntk configFile=myConfigFile RootDir=../..
|
||||
# For running from Visual Studio add
|
||||
|
|
@ -16,7 +16,7 @@ See License.md in the root level folder of the CNTK repository for full license
|
|||
|Purpose: |Showcase how to train feed forward and LSTM networks for speech data
|
||||
|Network: |SimpleNetworkBuilder for 2-layer FF, NdlNetworkBuilder for 3-layer LSTM network
|
||||
|Training: |Data-parallel 1-Bit SGD with automatic mini batch rescaling (FF)
|
||||
|Comments: |There are two config files: FeedForward.cntk and LSTM-NDL.cntk for FF and LSTM training respectively
|
||||
|Comments: |There are two config files: FeedForward.cntk and LSTM-NDL_ndl_deprecated.cntk for FF and LSTM training respectively
|
||||
|
||||
## Running the example
|
||||
|
||||
|
|
@ -61,7 +61,7 @@ To run on CPU set `deviceId = -1`, to run on GPU set deviceId to "auto" or a spe
|
|||
|
||||
The FeedForward.cntk file uses the SimpleNetworkBuilder to create a 2-layer
|
||||
feed forward network with sigmoid nodes and a softmax layer.
|
||||
The LSTM-NDL.cntk file uses the NdlNetworkBuilder and refers to the lstmp-3layer-opt.ndl file.
|
||||
The LSTM-NDL_ndl_deprecated.cntk file uses the NdlNetworkBuilder and refers to the lstmp-3layer-opt.ndl file.
|
||||
In the ndl file an LSTM component is defined and used to create a 3-layer LSTM network with a softmax layer.
|
||||
Both configuration only define and execute a single training task:
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$/configs
|
||||
command=$command$
|
||||
numCPUThreads=$numThreads$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$/configs
|
||||
command=$command$
|
||||
numCPUThreads=$numThreads$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$/configs
|
||||
command=$command$
|
||||
numCPUThreads=$numThreads$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$
|
||||
DeviceNumber=$DeviceNumber$
|
||||
stderr=$ExpDir$/log/$logFile$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$/configs
|
||||
command=$command$
|
||||
numCPUThreads=$numThreads$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
NdlDir=$ExpDir$/ndl
|
||||
DeviceNumber=$DeviceNumber$
|
||||
stderr=$ExpDir$/log/$logFile$
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
stderr= # your log file
|
||||
command=speechTrainNDL
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# configuration file for one layer LSTM training on TIMIT
|
||||
|
||||
ExpFolder= # your exp root path
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
stderr= # your log file
|
||||
command=speechTrainNDL
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# configuration file for one layer LSTM training on TIMIT
|
||||
|
||||
ExpFolder= # your exp root path
|
||||
|
|
@ -22,13 +22,13 @@ Path Definitions:
|
|||
Network Training Examples:
|
||||
==========================
|
||||
* TIMIT_TrainSimpleNetwork.cntk [train basic feedforward fully connected neural network]
|
||||
* TIMIT_TrainNDLNetwork.cntk [train a neural network defined using NDL]
|
||||
* TIMIT_TrainNDLNetwork_ndl_deprecated.cntk [train a neural network defined using NDL]
|
||||
* TIMIT_AdaptLearnRate.cntk [similar to simple network example, but learning rate adapted based on dev set]
|
||||
* TIMIT_TrainAutoEncoder.cntk [train autoencoder with bottleneck layer]
|
||||
* TIMIT_TrainWithPreTrain.cntk [pre-train using layerwise discriminative pre-training, then do full network training]
|
||||
* TIMIT_TrainMultiTask.cntk [train with multi-task learning with joint prediction of senone labels and dialect region]
|
||||
* TIMIT_TrainMultiInput.cntk [train with 2 different inputs: fbank and mfcc]
|
||||
* TIMIT_TrainLSTM.cntk [train single layer LSTM network]
|
||||
* TIMIT_TrainAutoEncoder_ndl_deprecated.cntk [train autoencoder with bottleneck layer]
|
||||
* TIMIT_TrainWithPreTrain_ndl_deprecated.cntk [pre-train using layerwise discriminative pre-training, then do full network training]
|
||||
* TIMIT_TrainMultiTask_ndl_deprecated.cntk [train with multi-task learning with joint prediction of senone labels and dialect region]
|
||||
* TIMIT_TrainMultiInput_ndl_deprecated.cntk [train with 2 different inputs: fbank and mfcc]
|
||||
* TIMIT_TrainLSTM_ndl_deprecated.cntk [train single layer LSTM network]
|
||||
|
||||
Network Evaluation Examples:
|
||||
============================
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
|
|
@ -1,3 +1,7 @@
|
|||
# Note: This sample uses the deprecated NdlNetworkBuilder.
|
||||
# An updated version using BrainScript is coming soon.
|
||||
# Please find updated samples on Github, https://github.com/Microsoft/CNTK/tree/master/Examples /...
|
||||
#
|
||||
# If set to true, always initialize the network on CPU, making initialization consistent across CPU and GPU targets (for testing).
|
||||
initOnCPUOnly=true
|
||||
|
||||
33
Makefile
33
Makefile
|
|
@ -26,7 +26,7 @@
|
|||
# CUB_PATH= path to NVIDIA CUB installation, so $(CUB_PATH)/cub/cub.cuh exists
|
||||
# defaults to /usr/local/cub-1.4.1
|
||||
# CUDNN_PATH= path to NVIDIA cuDNN installation so $(CUDNN_PATH)/cuda/include/cudnn.h exists
|
||||
# If not specified, CNTK will be be built without cuDNN.
|
||||
# CuDNN version needs to be 5.0 or higher.
|
||||
# KALDI_PATH= Path to Kaldi
|
||||
# If not specified, Kaldi plugins will not be built
|
||||
# OPENCV_PATH= path to OpenCV 3.1.0 installation, so $(OPENCV_PATH) exists
|
||||
|
|
@ -92,7 +92,7 @@ SRC:=
|
|||
all : buildall
|
||||
|
||||
# Set up basic nvcc options and add CUDA targets from above
|
||||
CUFLAGS = -m 64
|
||||
CUFLAGS = -m 64
|
||||
|
||||
ifdef CUDA_PATH
|
||||
ifndef GDK_INCLUDE_PATH
|
||||
|
|
@ -261,10 +261,11 @@ READER_SRC =\
|
|||
$(SOURCEDIR)/Readers/ReaderLib/TruncatedBpttPacker.cpp \
|
||||
$(SOURCEDIR)/Readers/ReaderLib/PackerBase.cpp \
|
||||
$(SOURCEDIR)/Readers/ReaderLib/FramePacker.cpp \
|
||||
$(SOURCEDIR)/Readers/ReaderLib/ChunkCache.cpp \
|
||||
$(SOURCEDIR)/Readers/ReaderLib/ChunkCache.cpp \
|
||||
|
||||
COMMON_SRC =\
|
||||
$(SOURCEDIR)/Common/Config.cpp \
|
||||
$(SOURCEDIR)/Common/Globals.cpp \
|
||||
$(SOURCEDIR)/Common/DataReader.cpp \
|
||||
$(SOURCEDIR)/Common/DataWriter.cpp \
|
||||
$(SOURCEDIR)/Common/ExceptionWithCallStack.cpp \
|
||||
|
|
@ -274,19 +275,19 @@ COMMON_SRC =\
|
|||
$(SOURCEDIR)/Common/fileutil.cpp \
|
||||
|
||||
MATH_SRC =\
|
||||
$(SOURCEDIR)/Math/BatchNormalizationEngine.cpp \
|
||||
$(SOURCEDIR)/Math/BlockHandlerSSE.cpp \
|
||||
$(SOURCEDIR)/Math/CUDAPageLockedMemAllocator.cpp \
|
||||
$(SOURCEDIR)/Math/CPUMatrix.cpp \
|
||||
$(SOURCEDIR)/Math/CPUSparseMatrix.cpp \
|
||||
$(SOURCEDIR)/Math/CPURNGHandle.cpp \
|
||||
$(SOURCEDIR)/Math/CPUSparseMatrix.cpp \
|
||||
$(SOURCEDIR)/Math/ConvolutionEngine.cpp \
|
||||
$(SOURCEDIR)/Math/MatrixQuantizerImpl.cpp \
|
||||
$(SOURCEDIR)/Math/MatrixQuantizerCPU.cpp \
|
||||
$(SOURCEDIR)/Math/QuantizedMatrix.cpp \
|
||||
$(SOURCEDIR)/Math/Matrix.cpp \
|
||||
$(SOURCEDIR)/Math/QuantizedMatrix.cpp \
|
||||
$(SOURCEDIR)/Math/RNGHandle.cpp \
|
||||
$(SOURCEDIR)/Math/TensorView.cpp \
|
||||
$(SOURCEDIR)/Math/CUDAPageLockedMemAllocator.cpp \
|
||||
$(SOURCEDIR)/Math/ConvolutionEngine.cpp \
|
||||
$(SOURCEDIR)/Math/BatchNormalizationEngine.cpp \
|
||||
|
||||
ifdef SUPPORT_AVX2
|
||||
MATH_SRC +=\
|
||||
|
|
@ -296,16 +297,17 @@ endif
|
|||
|
||||
ifdef CUDA_PATH
|
||||
MATH_SRC +=\
|
||||
$(SOURCEDIR)/Math/CuDnnBatchNormalization.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnCommon.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnConvolutionEngine.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnRNN.cpp \
|
||||
$(SOURCEDIR)/Math/GPUDataTransferer.cpp \
|
||||
$(SOURCEDIR)/Math/GPUMatrix.cu \
|
||||
$(SOURCEDIR)/Math/GPUTensor.cu \
|
||||
$(SOURCEDIR)/Math/GPUSparseMatrix.cu \
|
||||
$(SOURCEDIR)/Math/GPUTensor.cu \
|
||||
$(SOURCEDIR)/Math/GPUWatcher.cu \
|
||||
$(SOURCEDIR)/Math/GPURNGHandle.cu \
|
||||
$(SOURCEDIR)/Math/MatrixQuantizerGPU.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnCommon.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnConvolutionEngine.cu \
|
||||
$(SOURCEDIR)/Math/CuDnnBatchNormalization.cu \
|
||||
$(SOURCEDIR)/Math/GPUDataTransferer.cpp \
|
||||
|
||||
else
|
||||
MATH_SRC +=\
|
||||
|
|
@ -341,6 +343,7 @@ COMPUTATION_NETWORK_LIB_SRC =\
|
|||
$(SOURCEDIR)/ComputationNetworkLib/ComputationNodeScripting.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/InputAndParamNodes.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/ReshapingNodes.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/RNNNodes.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/SpecialPurposeNodes.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/ComputationNetwork.cpp \
|
||||
$(SOURCEDIR)/ComputationNetworkLib/ComputationNetworkEvaluation.cpp \
|
||||
|
|
@ -368,6 +371,7 @@ endif
|
|||
CNTKLIBRARY_SRC =\
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/BackCompat.cpp \
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/Common.cpp \
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/ComputeInputStatistics.cpp \
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/Function.cpp \
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/MinibatchSource.cpp \
|
||||
$(SOURCEDIR)/CNTKv2LibraryDll/NDArrayView.cpp \
|
||||
|
|
@ -413,6 +417,7 @@ CNTKLIBRARY_TESTS_SRC =\
|
|||
Tests/UnitTests/V2LibraryTests/LearnerTests.cpp \
|
||||
Tests/UnitTests/V2LibraryTests/FunctionTests.cpp \
|
||||
Tests/UnitTests/V2LibraryTests/SequenceClassification.cpp \
|
||||
Tests/UnitTests/V2LibraryTests/Seq2Seq.cpp \
|
||||
|
||||
CNTKLIBRARY_TESTS:=$(BINDIR)/v2librarytests
|
||||
CNTKLIBRARY_TESTS_OBJ := $(patsubst %.cu, $(OBJDIR)/%.o, $(patsubst %.cpp, $(OBJDIR)/%.o, $(CNTKLIBRARY_TESTS_SRC)))
|
||||
|
|
@ -903,6 +908,8 @@ UNITTEST_MATH_SRC = \
|
|||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/CPUMatrixTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/CPUSparseMatrixTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/fixtures.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/QuantizersTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/TensorTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/GPUMatrixCudaBlasTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/GPUMatrixTests.cpp \
|
||||
$(SOURCEDIR)/../Tests/UnitTests/MathTests/GPUSparseMatrixTests.cpp \
|
||||
|
|
|
|||
23
README.md
23
README.md
|
|
@ -1,18 +1,29 @@
|
|||
# CNTK
|
||||
|
||||
## Latest news
|
||||
*2016-08-10.* CNTK now supports **Gated Recurrent Units (GRU)**. Read more in the [corresponding article](https://github.com/Microsoft/CNTK/wiki/GRUs-on-CNTK-with-BrainScript).
|
||||
*2016-08-31.* V 1.7 Binary release
|
||||
Highlights of this Release:
|
||||
* Improvements in BrainScript (New library of predefined common layer types, Support of cuDNN5 RNN and Common random-initialization types, improved handling of GRUs)
|
||||
* Support of NVIDIA cuDNN 5.1
|
||||
* Improvements in Readers and Deserializers
|
||||
* Additions to Evaluator Library (Eval Client Sample, Strong Name for EvalWrapper)
|
||||
* New in Unit Tests (Linux support, Randomization engines)
|
||||
* Python API Preview (since V.1.5)
|
||||
* Multiple bug fixes
|
||||
|
||||
See more in the [Release Notes](https://github.com/Microsoft/CNTK/wiki/CNTK_1_7_Release_Notes)
|
||||
Get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases)
|
||||
|
||||
*2016-08-29.* Two new Tutorials are available:
|
||||
[Image recognition](https://github.com/Microsoft/CNTK/wiki/Hands-On-Labs-Image-Recognition) (CIFAR-10) and [Language understanding](https://github.com/Microsoft/CNTK/wiki/Hands-On-Labs-Image-Recognition) (ATIS).
|
||||
|
||||
*2016-08-10.* We have significantly simplified handling of **Gated Recurrent Units (GRU)**. Read more in the [corresponding article](https://github.com/Microsoft/CNTK/wiki/GRUs-on-CNTK-with-BrainScript).
|
||||
|
||||
*2016-07-15.* V 1.6 Binary release
|
||||
CNTK v.1.6 binaries are on the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases)
|
||||
|
||||
*2016-07-12.* We have further expanded Licensing options for CNTK 1bit-SGD and related components. See the details at the [Wiki page](https://github.com/microsoft/cntk/wiki/CNTK-1bit-SGD-License). These new options are an extension of the new CNTK 1bit-SGD License that we have announced on Jun 23, 2016.
|
||||
|
||||
*2016-07-05.* CNTK now supports *Deconvolution* and *Unpooling*. See the usage example in the Network number 4 in [MNIST Sample](https://github.com/Microsoft/CNTK/blob/master/Examples/Image/MNIST/README.md).
|
||||
|
||||
*2016-06-23.* New License Terms for CNTK 1bit-SGD and related components.
|
||||
Effective immediately the License Terms for CNTK 1bit-SGD and related components have changed. The new Terms provide more flexibility and enable new usage scenarios, especially in commercial environments. Read the new Terms at the [standard location](https://cntk1bitsgd.codeplex.com/license). Please note, that while the new Terms are significantly more flexible comparing to the previous ones, they are still **more restrictive** than the main CNTK License. Consequently everything described in [Enabling 1bit-SGD](https://github.com/Microsoft/CNTK/wiki/Enabling-1bit-SGD) section of the Wiki remains valid.
|
||||
|
||||
See [all news](https://github.com/Microsoft/CNTK/wiki/News).
|
||||
|
||||
## What is CNTK
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# This script takes a list of dictionary files and a plain text file and converts this text input file to CNTK text format.
|
||||
# This script takes a list of dictionary files and a plain text utf-8 file and converts this text input file to CNTK text format.
|
||||
#
|
||||
# The input text file must contain N streams per line (N TAB-separated "columns") and should be accompanied by N dictionary files.
|
||||
# The input text file must be in the following form:
|
||||
|
|
@ -72,21 +72,23 @@ if __name__ == "__main__":
|
|||
# creating inputs
|
||||
inputs = [sys.stdin]
|
||||
if len(args.input) != 0:
|
||||
inputs = [open(i) for i in args.input]
|
||||
inputs = [open(i, encoding="utf-8") for i in args.input]
|
||||
|
||||
# creating output
|
||||
output = sys.stdout
|
||||
if args.output != "":
|
||||
output = open(args.output, "w")
|
||||
|
||||
convert([open(d) for d in args.map], inputs, output, args.annotated == "True")
|
||||
convert([open(d, encoding="utf-8") for d in args.map], inputs, output, args.annotated == "True")
|
||||
|
||||
|
||||
#####################################################################################################
|
||||
# Tests
|
||||
#####################################################################################################
|
||||
|
||||
import StringIO
|
||||
try:
|
||||
import StringIO
|
||||
except ImportError:
|
||||
from io import StringIO
|
||||
import pytest
|
||||
|
||||
def test_simpleSanityCheck():
|
||||
|
|
|
|||
|
|
@ -45,6 +45,8 @@ void DoEdit(const ConfigParameters& config);
|
|||
|
||||
// evaluation (EvalActions.cpp)
|
||||
template <typename ElemType>
|
||||
void DoEvalBN(const ConfigParameters& config);
|
||||
template <typename ElemType>
|
||||
void DoEval(const ConfigParameters& config);
|
||||
template <typename ElemType>
|
||||
void DoCrossValidate(const ConfigParameters& config);
|
||||
|
|
|
|||
|
|
@ -78,6 +78,62 @@ static void DoEvalBase(const ConfigParameters& config, IDataReader& reader)
|
|||
eval.Evaluate(&reader, evalNodeNamesVector, mbSize[0], epochSize);
|
||||
}
|
||||
|
||||
// ===========================================================================
|
||||
// DoEvalBNBase() - implements CNTK "pbn" command
|
||||
// ===========================================================================
|
||||
|
||||
template <typename ElemType>
|
||||
static void DoEvalBNBase(const ConfigParameters& config, IDataReader& reader)
|
||||
{
|
||||
// DEVICEID_TYPE deviceId = DeviceFromConfig(config);
|
||||
ConfigArray minibatchSize = config(L"minibatchSize", "40960");
|
||||
size_t epochSize = config(L"epochSize", "0");
|
||||
if (epochSize == 0)
|
||||
{
|
||||
epochSize = requestDataSize;
|
||||
}
|
||||
wstring modelPath = config(L"modelPath");
|
||||
wstring exportPath = modelPath + L".PBN";
|
||||
intargvector mbSize = minibatchSize;
|
||||
|
||||
int iters = config(L"iters", 240);
|
||||
|
||||
int traceLevel = config(L"traceLevel", "0");
|
||||
size_t numMBsToShowResult = config(L"numMBsToShowResult", "100");
|
||||
size_t firstMBsToShowResult = config(L"firstMBsToShowResult", "0");
|
||||
size_t maxSamplesInRAM = config(L"maxSamplesInRAM", (size_t)SIZE_MAX);
|
||||
size_t numSubminiBatches = config(L"numSubminibatches", (size_t)1);
|
||||
|
||||
bool enableDistributedMBReading = config(L"distributedMBReading", false);
|
||||
|
||||
vector<wstring> evalNodeNamesVector;
|
||||
|
||||
let net = GetModelFromConfig<ConfigParameters, ElemType>(config, L"evalNodeNames", evalNodeNamesVector);
|
||||
|
||||
// set tracing flags
|
||||
net->EnableNodeTracing(config(L"traceNodeNamesReal", ConfigParameters::Array(stringargvector())),
|
||||
config(L"traceNodeNamesCategory", ConfigParameters::Array(stringargvector())),
|
||||
config(L"traceNodeNamesSparse", ConfigParameters::Array(stringargvector())));
|
||||
|
||||
SimpleEvaluator<ElemType> eval(net, MPIWrapper::GetInstance(), enableDistributedMBReading, numMBsToShowResult,
|
||||
firstMBsToShowResult, traceLevel, maxSamplesInRAM, numSubminiBatches);
|
||||
eval.EvaluateBN(&reader, evalNodeNamesVector, exportPath, mbSize[0], iters, epochSize);
|
||||
}
|
||||
|
||||
template <typename ElemType>
|
||||
void DoEvalBN(const ConfigParameters& config)
|
||||
{
|
||||
// evaluate batch normalization mean and various
|
||||
ConfigParameters readerConfig(config(L"reader"));
|
||||
|
||||
// Should trace level to zero in Post BN?
|
||||
//readerConfig.Insert("traceLevel", config(L"traceLevel", "0"));
|
||||
|
||||
DataReader evaBNDataReader(readerConfig);
|
||||
|
||||
DoEvalBNBase<ElemType>(config, evaBNDataReader);
|
||||
}
|
||||
|
||||
template <typename ElemType>
|
||||
void DoEval(const ConfigParameters& config)
|
||||
{
|
||||
|
|
@ -90,6 +146,8 @@ void DoEval(const ConfigParameters& config)
|
|||
DoEvalBase<ElemType>(config, testDataReader);
|
||||
}
|
||||
|
||||
template void DoEvalBN<double>(const ConfigParameters& config);
|
||||
template void DoEvalBN<float>(const ConfigParameters& config);
|
||||
template void DoEval<double>(const ConfigParameters& config);
|
||||
template void DoEval<float>(const ConfigParameters& config);
|
||||
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@
|
|||
#include "LinearAlgebraNodes.h"
|
||||
#include "RecurrentNodes.h"
|
||||
#include "ConvolutionalNodes.h"
|
||||
#include "RNNNodes.h"
|
||||
#include "NonlinearityNodes.h"
|
||||
#include "ReshapingNodes.h"
|
||||
#include "InputAndParamNodes.h"
|
||||
|
|
@ -498,7 +499,7 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
|
|||
else if (cnNodeType == OperationNameOf(BatchNormalizationNode))
|
||||
{
|
||||
if (parameter.size() != 5)
|
||||
RuntimeError("%ls should have 5 fixed parameters[inputValueNodeName, scale, bias, runMean, runInvStdDev].", cnNodeType.c_str());
|
||||
RuntimeError("%ls should have 5 fixed parameters[inputValueNodeName, scale, bias, runMean, runVariance].", cnNodeType.c_str());
|
||||
|
||||
// setup the parameter position of children so we can hook them up later
|
||||
nodeParamCount = 5;
|
||||
|
|
@ -506,7 +507,7 @@ void NDLNodeEvaluatorImpl<ElemType>::Evaluate(NDLNode<ElemType>* node, const wst
|
|||
|
||||
if (pass == ndlPassInitial)
|
||||
{
|
||||
int id = 5; // skip inputValueNode, scale and bias, runMean, runInvStdDev.
|
||||
int id = 5; // skip inputValueNode, scale and bias, runMean, runVariance.
|
||||
// evaluate only scalar parameters
|
||||
vector<void*> params = EvaluateParameters(node, baseName, id, parameter.size() - id, pass);
|
||||
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@
|
|||
#include "NDLNetworkBuilder.h"
|
||||
|
||||
#include "ConvolutionalNodes.h"
|
||||
#include "RNNNodes.h"
|
||||
#include "DeprecatedNodes.h"
|
||||
#include "EvaluationNodes.h"
|
||||
#include "InputAndParamNodes.h"
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@
|
|||
|
||||
# LinearLayer -- create a fully-connected linear projection layer
|
||||
# Note: outDim may describe a tensor as well.
|
||||
LinearLayer {outDim, bias = true, init='heNormal', initValueScale=1, inputRank=None, mapRank=None} =
|
||||
LinearLayer {outDim, bias = true, init='glorotUniform', initValueScale=1, inputRank=None, mapRank=None} =
|
||||
{
|
||||
# inputRank given: number of zeroes to add to W (mapRank must not be given)
|
||||
# mapRank given: expand W to leave exactly mapRank axes (inputRank must not be given)
|
||||
|
|
@ -53,15 +53,16 @@ LinearLayer {outDim, bias = true, init='heNormal', initValueScale=1, inputRank=N
|
|||
}.apply
|
||||
|
||||
# DenseLayer -- create a fully-connected layer with optional non-linearity
|
||||
DenseLayer{outDim, bias = true, activation=(x=>x), init='heNormal', initValueScale=1, inputRank=None, mapRank=None} = Sequential ( LinearLayer{outDim, bias=bias, init=init, initValueScale=initValueScale, inputRank=inputRank, mapRank=mapRank} : activation )
|
||||
DenseLayer{outDim, bias = true, activation=(x=>x), init='glorotUniform', initValueScale=1, inputRank=None, mapRank=None} = Sequential ( LinearLayer{outDim, bias=bias, init=init, initValueScale=initValueScale, inputRank=inputRank, mapRank=mapRank} : activation )
|
||||
|
||||
# EmbeddingLayer -- create a linear embedding layer
|
||||
EmbeddingLayer {outDim, # dimension of embedding
|
||||
init='glorotUniform', initValueScale=1,
|
||||
embeddingPath = '', transpose = false} = # load a fixed embedding from a path instead
|
||||
{
|
||||
shape = if transpose then (Inferred : outDim) else (outDim : Inferred)
|
||||
E = if embeddingPath == ''
|
||||
then ParameterTensor {shape, init='heNormal'} # learnable
|
||||
then ParameterTensor {shape, init=init, initValueScale=initValueScale} # learnable
|
||||
else ParameterTensor {shape, initFromFilePath = embeddingPath, learningRateMultiplier = 0} # fixed from file
|
||||
TimesOp = if transpose then TransposeTimes else Times
|
||||
apply (x) = TimesOp (E, x) # x is expected to be sparse one-hot
|
||||
|
|
@ -76,7 +77,7 @@ ConvolutionalLayer {numOutputChannels, # e.g. (1) or BS.Constants.None
|
|||
filterShape, # e.g. (3:3)
|
||||
bias = true,
|
||||
activation = (x=>x),
|
||||
init = "heNormal",
|
||||
init = 'glorotUniform',
|
||||
initValueScale = 1, # TODO: rename to initScale
|
||||
#reductionRank = 1, # TODO: support this
|
||||
stride = 1, pad = false,
|
||||
|
|
@ -89,8 +90,7 @@ ConvolutionalLayer {numOutputChannels, # e.g. (1) or BS.Constants.None
|
|||
outputRank = Length (outputChannelsShape)
|
||||
filterRank = Length (filterShape)
|
||||
kernelShape = _ConcatArrays (filterShape, Repeat (reductionRank, Inferred)) # kernel := filter plus reductionDims
|
||||
#W = ParameterTensor{_ConcatArrays ( kernelShape, outputChannelsShape), init = init, initValueScale = initValueScale, initOutputRank = -1} # [ W x H x C x K ]
|
||||
W = ParameterTensor{(outputChannelsShape : Inferred), init = init, initValueScale = initValueScale} # old-style for backwards-compatible random initialization
|
||||
W = ParameterTensor{_ConcatArrays (kernelShape, outputChannelsShape), init = init, initValueScale = initValueScale, initFilterRank = filterRank, initOutputRank = -1} # [ W x H x C x K ]
|
||||
b = ParameterTensor(_ConcatArrays (Repeat (Length (filterShape), 1), outputChannelsShape), initValue = 0) # [ 1 x 1 x K ]
|
||||
sharing = true # TODO: support this
|
||||
transpose = false # TODO: support this
|
||||
|
|
@ -115,13 +115,22 @@ AveragePoolingLayer {filterShape, stride = 1, pad = false, lowerPad = 0, upperPa
|
|||
|
||||
# RecurrentLSTMLayer -- create an LSTM layer
|
||||
RecurrentLSTMLayer {outputDim,
|
||||
cellShape = BS.Constants.None, # if set then use a projection
|
||||
cellShape = None, # if set then use a projection
|
||||
goBackwards = false,
|
||||
init='heNormal', initValueScale=1,
|
||||
enableSelfStabilization = false} =
|
||||
usePeepholes = false,
|
||||
init = 'glorotUniform', initValueScale = 1,
|
||||
enableSelfStabilization = false,
|
||||
allowOptimizedEngine = false} =
|
||||
if allowOptimizedEngine && BS.Constants.IsNone (cellShape) && !goBackwards && !usePeepholes && !enableSelfStabilization then
|
||||
{
|
||||
# use cudnn instead
|
||||
W = ParameterTensor {0:0, initFilterRank=0, initOutputRank=-1, init=init, initValueScale=initValueScale}
|
||||
apply (x) = OptimizedRNNStack (W, x, outputDim, numLayers=1)
|
||||
}.apply
|
||||
else
|
||||
{
|
||||
previousHook = if goBackwards then BS.RNNs.NextHC else BS.RNNs.PreviousHC
|
||||
lstm = BS.RNNs.LSTMBlock {outputDim, cellShape = cellShape, enableSelfStabilization = enableSelfStabilization, init=init, initValueScale=initValueScale}
|
||||
lstm = BS.RNNs.LSTMBlock {outputDim, cellShape=cellShape, usePeepholes=usePeepholes, enableSelfStabilization=enableSelfStabilization, init=init, initValueScale=initValueScale}
|
||||
apply (x) = {
|
||||
prevState = previousHook (lstmState) # recurrent memory. E.g. Previous or Next, with or without initial state, beam reordering etc.
|
||||
|
||||
|
|
@ -131,6 +140,35 @@ RecurrentLSTMLayer {outputDim,
|
|||
}.lstmState.h // that's the value we return
|
||||
}.apply
|
||||
|
||||
# helper to check whether all elements of an array are the same
|
||||
_AreElementsTheSame (arr, L) =
|
||||
if L < 2 then true
|
||||
else arr[L-1] == arr[L-2] && _AreElementsTheSame (arr, L-1)
|
||||
|
||||
# RecurrentLSTMLayer -- create a whole stack of LSTM layers
|
||||
RecurrentLSTMLayerStack {layerDims, # an array of dimensions for each layer (last one is output dimension)
|
||||
cellShapes = None, # if set then use a projection
|
||||
goBackwards = false,
|
||||
usePeepholes = false,
|
||||
init = 'glorotUniform', initValueScale = 1,
|
||||
enableSelfStabilization = false,
|
||||
allowOptimizedEngine = false} =
|
||||
if allowOptimizedEngine && BS.Constants.IsNone (cellShapes) && !goBackwards && !usePeepholes && !enableSelfStabilization && _AreElementsTheSame (layerDims, Length(layerDims)) then
|
||||
{
|
||||
# use cudnn instead
|
||||
W = ParameterTensor {0:0, initFilterRank=0, initOutputRank=-1, init=init, initValueScale=initValueScale}
|
||||
apply (x) = OptimizedRNNStack (W, x, layerDims[0], numLayers=Length(layerDims))
|
||||
}.apply
|
||||
else
|
||||
LayerStack {Length(layerDims), i => RecurrentLSTMLayer {
|
||||
layerDims[i],
|
||||
cellShape = if BS.Constants.IsNone (cellShapes) then None else cellShapes[i], # if set then use a projection
|
||||
goBackwards = goBackwards,
|
||||
usePeepholes = usePeepholes,
|
||||
init = init, initValueScale = initValueScale,
|
||||
enableSelfStabilization = enableSelfStabilization,
|
||||
allowOptimizedEngine = allowOptimizedEngine}}
|
||||
|
||||
# DelayLayer -- delay input
|
||||
DelayLayer {T=1, defaultHiddenActivation=0} =
|
||||
{
|
||||
|
|
@ -150,22 +188,25 @@ DelayLayer {T=1, defaultHiddenActivation=0} =
|
|||
# BatchNormalizationLayer -- create a batch-normalization layer
|
||||
BatchNormalizationLayer {spatialRank = 0, # reduce over these dims. E.g. 2 to reduce over (w,h) in a [W x H x C]-shaped input
|
||||
initialScale = 1,
|
||||
normalizationTimeConstant = 0, blendTimeConstant = 0, # TODO: normTimeConst should be INF, not 0
|
||||
normalizationTimeConstant = 5000, blendTimeConstant = 0,
|
||||
epsilon = 0.00001, useCntkEngine = true} =
|
||||
{
|
||||
#normShape = _ConcatArrays (Repeat (spatialRank, 1), 0) # spatial dims get a dimension of 1 (broadcasting, while all others are inferred from input)
|
||||
normShape = (0:1) # TODO: Update this once we support broadcasting-style parameters.
|
||||
scale = ParameterTensor {normShape, initValue = initialScale}
|
||||
bias = ParameterTensor {normShape, initValue = 0}
|
||||
runMean = ParameterTensor {normShape, initValue = 0, learningRateMultiplier = 0} # note: disable learning since these are updated differently
|
||||
runInvStdDev = ParameterTensor {normShape, initValue = 0, learningRateMultiplier = 0}
|
||||
apply (x) = BatchNormalization (x, scale, bias, runMean, runInvStdDev, spatialRank > 0, normalizationTimeConstant = normalizationTimeConstant, blendTimeConstant = blendTimeConstant, epsilon = epsilon, useCntkEngine = useCntkEngine)
|
||||
#normShape = _ConcatArrays (Repeat (spatialRank, 1), 0) # spatial dims get a dimension of 1 (broadcasting, while all others are inferred from input)
|
||||
normShape = (0:1) # TODO: Update this once we support broadcasting-style parameters.
|
||||
scale = ParameterTensor {normShape, initValue = initialScale}
|
||||
bias = ParameterTensor {normShape, initValue = 0}
|
||||
runMean = ParameterTensor {normShape, initValue = 0, learningRateMultiplier = 0} # note: disable learning since these are updated differently
|
||||
runVariance = ParameterTensor {normShape, initValue = 0, learningRateMultiplier = 0}
|
||||
apply (x) = BatchNormalization (x, scale, bias, runMean, runVariance, spatialRank > 0, normalizationTimeConstant = normalizationTimeConstant, blendTimeConstant = blendTimeConstant, epsilon = epsilon, useCntkEngine = useCntkEngine)
|
||||
}.apply
|
||||
|
||||
# LayerNormalizationLayer -- create a layer-normalization layer
|
||||
LayerNormalizationLayer {initScale = 1, initBias = 0} =
|
||||
{
|
||||
gain = ParameterTensor{(1), initValue = initScale}
|
||||
gain = ParameterTensor{(1), initValue = initScale} # TODO: offer Softplus version for protection, as for Stabilizer
|
||||
# f = ConstantTensor (4, (1))
|
||||
# fInv = Reciprocal (f)
|
||||
# gain = fInv .* Log (BS.Constants.One + Exp (f .* ParameterTensor ((1), initValue=0.99537863/* 1/f*ln (e^f-1) */))) # init value is 1
|
||||
bias = ParameterTensor{(1), initValue = initBias}
|
||||
|
||||
apply (x) = {
|
||||
|
|
@ -304,7 +345,7 @@ CNTK2 = [
|
|||
// TODO: The API for Parameter is different in current 2.0 design, getting a constant as input for the initial values.
|
||||
// This needs to be fixed to follow the way the Constant() is exposed in Python
|
||||
// Making this an internal node with "_" until we agree on the final interface:
|
||||
_Parameter(shape, value = 0, initValue = '', learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, initOutputRank = 1, initFromFilePath = '', initFromLiteral = '', initOnCPUOnly=true, randomSeed=-1, tag='') = new ComputationNode [ operation = 'LearnableParameter' ; shape = new TensorShape [ /*shape */ ] /*plus the function args*/ ]
|
||||
_Parameter(shape, value = 0, initValue = '', learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, initFilterRank = 0, initOutputRank = 1, initFromFilePath = '', initFromLiteral = '', initOnCPUOnly=true, randomSeed=-1, tag='') = new ComputationNode [ operation = 'LearnableParameter' ; shape = new TensorShape [ /*shape */ ] /*plus the function args*/ ]
|
||||
|
||||
// 3. Shape operations
|
||||
// Changes: NewReshape -> Reshape, input -> _, dims -> shape
|
||||
|
|
@ -416,12 +457,12 @@ CNTK2 = [
|
|||
# - initFromLiteral="..." (deprecated) --> parse a string literal (obsolete with value=array form)
|
||||
# - init="fixedValue", value from 'value'
|
||||
# Warning: Current config will behave unexpected if user mistypes 'initValue' as 'value' (which will be ignored, defaulting to "uniform" init)
|
||||
Parameter {outputDim, inputDim, learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, value = 0/*deprecated*/, initValue = '', initFromFilePath = '', initFromLiteral = ''/*deprecated*/, initOnCPUOnly=true, randomSeed=-1, tag=''} = new ComputationNode [ operation = 'LearnableParameter' ; initOutputRank = 1 ; shape = new TensorShape [ dims = (outputDim : inputDim) ] /*plus the function args*/ ]
|
||||
Parameter {outputDim, inputDim, learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, value = 0/*deprecated*/, initValue = '', initFromFilePath = '', initFromLiteral = ''/*deprecated*/, initOnCPUOnly=true, randomSeed=-1, tag=''} = new ComputationNode [ operation = 'LearnableParameter' ; initFilterRank = 0 ; initOutputRank = 1 ; shape = new TensorShape [ dims = (outputDim : inputDim) ] /*plus the function args*/ ]
|
||||
|
||||
LearnableParameter = Parameter // deprecated
|
||||
|
||||
# TODO: make Parameter take tensor dims?
|
||||
ParameterTensor {dims, learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, value = 0, initValue = '', initOutputRank = 1, initFromFilePath = '', initFromLiteral = '', initOnCPUOnly=true, randomSeed=-1, tag=''} = new ComputationNode [ operation = 'LearnableParameter' ; shape = new TensorShape [ /*dims*/ ] /*plus the function args*/ ]
|
||||
ParameterTensor {dims, learningRateMultiplier = 1.0, init = ''/*|uniform|fixedValue|gaussian|fromFile|fromLiteral*/, initValueScale = 1, value = 0, initValue = '', initFilterRank = 0, initOutputRank = 1, initFromFilePath = '', initFromLiteral = '', initOnCPUOnly=true, randomSeed=-1, tag=''} = new ComputationNode [ operation = 'LearnableParameter' ; shape = new TensorShape [ /*dims*/ ] /*plus the function args*/ ]
|
||||
ConstantFromString(literal, tag='') = ParameterTensor((0)/*dim, will be inferred*/, initFromLiteral = literal, learningRateMultiplier = 0.0)
|
||||
# TODO: Deprecate ConstantFromString() in favor of Constant(array expression)
|
||||
DynamicAxis(tag='') = new ComputationNode [ operation = 'DynamicAxis' ; /*plus the function args*/ ]
|
||||
|
|
@ -478,7 +519,7 @@ ColumnwiseCrossProduct = KhatriRaoProduct // deprecated
|
|||
ErrorPrediction = ClassificationError # legacy name
|
||||
Delay = PastValue
|
||||
|
||||
BatchNormalization(input, scale, bias, runMean, runInvStdDev, spatial, normalizationTimeConstant = 0, blendTimeConstant = 0, epsilon = 0.00001, useCntkEngine = true, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'BatchNormalization' ; inputs = (input : scale : bias : runMean : runInvStdDev) /*plus the function args*/ ]
|
||||
BatchNormalization(input, scale, bias, runMean, runVariance, spatial, normalizationTimeConstant = 0, blendTimeConstant = 0, epsilon = 0.00001, useCntkEngine = true, imageLayout='CHW', tag='') = new ComputationNode [ operation = 'BatchNormalization' ; inputs = (input : scale : bias : runMean : runVariance) /*plus the function args*/ ]
|
||||
ClassBasedCrossEntropyWithSoftmax(labelClassDescriptorVectorSequence, mainInputInfo, mainWeight, classLogProbsBeforeSoftmax, tag='') = new ComputationNode [ operation = 'ClassBasedCrossEntropyWithSoftmax' ; inputs = (labelClassDescriptorVectorSequence : mainInputInfo : mainWeight : classLogProbsBeforeSoftmax) /*plus the function args*/ ]
|
||||
Clip(minValue, maxValue, x, tag='') = new ComputationNode [ operation = 'Clip' ; inputs = (minValue : maxValue : x) /* plus the function args*/ ]
|
||||
ColumnElementTimes(aVectorSequence, anotherVectorSequence, tag='') = new ComputationNode [ operation = 'ColumnElementTimes' ; inputs = (aVectorSequence : anotherVectorSequence) /*plus the function args*/ ]
|
||||
|
|
@ -508,6 +549,10 @@ PerDimMeanVarDeNormalization(dataVectorSequence, meanVector, invStdDevVector, ta
|
|||
PerDimMeanVarNormalization (x, mean, invStdDev) = (x - mean) .* invStdDev
|
||||
Reciprocal(z, tag='') = new ComputationNode [ operation = 'Reciprocal' ; inputs = z /*plus the function args*/ ]
|
||||
//# the following is a temporary workaround until we have the C++ version
|
||||
# TODO: change hiddenDims to hiddenShape and pass as a TensorShape (currently, the node only supports rank-1 data)
|
||||
OptimizedRNNStack(weights, input, hiddenDims, numLayers=1, bidirectional=false, recurrentOp='lstm', axis=-1, tag='') = new ComputationNode [ operation = 'OptimizedRNNStack' ; inputs = ( weights : input ) /*plus the function args*/ ]
|
||||
# legacy:
|
||||
RNNStack(x, W, hiddenSize=10, numLayers=1, bidirectional=false, rnnMode='lstm', tag='') = OptimizedRNNStack(W, x, hiddenSize, numLayers=1, bidirectional=false, recurrentOp=rnnMode, tag='')
|
||||
Scale(scalarScalingFactor, matrix, tag='') = new ComputationNode [ operation = 'Scale' ; inputs = (scalarScalingFactor : matrix) /*plus the function args*/ ]
|
||||
# TODO: Scale = ElementTimes
|
||||
ScatterPacked(cond, indexSequence, sourceData, tag='') = new ComputationNode [ operation = 'ScatterPacked' ; inputs = (cond : indexSequence : sourceData) /*plus the function args*/ ]
|
||||
|
|
@ -875,17 +920,19 @@ RNNs =
|
|||
# This is the stateless version that takes the previous state as an input.
|
||||
# It returns a dictionary with three members: h and c, and dim=h.dim for convenience. prevState must have h and c.
|
||||
# This function also takes an optional auxiliary input, e.g. for suporting attention models.
|
||||
LSTMBlock (outputDim, cellShape=None, init='heNormal', initValueScale=1, enableSelfStabilization=false) =
|
||||
LSTMBlock (outputDim, cellShape=None, usePeepholes=false, init='glorotUniform', initValueScale=1, enableSelfStabilization=false) =
|
||||
{
|
||||
cellDim = if Constants.IsNone (cellShape) then outputDim else cellShape
|
||||
|
||||
# parameter helpers
|
||||
# we group 4 matrices into one, gives 60% speed-up in some cases
|
||||
# Not merging inputs since initialization would be off.
|
||||
# note: invoked multiple times, each invocation comes with its own set of weights
|
||||
B{} = ParameterTensor {(cellDim), initValue=0} # a bias
|
||||
W{} = ParameterTensor {(cellDim : Inferred), init=init, initValueScale=initValueScale} # a input
|
||||
A{} = ParameterTensor {(cellDim : Inferred), init=init, initValueScale=initValueScale} # an aux input
|
||||
H{} = ParameterTensor {(cellDim : outputDim), init=init, initValueScale=initValueScale} # a hidden-to-hidden
|
||||
C{} = ParameterTensor {(cellDim), init=init, initValueScale=initValueScale} # a cell-to-hiddden {note: applied elementwise}
|
||||
B{} = ParameterTensor {(4 * cellDim), initValue=0} # a bias
|
||||
W{} = ParameterTensor {(4 * cellDim : Inferred), init=init, initValueScale=initValueScale} # a input
|
||||
A{} = ParameterTensor {(4 * cellDim : Inferred), init=init, initValueScale=initValueScale} # an aux input
|
||||
H{} = ParameterTensor {(4 * cellDim : outputDim), init=init, initValueScale=initValueScale} # a hidden-to-hidden
|
||||
C{} = ParameterTensor {( cellDim), init=init, initValueScale=initValueScale} # a cell-to-hiddden {note: applied elementwise}
|
||||
|
||||
Wmr = ParameterTensor {(outputDim : cellDim), init=init, initValueScale=initValueScale}; # final projection
|
||||
|
||||
|
|
@ -902,21 +949,30 @@ RNNs =
|
|||
dcs = S(dc)
|
||||
# note: input does not get a stabilizer here, user is meant to do that outside
|
||||
|
||||
# projected contribution from input(s) and bias
|
||||
pin() = if Constants.IsNone (aux)
|
||||
then B{} + W{} * x
|
||||
else B{} + W{} * x + A{} * aux
|
||||
# projected contribution from input(s), hidden, and bias
|
||||
proj() = if Constants.IsNone (aux)
|
||||
then B{} + W{} * x + H{} * dhs
|
||||
else B{} + W{} * x + H{} * dhs + A{} * aux
|
||||
|
||||
it = Sigmoid (pin() + H{} * dhs + C{} .* dcs) // input gate(t)
|
||||
bit = it .* Tanh (pin() + H{} * dhs) // applied to tanh of input network
|
||||
# add peephole connection if requested
|
||||
peep(x, c) = if usePeepholes then x + C{} .* c else x
|
||||
|
||||
ft = Sigmoid (pin() + H{} * dhs + C{} .* dcs) // forget-me-not gate(t)
|
||||
bft = ft .* dc // applied to cell(t-1)
|
||||
proj4 = proj() # do all 4 sub-networks at once (not using an extra tensor dim, for efficiency)
|
||||
itProj = Slice (0*cellDim, 1*cellDim, proj4, axis=1)
|
||||
bitProj = Slice (1*cellDim, 2*cellDim, proj4, axis=1)
|
||||
ftProj = Slice (2*cellDim, 3*cellDim, proj4, axis=1)
|
||||
otProj = Slice (3*cellDim, 4*cellDim, proj4, axis=1)
|
||||
|
||||
ct = bft + bit // c(t) is sum of both
|
||||
it = Sigmoid (peep (itProj, dcs)) // input gate(t)
|
||||
bit = it .* Tanh (bitProj) // applied to tanh of input network
|
||||
|
||||
ot = Sigmoid (pin() + H{} * dhs + C{} .* S(ct)) // output gate(t)
|
||||
ht = ot .* Tanh (ct) // applied to tanh(cell(t))
|
||||
ft = Sigmoid (peep (ftProj, dcs)) // forget-me-not gate(t)
|
||||
bft = ft .* dc // applied to cell(t-1)
|
||||
|
||||
ct = bft + bit // c(t) is sum of both
|
||||
|
||||
ot = Sigmoid (peep (otProj, S(ct))) // output gate(t)
|
||||
ht = ot .* Tanh (ct) // applied to tanh(cell(t))
|
||||
}
|
||||
|
||||
# our return values
|
||||
|
|
@ -1038,7 +1094,7 @@ RNNs =
|
|||
|
||||
# a stack of recurrent LSTMs (bidirectional)
|
||||
# TODO: Should we define layerDims as the total (sum of both forward and backward direction)?
|
||||
RecurrentBirectionalLSTMPStack (layerDims, cellDims=layerDims, input, inputDim=input.dim, previousHook=PreviousHC, nextHook=NextHC, enableSelfStabilization=false) = [
|
||||
RecurrentBidirectionalLSTMPStack (layerDims, cellDims=layerDims, input, inputDim=input.dim, previousHook=PreviousHC, nextHook=NextHC, enableSelfStabilization=false) = [
|
||||
previousHook1 = previousHook ; nextHook1 = nextHook ; useStabilizer = enableSelfStabilization
|
||||
layers[i:0..Length (layerDims)-1] =
|
||||
[
|
||||
|
|
@ -1152,7 +1208,7 @@ RNNs =
|
|||
|
||||
# a stack of recurrent GRUs (bidirectional)
|
||||
# TODO: Should we define layerDims as the total (sum of both forward and backward direction)?
|
||||
RecurrentBirectionalGRUStack (layerDims, cellDims=layerDims, input, inputDim=input.dim, previousHook=PreviousHC, nextHook=NextHC, enableSelfStabilization=false) = [
|
||||
RecurrentBidirectionalGRUStack (layerDims, cellDims=layerDims, input, inputDim=input.dim, previousHook=PreviousHC, nextHook=NextHC, enableSelfStabilization=false) = [
|
||||
previousHook1 = previousHook ; nextHook1 = nextHook ; useStabilizer = enableSelfStabilization
|
||||
layers[i:0..Length (layerDims)-1] =
|
||||
[
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@
|
|||
#endif
|
||||
|
||||
#include "Basics.h"
|
||||
#include "Globals.h"
|
||||
#include "Actions.h"
|
||||
#include "ComputationNetwork.h"
|
||||
#include "ComputationNode.h"
|
||||
|
|
@ -153,7 +154,7 @@ static void DisableLegacyUsage(const ConfigParameters& TopLevelConfig, const Con
|
|||
|
||||
// When running in parallel with MPI, only commands in 'commandstoRunOnAllRanks' should
|
||||
// be run in parallel across multiple ranks. Others should only run on rank 0
|
||||
const std::set<std::string> commandstoRunOnAllRanks = { "train", "trainRNN", "adapt", "test", "eval", "cv", "devtest" };
|
||||
const std::set<std::string> commandstoRunOnAllRanks = { "train", "trainRNN", "adapt", "test", "eval", "cv", "devtest", "pbn" };
|
||||
|
||||
// process the command
|
||||
template <typename ElemType>
|
||||
|
|
@ -242,6 +243,10 @@ void DoCommands(const ConfigParameters& config, const shared_ptr<MPIWrapper>& mp
|
|||
LOGPRINTF(stderr, "CNTKCommandTrainEnd: %s\n", command[i].c_str());
|
||||
fullEpochsOffset += GetMaxEpochs(commandParams);
|
||||
}
|
||||
else if (thisAction == "pbn")
|
||||
{
|
||||
DoEvalBN<ElemType>(commandParams);
|
||||
}
|
||||
else if (thisAction == "adapt")
|
||||
{
|
||||
DoAdapt<ElemType>(commandParams);
|
||||
|
|
@ -480,6 +485,9 @@ int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapp
|
|||
let valp = BS::Evaluate(expr); // evaluate parse into a dictionary
|
||||
let& config = valp.AsRef<ScriptableObjects::IConfigRecord>(); // this is the dictionary
|
||||
|
||||
if (config(L"forceDeterministicAlgorithms", false))
|
||||
Globals::ForceDeterministicAlgorithms();
|
||||
|
||||
#ifndef CPUONLY
|
||||
auto valpp = config.Find(L"deviceId");
|
||||
if (valpp)
|
||||
|
|
@ -613,6 +621,9 @@ int wmainOldCNTKConfig(int argc, wchar_t* argv[])
|
|||
ProgressTracing::SetTimestampingFlag();
|
||||
}
|
||||
|
||||
if (config(L"forceDeterministicAlgorithms", false))
|
||||
Globals::ForceDeterministicAlgorithms();
|
||||
|
||||
// get the command param set they want
|
||||
wstring logpath = config(L"stderr", L"");
|
||||
|
||||
|
|
|
|||
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