Moved Examples for directory resturcturing
This commit is contained in:
pkranen
Родитель
a9c4922a5e
Коммит
9ec5b2c522
355
CNTK.sln
355
CNTK.sln
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@ -10,9 +10,6 @@ Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "CNTK", "Source\CNTK\CNTK.vc
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Tests", "Tests", "{D45DF403-6781-444E-B654-A96868C5BE68}"
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ProjectSection(SolutionItems) = preProject
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Tests\TestDriver.py = Tests\TestDriver.py
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Reader Plugins", "Reader Plugins", "{33EBFE78-A1A8-4961-8938-92A271941F94}"
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EndProject
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@ -386,82 +383,19 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple", "Simple", "{81AE01
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Tests\EndToEndTests\Speech\Simple\testcases.yml = Tests\EndToEndTests\Speech\Simple\testcases.yml
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Demos", "Demos", "{47755F2E-D674-4175-9E38-8EA053455072}"
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Examples", "Examples", "{47755F2E-D674-4175-9E38-8EA053455072}"
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ProjectSection(SolutionItems) = preProject
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Demos\README.md = Demos\README.md
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Examples\README.md = Examples\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Image", "Image", "{9BDFA4BE-790E-408F-915B-5979BB5078C6}"
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ProjectSection(SolutionItems) = preProject
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Demos\Image\README.md = Demos\Image\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{C0677F34-0267-44C9-8460-6D3A9A277DF6}"
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ProjectSection(SolutionItems) = preProject
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Demos\Simple2d\README.md = Demos\Simple2d\README.md
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Examples\Image\README.md = Examples\Image\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Speech", "Speech", "{3CE841C0-02E5-46DB-B401-6F8784880173}"
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ProjectSection(SolutionItems) = preProject
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Demos\Speech\README.md = Demos\Speech\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Text", "Text", "{97AAB0C8-D553-49CB-A539-004FCD7FD59F}"
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ProjectSection(SolutionItems) = preProject
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Demos\Text\README.md = Demos\Text\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{B478F99A-D37F-4169-9493-5A312019B1EF}"
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ProjectSection(SolutionItems) = preProject
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Demos\Simple2d\Data\SimpleDataTest.txt = Demos\Simple2d\Data\SimpleDataTest.txt
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Demos\Simple2d\Data\SimpleDataTrain.txt = Demos\Simple2d\Data\SimpleDataTrain.txt
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Demos\Simple2d\Data\SimpleMapping.txt = Demos\Simple2d\Data\SimpleMapping.txt
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{4258EAB1-C16E-4EE7-9640-A41F53F5072D}"
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ProjectSection(SolutionItems) = preProject
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Demos\Simple2d\Config\Multigpu.config = Demos\Simple2d\Config\Multigpu.config
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Demos\Simple2d\Config\Simple.config = Demos\Simple2d\Config\Simple.config
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{88A27DB7-181B-4823-BEB3-B9A7E57024F9}"
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ProjectSection(SolutionItems) = preProject
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Demos\Simple2d\AdditionalFiles\MakeData.m = Demos\Simple2d\AdditionalFiles\MakeData.m
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Demos\Simple2d\AdditionalFiles\SimpleDemoDataReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoDataReference.png
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Demos\Simple2d\AdditionalFiles\SimpleDemoErrorRateReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoErrorRateReference.png
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Demos\Simple2d\AdditionalFiles\SimpleDemoOutputReference.png = Demos\Simple2d\AdditionalFiles\SimpleDemoOutputReference.png
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Data", "Data", "{8BBD0355-B450-406B-814B-E58792C0F7FE}"
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ProjectSection(SolutionItems) = preProject
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Demos\Speech\Data\000000000.chunk = Demos\Speech\Data\000000000.chunk
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Demos\Speech\Data\glob_0000.mlf = Demos\Speech\Data\glob_0000.mlf
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Demos\Speech\Data\glob_0000.scp = Demos\Speech\Data\glob_0000.scp
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Demos\Speech\Data\state.list = Demos\Speech\Data\state.list
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{C613F7AC-B729-43B9-BA31-9A943F96DA55}"
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ProjectSection(SolutionItems) = preProject
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Demos\Speech\Config\FeedForward.config = Demos\Speech\Config\FeedForward.config
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Demos\Speech\Config\LSTM-NDL.config = Demos\Speech\Config\LSTM-NDL.config
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Demos\Speech\Config\lstmp-3layer-opt.ndl = Demos\Speech\Config\lstmp-3layer-opt.ndl
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{EDE295D1-37F6-48C2-A5AF-8FC66BF20E68}"
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ProjectSection(SolutionItems) = preProject
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Demos\Speech\AdditionalFiles\AN4.LICENSE.html = Demos\Speech\AdditionalFiles\AN4.LICENSE.html
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{A07A002C-A05C-477C-9DED-F01272724C6E}"
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ProjectSection(SolutionItems) = preProject
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Demos\Text\Config\rnn.config = Demos\Text\Config\rnn.config
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{8EBD7E6F-415C-4B6A-927C-1AF82905B16C}"
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ProjectSection(SolutionItems) = preProject
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Demos\Text\AdditionalFiles\ExpectedResults.txt = Demos\Text\AdditionalFiles\ExpectedResults.txt
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Demos\Text\AdditionalFiles\perplexity.txt = Demos\Text\AdditionalFiles\perplexity.txt
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EndProjectSection
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EndProject
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Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "ReaderTests", "Tests\UnitTests\ReaderTests\ReaderTests.vcxproj", "{A4FC3467-4787-43E8-BBC0-D79AE56B468D}"
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ProjectSection(ProjectDependencies) = postProject
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@ -572,6 +506,248 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{BFBC6BE1-C33
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Tests\EndToEndTests\SLU\rnnlu.ndl.config = Tests\EndToEndTests\SLU\rnnlu.ndl.config
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "MNIST", "MNIST", "{FA33A61E-95C7-4049-8111-22058CE361A3}"
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ProjectSection(SolutionItems) = preProject
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Examples\Image\MNIST\README.md = Examples\Image\MNIST\README.md
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{F99E1E80-50D8-421C-AD94-8ED0DF08C355}"
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{ED57E827-B28F-4BEE-BFB7-398EF8D83357}"
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ProjectSection(SolutionItems) = preProject
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Examples\Image\MNIST\AdditionalFiles\mnist_convert.py = Examples\Image\MNIST\AdditionalFiles\mnist_convert.py
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{6E5A252C-ACCE-42E0-9819-FF4DEF6D739E}"
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ProjectSection(SolutionItems) = preProject
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Examples\Image\MNIST\Config\01_OneHidden.config = Examples\Image\MNIST\Config\01_OneHidden.config
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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.config = Examples\Image\MNIST\Config\02_Convolution.config
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Examples\Image\MNIST\Config\02_Convolution.ndl = Examples\Image\MNIST\Config\02_Convolution.ndl
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Examples\Image\MNIST\Config\03_ConvBatchNorm.config = Examples\Image\MNIST\Config\03_ConvBatchNorm.config
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Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl = Examples\Image\MNIST\Config\03_ConvBatchNorm.ndl
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Examples\Image\MNIST\Config\Macros.ndl = Examples\Image\MNIST\Config\Macros.ndl
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CIFAR-10", "CIFAR-10", "{77125562-3BF2-45D2-9B73-72CA8E03C78C}"
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ProjectSection(SolutionItems) = preProject
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Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config = Examples\Image\Miscellaneous\CIFAR-10\01_Conv.config
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Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl = Examples\Image\Miscellaneous\CIFAR-10\01_Convolution.ndl
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Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.config
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Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.mel
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Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl = Examples\Image\Miscellaneous\CIFAR-10\02_BatchNormConv.ndl
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Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py = Examples\Image\Miscellaneous\CIFAR-10\CIFAR_convert.py
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Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt = Examples\Image\Miscellaneous\CIFAR-10\labelsmap.txt
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Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl = Examples\Image\Miscellaneous\CIFAR-10\Macros.ndl
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Examples\Image\Miscellaneous\CIFAR-10\readme.txt = Examples\Image\Miscellaneous\CIFAR-10\readme.txt
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EndProjectSection
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ImageNet", "ImageNet", "{EF710C5A-E616-442A-889D-C997D39AF2E1}"
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EndProject
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AlexNet", "AlexNet", "{D29DC402-98A3-40C7-B683-4CC84DEC5C18}"
|
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ProjectSection(SolutionItems) = preProject
|
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Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\AlexNet\add_top5_layer.mel
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Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.config
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Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\AlexNet.ndl
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Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\AlexNet\Macros.ndl
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EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "VGG", "VGG", "{BC0D6DFF-80CF-4B41-AD55-6F561C6FD229}"
|
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ProjectSection(SolutionItems) = preProject
|
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Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel = Examples\Image\Miscellaneous\ImageNet\VGG\add_top5_layer.mel
|
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Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml = Examples\Image\Miscellaneous\ImageNet\VGG\ImageNet1K_mean.xml
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Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\Macros.ndl
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Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.config
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Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_A.ndl
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Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.config
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Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl = Examples\Image\Miscellaneous\ImageNet\VGG\VGG_E.ndl
|
||||
EndProjectSection
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{CCD56F12-BA17-4753-B5EE-4995FE682995}"
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Simple2d", "Simple2d", "{D2A060F1-128E-42A1-A0D0-3E3E1DFBC427}"
|
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ProjectSection(SolutionItems) = preProject
|
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Examples\Miscellaneous\Simple2d\README.md = Examples\Miscellaneous\Simple2d\README.md
|
||||
EndProjectSection
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "NdlExamples", "NdlExamples", "{FC573A62-6DAE-40A4-8153-520C8571A007}"
|
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ProjectSection(SolutionItems) = preProject
|
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Examples\Miscellaneous\NdlExamples\NDLExamples.ndl = Examples\Miscellaneous\NdlExamples\NDLExamples.ndl
|
||||
EndProjectSection
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{1E37CE40-556D-4693-B58C-F8D4CE349BB7}"
|
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ProjectSection(SolutionItems) = preProject
|
||||
Examples\Miscellaneous\Simple2d\Config\Multigpu.config = Examples\Miscellaneous\Simple2d\Config\Multigpu.config
|
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Examples\Miscellaneous\Simple2d\Config\Simple.config = Examples\Miscellaneous\Simple2d\Config\Simple.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{BF1A621D-528B-4B84-AAFC-EF1455FC6830}"
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Miscellaneous", "Miscellaneous", "{8C128B1D-87E0-4643-AB93-2581589AE425}"
|
||||
EndProject
|
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Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AN4", "AN4", "{EDA80B25-B181-4F70-844E-FBB41D40EA34}"
|
||||
ProjectSection(SolutionItems) = preProject
|
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Examples\Speech\AN4\README.md = Examples\Speech\AN4\README.md
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\AN4\Config\FeedForward.config = Examples\Speech\AN4\Config\FeedForward.config
|
||||
Examples\Speech\AN4\Config\LSTM-NDL.config = Examples\Speech\AN4\Config\LSTM-NDL.config
|
||||
Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl = Examples\Speech\AN4\Config\lstmp-3layer-opt.ndl
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AdditionalFiles", "AdditionalFiles", "{6FCDB701-C23D-4BD7-BD48-12D46DC51084}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html = Examples\Speech\AN4\AdditionalFiles\AN4.LICENSE.html
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "AMI", "AMI", "{0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\AMI\Readme = Examples\Speech\Miscellaneous\AMI\Readme
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "TIMIT", "TIMIT", "{FB65FA58-C47B-4A49-9566-40FD5D75FC59}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "G2P", "G2P", "{7FC6C635-D664-4244-9804-962165C3B037}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\G2P\README.txt = Examples\Speech\Miscellaneous\G2P\README.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "cntk_config", "cntk_config", "{439F8840-309E-4B7F-BCEC-196E0EADE949}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\40fbank.conf
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf = Examples\Speech\Miscellaneous\AMI\cntk_config\80fbank.conf
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config = Examples\Speech\Miscellaneous\AMI\cntk_config\Align.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_dnn_smbr.mel
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_lstmp_smbr.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_smbr.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK2_write.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config = Examples\Speech\Miscellaneous\AMI\cntk_config\CNTK_write.config
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\default_macros.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_3layer.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\dnn_6layer_smbr.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstm_3layer_delay.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-delay.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway-dropout.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer-highway.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-3layer.txt
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer-highway.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-8layer.ndl
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel = Examples\Speech\Miscellaneous\AMI\cntk_config\lstmp-smbr.mel
|
||||
Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt = Examples\Speech\Miscellaneous\AMI\cntk_config\PACRnn.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "setups", "setups", "{654756BA-9A00-4225-A2BF-BA4547801848}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\G2P\setups\bilstm.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\bilstm.indstreams.joint.conditional.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.indstreams.joint.conditional.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\bilstm.ndl.config = Examples\Speech\Miscellaneous\G2P\setups\bilstm.ndl.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\global.lstm.config = Examples\Speech\Miscellaneous\G2P\setups\global.lstm.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.lw7.conditional.mb100.fw6.config = Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.lw7.conditional.mb100.fw6.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.mb100.noemb.config = Examples\Speech\Miscellaneous\G2P\setups\lstm.2streams.mb100.noemb.config
|
||||
Examples\Speech\Miscellaneous\G2P\setups\lstm.ndl = Examples\Speech\Miscellaneous\G2P\setups\lstm.ndl
|
||||
Examples\Speech\Miscellaneous\G2P\setups\s2s.mpd.rnd.hiddenstate.2nets.500.100mb.2layers.config = Examples\Speech\Miscellaneous\G2P\setups\s2s.mpd.rnd.hiddenstate.2nets.500.100mb.2layers.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "config", "config", "{1C7D222F-E17B-444F-A18C-6205DEEF27BA}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel = Examples\Speech\Miscellaneous\TIMIT\config\add_layer.mel
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl = Examples\Speech\Miscellaneous\TIMIT\config\ae.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl = Examples\Speech\Miscellaneous\TIMIT\config\classify.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl = Examples\Speech\Miscellaneous\TIMIT\config\create_1layer.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl = Examples\Speech\Miscellaneous\TIMIT\config\default_macros.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\globals.config = Examples\Speech\Miscellaneous\TIMIT\config\globals.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl = Examples\Speech\Miscellaneous\TIMIT\config\lstm.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_fbank_mfcc.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl = Examples\Speech\Miscellaneous\TIMIT\config\mtl_senones_dr.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl = Examples\Speech\Miscellaneous\TIMIT\config\PAC-RNN.ndl
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\README.txt = Examples\Speech\Miscellaneous\TIMIT\config\README.txt
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_AdaptLearnRate.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_CrossValidateSimpleNetwork.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_EvalSimpleNetwork.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainAutoEncoder.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainLSTM.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiInput.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainMultiTask.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainNDLNetwork.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainSimpleNetwork.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_TrainWithPreTrain.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteBottleneck.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config = Examples\Speech\Miscellaneous\TIMIT\config\TIMIT_WriteScaledLogLike.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "CPU", "CPU", "{5ED4F5DC-E016-4E10-BACD-6A760A0CDE89}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_DNN.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\CPU\TIMIT_LSTM.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "GPU", "GPU", "{35CFD8E3-7206-4243-AB5C-AAF610109A5C}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_DNN.config
|
||||
Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config = Examples\Speech\Miscellaneous\TIMIT\GPU\TIMIT_LSTM.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "PennTreebank", "PennTreebank", "{6F4125B5-220F-4FB7-B6C4-85A966A0268C}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\PennTreebank\README.md = Examples\Text\PennTreebank\README.md
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Config", "Config", "{850008BC-36B0-4A0A-BD0C-B6D5C2184227}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\PennTreebank\Config\rnn.config = Examples\Text\PennTreebank\Config\rnn.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "News", "News", "{52556781-2778-4BA0-9A0C-1FA0C92B3896}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\Miscellaneous\News\README.txt = Examples\Text\Miscellaneous\News\README.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SLU", "SLU", "{E6DC3B7D-303D-4A54-B040-D8DCF8C56E17}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\Miscellaneous\SLU\lstmNDL.txt = Examples\Text\Miscellaneous\SLU\lstmNDL.txt
|
||||
Examples\Text\Miscellaneous\SLU\README.txt = Examples\Text\Miscellaneous\SLU\README.txt
|
||||
Examples\Text\Miscellaneous\SLU\rnnlu.config = Examples\Text\Miscellaneous\SLU\rnnlu.config
|
||||
Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt = Examples\Text\Miscellaneous\SLU\rnnluModelEditor.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "SMT", "SMT", "{AED49E07-AC6B-4EE0-81AC-A572ABCA0518}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\Miscellaneous\SMT\README.txt = Examples\Text\Miscellaneous\SMT\README.txt
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "setups", "setups", "{906E41CB-F603-4E83-98D8-4F77350124BC}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\Miscellaneous\News\setups\global.config = Examples\Text\Miscellaneous\News\setups\global.config
|
||||
Examples\Text\Miscellaneous\News\setups\global.s2s.config = Examples\Text\Miscellaneous\News\setups\global.s2s.config
|
||||
Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.2stream.config = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.2stream.config
|
||||
Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config
|
||||
Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config.txt = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.classlm.config.txt
|
||||
Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.nce.config.txt = Examples\Text\Miscellaneous\News\setups\lstmlm.gpu.nce.config.txt
|
||||
Examples\Text\Miscellaneous\News\setups\s2s.class.alignment.config = Examples\Text\Miscellaneous\News\setups\s2s.class.alignment.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "setups", "setups", "{7734DDA6-5997-4F43-BC99-DF0495DA5D1D}"
|
||||
ProjectSection(SolutionItems) = preProject
|
||||
Examples\Text\Miscellaneous\SMT\setups\global.config = Examples\Text\Miscellaneous\SMT\setups\global.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\global.cs-en.config = Examples\Text\Miscellaneous\SMT\setups\global.cs-en.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\global.local.config = Examples\Text\Miscellaneous\SMT\setups\global.local.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\s2s.2layers.config = Examples\Text\Miscellaneous\SMT\setups\s2s.2layers.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\s2s.class.alignment.config = Examples\Text\Miscellaneous\SMT\setups\s2s.class.alignment.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\s2s.class.config = Examples\Text\Miscellaneous\SMT\setups\s2s.class.config
|
||||
Examples\Text\Miscellaneous\SMT\setups\s2sfb.class.alignment.config = Examples\Text\Miscellaneous\SMT\setups\s2sfb.class.alignment.config
|
||||
EndProjectSection
|
||||
EndProject
|
||||
Global
|
||||
GlobalSection(SolutionConfigurationPlatforms) = preSolution
|
||||
Debug|x64 = Debug|x64
|
||||
|
|
@ -707,17 +883,8 @@ Global
|
|||
{81AE014F-DD63-47C7-B6E2-DB1D2833DCD1} = {C47CDAA5-6D6C-429E-BC89-7CA0F868FDC8}
|
||||
{47755F2E-D674-4175-9E38-8EA053455072} = {39E42C4B-A078-4CA4-9D92-B883D8129601}
|
||||
{9BDFA4BE-790E-408F-915B-5979BB5078C6} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{C0677F34-0267-44C9-8460-6D3A9A277DF6} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{3CE841C0-02E5-46DB-B401-6F8784880173} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{97AAB0C8-D553-49CB-A539-004FCD7FD59F} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{B478F99A-D37F-4169-9493-5A312019B1EF} = {C0677F34-0267-44C9-8460-6D3A9A277DF6}
|
||||
{4258EAB1-C16E-4EE7-9640-A41F53F5072D} = {C0677F34-0267-44C9-8460-6D3A9A277DF6}
|
||||
{88A27DB7-181B-4823-BEB3-B9A7E57024F9} = {C0677F34-0267-44C9-8460-6D3A9A277DF6}
|
||||
{8BBD0355-B450-406B-814B-E58792C0F7FE} = {3CE841C0-02E5-46DB-B401-6F8784880173}
|
||||
{C613F7AC-B729-43B9-BA31-9A943F96DA55} = {3CE841C0-02E5-46DB-B401-6F8784880173}
|
||||
{EDE295D1-37F6-48C2-A5AF-8FC66BF20E68} = {3CE841C0-02E5-46DB-B401-6F8784880173}
|
||||
{A07A002C-A05C-477C-9DED-F01272724C6E} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F}
|
||||
{8EBD7E6F-415C-4B6A-927C-1AF82905B16C} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F}
|
||||
{A4FC3467-4787-43E8-BBC0-D79AE56B468D} = {6F19321A-65E7-4829-B00C-3886CD6C6EDE}
|
||||
{482999D1-B7E2-466E-9F8D-2119F93EAFD9} = {DD043083-71A4-409A-AA91-F9C548DCF7EC}
|
||||
{60BDB847-D0C4-4FD3-A947-0C15C08BCDB5} = {DD043083-71A4-409A-AA91-F9C548DCF7EC}
|
||||
|
|
@ -739,5 +906,37 @@ Global
|
|||
{96012801-5187-4FAF-A54E-BF4B73C855F8} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1}
|
||||
{2A1F0FB0-2304-4F35-87B3-66230C6E58F0} = {811924DE-2F12-4EA0-BE58-E57BEF3B74D1}
|
||||
{BFBC6BE1-C33E-4A80-B8F3-A33410EC00FC} = {6E565B48-1923-49CE-9787-9BBB9D96F4C5}
|
||||
{FA33A61E-95C7-4049-8111-22058CE361A3} = {9BDFA4BE-790E-408F-915B-5979BB5078C6}
|
||||
{F99E1E80-50D8-421C-AD94-8ED0DF08C355} = {9BDFA4BE-790E-408F-915B-5979BB5078C6}
|
||||
{ED57E827-B28F-4BEE-BFB7-398EF8D83357} = {FA33A61E-95C7-4049-8111-22058CE361A3}
|
||||
{6E5A252C-ACCE-42E0-9819-FF4DEF6D739E} = {FA33A61E-95C7-4049-8111-22058CE361A3}
|
||||
{77125562-3BF2-45D2-9B73-72CA8E03C78C} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355}
|
||||
{EF710C5A-E616-442A-889D-C997D39AF2E1} = {F99E1E80-50D8-421C-AD94-8ED0DF08C355}
|
||||
{D29DC402-98A3-40C7-B683-4CC84DEC5C18} = {EF710C5A-E616-442A-889D-C997D39AF2E1}
|
||||
{BC0D6DFF-80CF-4B41-AD55-6F561C6FD229} = {EF710C5A-E616-442A-889D-C997D39AF2E1}
|
||||
{CCD56F12-BA17-4753-B5EE-4995FE682995} = {47755F2E-D674-4175-9E38-8EA053455072}
|
||||
{D2A060F1-128E-42A1-A0D0-3E3E1DFBC427} = {CCD56F12-BA17-4753-B5EE-4995FE682995}
|
||||
{FC573A62-6DAE-40A4-8153-520C8571A007} = {CCD56F12-BA17-4753-B5EE-4995FE682995}
|
||||
{1E37CE40-556D-4693-B58C-F8D4CE349BB7} = {D2A060F1-128E-42A1-A0D0-3E3E1DFBC427}
|
||||
{BF1A621D-528B-4B84-AAFC-EF1455FC6830} = {3CE841C0-02E5-46DB-B401-6F8784880173}
|
||||
{8C128B1D-87E0-4643-AB93-2581589AE425} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F}
|
||||
{EDA80B25-B181-4F70-844E-FBB41D40EA34} = {3CE841C0-02E5-46DB-B401-6F8784880173}
|
||||
{B3E3AF4A-FEF5-46AB-A72A-19AF4F1FDD49} = {EDA80B25-B181-4F70-844E-FBB41D40EA34}
|
||||
{6FCDB701-C23D-4BD7-BD48-12D46DC51084} = {EDA80B25-B181-4F70-844E-FBB41D40EA34}
|
||||
{0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830}
|
||||
{FB65FA58-C47B-4A49-9566-40FD5D75FC59} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830}
|
||||
{7FC6C635-D664-4244-9804-962165C3B037} = {BF1A621D-528B-4B84-AAFC-EF1455FC6830}
|
||||
{439F8840-309E-4B7F-BCEC-196E0EADE949} = {0D0056B3-7068-4FC9-AFCF-DD1E78D4CCBC}
|
||||
{654756BA-9A00-4225-A2BF-BA4547801848} = {7FC6C635-D664-4244-9804-962165C3B037}
|
||||
{1C7D222F-E17B-444F-A18C-6205DEEF27BA} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59}
|
||||
{5ED4F5DC-E016-4E10-BACD-6A760A0CDE89} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59}
|
||||
{35CFD8E3-7206-4243-AB5C-AAF610109A5C} = {FB65FA58-C47B-4A49-9566-40FD5D75FC59}
|
||||
{6F4125B5-220F-4FB7-B6C4-85A966A0268C} = {97AAB0C8-D553-49CB-A539-004FCD7FD59F}
|
||||
{850008BC-36B0-4A0A-BD0C-B6D5C2184227} = {6F4125B5-220F-4FB7-B6C4-85A966A0268C}
|
||||
{52556781-2778-4BA0-9A0C-1FA0C92B3896} = {8C128B1D-87E0-4643-AB93-2581589AE425}
|
||||
{E6DC3B7D-303D-4A54-B040-D8DCF8C56E17} = {8C128B1D-87E0-4643-AB93-2581589AE425}
|
||||
{AED49E07-AC6B-4EE0-81AC-A572ABCA0518} = {8C128B1D-87E0-4643-AB93-2581589AE425}
|
||||
{906E41CB-F603-4E83-98D8-4F77350124BC} = {52556781-2778-4BA0-9A0C-1FA0C92B3896}
|
||||
{7734DDA6-5997-4F43-BC99-DF0495DA5D1D} = {AED49E07-AC6B-4EE0-81AC-A572ABCA0518}
|
||||
EndGlobalSection
|
||||
EndGlobal
|
||||
|
|
|
|||
|
|
@ -1,56 +0,0 @@
|
|||
$ grep -a --text Finish gpulstmlm_train_test.log | grep Train
|
||||
Finished Epoch[1]: [Training Set] Train Loss Per Sample = 6.0689292 EvalErr Per Sample = 6.0689292 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.42099
|
||||
Finished Epoch[1]: [Validation Set] Train Loss Per Sample = 5.7789721 EvalErr Per Sample = 5.7789721
|
||||
Finished Epoch[2]: [Training Set] Train Loss Per Sample = 5.6369767 EvalErr Per Sample = 5.6369767 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.62399
|
||||
Finished Epoch[2]: [Validation Set] Train Loss Per Sample = 5.5511756 EvalErr Per Sample = 5.5511756
|
||||
Finished Epoch[3]: [Training Set] Train Loss Per Sample = 5.4327216 EvalErr Per Sample = 5.4327216 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=501.52399
|
||||
Finished Epoch[3]: [Validation Set] Train Loss Per Sample = 5.4396544 EvalErr Per Sample = 5.4396544
|
||||
Finished Epoch[4]: [Training Set] Train Loss Per Sample = 5.2914305 EvalErr Per Sample = 5.2914305 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=503.44199
|
||||
Finished Epoch[4]: [Validation Set] Train Loss Per Sample = 5.345387 EvalErr Per Sample = 5.345387
|
||||
Finished Epoch[5]: [Training Set] Train Loss Per Sample = 5.1821184 EvalErr Per Sample = 5.1821184 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=508.905
|
||||
Finished Epoch[5]: [Validation Set] Train Loss Per Sample = 5.2927375 EvalErr Per Sample = 5.2927375
|
||||
Finished Epoch[6]: [Training Set] Train Loss Per Sample = 5.0916734 EvalErr Per Sample = 5.0916734 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=522.10303
|
||||
Finished Epoch[6]: [Validation Set] Train Loss Per Sample = 5.2429872 EvalErr Per Sample = 5.2429872
|
||||
Finished Epoch[7]: [Training Set] Train Loss Per Sample = 5.0149188 EvalErr Per Sample = 5.0149188 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=526.94702
|
||||
Finished Epoch[7]: [Validation Set] Train Loss Per Sample = 5.1904435 EvalErr Per Sample = 5.1904435
|
||||
Finished Epoch[8]: [Training Set] Train Loss Per Sample = 4.9472914 EvalErr Per Sample = 4.9472914 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=544.46198
|
||||
Finished Epoch[8]: [Validation Set] Train Loss Per Sample = 5.1749911 EvalErr Per Sample = 5.1749911
|
||||
Finished Epoch[9]: [Training Set] Train Loss Per Sample = 4.8864255 EvalErr Per Sample = 4.8864255 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=531.33002
|
||||
Finished Epoch[9]: [Validation Set] Train Loss Per Sample = 5.1553888 EvalErr Per Sample = 5.1553888
|
||||
Finished Epoch[10]: [Training Set] Train Loss Per Sample = 4.831315 EvalErr Per Sample = 4.831315 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=523.29999
|
||||
Finished Epoch[10]: [Validation Set] Train Loss Per Sample = 5.1265602 EvalErr Per Sample = 5.1265602
|
||||
Finished Epoch[11]: [Training Set] Train Loss Per Sample = 4.7812014 EvalErr Per Sample = 4.7812014 Ave Learn Rate Per Sample = 0.009999999776 Epoch Time=521.64697
|
||||
Finished Epoch[11]: [Validation Set] Train Loss Per Sample = 5.1217532 EvalErr Per Sample = 5.1217532
|
||||
Finished Epoch[12]: [Training Set] Train Loss Per Sample = 4.6650786 EvalErr Per Sample = 4.6650786 Ave Learn Rate Per Sample = 0.004999999888 Epoch Time=523.90198
|
||||
Finished Epoch[12]: [Validation Set] Train Loss Per Sample = 5.0993018 EvalErr Per Sample = 5.0993018
|
||||
Finished Epoch[13]: [Training Set] Train Loss Per Sample = 4.5911136 EvalErr Per Sample = 4.5911136 Ave Learn Rate Per Sample = 0.002499999944 Epoch Time=520.66101
|
||||
Finished Epoch[13]: [Validation Set] Train Loss Per Sample = 5.0787692 EvalErr Per Sample = 5.0787692
|
||||
Finished Epoch[14]: [Training Set] Train Loss Per Sample = 4.5472517 EvalErr Per Sample = 4.5472517 Ave Learn Rate Per Sample = 0.001249999972 Epoch Time=517.742
|
||||
Finished Epoch[14]: [Validation Set] Train Loss Per Sample = 5.0703268 EvalErr Per Sample = 5.0703268
|
||||
Finished Epoch[15]: [Training Set] Train Loss Per Sample = 4.5230498 EvalErr Per Sample = 4.5230498 Ave Learn Rate Per Sample = 0.000624999986 Epoch Time=516.28998
|
||||
Finished Epoch[15]: [Validation Set] Train Loss Per Sample = 5.0566416 EvalErr Per Sample = 5.0566416
|
||||
Finished Epoch[16]: [Training Set] Train Loss Per Sample = 4.510385 EvalErr Per Sample = 4.510385 Ave Learn Rate Per Sample = 0.000312499993 Epoch Time=513.30499
|
||||
Finished Epoch[16]: [Validation Set] Train Loss Per Sample = 5.0524426 EvalErr Per Sample = 5.0524426
|
||||
|
||||
$ grep -a --text Finish gpulstmlm_train_test.log | grep Validation
|
||||
Finished Epoch[1]: [Validation Set] Train Loss Per Sample = 5.7789721 EvalErr Per Sample = 5.7789721
|
||||
Finished Epoch[2]: [Validation Set] Train Loss Per Sample = 5.5511756 EvalErr Per Sample = 5.5511756
|
||||
Finished Epoch[3]: [Validation Set] Train Loss Per Sample = 5.4396544 EvalErr Per Sample = 5.4396544
|
||||
Finished Epoch[4]: [Validation Set] Train Loss Per Sample = 5.345387 EvalErr Per Sample = 5.345387
|
||||
Finished Epoch[5]: [Validation Set] Train Loss Per Sample = 5.2927375 EvalErr Per Sample = 5.2927375
|
||||
Finished Epoch[6]: [Validation Set] Train Loss Per Sample = 5.2429872 EvalErr Per Sample = 5.2429872
|
||||
Finished Epoch[7]: [Validation Set] Train Loss Per Sample = 5.1904435 EvalErr Per Sample = 5.1904435
|
||||
Finished Epoch[8]: [Validation Set] Train Loss Per Sample = 5.1749911 EvalErr Per Sample = 5.1749911
|
||||
Finished Epoch[9]: [Validation Set] Train Loss Per Sample = 5.1553888 EvalErr Per Sample = 5.1553888
|
||||
Finished Epoch[10]: [Validation Set] Train Loss Per Sample = 5.1265602 EvalErr Per Sample = 5.1265602
|
||||
Finished Epoch[11]: [Validation Set] Train Loss Per Sample = 5.1217532 EvalErr Per Sample = 5.1217532
|
||||
Finished Epoch[12]: [Validation Set] Train Loss Per Sample = 5.0993018 EvalErr Per Sample = 5.0993018
|
||||
Finished Epoch[13]: [Validation Set] Train Loss Per Sample = 5.0787692 EvalErr Per Sample = 5.0787692
|
||||
Finished Epoch[14]: [Validation Set] Train Loss Per Sample = 5.0703268 EvalErr Per Sample = 5.0703268
|
||||
Finished Epoch[15]: [Validation Set] Train Loss Per Sample = 5.0566416 EvalErr Per Sample = 5.0566416
|
||||
Finished Epoch[16]: [Validation Set] Train Loss Per Sample = 5.0524426 EvalErr Per Sample = 5.0524426
|
||||
|
||||
|
||||
# test
|
||||
Final Results: Minibatch[1-82430]: Samples Seen = 82430 TrainNodeClassBasedCrossEntropy/Sample = 4.9702353 TrainNodeClassBasedCrossEntropy/Sample = 4.9702353
|
||||
corresponding PPL = 144
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
ExpDir=d:\temp\lstm
|
||||
ConfigDir=D:\zhaoyg\2015-5-6\cntk\ExampleSetups\LM\LSTMLM\
|
||||
DataDir=\\speechstore5\transient\kaishengy\data\lm\PennTreeBank
|
||||
|
||||
|
||||
# to run this
|
||||
# ..\..\..\x64\debug\cntk.exe configFile=global.config+lstmlm.gpu.config ExpDir=d:\temp\lm\ptb
|
||||
|
|
@ -1,432 +0,0 @@
|
|||
# configuration file for class based RNN training
|
||||
# final test PPL=122.54
|
||||
ExpFolder=$ExpDir$
|
||||
ConfigFolder=$ConfigDir$
|
||||
DataFolder=$DataDir$
|
||||
|
||||
stderr=$ExpFolder$
|
||||
numCPUThreads=4
|
||||
# command=dumpNodeInfo
|
||||
#command=train
|
||||
#command=test
|
||||
command=writeWordAndClassInfo:train:test
|
||||
command=train:test
|
||||
type=double
|
||||
|
||||
DEVICEID=-1
|
||||
|
||||
NOISE=100
|
||||
RATE=0.1
|
||||
VOCABSIZE=10000
|
||||
CLASSSIZE=50
|
||||
makeMode=true
|
||||
TRAINFILE=ptb.train.cntk.txt
|
||||
VALIDFILE=ptb.valid.cntk.txt
|
||||
TESTFILE=ptb.test.cntk.txt
|
||||
|
||||
#number of threads
|
||||
nthreads=4
|
||||
|
||||
writeWordAndClassInfo=[
|
||||
action=writeWordAndClass
|
||||
inputFile=$DataFolder$\$TRAINFILE$
|
||||
outputVocabFile=$DataFolder$\vocab.txt
|
||||
outputWord2Cls=$ExpFolder$\word2cls.txt
|
||||
outputCls2Index=$ExpFolder$\cls2idx.txt
|
||||
vocabSize=$VOCABSIZE$
|
||||
cutoff=0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
dumpNodeInfo=[
|
||||
action=dumpnode
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
#nodeName=W0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
devtest=[action=devtest]
|
||||
|
||||
train=[
|
||||
action=train
|
||||
minibatchSize=10
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=NCELSTM
|
||||
#CLASSLSTM
|
||||
|
||||
# uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM
|
||||
# NDLNetworkBuilder=[
|
||||
# networkDescription=$ConfigFolder$\rnnlm.ndl
|
||||
# ]
|
||||
|
||||
SimpleNetworkBuilder=[
|
||||
trainingCriterion=NoiseContrastiveEstimationNode
|
||||
evalCriterion=NoiseContrastiveEstimationNode
|
||||
nodeType=Sigmoid
|
||||
initValueScale=6.0
|
||||
layerSizes=$VOCABSIZE$:200:$VOCABSIZE$
|
||||
addPrior=false
|
||||
addDropoutNodes=false
|
||||
applyMeanVarNorm=false
|
||||
uniformInit=true;
|
||||
|
||||
# these are for the class information for class-based language modeling
|
||||
vocabSize=$VOCABSIZE$
|
||||
#nbrClass=$CLASSSIZE$
|
||||
noise_number=$NOISE$
|
||||
]
|
||||
|
||||
# configuration file, base parameters
|
||||
SGD=[
|
||||
makeMode=true
|
||||
learningRatesPerSample=$RATE$
|
||||
momentumPerMB=0
|
||||
gradientClippingWithTruncation=true
|
||||
clippingThresholdPerSample=15.0
|
||||
maxEpochs=40
|
||||
unroll=false
|
||||
numMBsToShowResult=2000
|
||||
# gradUpdateType=AdaGrad
|
||||
gradUpdateType=None
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
loadBestModel=true
|
||||
|
||||
# settings for Auto Adjust Learning Rate
|
||||
AutoAdjust=[
|
||||
# auto learning rate adjustment
|
||||
autoAdjustLR=adjustafterepoch
|
||||
reduceLearnRateIfImproveLessThan=0.001
|
||||
continueReduce=false
|
||||
increaseLearnRateIfImproveMoreThan=1000000000
|
||||
learnRateDecreaseFactor=0.5
|
||||
learnRateIncreaseFactor=1.382
|
||||
numMiniBatch4LRSearch=100
|
||||
numPrevLearnRates=5
|
||||
numBestSearchEpoch=1
|
||||
]
|
||||
|
||||
dropoutRate=0.0
|
||||
]
|
||||
|
||||
reader=[
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
nbruttsineachrecurrentiter=10
|
||||
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
noise_number=$NOISE$
|
||||
mode=nce
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TRAINFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
cvReader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
mode=softmax
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.valid.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$VALIDFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
# it should be the same as that in the training set
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
|
||||
test=[
|
||||
action=eval
|
||||
|
||||
# correspond to the number of words/characteres to train in a minibatch
|
||||
minibatchSize=1
|
||||
# need to be small since models are updated for each minibatch
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=NCELSTM
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
|
||||
reader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
mode=softmax
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
# wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
# windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TESTFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
|
@ -1,425 +0,0 @@
|
|||
# configuration file for class based RNN training
|
||||
# ppl=133.35
|
||||
ExpFolder=$ExpDir$
|
||||
ConfigFolder=$ConfigDir$
|
||||
DataFolder=$DataDir$
|
||||
|
||||
stderr=$ExpFolder$
|
||||
|
||||
# command=dumpNodeInfo
|
||||
#command=train
|
||||
#command=test
|
||||
command=writeWordAndClassInfo:train:test
|
||||
#command=writeWordAndClassInfo
|
||||
type=double
|
||||
|
||||
DEVICEID=Auto
|
||||
numCPUThreads=4
|
||||
|
||||
VOCABSIZE=10000
|
||||
CLASSSIZE=50
|
||||
|
||||
TRAINFILE=ptb.train.cntk.txt
|
||||
VALIDFILE=ptb.valid.cntk.txt
|
||||
TESTFILE=ptb.test.cntk.txt
|
||||
|
||||
writeWordAndClassInfo=[
|
||||
action=writeWordAndClass
|
||||
inputFile=$DataFolder$\$TRAINFILE$
|
||||
outputVocabFile=$ExpFolder$\vocab.txt
|
||||
outputWord2Cls=$ExpFolder$\word2cls.txt
|
||||
outputCls2Index=$ExpFolder$\cls2idx.txt
|
||||
vocabSize=$VOCABSIZE$
|
||||
nbrClass=$CLASSSIZE$
|
||||
cutoff=0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
dumpNodeInfo=[
|
||||
action=dumpnode
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
#nodeName=W0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
devtest=[action=devtest]
|
||||
|
||||
train=[
|
||||
action=train
|
||||
minibatchSize=10
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=CLASSLSTM
|
||||
|
||||
# uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM
|
||||
# NDLNetworkBuilder=[
|
||||
# networkDescription=$ConfigFolder$\rnnlm.ndl
|
||||
# ]
|
||||
|
||||
SimpleNetworkBuilder=[
|
||||
trainingCriterion=classcrossentropywithsoftmax
|
||||
evalCriterion=classcrossentropywithsoftmax
|
||||
nodeType=Sigmoid
|
||||
initValueScale=6.0
|
||||
layerSizes=$VOCABSIZE$:150:200:$VOCABSIZE$
|
||||
addPrior=false
|
||||
addDropoutNodes=false
|
||||
applyMeanVarNorm=false
|
||||
uniformInit=true;
|
||||
lookupTableOrder=1
|
||||
# these are for the class information for class-based language modeling
|
||||
vocabSize=$VOCABSIZE$
|
||||
nbrClass=$CLASSSIZE$
|
||||
]
|
||||
|
||||
# configuration file, base parameters
|
||||
SGD=[
|
||||
learningRatesPerSample=0.1
|
||||
momentumPerMB=0
|
||||
gradientClippingWithTruncation=true
|
||||
clippingThresholdPerSample=15.0
|
||||
maxEpochs=40
|
||||
unroll=false
|
||||
numMBsToShowResult=2000
|
||||
# gradUpdateType=AdaGrad
|
||||
gradUpdateType=None
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
loadBestModel=true
|
||||
|
||||
# settings for Auto Adjust Learning Rate
|
||||
AutoAdjust=[
|
||||
# auto learning rate adjustment
|
||||
autoAdjustLR=adjustafterepoch
|
||||
reduceLearnRateIfImproveLessThan=0.001
|
||||
continueReduce=false
|
||||
increaseLearnRateIfImproveMoreThan=1000000000
|
||||
learnRateDecreaseFactor=0.5
|
||||
learnRateIncreaseFactor=1.382
|
||||
numMiniBatch4LRSearch=100
|
||||
numPrevLearnRates=5
|
||||
numBestSearchEpoch=1
|
||||
]
|
||||
|
||||
dropoutRate=0.0
|
||||
]
|
||||
|
||||
reader=[
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
nbruttsineachrecurrentiter=10
|
||||
|
||||
# word class info
|
||||
wordclass=$ExpFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TRAINFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
cvReader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
|
||||
# word class info
|
||||
wordclass=$ExpFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.valid.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$VALIDFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
# it should be the same as that in the training set
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
|
||||
test=[
|
||||
action=eval
|
||||
|
||||
# correspond to the number of words/characteres to train in a minibatch
|
||||
minibatchSize=1
|
||||
# need to be small since models are updated for each minibatch
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=CLASSLM
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
|
||||
reader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
|
||||
# word class info
|
||||
wordclass=$ExpFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
# wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
# windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TESTFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
|
@ -1,432 +0,0 @@
|
|||
# configuration file for class based RNN training
|
||||
# final test PPL=122.54
|
||||
ExpFolder=$ExpDir$
|
||||
ConfigFolder=$ConfigDir$
|
||||
DataFolder=$DataDir$
|
||||
|
||||
stderr=$ExpFolder$
|
||||
numCPUThreads=4
|
||||
# command=dumpNodeInfo
|
||||
#command=train
|
||||
#command=test
|
||||
command=writeWordAndClassInfo:train:test
|
||||
command=train:test
|
||||
type=double
|
||||
|
||||
DEVICEID=-1
|
||||
|
||||
NOISE=100
|
||||
RATE=0.1
|
||||
VOCABSIZE=10000
|
||||
CLASSSIZE=50
|
||||
makeMode=true
|
||||
TRAINFILE=ptb.train.cntk.txt
|
||||
VALIDFILE=ptb.valid.cntk.txt
|
||||
TESTFILE=ptb.test.cntk.txt
|
||||
|
||||
#number of threads
|
||||
nthreads=4
|
||||
|
||||
writeWordAndClassInfo=[
|
||||
action=writeWordAndClass
|
||||
inputFile=$DataFolder$\$TRAINFILE$
|
||||
outputVocabFile=$DataFolder$\vocab.txt
|
||||
outputWord2Cls=$ExpFolder$\word2cls.txt
|
||||
outputCls2Index=$ExpFolder$\cls2idx.txt
|
||||
vocabSize=$VOCABSIZE$
|
||||
cutoff=0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
dumpNodeInfo=[
|
||||
action=dumpnode
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
#nodeName=W0
|
||||
printValues=true
|
||||
]
|
||||
|
||||
devtest=[action=devtest]
|
||||
|
||||
train=[
|
||||
action=train
|
||||
minibatchSize=10
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=NCELSTM
|
||||
#CLASSLSTM
|
||||
|
||||
# uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM
|
||||
# NDLNetworkBuilder=[
|
||||
# networkDescription=$ConfigFolder$\rnnlm.ndl
|
||||
# ]
|
||||
|
||||
SimpleNetworkBuilder=[
|
||||
trainingCriterion=NoiseContrastiveEstimationNode
|
||||
evalCriterion=NoiseContrastiveEstimationNode
|
||||
nodeType=Sigmoid
|
||||
initValueScale=6.0
|
||||
layerSizes=$VOCABSIZE$:200:$VOCABSIZE$
|
||||
addPrior=false
|
||||
addDropoutNodes=false
|
||||
applyMeanVarNorm=false
|
||||
uniformInit=true;
|
||||
|
||||
# these are for the class information for class-based language modeling
|
||||
vocabSize=$VOCABSIZE$
|
||||
#nbrClass=$CLASSSIZE$
|
||||
noise_number=$NOISE$
|
||||
]
|
||||
|
||||
# configuration file, base parameters
|
||||
SGD=[
|
||||
makeMode=true
|
||||
learningRatesPerSample=$RATE$
|
||||
momentumPerMB=0
|
||||
gradientClippingWithTruncation=true
|
||||
clippingThresholdPerSample=15.0
|
||||
maxEpochs=40
|
||||
unroll=false
|
||||
numMBsToShowResult=2000
|
||||
# gradUpdateType=AdaGrad
|
||||
gradUpdateType=None
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
loadBestModel=true
|
||||
|
||||
# settings for Auto Adjust Learning Rate
|
||||
AutoAdjust=[
|
||||
# auto learning rate adjustment
|
||||
autoAdjustLR=adjustafterepoch
|
||||
reduceLearnRateIfImproveLessThan=0.001
|
||||
continueReduce=false
|
||||
increaseLearnRateIfImproveMoreThan=1000000000
|
||||
learnRateDecreaseFactor=0.5
|
||||
learnRateIncreaseFactor=1.382
|
||||
numMiniBatch4LRSearch=100
|
||||
numPrevLearnRates=5
|
||||
numBestSearchEpoch=1
|
||||
]
|
||||
|
||||
dropoutRate=0.0
|
||||
]
|
||||
|
||||
reader=[
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
nbruttsineachrecurrentiter=10
|
||||
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
noise_number=$NOISE$
|
||||
mode=nce
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TRAINFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
cvReader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
mode=softmax
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.valid.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$VALIDFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
# it should be the same as that in the training set
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
|
||||
test=[
|
||||
action=eval
|
||||
|
||||
# correspond to the number of words/characteres to train in a minibatch
|
||||
minibatchSize=1
|
||||
# need to be small since models are updated for each minibatch
|
||||
traceLevel=1
|
||||
deviceId=$DEVICEID$
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=NCELSTM
|
||||
|
||||
modelPath=$ExpFolder$\modelRnnCNTK
|
||||
|
||||
reader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
mode=softmax
|
||||
# word class info
|
||||
wordclass=$DataFolder$\vocab.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=$ExpFolder$\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
# wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
# windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=$VOCABSIZE$
|
||||
|
||||
file=$DataFolder$\$TESTFILE$
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=$VOCABSIZE$
|
||||
|
||||
labelMappingFile=$ExpFolder$\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
|
@ -1,415 +0,0 @@
|
|||
# configuration file for CNTK MNIST Before Check-In Tests
|
||||
|
||||
stderr=c:\temp\\penntreebank\lstmpenntreebank.log
|
||||
command=penntreebanklstmrndmb10lr01:penntreebanktestlstmrndmb10lr01
|
||||
|
||||
#
|
||||
# Class-based cross entropy LSTM experiments
|
||||
#
|
||||
# learning rate: 0.15
|
||||
# minibatch size 10
|
||||
# number of sentences in each minibatch nbruttsineachrecurrentiter=10
|
||||
#
|
||||
# iter 30: PPL = exp(4.9226532) = 137.37
|
||||
# iter 20: PPL = exp(4.9226532) = 137.37
|
||||
# iter 10: PPL = exp(4.9226532) = 137.37
|
||||
# iter 1: PPL = exp(5.087323) = 161.96
|
||||
|
||||
#
|
||||
# baseline from Thomas Mikolov's paper
|
||||
# http://www.fit.vutbr.cz/research/groups/speech/publi/2011/mikolov_icassp2011_5528.pdf
|
||||
# test PPL : 136
|
||||
#
|
||||
# LSTM results from Alex Graves LSTM paper
|
||||
# http://arxiv.org/pdf/1308.0850v2.pdf
|
||||
# test PPL : 138
|
||||
# notice that Alex's paper doesn't use class for speed-up.
|
||||
#
|
||||
|
||||
|
||||
penntreebanklstmrndmb10lr01=[
|
||||
# this is the maximum size for the minibatch, since sequence minibatches are really just a single sequence
|
||||
# can be considered as the maximum length of a sentence
|
||||
action=train
|
||||
|
||||
# correspond to the number of words/characteres to train in a minibatch
|
||||
minibatchSize=6
|
||||
# need to be small since models are updated for each minibatch
|
||||
traceLevel=1
|
||||
deviceId=-1
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
SimpleNetworkBuilder=[
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
rnnType=CLASSLSTM
|
||||
|
||||
trainingCriterion=classcrossentropywithsoftmax
|
||||
evalCriterion=classcrossentropywithsoftmax
|
||||
nodeType=Sigmoid
|
||||
initValueScale=6.0
|
||||
layerSizes=10000:200:10000
|
||||
addPrior=false
|
||||
addDropoutNodes=false
|
||||
applyMeanVarNorm=false
|
||||
uniformInit=true;
|
||||
vocabSize=10000
|
||||
nbrClass=50
|
||||
]
|
||||
|
||||
# configuration file, base parameters
|
||||
SGD=[
|
||||
learningRatesPerSample=0.15
|
||||
momentumPerMB=0.90
|
||||
# momentumPerMB=0.0
|
||||
# gradientClippingWithTruncation=false
|
||||
# clippingThresholdPerSample=15.0
|
||||
|
||||
maxEpochs=150
|
||||
unroll=false
|
||||
numMBsToShowResult=1000
|
||||
useAdagrad=true
|
||||
|
||||
modelPath=c:\temp\penntreebank\cntkdebug.dnn
|
||||
loadBestModel=true
|
||||
|
||||
# settings for Auto Adjust Learning Rate
|
||||
AutoAdjust=[
|
||||
# auto learning rate adjustment
|
||||
autoAdjustLR=adjustafterepoch
|
||||
reduceLearnRateIfImproveLessThan=0
|
||||
increaseLearnRateIfImproveMoreThan=1000000000
|
||||
learnRateDecreaseFactor=0.618
|
||||
learnRateIncreaseFactor=1.382
|
||||
numMiniBatch4LRSearch=100
|
||||
numPrevLearnRates=5
|
||||
numBestSearchEpoch=1
|
||||
]
|
||||
|
||||
|
||||
dropoutRate=0.0
|
||||
]
|
||||
|
||||
reader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
|
||||
# word class info
|
||||
wordclass=c:\exp\penntreebank\data\wordclass.txt
|
||||
|
||||
# number of utterances to be allocated for each minibatch
|
||||
nbruttsineachrecurrentiter=10
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=c:\temp\penntreebank\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=10000
|
||||
|
||||
# file=c:\exp\penntreebank\data\ptb.train.cntk.100.txt
|
||||
file=c:\exp\penntreebank\data\ptb.train.cntk.txt
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=10000
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=10000
|
||||
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
cvReader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
|
||||
# word class info
|
||||
wordclass=c:\exp\penntreebank\data\wordclass.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=c:\temp\penntreebank\sequenceSentence.valid.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=10000
|
||||
|
||||
# file=c:\exp\penntreebank\data\ptb.valid.cntk.100.txt
|
||||
file=c:\exp\penntreebank\data\ptb.valid.cntk.txt
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
# it should be the same as that in the training set
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=10000
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.out.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
labelDim=10000
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
penntreebanktestlstmrndmb10lr01=[
|
||||
# this is the maximum size for the minibatch, since sequence minibatches are really just a single sequence
|
||||
# can be considered as the maximum length of a sentence
|
||||
action=eval
|
||||
|
||||
# correspond to the number of words/characteres to train in a minibatch
|
||||
minibatchSize=6
|
||||
# need to be small since models are updated for each minibatch
|
||||
traceLevel=1
|
||||
deviceId=-1
|
||||
epochSize=4430000
|
||||
# which is 886 * 5000
|
||||
recurrentLayer=1
|
||||
defaultHiddenActivity=0.1
|
||||
useValidation=true
|
||||
rnnType=CLASSLM
|
||||
|
||||
modelPath=c:\temp\penntreebank\cntkdebug.dnn
|
||||
|
||||
reader=[
|
||||
# reader to use
|
||||
readerType=LMSequenceReader
|
||||
randomize=None
|
||||
|
||||
# word class info
|
||||
wordclass=c:\exp\penntreebank\data\wordclass.txt
|
||||
|
||||
# if writerType is set, we will cache to a binary file
|
||||
# if the binary file exists, we will use it instead of parsing this file
|
||||
# writerType=BinaryReader
|
||||
|
||||
#### write definition
|
||||
wfile=c:\temp\penntreebank\sequenceSentence.bin
|
||||
#wsize - inital size of the file in MB
|
||||
# if calculated size would be bigger, that is used instead
|
||||
wsize=256
|
||||
|
||||
#wrecords - number of records we should allocate space for in the file
|
||||
# files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
|
||||
wrecords=1000
|
||||
#windowSize - number of records we should include in BinaryWriter window
|
||||
windowSize=10000
|
||||
|
||||
# file=c:\exp\penntreebank\data\ptb.test.cntk.100.txt
|
||||
file=c:\exp\penntreebank\data\ptb.test.cntk.txt
|
||||
|
||||
#additional features sections
|
||||
#for now store as expanded category data (including label in)
|
||||
features=[
|
||||
# sentence has no features, so need to set dimension to zero
|
||||
dim=0
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
# sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
|
||||
sequence=[
|
||||
dim=1
|
||||
wrecords=2
|
||||
### write definition
|
||||
sectionType=data
|
||||
]
|
||||
#labels sections
|
||||
labelIn=[
|
||||
dim=1
|
||||
|
||||
# vocabulary size
|
||||
labelDim=10000
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.txt
|
||||
labelType=Category
|
||||
beginSequence="</s>"
|
||||
endSequence="</s>"
|
||||
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=11
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=11
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
#labels sections
|
||||
labels=[
|
||||
dim=1
|
||||
labelType=NextWord
|
||||
beginSequence="O"
|
||||
endSequence="O"
|
||||
|
||||
# vocabulary size
|
||||
labelDim=10000
|
||||
|
||||
labelMappingFile=c:\temp\penntreebank\sentenceLabels.out.txt
|
||||
#### Write definition ####
|
||||
# sizeof(unsigned) which is the label index type
|
||||
elementSize=4
|
||||
sectionType=labels
|
||||
mapping=[
|
||||
#redefine number of records for this section, since we don't need to save it for each data record
|
||||
wrecords=3
|
||||
#variable size so use an average string size
|
||||
elementSize=10
|
||||
sectionType=labelMapping
|
||||
]
|
||||
category=[
|
||||
dim=3
|
||||
#elementSize=sizeof(ElemType) is default
|
||||
sectionType=categoryLabels
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
Двоичные данные
ExampleSetups/LM/LSTMLM/perplexity.class50.lr0.1.txt
Двоичные данные
ExampleSetups/LM/LSTMLM/perplexity.class50.lr0.1.txt
Двоичный файл не отображается.
|
|
@ -1,40 +0,0 @@
|
|||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.4415899 Perplexity = 230.80885 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.4415899 Perplexity = 230.80885
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.2513086 Perplexity = 190.8158 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.2513086 Perplexity = 190.8158
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.1372039 Perplexity = 170.23909 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.1372039 Perplexity = 170.23909
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0720036 Perplexity = 159.49358 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0720036 Perplexity = 159.49358
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0618825 Perplexity = 157.88746 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0618825 Perplexity = 157.88746
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0321352 Perplexity = 153.25991 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0321352 Perplexity = 153.25991
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0083887 Perplexity = 149.66339 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0083887 Perplexity = 149.66339
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0220441 Perplexity = 151.72111 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 5.0220441 Perplexity = 151.72111
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9666183 Perplexity = 143.54066 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9666183 Perplexity = 143.54066
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9719939 Perplexity = 144.31436 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9719939 Perplexity = 144.31436
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9397468 Perplexity = 139.73487 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9397468 Perplexity = 139.73487
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9442405 Perplexity = 140.36421 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9442405 Perplexity = 140.36421
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9287878 Perplexity = 138.21187 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9287878 Perplexity = 138.21187
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9292672 Perplexity = 138.27814 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9292672 Perplexity = 138.27814
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9187232 Perplexity = 136.82781 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9187232 Perplexity = 136.82781
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9224714 Perplexity = 137.34162 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9224714 Perplexity = 137.34162
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9124989 Perplexity = 135.97878 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9124989 Perplexity = 135.97878
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9146056 Perplexity = 136.26556 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9146056 Perplexity = 136.26556
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9083939 Perplexity = 135.42174 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9083939 Perplexity = 135.42174
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9042288 Perplexity = 134.85886 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9042288 Perplexity = 134.85886
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9007954 Perplexity = 134.39664 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.9007954 Perplexity = 134.39664
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8977358 Perplexity = 133.98607 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8977358 Perplexity = 133.98607
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8960381 Perplexity = 133.75879 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8960381 Perplexity = 133.75879
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8950807 Perplexity = 133.63079 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8950807 Perplexity = 133.63079
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8947338 Perplexity = 133.58444 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8947338 Perplexity = 133.58444
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8945776 Perplexity = 133.56358 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8945776 Perplexity = 133.56358
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944772 Perplexity = 133.55017 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944772 Perplexity = 133.55017
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944128 Perplexity = 133.54157 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8944128 Perplexity = 133.54157
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943936 Perplexity = 133.53901 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943936 Perplexity = 133.53901
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.89438 Perplexity = 133.53718 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.89438 Perplexity = 133.53718
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943731 Perplexity = 133.53627 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943731 Perplexity = 133.53627
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943699 Perplexity = 133.53584 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943699 Perplexity = 133.53584
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894368 Perplexity = 133.53558 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894368 Perplexity = 133.53558
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894367 Perplexity = 133.53545 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.894367 Perplexity = 133.53545
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943667 Perplexity = 133.53541 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943667 Perplexity = 133.53541
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943665 Perplexity = 133.53538 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943665 Perplexity = 133.53538
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943664 Perplexity = 133.53537
|
||||
Final Results: Minibatch[1-8905]: Samples Seen = 73760 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943663 Perplexity = 133.53537 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8943663 Perplexity = 133.53537
|
||||
Final Results: Minibatch[1-82430]: Samples Seen = 82430 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8084526 Perplexity = 122.54185 TrainNodeNCEBasedCrossEntropy: NCEBasedCrossEntropyWithSoftmax/Sample = 4.8084526 Perplexity = 122.54185
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
# LSTM LM setup on PennTreebank.
|
||||
|
||||
to use it, call
|
||||
cntk.exe configFile=global.config+lstmlm.gpu.config ExpDir=<your exp dir>
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
# CNTK Demos and Example Setups
|
||||
|
||||
This folder contains examples that correspond to popular data sets and tasks.
|
||||
These data sets often require a license and are therefore not included in the repository.
|
||||
The 'Demos' folder contains a few self-contained and documented demos to get started with CNTK.
|
||||
|
||||
The four examples shown in the table below provide a good introduction to CNTK.
|
||||
|
||||
|Folder | Domain | Network types |
|
||||
|:------------------------|:-------------------------------------------------|:----------------|
|
||||
Demos/Simple2d | Synthetic 2d data | FF (CPU and GPU)
|
||||
Demos/Speech | Speech data (CMU AN4) | FF and LSTM
|
||||
Demos/Text | Text data (penn treebank) | RNN
|
||||
ExampleSetups/Image/MNIST | Image data (MNIST handwritten digit recognition) | CNN
|
||||
0
ExampleSetups/Image/MNIST/AdditionalFiles/mnist_convert.py → Examples/Image/MNIST/AdditionalFiles/mnist_convert.py
Executable file → Normal file
0
ExampleSetups/Image/MNIST/AdditionalFiles/mnist_convert.py → Examples/Image/MNIST/AdditionalFiles/mnist_convert.py
Executable file → Normal file
0
ExampleSetups/Image/CIFAR-10/CIFAR_convert.py → Examples/Image/Miscellaneous/CIFAR-10/CIFAR_convert.py
Executable file → Normal file
0
ExampleSetups/Image/CIFAR-10/CIFAR_convert.py → Examples/Image/Miscellaneous/CIFAR-10/CIFAR_convert.py
Executable file → Normal file
|
До Ширина: | Высота: | Размер: 177 KiB После Ширина: | Высота: | Размер: 177 KiB |
|
До Ширина: | Высота: | Размер: 11 KiB После Ширина: | Высота: | Размер: 11 KiB |
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ExampleSetups/ASR/AMI/scripts/Convert_label_to_cntk.py → Examples/Speech/Miscellaneous/AMI/scripts/Convert_label_to_cntk.py
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ExampleSetups/ASR/AMI/scripts/Convert_label_to_cntk.py → Examples/Speech/Miscellaneous/AMI/scripts/Convert_label_to_cntk.py
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