Updated samples.
This commit is contained in:
Alexey Kamenev
Родитель
3943020d86
Коммит
ad2c6bc37e
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@ -19,6 +19,8 @@ ndlMacros = "$ConfigDir$/Macros.ndl"
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# comment the following line to write logs to the console
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stderr = "$OutputDir$/01_OneHidden_out"
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traceLevel=1
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numMBsToShowResult=500
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#######################################
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# TRAINING CONFIG #
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@ -63,6 +65,7 @@ train = [
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test = [
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action = "test"
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minibatchSize = 16
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NDLNetworkBuilder=[
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networkDescription = "$ConfigDir$/01_OneHidden.ndl"
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@ -19,6 +19,10 @@ ndlMacros = "$ConfigDir$/Macros.ndl"
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# comment the following line to write logs to the console
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stderr = "$OutputDir$/02_Convolution_out"
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traceLevel=1
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numMBsToShowResult=500
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prefetch=true
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#######################################
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# TRAINING CONFIG #
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@ -63,6 +67,7 @@ train = [
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test = [
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action = test
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minibatchSize = 16
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NDLNetworkBuilder = [
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networkDescription = "$ConfigDir$/02_Convolution.ndl"
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@ -1,20 +1,28 @@
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WorkDir=.
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ModelDir=$WorkDir$/_out/$ConfigName$
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stderr=$WorkDir$/_out/$ConfigName$
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RootDir = "."
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ndlMacros=$WorkDir$/Macros.ndl
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ConfigDir = "$RootDir$"
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DataDir = "$RootDir$"
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OutputDir = "$RootDir$/Output"
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ModelDir = "$OutputDir$/Models"
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ndlMacros=$ConfigDir$/Macros.ndl
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precision=float
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deviceId=Auto
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prefetch=true
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command=Train:Test
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stderr=$OutputDir$/01_Conv
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traceLevel=1
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numMBsToShowResult=500
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Train=[
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action=train
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modelPath=$ModelDir$/01_Convolution
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NDLNetworkBuilder=[
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networkDescription=$WorkDir$/01_Convolution.ndl
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networkDescription=$ConfigDir$/01_Convolution.ndl
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]
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SGD=[
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@ -29,7 +37,7 @@ Train=[
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reader=[
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readerType=UCIFastReader
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file=$WorkDir$/Train.txt
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file=$DataDir$/Train.txt
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randomize=None
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features=[
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dim=3072
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@ -39,7 +47,7 @@ Train=[
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dim=1
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start=0
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labelDim=10
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labelMappingFile=$WorkDir$/labelsmap.txt
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labelMappingFile=$DataDir$/labelsmap.txt
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]
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]
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]
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@ -48,15 +56,15 @@ Test=[
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action=test
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modelPath=$ModelDir$/01_Convolution
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# Set minibatch size for testing.
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minibatchSize=128
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minibatchSize=16
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NDLNetworkBuilder=[
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networkDescription=$WorkDir$/01_Convolution.ndl
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networkDescription=$ConfigDir$/01_Convolution.ndl
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]
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reader=[
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readerType=UCIFastReader
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file=$WorkDir$/Test.txt
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file=$DataDir$/Test.txt
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randomize=None
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features=[
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dim=3072
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@ -66,7 +74,7 @@ Test=[
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dim=1
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start=0
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labelDim=10
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labelMappingFile=$WorkDir$/labelsmap.txt
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labelMappingFile=$DataDir$/labelsmap.txt
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]
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]
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]
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@ -28,50 +28,50 @@ DNN=[
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# conv1
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kW1 = 5
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kH1 = 5
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cMap1 = 36
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cMap1 = 32
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hStride1 = 1
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vStride1 = 1
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# weight[cMap1, kW1 * kH1 * ImageC]
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conv1_act = ConvReLULayer(featScaled, cMap1, ImageC, kW1, kH1, hStride1, vStride1, conv1WScale, conv1BValue)
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conv1_act = ConvReLULayer(featScaled, cMap1, 75, kW1, kH1, hStride1, vStride1, conv1WScale, conv1BValue)
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# pool1
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pool1W = 3
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pool1H = 3
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pool1hStride = 2
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pool1vStride = 2
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pool1 = MaxPooling(conv1_act, pool1W, pool1H, pool1hStride, pool1vStride)
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pool1 = MaxPooling(conv1_act, pool1W, pool1H, pool1hStride, pool1vStride, imageLayout = "cudnn")
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# conv2
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kW2 = 5
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kH2 = 5
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cMap2 = 28
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cMap2 = 32
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hStride2 = 1
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vStride2 = 1
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# weight[cMap2, kW2 * kH2 * cMap1]
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conv2_act = ConvReLULayer(pool1, cMap2, cMap1, kW2, kH2, hStride2, vStride2, conv2WScale, conv2BValue)
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conv2_act = ConvReLULayer(pool1, cMap2, 800, kW2, kH2, hStride2, vStride2, conv2WScale, conv2BValue)
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# pool2
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pool2W = 3
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pool2H = 3
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pool2hStride = 2
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pool2vStride = 2
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pool2 = MaxPooling(conv2_act, pool2W, pool2H, pool2hStride, pool2vStride)
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pool2 = MaxPooling(conv2_act, pool2W, pool2H, pool2hStride, pool2vStride, imageLayout = "cudnn")
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# conv3
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kW3 = 5
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kH3 = 5
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cMap3 = 68
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cMap3 = 64
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hStride3 = 1
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vStride3 = 1
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# weight[cMap3, kW3 * kH3 * cMap2]
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conv3_act = ConvReLULayer(pool2, cMap3, cMap2, kW3, kH3, hStride3, vStride3, conv3WScale, conv3BValue)
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conv3_act = ConvReLULayer(pool2, cMap3, 800, kW3, kH3, hStride3, vStride3, conv3WScale, conv3BValue)
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# pool3
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pool3W = 3
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pool3H = 3
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pool3hStride = 2
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pool3vStride = 2
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pool3 = MaxPooling(conv3_act, pool3W, pool3H, pool3hStride, pool3vStride)
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pool3 = MaxPooling(conv3_act, pool3W, pool3H, pool3hStride, pool3vStride, imageLayout = "cudnn")
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hiddenDim = 64
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h1 = DNNReLULayer(576, hiddenDim, pool3, fc1WScale, fc1BValue)
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@ -1,37 +1,43 @@
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WorkDir=.
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ModelDir=$WorkDir$/_out/$ConfigName$
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stderr=$WorkDir$/_out/$ConfigName$
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RootDir = "."
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ndlMacros=$WorkDir$/Macros.ndl
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ConfigDir = "$RootDir$"
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DataDir = "$RootDir$"
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OutputDir = "$RootDir$/Output"
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ModelDir = "$OutputDir$/Models"
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ndlMacros=$ConfigDir$/Macros.ndl
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precision=float
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deviceId=Auto
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prefetch=true
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parallelTrain=false
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command=Train:AddBNEval:Test
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stderr=$OutputDir$/02_BatchNormConv
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traceLevel=1
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numMBsToShowResult=500
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Train=[
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action=train
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modelPath=$ModelDir$/02_BatchNormConv
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NDLNetworkBuilder=[
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networkDescription=$WorkDir$/02_BatchNormConv.ndl
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networkDescription=$ConfigDir$/02_BatchNormConv.ndl
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]
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SGD=[
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epochSize=49984
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minibatchSize=64
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learningRatesPerMB=0.03*7:0.01*8:0.003
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#momentumPerMB=0.9*10:0.99
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maxEpochs=10
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#L2RegWeight=0.03
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learningRatesPerMB=0.03*7:0.01
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momentumPerMB=0
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maxEpochs=1
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L2RegWeight=0
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dropoutRate=0*1:0.5
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]
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reader=[
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readerType=UCIFastReader
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file=$WorkDir$/Train.txt
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file=$DataDir$/Train.txt
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randomize=None
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features=[
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dim=3072
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@ -41,7 +47,7 @@ Train=[
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dim=1
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start=0
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labelDim=10
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labelMappingFile=$WorkDir$/labelsmap.txt
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labelMappingFile=$DataDir$/labelsmap.txt
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]
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]
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]
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@ -50,22 +56,22 @@ AddBNEval=[
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action=edit
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CurModel=$ModelDir$/02_BatchNormConv
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NewModel=$ModelDir$/02_BatchNormConv.Eval
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editPath=$WorkDir$/02_BatchNormConv.mel
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editPath=$ConfigDir$/02_BatchNormConv.mel
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]
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Test=[
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action=test
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modelPath=$ModelDir$/02_BatchNormConv.Eval
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# Set minibatch size for testing.
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minibatchSize=128
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minibatchSize=16
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NDLNetworkBuilder=[
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networkDescription=$WorkDir$/02_BatchNormConv.ndl
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networkDescription=$ConfigDir$/02_BatchNormConv.ndl
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]
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reader=[
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readerType=UCIFastReader
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file=$WorkDir$/Test.txt
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file=$DataDir$/Test.txt
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randomize=None
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features=[
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dim=3072
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@ -75,7 +81,7 @@ Test=[
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dim=1
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start=0
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labelDim=10
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labelMappingFile=$WorkDir$/labelsmap.txt
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labelMappingFile=$DataDir$/labelsmap.txt
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]
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]
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]
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@ -1,16 +1,16 @@
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m=LoadModel($CurModel$, format=cntk)
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SetDefaultModel(m)
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ibn_e = BatchNormalization(featScaled, isc, ib, im, iisd, eval = true, spatial = true)
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ibn_e = BatchNormalization(featScaled, isc, ib, im, iisd, eval = true, spatial = true, imageLayout = "cudnn")
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SetNodeInput(conv1.c, 1, ibn_e)
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conv2.bn_e = BatchNormalization(pool1, conv2.sc, conv2.b, conv2.m, conv2.isd, eval = true, spatial = true)
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conv2.bn_e = BatchNormalization(pool1, conv2.sc, conv2.b, conv2.m, conv2.isd, eval = true, spatial = true, imageLayout = "cudnn")
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SetNodeInput(conv2.c, 1, conv2.bn_e)
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conv3.bn_e = BatchNormalization(pool2, conv3.sc, conv3.b, conv3.m, conv3.isd, eval = true, spatial = true)
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conv3.bn_e = BatchNormalization(pool2, conv3.sc, conv3.b, conv3.m, conv3.isd, eval = true, spatial = true, imageLayout = "cudnn")
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SetNodeInput(conv3.c, 1, conv3.bn_e)
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h1.bn_e = BatchNormalization(pool3, h1.sc, h1.b, h1.m, h1.isd, eval = true, spatial = false)
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h1.bn_e = BatchNormalization(pool3, h1.sc, h1.b, h1.m, h1.isd, eval = true, spatial = false, imageLayout = "cudnn")
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SetNodeInput(h1.t, 1, h1.bn_e)
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SaveModel(m, $NewModel$, format=cntk)
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@ -7,8 +7,8 @@ ndlMnistMacros = [
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ImageC = 3
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LabelDim = 10
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features = ImageInput(ImageW, ImageH, ImageC, tag = feature)
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featOffs = Const(128, rows = 3072)
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features = ImageInput(ImageW, ImageH, ImageC, tag = feature, imageLayout = "cudnn")
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featOffs = Const(128)
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featScaled = Minus(features, featOffs)
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labels = Input(LabelDim, tag = label)
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@ -18,6 +18,9 @@ ndlMnistMacros = [
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conv2BValue = 0
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conv3WScale = 1.414
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conv3BValue = 0
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scScale = 0.03
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fc1WScale = 12
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fc1BValue = 0
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fc2WScale = 1.5
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@ -25,12 +28,6 @@ ndlMnistMacros = [
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]
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DNN=[
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ib = Parameter(ImageC, 1, init = Uniform, initValueScale = 100)
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isc = Parameter(ImageC, 1, init = Uniform, initValueScale = 100)
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im = Parameter(ImageC, 1, init = fixedValue, value = 0, needGradient = false)
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iisd = Parameter(ImageC, 1, init = fixedValue, value = 0, needGradient = false)
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ibn = BatchNormalization(featScaled, isc, ib, im, iisd, eval = false, spatial = true)
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# conv1
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kW1 = 5
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kH1 = 5
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@ -38,14 +35,14 @@ DNN=[
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hStride1 = 1
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vStride1 = 1
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# weight[cMap1, kW1 * kH1 * ImageC]
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conv1 = ConvReLULayer(ibn, cMap1, 75, kW1, kH1, hStride1, vStride1, conv1WScale, conv1BValue)
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conv1 = ConvBNReLULayer(featScaled, cMap1, 75, kW1, kH1, hStride1, vStride1, conv1WScale, conv1BValue, scScale)
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# pool1
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pool1W = 3
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pool1H = 3
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pool1hStride = 2
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pool1vStride = 2
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pool1 = MaxPooling(conv1, pool1W, pool1H, pool1hStride, pool1vStride)
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pool1 = MaxPooling(conv1, pool1W, pool1H, pool1hStride, pool1vStride, imageLayout = "cudnn")
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# conv2
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kW2 = 5
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@ -54,14 +51,14 @@ DNN=[
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hStride2 = 1
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vStride2 = 1
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# weight[cMap2, kW2 * kH2 * cMap1]
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conv2 = ConvBNReLULayer(pool1, cMap1, cMap2, 800, kW2, kH2, hStride2, vStride2, conv2WScale, conv2BValue)
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conv2 = ConvBNReLULayer(pool1, cMap2, 800, kW2, kH2, hStride2, vStride2, conv2WScale, conv2BValue, scScale)
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# pool2
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pool2W = 3
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pool2H = 3
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pool2hStride = 2
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pool2vStride = 2
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pool2 = MaxPooling(conv2, pool2W, pool2H, pool2hStride, pool2vStride)
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pool2 = MaxPooling(conv2, pool2W, pool2H, pool2hStride, pool2vStride, imageLayout = "cudnn")
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# conv3
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kW3 = 5
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@ -70,14 +67,14 @@ DNN=[
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hStride3 = 1
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vStride3 = 1
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# weight[cMap3, kW3 * kH3 * cMap2]
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conv3 = ConvBNReLULayer(pool2, cMap2, cMap3, 800, kW3, kH3, hStride3, vStride3, conv3WScale, conv3BValue)
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conv3 = ConvBNReLULayer(pool2, cMap3, 800, kW3, kH3, hStride3, vStride3, conv3WScale, conv3BValue, scScale)
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# pool3
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pool3W = 3
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pool3H = 3
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pool3hStride = 2
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pool3vStride = 2
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pool3 = MaxPooling(conv3, pool3W, pool3H, pool3hStride, pool3vStride)
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pool3 = MaxPooling(conv3, pool3W, pool3H, pool3hStride, pool3vStride, imageLayout = "cudnn")
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hiddenDim = 64
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h1 = DnnBNReLULayer(576, hiddenDim, pool3, fc1WScale, fc1BValue)
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@ -1,83 +1,71 @@
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ConvReLULayer(inp, outMap, inMap, kW, kH, hStride, vStride, wScale, bValue)
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ConvReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue)
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{
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W = ImageParameter(kW, kH, inMap, init = Gaussian, initValueScale = wScale, imageLayout = "cudnn")
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W = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
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b = ImageParameter(1, 1, outMap, init = fixedValue, value = bValue, imageLayout = "cudnn")
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c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = "cudnn")
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p = Plus(c, b);
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y = RectifiedLinear(p);
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}
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ConvBNReLULayer(inp, inMap, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue)
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{
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W = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
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b = Parameter(inMap, 1, init = Gaussian, initValueScale = 0.03)
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sc = Parameter(inMap, 1, init = Gaussian, initValueScale = 0.03)
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m = Parameter(inMap, 1, init = fixedValue, value = 0, needGradient = false)
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isd = Parameter(inMap, 1, init = fixedValue, value = 0, needGradient = false)
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bn = BatchNormalization(inp, sc, b, m, isd, eval = false, spatial = true)
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c = Convolution(W, bn, kW, kH, outMap, hStride, vStride, zeroPadding = true)
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y = RectifiedLinear(c);
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}
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ConvBNReLULayer2(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scValue)
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ConvBNReLULayer(inp, outMap, inWCount, kW, kH, hStride, vStride, wScale, bValue, scScale)
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{
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W = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
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b = Parameter(outMap, 1, init = fixedValue, value = bValue)
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sc = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
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sc = Parameter(outMap, 1, init = Gaussian, initValueScale = scScale)
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m = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
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isd = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
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|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true)
|
||||
bn = BatchNormalization(c, sc, b, m, isd, eval = false, spatial = true, expAvgFactor = 1.0)
|
||||
c = Convolution(W, inp, kW, kH, outMap, hStride, vStride, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn = BatchNormalization(c, sc, b, m, isd, eval = false, spatial = true, expAvgFactor = 1.0, imageLayout = "cudnn")
|
||||
y = RectifiedLinear(bn);
|
||||
}
|
||||
|
||||
ResNetNode2(inp, outMap, inWCount, kW, kH, wScale, bValue, scValue)
|
||||
ResNetNode2(inp, outMap, inWCount, kW, kH, wScale, bValue, scScale)
|
||||
{
|
||||
W1 = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
|
||||
b1 = Parameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc1 = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
|
||||
sc1 = Parameter(outMap, 1, init = Gaussian, initValueScale = scScale)
|
||||
m1 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
isd1 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
|
||||
c1 = Convolution(W1, inp, kW, kH, outMap, 1, 1, zeroPadding = true)
|
||||
bn1 = BatchNormalization(c1, sc1, b1, m1, isd1, eval = false, spatial = true, expAvgFactor = 1.0)
|
||||
c1 = Convolution(W1, inp, kW, kH, outMap, 1, 1, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn1 = BatchNormalization(c1, sc1, b1, m1, isd1, eval = false, spatial = true, expAvgFactor = 1.0, imageLayout = "cudnn")
|
||||
y1 = RectifiedLinear(bn1);
|
||||
|
||||
W2 = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
|
||||
b2 = Parameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc2 = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
|
||||
sc2 = Parameter(outMap, 1, init = Gaussian, initValueScale = scScale)
|
||||
m2 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
isd2 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
|
||||
c2 = Convolution(W2, y1, kW, kH, outMap, 1, 1, zeroPadding = true)
|
||||
bn2 = BatchNormalization(c2, sc2, b2, m2, isd2, eval = false, spatial = true, expAvgFactor = 1.0)
|
||||
c2 = Convolution(W2, y1, kW, kH, outMap, 1, 1, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn2 = BatchNormalization(c2, sc2, b2, m2, isd2, eval = false, spatial = true, expAvgFactor = 1.0, imageLayout = "cudnn")
|
||||
p = Plus(bn2, inp)
|
||||
y2 = RectifiedLinear(p);
|
||||
}
|
||||
|
||||
ResNetNode2Conv(inp, outMap, inWCount, wCount, kW, kH, wScale, bValue, scValue, Wproj)
|
||||
ResNetNode2Conv(inp, outMap, inWCount, wCount, kW, kH, wScale, bValue, scScale, Wproj)
|
||||
{
|
||||
W1 = Parameter(outMap, inWCount, init = Gaussian, initValueScale = wScale)
|
||||
b1 = Parameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc1 = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
|
||||
sc1 = Parameter(outMap, 1, init = Gaussian, initValueScale = scScale)
|
||||
m1 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
isd1 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
|
||||
c1 = Convolution(W1, inp, kW, kH, outMap, 2, 2, zeroPadding = true)
|
||||
bn1 = BatchNormalization(c1, sc1, b1, m1, isd1, eval = false, spatial = true, expAvgFactor = 1.0)
|
||||
c1 = Convolution(W1, inp, kW, kH, outMap, 2, 2, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn1 = BatchNormalization(c1, sc1, b1, m1, isd1, eval = false, spatial = true, expAvgFactor = 1.0, imageLayout = "cudnn")
|
||||
y1 = RectifiedLinear(bn1);
|
||||
|
||||
W2 = Parameter(outMap, wCount, init = Gaussian, initValueScale = wScale)
|
||||
b2 = Parameter(outMap, 1, init = fixedValue, value = bValue)
|
||||
sc2 = Parameter(outMap, 1, init = Gaussian, initValueScale = scValue)
|
||||
sc2 = Parameter(outMap, 1, init = Gaussian, initValueScale = scScale)
|
||||
m2 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
isd2 = Parameter(outMap, 1, init = fixedValue, value = 0, needGradient = false)
|
||||
|
||||
c2 = Convolution(W2, y1, kW, kH, outMap, 1, 1, zeroPadding = true)
|
||||
bn2 = BatchNormalization(c2, sc2, b2, m2, isd2, eval = false, spatial = true, expAvgFactor = 1.0)
|
||||
c2 = Convolution(W2, y1, kW, kH, outMap, 1, 1, zeroPadding = true, imageLayout = "cudnn")
|
||||
bn2 = BatchNormalization(c2, sc2, b2, m2, isd2, eval = false, spatial = true, expAvgFactor = 1.0, imageLayout = "cudnn")
|
||||
|
||||
cproj = Convolution(Wproj, inp, 1, 1, outMap, 2, 2, zeroPadding = false)
|
||||
cproj = Convolution(Wproj, inp, 1, 1, outMap, 2, 2, zeroPadding = false, imageLayout = "cudnn")
|
||||
p = Plus(bn2, cproj)
|
||||
y2 = RectifiedLinear(p);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ Short description of the network:
|
|||
01_Convolution.ndl is a convolutional network which has 3 convolutional and 3 max pooling layers and resembles the network described here:
|
||||
https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-80sec.cfg
|
||||
(main differences are usage of max pooling layers everywhere rather than mix of max and average pooling, as well as dropout in fully-connected layer).
|
||||
The network produces 22% of error after training for about 4 minutes on GPU.
|
||||
The network produces 21% of error after training for about 3 minutes on GPU.
|
||||
To run the sample, navigate to this folder and run the following command:
|
||||
<path to CNTK executable> configFile=01_Conv.config configName=01_Conv
|
||||
|
||||
|
|
|
|||
|
|
@ -152,7 +152,7 @@ bool CheckFunction(std::string& p_nodeType, bool* allowUndeterminedVariable)
|
|||
ret = true;
|
||||
else if (EqualInsensitive(nodeType, OperationNameOf(SparseInputValue), L"SparseInput"))
|
||||
ret = true;
|
||||
else if (EqualInsensitive(nodeType, OperationNameOf(LearnableParameter), L"Parameter"), L"ImageParameter")
|
||||
else if (EqualInsensitive(nodeType, OperationNameOf(LearnableParameter), L"Parameter"))
|
||||
ret = true;
|
||||
else if (EqualInsensitive(nodeType, L"ImageParameter"))
|
||||
ret = true;
|
||||
|
|
|
|||
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Ссылка в новой задаче