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renamed ErrorPrediction to ClassificationError, second attempt

This commit is contained in:
Frank Seide 2016-08-22 11:16:39 -07:00
Родитель 9bd57387bb
Коммит 536e264749
202 изменённых файлов: 14923 добавлений и 14923 удалений
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26
Documentation/CNTK-TechReport/lyx/CNTKBook_CNTK_Adv_Chapter.lyx
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@ -259,7 +259,7 @@ CE=CrossEntropyWithSoftmax(labels, Plus2)
\begin_layout Plain Layout
ErrPredict=ErrorPrediction(labels, Plus2)
ErrPredict=ClassificationError(labels, Plus2)
\end_layout
\begin_layout Plain Layout
@ -616,7 +616,7 @@ CE=CrossEntropyWithSoftmax(labels, Plus2)
\begin_layout Plain Layout
ErrPredict=ErrorPrediction(labels, Plus2)
ErrPredict=ClassificationError(labels, Plus2)
\end_layout
\end_inset
@ -633,19 +633,19 @@ CrossEntropyWithSoftmax
\end_inset
() to compute the training criterion and the operator ErrorPrediction
() to compute the training criterion and the operator ClassificationError
\begin_inset Index idx
status open
\begin_layout Plain Layout
ErrorPrediction
ClassificationError
\end_layout
\end_inset
() to compute the testing criterion.
These operators are internally represented as computation nodes CrossEntropyWit
hSoftmaxNode and ErrorPredictionNode with names CE and ErrPredict, respectively.
hSoftmaxNode and ClassificationErrorNode with names CE and ErrPredict, respectively.
\end_layout
\begin_layout Subsubsection
@ -740,7 +740,7 @@ status open
\begin_layout Plain Layout
ErrPredict=ErrorPrediction(labels, Plus2) # classification error
ErrPredict=ClassificationError(labels, Plus2) # classification error
\end_layout
\begin_layout Plain Layout
@ -1025,7 +1025,7 @@ reference "sub:NDL-Basic-Concepts"
but is much simpler and easier to understand because of the use of macros.
One new feature shown in this network definition is the access to macro-region
variables.
ErrorPrediction() needs to access an intermediate result from SMBFF before
ClassificationError() needs to access an intermediate result from SMBFF before
the CrossEntropyWithSoftmax() is applied.
Although the needed variable is local to the macro, it can be accessed
via the
@ -1107,7 +1107,7 @@ CE = SMBFF(L1, LDim, HDim, labels)
\begin_layout Plain Layout
Err=ErrorPrediction(labels, CE.F)
Err=ClassificationError(labels, CE.F)
\end_layout
\end_inset
@ -1280,7 +1280,7 @@ CE = SMBFF(L3, LDim, HDim, labels, tag="criterion")
\begin_layout Plain Layout
Err=ErrorPrediction(labels, CE.F, tag="evaluation")
Err=ClassificationError(labels, CE.F, tag="evaluation")
\end_layout
\end_inset
@ -2900,12 +2900,12 @@ classProbBeforeSoftmax - applying softmax on this matrix will result in
\end_layout
\begin_layout Subsubsection
ErrorPrediction
ClassificationError
\begin_inset Index idx
status open
\begin_layout Plain Layout
ErrorPrediction
ClassificationError
\end_layout
\end_inset
@ -2941,7 +2941,7 @@ status open
\begin_layout Plain Layout
ErrorPrediction(labels, m)
ClassificationError(labels, m)
\end_layout
\begin_layout Plain Layout
@ -4059,7 +4059,7 @@ CE = SMBFF(L3, LDim, HDim, labels, tag="criterion")
\begin_layout Plain Layout
Err=ErrorPrediction(labels, CE.F, tag="evaluation")
Err=ClassificationError(labels, CE.F, tag="evaluation")
\end_layout
\begin_layout Plain Layout

6
Documentation/CNTK-TechReport/lyx/CNTKBook_ExampleSetup_Chapter.lyx
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@ -290,7 +290,7 @@ cntkSpeech.dnn"
\begin_layout Plain Layout
evalCriterion="ErrorPrediction"
evalCriterion="ClassificationError"
\end_layout
\begin_layout Plain Layout
@ -1610,7 +1610,7 @@ CE1=CrossEntropyWithSoftmax(labels,BFF1.FF.P,tag="evaluation")
\begin_layout Plain Layout
FER1 = ErrorPrediction(labels,BFF1.FF.P,tag="evaluation")
FER1 = ClassificationError(labels,BFF1.FF.P,tag="evaluation")
\end_layout
\begin_layout Plain Layout
@ -1634,7 +1634,7 @@ CE2=CrossEntropyWithSoftmax(regions,BFF2.FF.P,tag="evaluation")
\begin_layout Plain Layout
FER2 = ErrorPrediction(regions,BFF2.FF.P,tag="evaluation")
FER2 = ClassificationError(regions,BFF2.FF.P,tag="evaluation")
\end_layout
\begin_layout Plain Layout

2
Documentation/CNTK-TechReport/lyx/CNTKBook_Introduction.lyx
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@ -514,7 +514,7 @@ Simple_Demo=[
\begin_layout Plain Layout
evalCriterion="ErrorPrediction"
evalCriterion="ClassificationError"
\end_layout
\begin_layout Plain Layout

4
Examples/Image/MNIST/Config/01_OneHidden.cntk
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@ -52,12 +52,12 @@ train = [
z = DNNLayer (hiddenDim, labelDim, h1, 1)
ce = CrossEntropyWithSoftmax (labels, z)
errs = ErrorPrediction (labels, z)
errs = ClassificationError (labels, z)
# set top5Errs as an evaluation node to compute the top-5 error rate
# This is not marked tag="evaluation" since expensive during training.
# We explicitly select it as an output node in the "test" command.
top5Errs = ErrorPrediction (labels, z, topN=5)
top5Errs = ClassificationError (labels, z, topN=5)
# declare special nodes
featureNodes = (features)

4
Examples/Image/MNIST/Config/01_OneHidden.ndl
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@ -22,8 +22,8 @@ DNN = [
ol = DNNLayer(hiddenDim, labelDim, h1, 1)
ce = CrossEntropyWithSoftmax(labels, ol)
errs = ErrorPrediction(labels, ol)
top5Errs = ErrorPrediction(labels, ol, Const(5), tag="eval") # only used in testing
errs = ClassificationError(labels, ol)
top5Errs = ClassificationError(labels, ol, Const(5), tag="eval") # only used in testing
# Special Nodes
FeatureNodes = (features)

2
Examples/Image/MNIST/Config/02_Convolution.ndl
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@ -58,7 +58,7 @@ DNN=[
ol = DNNLayer(h1Dim, labelDim, h1, 1)
ce = CrossEntropyWithSoftmax(labels, ol)
errs = ErrorPrediction(labels, ol)
errs = ClassificationError(labels, ol)
# Special Nodes
FeatureNodes = (features)

2
Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl
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@ -64,7 +64,7 @@ DNN = [
ol = DNNLayer(h1Dim, labelDim, h1, 1)
ce = CrossEntropyWithSoftmax(labels, ol)
errs = ErrorPrediction(labels, ol)
errs = ClassificationError(labels, ol)
# Special Nodes
FeatureNodes = (features)

2
Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl
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@ -79,7 +79,7 @@ DNN=[
ol = DNNLastLayer(hiddenDim, labelDim, h1_d, fc2WScale, fc2BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl
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@ -84,7 +84,7 @@ DNN=[
ol = DNNLastLayer(hiddenDim, labelDim, h1, fc2WScale, fc2BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/CIFAR-10/03_ResNet.ndl
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@ -61,7 +61,7 @@ DNN=[
ol = DnnLastLayer(cMap3, labelDim, pool, fc1WScale, fc1BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/CIFAR-10/04_ResNet_56.ndl
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@ -106,7 +106,7 @@ DNN=[
ol = DnnLastLayer(cMap3, labelDim, pool, fc1WScale, fc1BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/AlexNet/AlexNet.ndl
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@ -103,6 +103,6 @@ DNN=[
ol = DNNLastLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/ResNet/ResNet_152.ndl
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@ -111,6 +111,6 @@ DNN=[
ol = DnnLayer(cMap6, labelDim, pool2, fcWScale, fcBValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/ResNet/ResNet_34.ndl
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@ -74,6 +74,6 @@ DNN=[
ol = DnnLayer(cMap4, labelDim, pool5, fcWScale, fcBValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/ResNet/ResNet_50.ndl
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@ -77,6 +77,6 @@ DNN=[
ol = DnnLayer(cMap6, labelDim, pool2, fcWScale, fcBValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/VGG/VGG_A.ndl
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@ -71,6 +71,6 @@ DNN=[
ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/VGG/VGG_E.ndl
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@ -79,6 +79,6 @@ DNN=[
ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

2
Examples/Image/Miscellaneous/ImageNet/VGG/VGG_E_BN.ndl
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@ -80,6 +80,6 @@ DNN=[
ol = DnnLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
CE = CrossEntropyWithSoftmax(labels, ol, tag = Criteria)
Err = ErrorPrediction(labels, ol, tag = Eval)
Err = ClassificationError(labels, ol, tag = Eval)
OutputNodes = ol
]

16
Examples/Other/NdlExamples/NDLExamples.ndl
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@ -29,7 +29,7 @@ ndlTestCosDist=[
CD = CosDistance(L4, labels);
CDAll=SumElements(CD)
NCD=Negate(CDALL, tag="criterion")
Err=ErrorPrediction(labels, L4, tag="evaluation")
Err=ClassificationError(labels, L4, tag="evaluation")
# rootNodes defined here
OutputNodes=(L4)
@ -129,7 +129,7 @@ ndlFull=[
#SM=Softmax(Plus2)
#CE=CrossEntropy(labels, SM)
CE=CrossEntropyWithSoftmax(labels, Plus2)
ErrPredict=ErrorPrediction(labels, Plus2)
ErrPredict=ClassificationError(labels, Plus2)
FeatureNodes=(features)
LabelNodes=(labels)
CriterionNodes=(CE)
@ -233,7 +233,7 @@ ndlMacroUse2=[
L2 = RBFF(L1, HDim, HDim)
L3 = RBFF(L2, HDim, HDim)
CE = SMBFF(L3, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF)
@ -290,7 +290,7 @@ ndlMacroUseCNNSubSample2ZeroPadding=[
HDim=128
L1 = SBFF(mp, HDim, mpoutputSizePerSample)
CE = SMBFF(L1, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF)
@ -349,7 +349,7 @@ ndlMacroUseCNNSubSample2=[
HDim=128
L1 = SBFF(mp, HDim, mpoutputSizePerSample)
CE = SMBFF(L1, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF)
@ -399,7 +399,7 @@ ndlMacroUseCNN=[
HDim=128
L1 = SBFF(mp, HDim, 0)
CE = SMBFF(L1, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF)
@ -430,7 +430,7 @@ ndlMacroUseNoBase=[
L2 = RFFD(L1, HDim, HDim)
L3 = RFFD(L2, HDim, HDim)
CE = SMFF(L3, LDim, SDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF)
]
@ -463,7 +463,7 @@ ndlMacroUseMask=[
L4=ElementTimes(L3, ML2)
CE = SMBFF(L4, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF, tag="evaluation")
Err=ClassificationError(labels, CE.BFF, tag="evaluation")
# output nodes
Prior=Mean(labels)

2
Examples/Other/Simple2d/Config/Multigpu.cntk
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@ -39,7 +39,7 @@ Multigpu_Demo_Train=[
# 2 input, 2 50-element hidden, 2 output
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true

4
Examples/Other/Simple2d/Config/Simple.cntk
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@ -32,7 +32,7 @@ Simple_Demo_Train = [
# 2 input, 2 50-element hidden, 2 output
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -137,6 +137,6 @@ Simple_Demo_Output=[
# grep labels SimpleOutput.labels | awk '{print $1}' > L
# diff L P | grep "<" | wc -l
# wc -l P
# The ratio of the two numbers gives the same error rate as ErrorPrediction/Sample in the log.
# The ratio of the two numbers gives the same error rate as ClassificationError/Sample in the log.
]
]

2
Examples/Speech/AN4/Config/FeedForward.cntk
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@ -29,7 +29,7 @@ speechTrain = [
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true

2
Examples/Speech/AN4/Config/lstmp-3layer-opt.ndl
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@ -199,7 +199,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3 = [
LSTMoutputW = Plus(Times(W, LSTMoutput3), b);
ce = CrossEntropyWithSoftmax(labels, LSTMoutputW);
err = ErrorPrediction(labels, LSTMoutputW);
err = ClassificationError(labels, LSTMoutputW);
logPrior = LogPrior(labels)
scaledLogLikelihood = Minus(LSTMoutputW, logPrior)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/PACRnn.txt
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@ -172,7 +172,7 @@ ndlCreateNetwork=[
criterion = Plus(Scale(cr2,criterion2), Scale(cr1,criterion1), tag=Criteria)
#CE = SMBFF(Dout,labelDim,hiddenDim,labels,tag=Criteria)
Err = ErrorPrediction(labels,DNN_A_CE_BFF,tag=Eval)
Err = ClassificationError(labels,DNN_A_CE_BFF,tag=Eval)
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/dnn_3layer.ndl
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@ -33,7 +33,7 @@ ndlCreateNetwork=[
L2 = SBFF(L1,hiddenDim,hiddenDim)
L3 = SBFF(L2,hiddenDim,hiddenDim)
CE = SMBFF(L3,labelDim,hiddenDim,labels,tag=Criteria)
Err = ErrorPrediction(labels,CE.BFF.FF.P,tag=Eval)
Err = ClassificationError(labels,CE.BFF.FF.P,tag=Eval)
# define output (scaled loglikelihood)
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/dnn_6layer.ndl
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@ -122,7 +122,7 @@ ndlCreateNetwork=[
L5 = SBFF(L4,hiddenDim,hiddenDim)
L6 = SBFF(L5,hiddenDim,hiddenDim)
CE = SMBFF(L6,labelDim,hiddenDim,labels,tag=Criteria)
Err = ErrorPrediction(labels,CE.BFF.FF.P,tag=Eval)
Err = ClassificationError(labels,CE.BFF.FF.P,tag=Eval)
# define output (scaled loglikelihood)
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/dnn_6layer_smbr.ndl
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@ -128,7 +128,7 @@ ndlCreateNetwork=[
# same name as the corresponding node in the non-sequence training model.
CE.BFF = BFF(L6, labelDim, hiddenDim)
Cr = DummyCriterion(objectives, derivatives, CE.BFF.FF.P, tag=Criteria)
Err = ErrorPrediction(labels, CE.BFF.FF.P, tag=Eval)
Err = ClassificationError(labels, CE.BFF.FF.P, tag=Eval)
# define output (scaled loglikelihood)
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstm_3layer_delay.ndl
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@ -106,7 +106,7 @@ ndlCreateNetwork=[
LSTMoutputW1 = Times(W1, LSTMoutput3)
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW1,tag=Criteria)
Err = ErrorPrediction(labels,LSTMoutputW1,tag=Eval)
Err = ClassificationError(labels,LSTMoutputW1,tag=Eval)
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW1,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-3layer-delay.ndl
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@ -142,7 +142,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
#LSTMoutputW = Plus(Times(W, LSTMoutput3), b);
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-3layer-highway-dropout.ndl
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@ -184,7 +184,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-3layer-highway.ndl
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@ -182,7 +182,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-3layer.ndl
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@ -111,7 +111,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
LSTMoutputW = Plus(Times(W, LSTMoutput3), b);
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-3layer.txt
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@ -112,7 +112,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-8layer-highway.ndl
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@ -187,7 +187,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/AMI/cntk_config/lstmp-8layer.ndl
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@ -116,7 +116,7 @@ ndlCreateNetwork_LSTMP_c1024_p256_x3=[
LSTMoutputW = Plus(Times(W, LSTMoutput8), b);
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW,tag=Criteria);
Err = ErrorPrediction(labels,LSTMoutputW,tag=Eval);
Err = ClassificationError(labels,LSTMoutputW,tag=Eval);
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW,logPrior,tag=Output)

2
Examples/Speech/Miscellaneous/TIMIT/config/PAC-RNN.ndl
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@ -163,7 +163,7 @@ ndlCreateNetwork=[
criterion2 = CrossEntropyWithSoftmax(statelabels, DNN_B_CE_BFF)
criterion = Plus(Scale(cr2,criterion2), Scale(cr1,criterion1), tag="criterion")
Err = ErrorPrediction(labels,DNN_A_CE_BFF,tag="evaluation")
Err = ClassificationError(labels,DNN_A_CE_BFF,tag="evaluation")
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/TIMIT/config/TIMIT_AdaptLearnRate.cntk
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@ -18,7 +18,7 @@ TIMIT_TrainAdaptLR=[
SimpleNetworkBuilder=[
layerSizes=792:512*3:183
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
evalCriterion=ClassificationError
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true

2
Examples/Speech/Miscellaneous/TIMIT/config/TIMIT_TrainSimpleNetwork.cntk
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@ -24,7 +24,7 @@ TIMIT_TrainSimple=[
SimpleNetworkBuilder=[
layerSizes=792:512*3:183
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
evalCriterion=ClassificationError
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true

2
Examples/Speech/Miscellaneous/TIMIT/config/classify.ndl
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@ -33,7 +33,7 @@ ndlCreateNetwork=[
L2 = SBFF(L1,hiddenDim,hiddenDim)
L3 = SBFF(L2,hiddenDim,hiddenDim)
CE = SMBFF(L3,labelDim,hiddenDim,myLabels,tag="criterion")
Err = ErrorPrediction(myLabels,CE.BFF.FF.P,tag="evaluation")
Err = ClassificationError(myLabels,CE.BFF.FF.P,tag="evaluation")
# define output (scaled loglikelihood)
logPrior = LogPrior(myLabels)

2
Examples/Speech/Miscellaneous/TIMIT/config/create_1layer.ndl
Просмотреть файл

@ -31,7 +31,7 @@ ndlCreateNetwork=[
featNorm = MeanVarNorm(features)
L1 = SBFF(featNorm,hiddenDim,featDim)
CE = SMBFF(L1,labelDim,hiddenDim,labels,tag="criterion")
Err = ErrorPrediction(labels,CE.BFF.FF.P,tag="evaluation")
Err = ClassificationError(labels,CE.BFF.FF.P,tag="evaluation")
# define output (scaled loglikelihood)
logPrior = LogPrior(labels)

2
Examples/Speech/Miscellaneous/TIMIT/config/lstm.ndl
Просмотреть файл

@ -102,7 +102,7 @@ ndlCreateNetwork=[
LSTMoutputW1 = Times(W1, LSTMoutput)
cr = CrossEntropyWithSoftmax(labels, LSTMoutputW1,tag="criterion")
Err = ErrorPrediction(labels,LSTMoutputW1,tag="evaluation")
Err = ClassificationError(labels,LSTMoutputW1,tag="evaluation")
logPrior = LogPrior(labels)
ScaledLogLikelihood=Minus(LSTMoutputW1,logPrior,tag="output")

2
Examples/Speech/Miscellaneous/TIMIT/config/mtl_fbank_mfcc.ndl
Просмотреть файл

@ -51,7 +51,7 @@ L1 = SBFF2(featInput1, HiddenDim, FeatDim1, featInput2, FeatDim2)
L2 = SBFF(L1, HiddenDim, HiddenDim)
L3 = SBFF(L2, HiddenDim, HiddenDim)
CE = SMBFF(L3, LabelDim1, HiddenDim, labels,tag="criterion") # do I need a tag?
FER = ErrorPrediction(labels,CE.BFF.FF.P,tag="evaluation")
FER = ClassificationError(labels,CE.BFF.FF.P,tag="evaluation")
# outputNodes
Prior=Mean(labels)

4
Examples/Speech/Miscellaneous/TIMIT/config/mtl_senones_dr.ndl
Просмотреть файл

@ -41,12 +41,12 @@ L3 = SBFF(L2, HiddenDim, HiddenDim2)
# objective function 1
BFF1=BFF(L3,LabelDim1,HiddenDim)
CE1=CrossEntropyWithSoftmax(labels,BFF1.FF.P,tag="evaluation")
FER1 = ErrorPrediction(labels,BFF1.FF.P,tag="evaluation")
FER1 = ClassificationError(labels,BFF1.FF.P,tag="evaluation")
# objective function 2
BFF2=BFF(L3,LabelDim2,HiddenDim)
CE2=CrossEntropyWithSoftmax(regions,BFF2.FF.P,tag="evaluation")
FER2 = ErrorPrediction(regions,BFF2.FF.P,tag="evaluation")
FER2 = ClassificationError(regions,BFF2.FF.P,tag="evaluation")
# weighted final objective function
Alpha1=0.8

2
Source/ActionsLib/SimpleNetworkBuilder.h
Просмотреть файл

@ -66,7 +66,7 @@ enum class EvalCriterion : int
CrossEntropy,
SquareError,
Logistic,
ErrorPrediction,
ClassificationError,
ClassCrossEntropyWithSoftmax,
NCECrossEntropyWithSoftmax,
CRF,

10
Source/CNTK/BrainScript/Doc/Notes.txt
Просмотреть файл

@ -216,13 +216,13 @@ TIMIT_TrainSimple = new TrainAction [ // new: added TrainAction; t
needPrior = true
// the following two belong into SGD, so they were removed here
//trainingCriterion = CrossEntropyWithSoftmax
//evalCriterion = ErrorPrediction
//evalCriterion = ClassificationError
// new: connect to input stream from source; and expose the output layer
input = source.features.data // these are also ComputeNodeRefs, exposed by the source
output = ComputeNodeRef [ dim = source.labels.dim ] // SimpleNetworkBuilder will put top layer affine transform output (input to softmax) here
// criteria are configurable here; these are ComputeNodes created here
trainingCriterion = CrossEntropyWithSoftmax (source.labels.data, output)
evalCriterion = ErrorPrediction (source.labels.data, output)
evalCriterion = ClassificationError (source.labels.data, output)
// new: (and half-baked) define Input nodes
myFeatures=Input(featDim) // reader stream will reference this
myLabels=Input(labelDim)
@ -245,7 +245,7 @@ TIMIT_TrainSimple = new TrainAction [ // new: added TrainAction; t
//L2 = SBFF(L1,hiddenDim,hiddenDim)
//L3 = SBFF(L2,hiddenDim,hiddenDim)
//CE = SMBFF(L3,labelDim,hiddenDim,myLabels,tag=Criteria)
//Err = ErrorPrediction(myLabels,CE.BFF.FF.P,tag=Eval)
//Err = ClassificationError(myLabels,CE.BFF.FF.P,tag=Eval)
//logPrior = LogPrior(myLabels)
//ScaledLogLikelihood=Minus(CE.BFF.FF.P,logPrior,tag=Output)
@ -279,7 +279,7 @@ TIMIT_TrainSimple = new TrainAction [ // new: added TrainAction; t
// define criterion nodes
CE = CrossEntropyWithSoftmax(myLabels, outZ)
Err = ErrorPrediction(myLabels, outZ)
Err = ClassificationError(myLabels, outZ)
// define output node for decoding
logPrior = LogPrior(myLabels)
@ -392,7 +392,7 @@ network = new NDL [
// define criterion nodes
CE = CrossEntropyWithSoftmax(myLabels, outZ)
Err = ErrorPrediction(myLabels, outZ)
Err = ClassificationError(myLabels, outZ)
// define output node for decoding
logPrior = LogPrior(myLabels)

2
Source/CNTK/modelEditorFromScratch.txt
Просмотреть файл

@ -23,7 +23,7 @@ m1=[
L2 = RBFF(L1, HDim, HDim)
L3 = RBFF(L2, HDim, HDim)
CE = SMBFF(L3, LDim, HDim, labels, tag="criterion")
Err=ErrorPrediction(labels, CE.BFF.FF.P, tag="evaluation")
Err=ClassificationError(labels, CE.BFF.FF.P, tag="evaluation")
# rootNodes defined here
OutputNodes=(CE.BFF.FF.P)

2
Source/CNTKv2LibraryDll/BackCompat.cpp
Просмотреть файл

@ -171,7 +171,7 @@ namespace CNTK
std::swap(inputVars[0], inputVars[1]);
opType = PrimitiveOpType::CrossEntropyWithSoftmax;
}
else if (node->OperationName() == OperationNameOf(ErrorPredictionNode))
else if (node->OperationName() == OperationNameOf(ClassificationErrorNode))
{
std::swap(inputVars[0], inputVars[1]);
opType = PrimitiveOpType::ClassificationError;

2
Source/CNTKv2LibraryDll/Function.cpp
Просмотреть файл

@ -289,7 +289,7 @@ namespace CNTK
computationNodePtr = builder.CrossEntropyWithSoftmax(input1Node, input0Node, function->Name());
break;
case PrimitiveOpType::ClassificationError:
computationNodePtr = builder.ErrorPrediction(input1Node, input0Node, function->Name());
computationNodePtr = builder.ClassificationError(input1Node, input0Node, function->Name());
break;
case PrimitiveOpType::PastValue:
case PrimitiveOpType::FutureValue:

4
Source/ComputationNetworkLib/ComputationNetwork.cpp
Просмотреть файл

@ -435,7 +435,7 @@ bool ComputationNetwork::IsTypicalCriterionNode(ComputationNodeBasePtr nodePtr)
nodePtr->OperationName() == OperationNameOf(SequenceWithSoftmaxNode) ||
nodePtr->OperationName() == OperationNameOf(CrossEntropyNode) ||
nodePtr->OperationName() == OperationNameOf(ClassBasedCrossEntropyWithSoftmaxNode) ||
nodePtr->OperationName() == OperationNameOf(ErrorPredictionNode) ||
nodePtr->OperationName() == OperationNameOf(ClassificationErrorNode) ||
#ifdef COMING_SOON
nodePtr->OperationName() == OperationNameOf(CRFNode) ||
#endif
@ -1228,7 +1228,7 @@ void ComputationNetwork::SaveToDbnFile(ComputationNetworkPtr net, const std::wst
};
// Get output node
std::list<ComputationNodeBasePtr> outputNodes = net->GetNodesWithType(OperationNameOf(ErrorPredictionNode));
std::list<ComputationNodeBasePtr> outputNodes = net->GetNodesWithType(OperationNameOf(ClassificationErrorNode));
ComputationNodeBasePtr outputNode = GetFirstNodeWithDifferentType(outputNodes.front()->GetInputs(), OperationNameOf(InputValue));
if (outputNode == nullptr)

2
Source/ComputationNetworkLib/ComputationNetworkBuilder.h
Просмотреть файл

@ -122,7 +122,7 @@ public:
ComputationNodePtr DummyCriterion(const ComputationNodePtr objectives, const ComputationNodePtr derivatives, const ComputationNodePtr prediction, const std::wstring nodeName = L"");
ComputationNodePtr ElementTimes(const ComputationNodePtr a, const ComputationNodePtr b, const std::wstring nodeName = L"");
ComputationNodePtr DynamicAxis(const ComputationNodePtr a, const std::wstring& nodeName = L"");
ComputationNodePtr ErrorPrediction(const ComputationNodePtr a, const ComputationNodePtr b, const std::wstring nodeName = L"");
ComputationNodePtr ClassificationError(const ComputationNodePtr a, const ComputationNodePtr b, const std::wstring nodeName = L"");
ComputationNodePtr Exp(const ComputationNodePtr a, const std::wstring nodeName = L"");
ComputationNodePtr Floor(const ComputationNodePtr a, const std::wstring nodeName = L"");
ComputationNodePtr FutureValue(const ComputationNodePtr a, const float initHiddenActivity, const size_t row_size, size_t timeStep, const std::wstring nodeName = L"");

2
Source/ComputationNetworkLib/ComputationNetworkScripting.cpp
Просмотреть файл

@ -447,7 +447,7 @@ ScriptableObjects::ConfigurableRuntimeTypeRegister::Add<ComputationNetworkWithEd
// refWeight = 0.9
// kldLabels = labels * (1-refWeight) + Softmax (zRef) * refWeight # interpolate with ref output
// ce = CrossEntropyWithSoftmax (z, kldLabels)
// errs = ErrorPrediction (z, labels)
// errs = ClassificationError (z, labels)
// criterionNodes = (ce)
// evaluationNodes = (errs)
// ===================================================================

20
Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/baseline.linux.txt
Просмотреть файл

@ -272,8 +272,8 @@ Post-processing network...
4 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop1 = ErrorPrediction()
err = ClassificationError()
errTop1 = ClassificationError()
ol.z = Plus()
Validating network. 17 nodes to process in pass 1.
@ -292,9 +292,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
Validating --> errTop1 = ClassificationError (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
Validating network. 9 nodes to process in pass 2.
@ -314,8 +314,8 @@ Post-processing network complete.
05/13/2016 15:10:02: Evaluation criterion node(s):
05/13/2016 15:10:02: errTop1 = ErrorPrediction
05/13/2016 15:10:02: err = ErrorPrediction
05/13/2016 15:10:02: errTop1 = ClassificationError
05/13/2016 15:10:02: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -390,8 +390,8 @@ Post-processing network...
4 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop1 = ErrorPrediction()
err = ClassificationError()
errTop1 = ClassificationError()
ol.z = Plus()
Validating network. 17 nodes to process in pass 1.
@ -410,9 +410,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *1] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
Validating --> errTop1 = ClassificationError (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
Validating network. 9 nodes to process in pass 2.

20
Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/baseline.windows.txt
Просмотреть файл

@ -270,8 +270,8 @@ Post-processing network...
4 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop1 = ErrorPrediction()
err = ClassificationError()
errTop1 = ClassificationError()
ol.z = Plus()
Validating network. 17 nodes to process in pass 1.
@ -290,9 +290,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
Validating --> errTop1 = ClassificationError (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
Validating network. 9 nodes to process in pass 2.
@ -312,8 +312,8 @@ Post-processing network complete.
05/13/2016 08:15:53: Evaluation criterion node(s):
05/13/2016 08:15:53: errTop1 = ErrorPrediction
05/13/2016 08:15:53: err = ErrorPrediction
05/13/2016 08:15:53: errTop1 = ClassificationError
05/13/2016 08:15:53: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -388,8 +388,8 @@ Post-processing network...
4 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop1 = ErrorPrediction()
err = ClassificationError()
errTop1 = ClassificationError()
ol.z = Plus()
Validating network. 17 nodes to process in pass 1.
@ -408,9 +408,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *1] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
Validating --> errTop1 = ClassificationError (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
Validating network. 9 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/baseline.linux.txt
Просмотреть файл

@ -284,7 +284,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 27 nodes to process in pass 1.
@ -315,7 +315,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -343,7 +343,7 @@ Post-processing network complete.
05/13/2016 15:10:11: Evaluation criterion node(s):
05/13/2016 15:10:11: err = ErrorPrediction
05/13/2016 15:10:11: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -429,7 +429,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 27 nodes to process in pass 1.
@ -460,7 +460,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *1] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/baseline.windows.txt
Просмотреть файл

@ -282,7 +282,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 27 nodes to process in pass 1.
@ -313,7 +313,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -341,7 +341,7 @@ Post-processing network complete.
05/13/2016 08:16:18: Evaluation criterion node(s):
05/13/2016 08:16:18: err = ErrorPrediction
05/13/2016 08:16:18: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -427,7 +427,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 27 nodes to process in pass 1.
@ -458,7 +458,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *1] -> [10 x 1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/baseline.linux.txt
Просмотреть файл

@ -287,7 +287,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 36 nodes to process in pass 1.
@ -329,7 +329,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *] -> [10 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -363,7 +363,7 @@ Post-processing network complete.
05/13/2016 15:10:29: Evaluation criterion node(s):
05/13/2016 15:10:29: err = ErrorPrediction
05/13/2016 15:10:29: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -462,7 +462,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 36 nodes to process in pass 1.
@ -502,7 +502,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *1] -> [10 x *1]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/baseline.windows.txt
Просмотреть файл

@ -285,7 +285,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 36 nodes to process in pass 1.
@ -327,7 +327,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *] -> [10 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -361,7 +361,7 @@ Post-processing network complete.
05/13/2016 08:16:58: Evaluation criterion node(s):
05/13/2016 08:16:58: err = ErrorPrediction
05/13/2016 08:16:58: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -460,7 +460,7 @@ Post-processing network...
3 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
ol.z = Plus()
Validating network. 36 nodes to process in pass 1.
@ -500,7 +500,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *1] -> [10 x *1]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/baseline.linux.txt
Просмотреть файл

@ -288,7 +288,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 34 nodes to process in pass 1.
@ -326,7 +326,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *], [10] -> [10 x 1 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 21 nodes to process in pass 2.
@ -358,7 +358,7 @@ Post-processing network complete.
05/13/2016 15:10:48: Evaluation criterion node(s):
05/13/2016 15:10:48: Err = ErrorPrediction
05/13/2016 15:10:48: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -492,7 +492,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 34 nodes to process in pass 1.
@ -530,7 +530,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *1], [10] -> [10 x 1 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 21 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/baseline.windows.txt
Просмотреть файл

@ -286,7 +286,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 34 nodes to process in pass 1.
@ -324,7 +324,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *], [10] -> [10 x 1 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 21 nodes to process in pass 2.
@ -356,7 +356,7 @@ Post-processing network complete.
05/13/2016 08:17:53: Evaluation criterion node(s):
05/13/2016 08:17:53: Err = ErrorPrediction
05/13/2016 08:17:53: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -490,7 +490,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 34 nodes to process in pass 1.
@ -528,7 +528,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *1], [10] -> [10 x 1 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 21 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/baseline.windows.txt
Просмотреть файл

@ -289,7 +289,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 45 nodes to process in pass 1.
@ -338,7 +338,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1.y) : [10 x 64], [64 x *]
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 20 nodes to process in pass 2.
@ -378,7 +378,7 @@ Post-processing network complete.
05/13/2016 08:18:26: Evaluation criterion node(s):
05/13/2016 08:18:26: Err = ErrorPrediction
05/13/2016 08:18:26: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -489,7 +489,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 45 nodes to process in pass 1.
@ -538,7 +538,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, h1.y) : [10 x 64], [64 x *1
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 20 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/03_ResNet/baseline.linux.gpu.txt
Просмотреть файл

@ -356,7 +356,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 184 nodes to process in pass 1.
@ -546,7 +546,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 75 nodes to process in pass 2.
@ -652,7 +652,7 @@ Post-processing network complete.
05/03/2016 18:13:08: Evaluation criterion node(s):
05/03/2016 18:13:08: Err = ErrorPrediction
05/03/2016 18:13:08: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -907,7 +907,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 184 nodes to process in pass 1.
@ -1095,7 +1095,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 75 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/03_ResNet/baseline.windows.txt
Просмотреть файл

@ -354,7 +354,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 184 nodes to process in pass 1.
@ -544,7 +544,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 75 nodes to process in pass 2.
@ -650,7 +650,7 @@ Post-processing network complete.
05/03/2016 14:04:12: Evaluation criterion node(s):
05/03/2016 14:04:12: Err = ErrorPrediction
05/03/2016 14:04:12: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -905,7 +905,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 184 nodes to process in pass 1.
@ -1093,7 +1093,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 75 nodes to process in pass 2.

2
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/03_ResNet/testcases.yml
Просмотреть файл

@ -23,7 +23,7 @@ Epochs must be finished with expected results:
patterns:
- Finished Epoch[{{integer}} of {{integer}}]
- CrossEntropyWithSoftmax = {{float,tolerance=2.0%}}
- EvalErrorPrediction = {{float,tolerance=2.0%}}
- EvalClassificationError = {{float,tolerance=2.0%}}
- learningRatePerSample = {{float,tolerance=0.001%}}
Per-minibatch training results must match:

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/04_ResNet_56/baseline.linux.gpu.txt
Просмотреть файл

@ -356,7 +356,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 949 nodes to process in pass 1.
@ -1311,7 +1311,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 390 nodes to process in pass 2.
@ -1777,7 +1777,7 @@ Post-processing network complete.
05/03/2016 18:17:55: Evaluation criterion node(s):
05/03/2016 18:17:55: Err = ErrorPrediction
05/03/2016 18:17:55: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -2932,7 +2932,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 949 nodes to process in pass 1.
@ -3885,7 +3885,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 390 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/04_ResNet_56/baseline.windows.txt
Просмотреть файл

@ -354,7 +354,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 949 nodes to process in pass 1.
@ -1309,7 +1309,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 390 nodes to process in pass 2.
@ -1775,7 +1775,7 @@ Post-processing network complete.
05/03/2016 14:05:00: Evaluation criterion node(s):
05/03/2016 14:05:00: Err = ErrorPrediction
05/03/2016 14:05:00: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -2930,7 +2930,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 949 nodes to process in pass 1.
@ -3883,7 +3883,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, pool) : [10 x 1 x 1 x 64],
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 390 nodes to process in pass 2.

2
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/04_ResNet_56/testcases.yml
Просмотреть файл

@ -23,7 +23,7 @@ Epochs must be finished with expected results:
patterns:
- Finished Epoch[{{integer}} of {{integer}}]
- CrossEntropyWithSoftmax = {{float,tolerance=2.0%}}
- EvalErrorPrediction = {{float,tolerance=2.0%}}
- EvalClassificationError = {{float,tolerance=2.0%}}
- learningRatePerSample = {{float,tolerance=0.001%}}
Per-minibatch training results must match:

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/baseline.linux.txt
Просмотреть файл

@ -282,7 +282,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 32 nodes to process in pass 1.
@ -318,7 +318,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, conv4.y) : [10 x 7 x 7 x 32
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 19 nodes to process in pass 2.
@ -350,7 +350,7 @@ Post-processing network complete.
05/13/2016 15:11:11: Evaluation criterion node(s):
05/13/2016 15:11:11: Err = ErrorPrediction
05/13/2016 15:11:11: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -446,7 +446,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 32 nodes to process in pass 1.
@ -482,7 +482,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, conv4.y) : [10 x 7 x 7 x 32
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 19 nodes to process in pass 2.

10
Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/baseline.windows.txt
Просмотреть файл

@ -280,7 +280,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 32 nodes to process in pass 1.
@ -316,7 +316,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, conv4.y) : [10 x 7 x 7 x 32
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *], [10] -> [10 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x *] -> [1]
Validating network. 19 nodes to process in pass 2.
@ -348,7 +348,7 @@ Post-processing network complete.
05/13/2016 08:19:02: Evaluation criterion node(s):
05/13/2016 08:19:02: Err = ErrorPrediction
05/13/2016 08:19:02: Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -444,7 +444,7 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax()
Err = ErrorPrediction()
Err = ClassificationError()
OutputNodes.z = Plus()
Validating network. 32 nodes to process in pass 1.
@ -480,7 +480,7 @@ Validating --> OutputNodes.t = Times (OutputNodes.W, conv4.y) : [10 x 7 x 7 x 32
Validating --> OutputNodes.b = LearnableParameter() : -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x *1], [10] -> [10 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ErrorPrediction (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x *1] -> [1]
Validating network. 19 nodes to process in pass 2.

272
Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/baseline.linux.cpu.txt
Просмотреть файл

@ -68,7 +68,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -169,7 +169,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -302,7 +302,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -370,7 +370,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -399,7 +399,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -423,14 +423,14 @@ Post-processing network complete.
05/03/2016 15:21:43: Evaluation criterion node(s):
05/03/2016 15:21:43: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:21:43: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
(nil): {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0x1abc7c8: {[InvStdOfFeatures Value[2]] }
0x1b40348: {[features Value[2 x *]] }
0x1b408b8: {[MeanOfFeatures Value[2]] }
@ -443,7 +443,7 @@ Memory Sharing Structure:
0x1b46708: {[labels Value[2 x *]] }
0x1b473e8: {[Prior Value[2]] }
0x1b4b138: {[ScaledLogLikelihood Value[2 x 1 x *]] }
0x1b4cc28: {[EvalErrorPrediction Value[1]] }
0x1b4cc28: {[EvalClassificationError Value[1]] }
0x1b4cea8: {[CrossEntropyWithSoftmax Value[1]] }
0x1b4d388: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
0x1b4d548: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
@ -473,139 +473,139 @@ Memory Sharing Structure:
05/03/2016 15:21:44: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:44: Starting minibatch loop.
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0538s; samplesPerSecond = 4647.4
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.1073s; samplesPerSecond = 2329.6
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0631s; samplesPerSecond = 3961.3
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0747s; samplesPerSecond = 3346.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0900s; samplesPerSecond = 2778.4
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0605s; samplesPerSecond = 4135.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0619s; samplesPerSecond = 4039.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0769s; samplesPerSecond = 3249.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0799s; samplesPerSecond = 3129.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0620s; samplesPerSecond = 4031.5
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.1278s; samplesPerSecond = 1955.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0821s; samplesPerSecond = 3044.1
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalErrorPrediction = 0.54000000 * 250; time = 0.0657s; samplesPerSecond = 3804.8
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.1049s; samplesPerSecond = 2382.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.1180s; samplesPerSecond = 2117.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.1065s; samplesPerSecond = 2347.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.2709s; samplesPerSecond = 922.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.1375s; samplesPerSecond = 1818.4
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.1143s; samplesPerSecond = 2186.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.1079s; samplesPerSecond = 2317.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0917s; samplesPerSecond = 2727.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0923s; samplesPerSecond = 2707.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0887s; samplesPerSecond = 2819.0
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0634s; samplesPerSecond = 3945.8
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0885s; samplesPerSecond = 2823.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0601s; samplesPerSecond = 4162.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0630s; samplesPerSecond = 3968.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0849s; samplesPerSecond = 2944.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0879s; samplesPerSecond = 2844.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0752s; samplesPerSecond = 3325.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0958s; samplesPerSecond = 2610.3
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.1009s; samplesPerSecond = 2478.7
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.1607s; samplesPerSecond = 1555.6
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalErrorPrediction = 0.30400000 * 250; time = 0.1131s; samplesPerSecond = 2211.4
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalErrorPrediction = 0.27200000 * 250; time = 0.1047s; samplesPerSecond = 2388.5
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0896s; samplesPerSecond = 2791.5
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0727s; samplesPerSecond = 3438.8
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.2624s; samplesPerSecond = 952.6
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0842s; samplesPerSecond = 2967.7
05/03/2016 15:21:47: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalErrorPrediction = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.93174s
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0538s; samplesPerSecond = 4647.4
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.1073s; samplesPerSecond = 2329.6
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0631s; samplesPerSecond = 3961.3
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0747s; samplesPerSecond = 3346.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalClassificationError = 0.57600000 * 250; time = 0.0900s; samplesPerSecond = 2778.4
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0605s; samplesPerSecond = 4135.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0619s; samplesPerSecond = 4039.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0769s; samplesPerSecond = 3249.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0799s; samplesPerSecond = 3129.0
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0620s; samplesPerSecond = 4031.5
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.1278s; samplesPerSecond = 1955.9
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0821s; samplesPerSecond = 3044.1
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalClassificationError = 0.54000000 * 250; time = 0.0657s; samplesPerSecond = 3804.8
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.1049s; samplesPerSecond = 2382.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.1180s; samplesPerSecond = 2117.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.1065s; samplesPerSecond = 2347.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.2709s; samplesPerSecond = 922.9
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.1375s; samplesPerSecond = 1818.4
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.1143s; samplesPerSecond = 2186.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.1079s; samplesPerSecond = 2317.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalClassificationError = 0.48400000 * 250; time = 0.0917s; samplesPerSecond = 2727.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0923s; samplesPerSecond = 2707.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0887s; samplesPerSecond = 2819.0
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalClassificationError = 0.43600000 * 250; time = 0.0634s; samplesPerSecond = 3945.8
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0885s; samplesPerSecond = 2823.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0601s; samplesPerSecond = 4162.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalClassificationError = 0.55600000 * 250; time = 0.0630s; samplesPerSecond = 3968.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0849s; samplesPerSecond = 2944.1
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0879s; samplesPerSecond = 2844.6
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0752s; samplesPerSecond = 3325.7
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0958s; samplesPerSecond = 2610.3
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalClassificationError = 0.44800000 * 250; time = 0.1009s; samplesPerSecond = 2478.7
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalClassificationError = 0.49200000 * 250; time = 0.1607s; samplesPerSecond = 1555.6
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalClassificationError = 0.30400000 * 250; time = 0.1131s; samplesPerSecond = 2211.4
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalClassificationError = 0.27200000 * 250; time = 0.1047s; samplesPerSecond = 2388.5
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0896s; samplesPerSecond = 2791.5
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0727s; samplesPerSecond = 3438.8
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.2624s; samplesPerSecond = 952.6
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0842s; samplesPerSecond = 2967.7
05/03/2016 15:21:47: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalClassificationError = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.93174s
05/03/2016 15:21:47: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
05/03/2016 15:21:47: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:47: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20720006 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0545s; samplesPerSecond = 4583.4
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19690290 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0641s; samplesPerSecond = 3899.7
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16064646 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0770s; samplesPerSecond = 3247.1
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13547171 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0640s; samplesPerSecond = 3904.2
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.18000261 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0732s; samplesPerSecond = 3413.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17787841 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0790s; samplesPerSecond = 3164.0
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16821879 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0880s; samplesPerSecond = 2839.4
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16363456 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0854s; samplesPerSecond = 2926.8
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19533907 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0774s; samplesPerSecond = 3228.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19318692 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0820s; samplesPerSecond = 3049.5
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12726279 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0766s; samplesPerSecond = 3261.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18620067 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0773s; samplesPerSecond = 3235.5
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11547500 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0797s; samplesPerSecond = 3136.6
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16675950 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0833s; samplesPerSecond = 2999.8
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15807389 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0822s; samplesPerSecond = 3042.5
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18389093 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18269750 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0897s; samplesPerSecond = 2787.7
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18737841 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0963s; samplesPerSecond = 2597.3
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20174757 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0811s; samplesPerSecond = 3081.1
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13336708 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0732s; samplesPerSecond = 3414.6
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13851332 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0879s; samplesPerSecond = 2843.0
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15422288 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0821s; samplesPerSecond = 3044.3
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15478799 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0815s; samplesPerSecond = 3069.2
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14530201 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0810s; samplesPerSecond = 3086.3
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12192809 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.2596s; samplesPerSecond = 962.9
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13975597 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0569s; samplesPerSecond = 4394.5
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12566363 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0911s; samplesPerSecond = 2744.6
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18963051 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0765s; samplesPerSecond = 3267.2
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17955467 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0914s; samplesPerSecond = 2736.4
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18862103 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0772s; samplesPerSecond = 3236.7
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17503073 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0775s; samplesPerSecond = 3225.8
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14741998 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0774s; samplesPerSecond = 3230.1
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13803981 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14139232 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0820s; samplesPerSecond = 3048.4
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13886877 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0766s; samplesPerSecond = 3264.1
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15025864 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0852s; samplesPerSecond = 2933.5
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14659342 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0903s; samplesPerSecond = 2767.4
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13078795 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0784s; samplesPerSecond = 3187.6
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19832882 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0772s; samplesPerSecond = 3240.4
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15828904 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0721s; samplesPerSecond = 3468.7
05/03/2016 15:21:51: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16210811 * 10000; EvalErrorPrediction = 0.07480000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=3.34279s
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20720006 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0545s; samplesPerSecond = 4583.4
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19690290 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0641s; samplesPerSecond = 3899.7
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16064646 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0770s; samplesPerSecond = 3247.1
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13547171 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0640s; samplesPerSecond = 3904.2
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.18000261 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0732s; samplesPerSecond = 3413.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17787841 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0790s; samplesPerSecond = 3164.0
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16821879 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0880s; samplesPerSecond = 2839.4
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16363456 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0854s; samplesPerSecond = 2926.8
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19533907 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0774s; samplesPerSecond = 3228.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19318692 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0820s; samplesPerSecond = 3049.5
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12726279 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0766s; samplesPerSecond = 3261.6
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18620067 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0773s; samplesPerSecond = 3235.5
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11547500 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0797s; samplesPerSecond = 3136.6
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16675950 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0833s; samplesPerSecond = 2999.8
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15807389 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0822s; samplesPerSecond = 3042.5
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18389093 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18269750 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0897s; samplesPerSecond = 2787.7
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18737841 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0963s; samplesPerSecond = 2597.3
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20174757 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0811s; samplesPerSecond = 3081.1
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13336708 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0732s; samplesPerSecond = 3414.6
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13851332 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0879s; samplesPerSecond = 2843.0
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15422288 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0821s; samplesPerSecond = 3044.3
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15478799 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0815s; samplesPerSecond = 3069.2
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14530201 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0810s; samplesPerSecond = 3086.3
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12192809 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.2596s; samplesPerSecond = 962.9
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13975597 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0569s; samplesPerSecond = 4394.5
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12566363 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0911s; samplesPerSecond = 2744.6
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18963051 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0765s; samplesPerSecond = 3267.2
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17955467 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0914s; samplesPerSecond = 2736.4
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18862103 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0772s; samplesPerSecond = 3236.7
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17503073 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0775s; samplesPerSecond = 3225.8
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14741998 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0774s; samplesPerSecond = 3230.1
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13803981 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14139232 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0820s; samplesPerSecond = 3048.4
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13886877 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0766s; samplesPerSecond = 3264.1
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15025864 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0852s; samplesPerSecond = 2933.5
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14659342 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0903s; samplesPerSecond = 2767.4
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13078795 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0784s; samplesPerSecond = 3187.6
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19832882 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0772s; samplesPerSecond = 3240.4
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15828904 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0721s; samplesPerSecond = 3468.7
05/03/2016 15:21:51: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16210811 * 10000; EvalClassificationError = 0.07480000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=3.34279s
05/03/2016 15:21:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
05/03/2016 15:21:51: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:51: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.19031988 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0960s; samplesPerSecond = 2604.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13920714 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0967s; samplesPerSecond = 2585.3
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14595162 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0869s; samplesPerSecond = 2877.8
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13324012 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0817s; samplesPerSecond = 3060.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17358728 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0804s; samplesPerSecond = 3109.2
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17949159 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0660s; samplesPerSecond = 3788.1
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15009323 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0653s; samplesPerSecond = 3829.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17060954 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0660s; samplesPerSecond = 3787.3
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10410764 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0762s; samplesPerSecond = 3280.0
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20572259 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.2571s; samplesPerSecond = 972.5
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16519130 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0640s; samplesPerSecond = 3906.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14908187 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0593s; samplesPerSecond = 4213.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19227612 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0688s; samplesPerSecond = 3632.8
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13670934 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0532s; samplesPerSecond = 4700.3
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21113164 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0693s; samplesPerSecond = 3609.4
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13129944 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0882s; samplesPerSecond = 2833.6
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17304376 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2975.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16479250 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0685s; samplesPerSecond = 3648.5
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14591786 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0976s; samplesPerSecond = 2561.0
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12562012 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0969s; samplesPerSecond = 2580.7
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13442773 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0959s; samplesPerSecond = 2607.8
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17125328 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0754s; samplesPerSecond = 3314.6
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22482522 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.1037s; samplesPerSecond = 2410.8
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18291792 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0650s; samplesPerSecond = 3844.3
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20296558 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0823s; samplesPerSecond = 3038.9
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22849719 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0828s; samplesPerSecond = 3020.2
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12500068 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0864s; samplesPerSecond = 2894.1
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15719802 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2976.4
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11520810 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0687s; samplesPerSecond = 3636.7
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14159592 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0974s; samplesPerSecond = 2567.1
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18509569 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0721s; samplesPerSecond = 3465.4
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15008345 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0905s; samplesPerSecond = 2763.6
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12866435 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0902s; samplesPerSecond = 2770.5
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17640526 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0896s; samplesPerSecond = 2789.2
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14982110 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.2845s; samplesPerSecond = 878.8
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11472753 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0867s; samplesPerSecond = 2882.5
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16524783 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14961037 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0958s; samplesPerSecond = 2608.8
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15972387 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0972s; samplesPerSecond = 2572.7
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17867958 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0969s; samplesPerSecond = 2581.0
05/03/2016 15:21:54: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16073358 * 10000; EvalErrorPrediction = 0.07780000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=3.65495s
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.19031988 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0960s; samplesPerSecond = 2604.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13920714 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0967s; samplesPerSecond = 2585.3
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14595162 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0869s; samplesPerSecond = 2877.8
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13324012 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0817s; samplesPerSecond = 3060.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17358728 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0804s; samplesPerSecond = 3109.2
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17949159 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0660s; samplesPerSecond = 3788.1
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15009323 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0653s; samplesPerSecond = 3829.5
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17060954 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0660s; samplesPerSecond = 3787.3
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10410764 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0762s; samplesPerSecond = 3280.0
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20572259 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.2571s; samplesPerSecond = 972.5
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16519130 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0640s; samplesPerSecond = 3906.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14908187 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0593s; samplesPerSecond = 4213.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19227612 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0688s; samplesPerSecond = 3632.8
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13670934 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0532s; samplesPerSecond = 4700.3
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21113164 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0693s; samplesPerSecond = 3609.4
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13129944 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0882s; samplesPerSecond = 2833.6
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17304376 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2975.2
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16479250 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0685s; samplesPerSecond = 3648.5
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14591786 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0976s; samplesPerSecond = 2561.0
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12562012 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0969s; samplesPerSecond = 2580.7
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13442773 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0959s; samplesPerSecond = 2607.8
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17125328 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0754s; samplesPerSecond = 3314.6
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22482522 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.1037s; samplesPerSecond = 2410.8
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18291792 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0650s; samplesPerSecond = 3844.3
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20296558 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0823s; samplesPerSecond = 3038.9
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22849719 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0828s; samplesPerSecond = 3020.2
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12500068 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0864s; samplesPerSecond = 2894.1
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15719802 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2976.4
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11520810 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0687s; samplesPerSecond = 3636.7
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14159592 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0974s; samplesPerSecond = 2567.1
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18509569 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0721s; samplesPerSecond = 3465.4
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15008345 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0905s; samplesPerSecond = 2763.6
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12866435 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0902s; samplesPerSecond = 2770.5
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17640526 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0896s; samplesPerSecond = 2789.2
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14982110 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.2845s; samplesPerSecond = 878.8
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11472753 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0867s; samplesPerSecond = 2882.5
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16524783 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14961037 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0958s; samplesPerSecond = 2608.8
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15972387 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0972s; samplesPerSecond = 2572.7
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17867958 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0969s; samplesPerSecond = 2581.0
05/03/2016 15:21:54: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16073358 * 10000; EvalClassificationError = 0.07780000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=3.65495s
05/03/2016 15:21:54: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
05/03/2016 15:21:54: CNTKCommandTrainEnd: Multigpu_Demo_Train
@ -623,7 +623,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -652,7 +652,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -676,7 +676,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0x1abbf28: {[B0 Value[50 x 1]] }
0x1b47908: {[W1 Value[50 x 50]] }
0x1b48278: {[W2 Value[2 x 50]] }
@ -688,7 +688,7 @@ Memory Sharing Structure:
0x1b50cd8: {[Prior Value[2]] }
0x1b514f8: {[W0 Value[50 x 2]] }
0x1b53938: {[B1 Value[50 x 1]] }
0x1c0fd98: {[EvalErrorPrediction Value[1]] }
0x1c0fd98: {[EvalClassificationError Value[1]] }
0x1c0fef8: {[CrossEntropyWithSoftmax Value[1]] }
0x1c10438: {[LogOfPrior Value[2]] }
0x1c11f48: {[MVNormalizedFeatures Value[2 x *1]] }
@ -701,7 +701,7 @@ Memory Sharing Structure:
0x1c12d78: {[W2*H1 Value[2 x 1 x *1]] }
0x1c12f38: {[HLast Value[2 x 1 x *1]] }
05/03/2016 15:21:55: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12790061 * 603; perplexity = 1.13644005
05/03/2016 15:21:55: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12790061 * 603; perplexity = 1.13644005
05/03/2016 15:21:55: Action "test" complete.

272
Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/baseline.linux.gpu.txt
Просмотреть файл

@ -68,7 +68,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -169,7 +169,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -302,7 +302,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -371,7 +371,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -400,7 +400,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -424,14 +424,14 @@ Post-processing network complete.
05/03/2016 15:21:55: Evaluation criterion node(s):
05/03/2016 15:21:55: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:21:55: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
(nil): {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0x12a62e8: {[features Value[2 x *]] }
0x20202b8: {[MeanOfFeatures Value[2]] }
0x20207c8: {[InvStdOfFeatures Value[2]] }
@ -444,7 +444,7 @@ Memory Sharing Structure:
0x278ae18: {[Prior Value[2]] }
0x278c158: {[LogOfPrior Value[2]] }
0x27908f8: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
0x2790a18: {[EvalErrorPrediction Value[1]] }
0x2790a18: {[EvalClassificationError Value[1]] }
0x2790d18: {[ScaledLogLikelihood Value[2 x 1 x *]] }
0x2790e78: {[CrossEntropyWithSoftmax Value[1]] }
0x27966e8: {[B0 Value[50 x 1]] }
@ -474,139 +474,139 @@ Memory Sharing Structure:
05/03/2016 15:21:56: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:56: Starting minibatch loop.
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0059s; samplesPerSecond = 42038.0
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50525.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0050s; samplesPerSecond = 50423.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0049s; samplesPerSecond = 50689.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0050s; samplesPerSecond = 50261.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0049s; samplesPerSecond = 50576.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50566.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0049s; samplesPerSecond = 50515.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0049s; samplesPerSecond = 50730.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0049s; samplesPerSecond = 50751.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0050s; samplesPerSecond = 50393.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0048s; samplesPerSecond = 51899.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0047s; samplesPerSecond = 53498.8
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53694.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 53879.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53890.9
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0047s; samplesPerSecond = 53728.8
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53786.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54265.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0047s; samplesPerSecond = 53441.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53339.0
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 53832.9
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0047s; samplesPerSecond = 53350.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0047s; samplesPerSecond = 53430.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 53960.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0047s; samplesPerSecond = 53453.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 53972.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0047s; samplesPerSecond = 53636.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0047s; samplesPerSecond = 52854.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
05/03/2016 15:21:56: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalErrorPrediction = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.194983s
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0059s; samplesPerSecond = 42038.0
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50525.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0050s; samplesPerSecond = 50423.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0049s; samplesPerSecond = 50689.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalClassificationError = 0.57600000 * 250; time = 0.0050s; samplesPerSecond = 50261.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0049s; samplesPerSecond = 50576.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50566.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0049s; samplesPerSecond = 50515.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0049s; samplesPerSecond = 50730.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0049s; samplesPerSecond = 50751.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0050s; samplesPerSecond = 50393.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0048s; samplesPerSecond = 51899.5
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0047s; samplesPerSecond = 53498.8
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53694.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 53879.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53890.9
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0047s; samplesPerSecond = 53728.8
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53786.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54265.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalClassificationError = 0.43600000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalClassificationError = 0.55600000 * 250; time = 0.0047s; samplesPerSecond = 53441.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53339.0
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 53832.9
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0047s; samplesPerSecond = 53350.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0047s; samplesPerSecond = 53430.2
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 53960.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalClassificationError = 0.44400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0047s; samplesPerSecond = 53453.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 53972.4
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0047s; samplesPerSecond = 53636.6
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0047s; samplesPerSecond = 52854.1
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalClassificationError = 0.12000000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
05/03/2016 15:21:56: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalClassificationError = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.194983s
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
05/03/2016 15:21:56: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:56: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27919647 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0093s; samplesPerSecond = 26818.3
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24468611 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19639892 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30982.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16397861 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0080s; samplesPerSecond = 31222.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19745002 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19548896 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0081s; samplesPerSecond = 30871.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18230148 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30910.0
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17531255 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0080s; samplesPerSecond = 31059.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20166559 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19749058 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0081s; samplesPerSecond = 31055.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13463336 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0081s; samplesPerSecond = 30963.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19006259 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12234776 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0079s; samplesPerSecond = 31605.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16962922 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32649.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16091639 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32743.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18624030 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32748.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18465726 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18514518 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0077s; samplesPerSecond = 32620.0
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20127224 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13418547 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32701.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13995001 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15602538 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32907.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15448171 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32864.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14780067 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12361633 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0077s; samplesPerSecond = 32628.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14079766 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32632.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12624363 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18913222 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17952681 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32786.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18825452 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17517656 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14744161 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13888184 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32795.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14156678 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13990591 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0077s; samplesPerSecond = 32607.3
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15059729 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14720846 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32799.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13021243 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32912.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19704037 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0076s; samplesPerSecond = 33029.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15858146 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
05/03/2016 15:21:56: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16938752 * 10000; EvalErrorPrediction = 0.07430000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.313881s
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27919647 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0093s; samplesPerSecond = 26818.3
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24468611 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19639892 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30982.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16397861 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0080s; samplesPerSecond = 31222.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19745002 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19548896 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0081s; samplesPerSecond = 30871.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18230148 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30910.0
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17531255 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0080s; samplesPerSecond = 31059.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20166559 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19749058 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0081s; samplesPerSecond = 31055.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13463336 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0081s; samplesPerSecond = 30963.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19006259 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12234776 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0079s; samplesPerSecond = 31605.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16962922 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32649.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16091639 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32743.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18624030 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32748.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18465726 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18514518 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0077s; samplesPerSecond = 32620.0
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20127224 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13418547 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32701.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13995001 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15602538 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32907.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15448171 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32864.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14780067 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12361633 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0077s; samplesPerSecond = 32628.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14079766 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32632.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12624363 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18913222 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17952681 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32786.9
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18825452 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17517656 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14744161 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13888184 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32795.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14156678 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13990591 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0077s; samplesPerSecond = 32607.3
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15059729 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14720846 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32799.8
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13021243 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32912.1
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19704037 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0076s; samplesPerSecond = 33029.5
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15858146 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
05/03/2016 15:21:56: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16938752 * 10000; EvalClassificationError = 0.07430000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.313881s
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
05/03/2016 15:21:56: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:56: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18888809 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0078s; samplesPerSecond = 32129.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.14084978 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14561895 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32666.9
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13238169 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32752.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17465335 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32765.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17752616 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32821.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15030556 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0077s; samplesPerSecond = 32645.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17118019 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0077s; samplesPerSecond = 32611.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10379908 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0077s; samplesPerSecond = 32637.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20636150 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16606704 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0077s; samplesPerSecond = 32543.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14937580 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32446.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19161901 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32731.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13684752 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21095939 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32688.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13216461 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32769.7
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17341094 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0077s; samplesPerSecond = 32586.0
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16532641 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32868.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14614740 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12551177 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32705.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13419939 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17050096 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22579789 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18219666 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0078s; samplesPerSecond = 32220.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20347898 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22972656 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12621914 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32890.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15674728 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11517532 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0077s; samplesPerSecond = 32658.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14187870 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18496784 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32929.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15026403 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12862609 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32925.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17651362 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32778.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14975908 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0076s; samplesPerSecond = 32981.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11465866 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16513610 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14972374 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32977.2
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15995582 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17898927 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
05/03/2016 15:21:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16083773 * 10000; EvalErrorPrediction = 0.07760000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.307973s
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18888809 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0078s; samplesPerSecond = 32129.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.14084978 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14561895 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32666.9
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13238169 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32752.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17465335 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32765.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17752616 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32821.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15030556 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0077s; samplesPerSecond = 32645.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17118019 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0077s; samplesPerSecond = 32611.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10379908 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0077s; samplesPerSecond = 32637.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20636150 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16606704 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0077s; samplesPerSecond = 32543.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14937580 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32446.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19161901 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32731.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13684752 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21095939 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32688.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13216461 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32769.7
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17341094 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0077s; samplesPerSecond = 32586.0
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16532641 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32868.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14614740 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12551177 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32705.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13419939 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17050096 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22579789 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18219666 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0078s; samplesPerSecond = 32220.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20347898 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22972656 * 250; EvalClassificationError = 0.12000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12621914 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32890.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15674728 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11517532 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0077s; samplesPerSecond = 32658.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14187870 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18496784 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32929.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15026403 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12862609 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32925.1
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17651362 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32778.3
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14975908 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0076s; samplesPerSecond = 32981.5
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11465866 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16513610 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14972374 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32977.2
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15995582 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17898927 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
05/03/2016 15:21:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16083773 * 10000; EvalClassificationError = 0.07760000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.307973s
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
05/03/2016 15:21:56: CNTKCommandTrainEnd: Multigpu_Demo_Train
@ -624,7 +624,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -653,7 +653,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -677,7 +677,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0x1222268: {[InvStdOfFeatures Value[2]] }
0x1223258: {[W2 Value[2 x 50]] }
0x12a56c8: {[B0 Value[50 x 1]] }
@ -697,12 +697,12 @@ Memory Sharing Structure:
0x2adcc08: {[W0*features Value[50 x *1]] }
0x2add0a8: {[W0 Value[50 x 2]] }
0x2ae0518: {[W1 Value[50 x 50]] }
0x68bf228: {[EvalErrorPrediction Value[1]] }
0x68bf228: {[EvalClassificationError Value[1]] }
0x68bf388: {[CrossEntropyWithSoftmax Value[1]] }
0x68bf988: {[LogOfPrior Value[2]] }
0x68d0438: {[features Value[2 x *1]] }
05/03/2016 15:21:57: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12736577 * 603; perplexity = 1.13583240
05/03/2016 15:21:57: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12736577 * 603; perplexity = 1.13583240
05/03/2016 15:21:57: Action "test" complete.

272
Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/baseline.windows.cpu.txt
Просмотреть файл

@ -66,7 +66,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -167,7 +167,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -300,7 +300,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -368,7 +368,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -397,7 +397,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -421,14 +421,14 @@ Post-processing network complete.
05/03/2016 15:29:48: Evaluation criterion node(s):
05/03/2016 15:29:48: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:29:48: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
000000CDDFBEECA0: {[features Value[2 x *]] }
000000CDDFC7B170: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
000000CDDFC7B490: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
@ -438,7 +438,7 @@ Memory Sharing Structure:
000000CDDFC7B990: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
000000CDDFC7BC10: {[LogOfPrior Value[2]] }
000000CDDFC7BCB0: {[MVNormalizedFeatures Value[2 x *]] }
000000CDDFC7BD50: {[EvalErrorPrediction Value[1]] }
000000CDDFC7BD50: {[EvalClassificationError Value[1]] }
000000CDDFC7BDF0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
000000CDDFC7BF30: {[ScaledLogLikelihood Value[2 x 1 x *]] }
000000CDDFC7C070: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
@ -471,139 +471,139 @@ Memory Sharing Structure:
05/03/2016 15:29:48: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:48: Starting minibatch loop.
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0377s; samplesPerSecond = 6637.8
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0300s; samplesPerSecond = 8341.4
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0285s; samplesPerSecond = 8758.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0290s; samplesPerSecond = 8610.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0285s; samplesPerSecond = 8776.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0289s; samplesPerSecond = 8652.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0288s; samplesPerSecond = 8670.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0277s; samplesPerSecond = 9033.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0271s; samplesPerSecond = 9209.5
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0247s; samplesPerSecond = 10134.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0270s; samplesPerSecond = 9243.5
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0268s; samplesPerSecond = 9333.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9435.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0269s; samplesPerSecond = 9309.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0253s; samplesPerSecond = 9880.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0267s; samplesPerSecond = 9357.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0270s; samplesPerSecond = 9264.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0257s; samplesPerSecond = 9716.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0257s; samplesPerSecond = 9742.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0295s; samplesPerSecond = 8477.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0261s; samplesPerSecond = 9562.8
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0252s; samplesPerSecond = 9924.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0262s; samplesPerSecond = 9530.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9720.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0270s; samplesPerSecond = 9259.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0264s; samplesPerSecond = 9486.2
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0261s; samplesPerSecond = 9595.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0244s; samplesPerSecond = 10248.8
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0220s; samplesPerSecond = 11388.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0222s; samplesPerSecond = 11277.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalErrorPrediction = 0.37200000 * 250; time = 0.0245s; samplesPerSecond = 10192.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.0243s; samplesPerSecond = 10305.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalErrorPrediction = 0.36400000 * 250; time = 0.0236s; samplesPerSecond = 10606.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10578.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0232s; samplesPerSecond = 10762.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0235s; samplesPerSecond = 10623.8
05/03/2016 15:29:49: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalErrorPrediction = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.06166s
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0377s; samplesPerSecond = 6637.8
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0300s; samplesPerSecond = 8341.4
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0285s; samplesPerSecond = 8758.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalClassificationError = 0.56000000 * 250; time = 0.0290s; samplesPerSecond = 8610.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0285s; samplesPerSecond = 8776.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0289s; samplesPerSecond = 8652.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0288s; samplesPerSecond = 8670.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0277s; samplesPerSecond = 9033.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0271s; samplesPerSecond = 9209.5
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0247s; samplesPerSecond = 10134.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0270s; samplesPerSecond = 9243.5
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0268s; samplesPerSecond = 9333.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9435.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0269s; samplesPerSecond = 9309.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0253s; samplesPerSecond = 9880.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0267s; samplesPerSecond = 9357.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0270s; samplesPerSecond = 9264.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0257s; samplesPerSecond = 9716.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0257s; samplesPerSecond = 9742.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0295s; samplesPerSecond = 8477.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0261s; samplesPerSecond = 9562.8
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0252s; samplesPerSecond = 9924.6
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0262s; samplesPerSecond = 9530.7
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9720.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0270s; samplesPerSecond = 9259.9
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0264s; samplesPerSecond = 9486.2
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0261s; samplesPerSecond = 9595.1
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0244s; samplesPerSecond = 10248.8
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0220s; samplesPerSecond = 11388.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0222s; samplesPerSecond = 11277.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalClassificationError = 0.37200000 * 250; time = 0.0245s; samplesPerSecond = 10192.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalClassificationError = 0.44800000 * 250; time = 0.0243s; samplesPerSecond = 10305.0
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalClassificationError = 0.36400000 * 250; time = 0.0236s; samplesPerSecond = 10606.3
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10578.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0232s; samplesPerSecond = 10762.4
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0235s; samplesPerSecond = 10623.8
05/03/2016 15:29:49: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalClassificationError = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.06166s
05/03/2016 15:29:49: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
05/03/2016 15:29:49: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:49: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:29:49: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28780429 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0246s; samplesPerSecond = 10181.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.28222478 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0246s; samplesPerSecond = 10178.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23589864 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0255s; samplesPerSecond = 9796.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.21209458 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20285913 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10283.0
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21300948 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0252s; samplesPerSecond = 9928.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17835594 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0256s; samplesPerSecond = 9753.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18830077 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0257s; samplesPerSecond = 9740.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14198478 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0250s; samplesPerSecond = 10019.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15895022 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0237s; samplesPerSecond = 10566.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21062646 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10517.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16081948 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0223s; samplesPerSecond = 11186.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15635713 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10700.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13008516 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0239s; samplesPerSecond = 10453.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16625347 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10674.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15001793 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0245s; samplesPerSecond = 10223.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22343917 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0234s; samplesPerSecond = 10692.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18006735 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0245s; samplesPerSecond = 10194.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15361620 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0235s; samplesPerSecond = 10636.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17039588 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0246s; samplesPerSecond = 10177.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15516786 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0237s; samplesPerSecond = 10544.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15969617 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0225s; samplesPerSecond = 11102.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15939439 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10697.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15300194 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0233s; samplesPerSecond = 10729.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14902476 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0231s; samplesPerSecond = 10811.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15043256 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0231s; samplesPerSecond = 10823.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15531360 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0229s; samplesPerSecond = 10936.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17990796 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0248s; samplesPerSecond = 10088.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22925668 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0229s; samplesPerSecond = 10913.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16843626 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0234s; samplesPerSecond = 10682.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18045325 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0236s; samplesPerSecond = 10585.6
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13337526 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0221s; samplesPerSecond = 11308.6
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14332977 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0245s; samplesPerSecond = 10219.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18749446 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0242s; samplesPerSecond = 10326.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15505967 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0236s; samplesPerSecond = 10587.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19616616 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0228s; samplesPerSecond = 10980.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17305907 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10610.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15197365 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10033.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12102416 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0238s; samplesPerSecond = 10483.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15278496 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0235s; samplesPerSecond = 10646.9
05/03/2016 15:29:50: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17643784 * 10000; EvalErrorPrediction = 0.07560000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.957696s
05/03/2016 15:29:49: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28780429 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0246s; samplesPerSecond = 10181.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.28222478 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0246s; samplesPerSecond = 10178.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23589864 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0255s; samplesPerSecond = 9796.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.21209458 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20285913 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10283.0
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21300948 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0252s; samplesPerSecond = 9928.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17835594 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0256s; samplesPerSecond = 9753.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18830077 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0257s; samplesPerSecond = 9740.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14198478 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0250s; samplesPerSecond = 10019.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15895022 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0237s; samplesPerSecond = 10566.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21062646 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10517.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16081948 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0223s; samplesPerSecond = 11186.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15635713 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10700.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13008516 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0239s; samplesPerSecond = 10453.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16625347 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10674.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15001793 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0245s; samplesPerSecond = 10223.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22343917 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0234s; samplesPerSecond = 10692.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18006735 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0245s; samplesPerSecond = 10194.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15361620 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0235s; samplesPerSecond = 10636.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17039588 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0246s; samplesPerSecond = 10177.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15516786 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0237s; samplesPerSecond = 10544.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15969617 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0225s; samplesPerSecond = 11102.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15939439 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10697.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15300194 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0233s; samplesPerSecond = 10729.2
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14902476 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0231s; samplesPerSecond = 10811.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15043256 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0231s; samplesPerSecond = 10823.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15531360 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0229s; samplesPerSecond = 10936.1
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17990796 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0248s; samplesPerSecond = 10088.4
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22925668 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0229s; samplesPerSecond = 10913.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16843626 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0234s; samplesPerSecond = 10682.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18045325 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0236s; samplesPerSecond = 10585.6
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13337526 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0221s; samplesPerSecond = 11308.6
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14332977 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0245s; samplesPerSecond = 10219.9
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18749446 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0242s; samplesPerSecond = 10326.7
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15505967 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0236s; samplesPerSecond = 10587.8
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19616616 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0228s; samplesPerSecond = 10980.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17305907 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10610.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15197365 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10033.3
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12102416 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0238s; samplesPerSecond = 10483.5
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15278496 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0235s; samplesPerSecond = 10646.9
05/03/2016 15:29:50: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17643784 * 10000; EvalClassificationError = 0.07560000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.957696s
05/03/2016 15:29:50: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
05/03/2016 15:29:50: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:50: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10623312 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10637.4
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17519442 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0236s; samplesPerSecond = 10608.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14133983 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0240s; samplesPerSecond = 10404.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16278491 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0233s; samplesPerSecond = 10749.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11783558 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0232s; samplesPerSecond = 10780.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16342188 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0243s; samplesPerSecond = 10305.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16272195 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10476.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19401477 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10370.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20186661 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0229s; samplesPerSecond = 10903.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13672539 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10631.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20069212 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0234s; samplesPerSecond = 10681.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17729039 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9928.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15906107 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0251s; samplesPerSecond = 9941.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16281632 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0247s; samplesPerSecond = 10121.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19834981 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0248s; samplesPerSecond = 10067.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10217642 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0247s; samplesPerSecond = 10105.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17011383 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0258s; samplesPerSecond = 9692.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16599137 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9911.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12648996 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11920298 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0248s; samplesPerSecond = 10091.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12883164 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0272s; samplesPerSecond = 9205.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18222479 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0250s; samplesPerSecond = 9988.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13443351 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0246s; samplesPerSecond = 10149.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19720325 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0244s; samplesPerSecond = 10230.8
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15586137 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0254s; samplesPerSecond = 9860.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11854887 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0250s; samplesPerSecond = 9991.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13705285 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10050.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20009941 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0240s; samplesPerSecond = 10411.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19078680 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10741.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16505705 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10507.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12232722 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0239s; samplesPerSecond = 10472.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16342047 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0238s; samplesPerSecond = 10514.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15875107 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10688.3
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12248772 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0232s; samplesPerSecond = 10793.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13457009 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0238s; samplesPerSecond = 10521.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20976565 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0238s; samplesPerSecond = 10494.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519102 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10862.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14971420 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0247s; samplesPerSecond = 10106.3
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16456633 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10858.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16971407 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10473.0
05/03/2016 15:29:51: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15787325 * 10000; EvalErrorPrediction = 0.07430000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.972052s
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10623312 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10637.4
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17519442 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0236s; samplesPerSecond = 10608.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14133983 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0240s; samplesPerSecond = 10404.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16278491 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0233s; samplesPerSecond = 10749.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11783558 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0232s; samplesPerSecond = 10780.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16342188 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0243s; samplesPerSecond = 10305.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16272195 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10476.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19401477 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10370.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20186661 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0229s; samplesPerSecond = 10903.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13672539 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10631.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20069212 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0234s; samplesPerSecond = 10681.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17729039 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9928.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15906107 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0251s; samplesPerSecond = 9941.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16281632 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0247s; samplesPerSecond = 10121.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19834981 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0248s; samplesPerSecond = 10067.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10217642 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0247s; samplesPerSecond = 10105.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17011383 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0258s; samplesPerSecond = 9692.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16599137 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9911.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12648996 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11920298 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0248s; samplesPerSecond = 10091.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12883164 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0272s; samplesPerSecond = 9205.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18222479 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0250s; samplesPerSecond = 9988.0
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13443351 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0246s; samplesPerSecond = 10149.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19720325 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0244s; samplesPerSecond = 10230.8
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15586137 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0254s; samplesPerSecond = 9860.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11854887 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0250s; samplesPerSecond = 9991.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13705285 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10050.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20009941 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0240s; samplesPerSecond = 10411.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19078680 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10741.6
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16505705 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10507.7
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12232722 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0239s; samplesPerSecond = 10472.1
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16342047 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0238s; samplesPerSecond = 10514.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15875107 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10688.3
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12248772 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0232s; samplesPerSecond = 10793.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13457009 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0238s; samplesPerSecond = 10521.4
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20976565 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0238s; samplesPerSecond = 10494.9
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519102 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10862.5
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14971420 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0247s; samplesPerSecond = 10106.3
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16456633 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10858.2
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16971407 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10473.0
05/03/2016 15:29:51: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15787325 * 10000; EvalClassificationError = 0.07430000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.972052s
05/03/2016 15:29:51: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
05/03/2016 15:29:51: CNTKCommandTrainEnd: Multigpu_Demo_Train
@ -621,7 +621,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -650,7 +650,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -674,7 +674,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
000000CDDFC7B490: {[W0 Value[50 x 2]] }
000000CDDFC7B530: {[features Value[2 x *1]] }
000000CDDFC7B710: {[W1 Value[50 x 50]] }
@ -690,7 +690,7 @@ Memory Sharing Structure:
000000CDDFC8C2B0: {[W1*H1+B1 Value[50 x 1 x *1]] }
000000CDDFC8C490: {[CrossEntropyWithSoftmax Value[1]] }
000000CDDFC8C5D0: {[LogOfPrior Value[2]] }
000000CDDFC8C670: {[EvalErrorPrediction Value[1]] }
000000CDDFC8C670: {[EvalClassificationError Value[1]] }
000000CDDFC8C990: {[MVNormalizedFeatures Value[2 x *1]] }
000000CDDFC8CA30: {[H2 Value[50 x 1 x *1]] }
000000CDDFC8CC10: {[W1*H1 Value[50 x 1 x *1]] }
@ -699,7 +699,7 @@ Memory Sharing Structure:
000000CDDFC8D610: {[HLast Value[2 x 1 x *1]] }
000000CDDFC8D750: {[W0*features+B0 Value[50 x 1 x *1]] }
05/03/2016 15:29:52: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05306799 * 603; CrossEntropyWithSoftmax = 0.11782631 * 603; perplexity = 1.12504868
05/03/2016 15:29:52: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05306799 * 603; CrossEntropyWithSoftmax = 0.11782631 * 603; perplexity = 1.12504868
05/03/2016 15:29:52: Action "test" complete.

272
Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/baseline.windows.gpu.txt
Просмотреть файл

@ -66,7 +66,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -167,7 +167,7 @@ Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -300,7 +300,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -369,7 +369,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -398,7 +398,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -422,14 +422,14 @@ Post-processing network complete.
05/03/2016 15:29:53: Evaluation criterion node(s):
05/03/2016 15:29:53: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:29:53: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
000000572B66ECA0: {[features Value[2 x *]] }
00000057420A1700: {[W1 Value[50 x 50]] }
00000057420A1980: {[MeanOfFeatures Value[2]] }
@ -448,7 +448,7 @@ Memory Sharing Structure:
00000057439283E0: {[LogOfPrior Value[2]] }
00000057439285C0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
0000005743928660: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
00000057439287A0: {[EvalErrorPrediction Value[1]] }
00000057439287A0: {[EvalClassificationError Value[1]] }
0000005743928980: {[CrossEntropyWithSoftmax Value[1]] }
0000005743928A20: {[B2 Gradient[2 x 1]] }
0000005743928E80: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
@ -472,139 +472,139 @@ Memory Sharing Structure:
05/03/2016 15:29:54: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:54: Starting minibatch loop.
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21832.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0095s; samplesPerSecond = 26326.9
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0100s; samplesPerSecond = 25067.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0096s; samplesPerSecond = 26079.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0115s; samplesPerSecond = 21692.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0124s; samplesPerSecond = 20127.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0091s; samplesPerSecond = 27439.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0112s; samplesPerSecond = 22246.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0115s; samplesPerSecond = 21739.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0105s; samplesPerSecond = 23814.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0115s; samplesPerSecond = 21763.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0120s; samplesPerSecond = 20835.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0130s; samplesPerSecond = 19305.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0108s; samplesPerSecond = 23184.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0117s; samplesPerSecond = 21385.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0120s; samplesPerSecond = 20917.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0112s; samplesPerSecond = 22301.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0113s; samplesPerSecond = 22079.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21514.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0107s; samplesPerSecond = 23406.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21546.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0115s; samplesPerSecond = 21756.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0113s; samplesPerSecond = 22065.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0114s; samplesPerSecond = 22018.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0111s; samplesPerSecond = 22604.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0111s; samplesPerSecond = 22504.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0113s; samplesPerSecond = 22118.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalErrorPrediction = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19278.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19632.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0095s; samplesPerSecond = 26329.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0112s; samplesPerSecond = 22361.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0130s; samplesPerSecond = 19168.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0094s; samplesPerSecond = 26729.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalErrorPrediction = 0.40800000 * 250; time = 0.0112s; samplesPerSecond = 22365.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0128s; samplesPerSecond = 19583.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0092s; samplesPerSecond = 27144.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0114s; samplesPerSecond = 21864.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0116s; samplesPerSecond = 21475.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalErrorPrediction = 0.16000000 * 250; time = 0.0094s; samplesPerSecond = 26567.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalErrorPrediction = 0.19200000 * 250; time = 0.0132s; samplesPerSecond = 18988.3
05/03/2016 15:29:54: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.453807s
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21832.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0095s; samplesPerSecond = 26326.9
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0100s; samplesPerSecond = 25067.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalClassificationError = 0.56000000 * 250; time = 0.0096s; samplesPerSecond = 26079.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalClassificationError = 0.52800000 * 250; time = 0.0115s; samplesPerSecond = 21692.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0124s; samplesPerSecond = 20127.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0091s; samplesPerSecond = 27439.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0112s; samplesPerSecond = 22246.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0115s; samplesPerSecond = 21739.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0105s; samplesPerSecond = 23814.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0115s; samplesPerSecond = 21763.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0120s; samplesPerSecond = 20835.1
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0130s; samplesPerSecond = 19305.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0108s; samplesPerSecond = 23184.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0117s; samplesPerSecond = 21385.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0120s; samplesPerSecond = 20917.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0112s; samplesPerSecond = 22301.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0113s; samplesPerSecond = 22079.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21514.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0107s; samplesPerSecond = 23406.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21546.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0115s; samplesPerSecond = 21756.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0113s; samplesPerSecond = 22065.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0114s; samplesPerSecond = 22018.7
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0111s; samplesPerSecond = 22604.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0111s; samplesPerSecond = 22504.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0113s; samplesPerSecond = 22118.0
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalClassificationError = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19278.2
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19632.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0095s; samplesPerSecond = 26329.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0112s; samplesPerSecond = 22361.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0130s; samplesPerSecond = 19168.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0094s; samplesPerSecond = 26729.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalClassificationError = 0.40800000 * 250; time = 0.0112s; samplesPerSecond = 22365.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0128s; samplesPerSecond = 19583.3
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0092s; samplesPerSecond = 27144.4
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0114s; samplesPerSecond = 21864.6
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalClassificationError = 0.20400000 * 250; time = 0.0116s; samplesPerSecond = 21475.8
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalClassificationError = 0.16000000 * 250; time = 0.0094s; samplesPerSecond = 26567.5
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalClassificationError = 0.19200000 * 250; time = 0.0132s; samplesPerSecond = 18988.3
05/03/2016 15:29:54: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalClassificationError = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.453807s
05/03/2016 15:29:54: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
05/03/2016 15:29:54: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:54: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.45075654 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0250s; samplesPerSecond = 10002.4
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40775497 * 250; EvalErrorPrediction = 0.14400000 * 250; time = 0.0219s; samplesPerSecond = 11420.2
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.34165228 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0230s; samplesPerSecond = 10859.6
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29708900 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0198s; samplesPerSecond = 12604.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26669365 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0211s; samplesPerSecond = 11860.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25328680 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0212s; samplesPerSecond = 11817.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.21017820 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0237s; samplesPerSecond = 10540.1
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21483054 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11699.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16626513 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0213s; samplesPerSecond = 11757.5
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17672434 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0239s; samplesPerSecond = 10454.6
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22140190 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0208s; samplesPerSecond = 12033.1
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17048554 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0237s; samplesPerSecond = 10553.4
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16438517 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10662.3
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13782141 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0218s; samplesPerSecond = 11449.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16909663 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0244s; samplesPerSecond = 10228.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15419129 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0229s; samplesPerSecond = 10924.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22229924 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18134995 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15616904 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0236s; samplesPerSecond = 10594.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17162733 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0262s; samplesPerSecond = 9530.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15676289 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0262s; samplesPerSecond = 9554.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16159542 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0262s; samplesPerSecond = 9558.8
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16102246 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0284s; samplesPerSecond = 8800.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15392923 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0248s; samplesPerSecond = 10089.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14898334 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0269s; samplesPerSecond = 9279.5
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15087969 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0285s; samplesPerSecond = 8785.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15494578 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0247s; samplesPerSecond = 10101.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17878713 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0250s; samplesPerSecond = 9986.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22845049 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0249s; samplesPerSecond = 10045.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16884430 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10376.5
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17970282 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0237s; samplesPerSecond = 10533.9
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13292468 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0257s; samplesPerSecond = 9721.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14167778 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0226s; samplesPerSecond = 11048.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18716852 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0237s; samplesPerSecond = 10534.7
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15480385 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0258s; samplesPerSecond = 9705.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19482328 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10115.7
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17488171 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0249s; samplesPerSecond = 10048.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15164433 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0281s; samplesPerSecond = 8901.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12142463 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0222s; samplesPerSecond = 11279.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15287631 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10489.7
05/03/2016 15:29:55: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19475469 * 10000; EvalErrorPrediction = 0.07830000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.964496s
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.45075654 * 250; EvalClassificationError = 0.15200000 * 250; time = 0.0250s; samplesPerSecond = 10002.4
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40775497 * 250; EvalClassificationError = 0.14400000 * 250; time = 0.0219s; samplesPerSecond = 11420.2
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.34165228 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0230s; samplesPerSecond = 10859.6
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29708900 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0198s; samplesPerSecond = 12604.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26669365 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0211s; samplesPerSecond = 11860.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25328680 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0212s; samplesPerSecond = 11817.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.21017820 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0237s; samplesPerSecond = 10540.1
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21483054 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11699.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16626513 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0213s; samplesPerSecond = 11757.5
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17672434 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0239s; samplesPerSecond = 10454.6
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22140190 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0208s; samplesPerSecond = 12033.1
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17048554 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0237s; samplesPerSecond = 10553.4
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16438517 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10662.3
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13782141 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0218s; samplesPerSecond = 11449.0
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16909663 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0244s; samplesPerSecond = 10228.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15419129 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0229s; samplesPerSecond = 10924.7
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22229924 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18134995 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15616904 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0236s; samplesPerSecond = 10594.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17162733 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0262s; samplesPerSecond = 9530.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15676289 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0262s; samplesPerSecond = 9554.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16159542 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0262s; samplesPerSecond = 9558.8
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16102246 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0284s; samplesPerSecond = 8800.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15392923 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0248s; samplesPerSecond = 10089.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14898334 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0269s; samplesPerSecond = 9279.5
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15087969 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0285s; samplesPerSecond = 8785.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15494578 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0247s; samplesPerSecond = 10101.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17878713 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0250s; samplesPerSecond = 9986.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22845049 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0249s; samplesPerSecond = 10045.4
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16884430 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10376.5
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17970282 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0237s; samplesPerSecond = 10533.9
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13292468 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0257s; samplesPerSecond = 9721.6
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14167778 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0226s; samplesPerSecond = 11048.3
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18716852 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0237s; samplesPerSecond = 10534.7
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15480385 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0258s; samplesPerSecond = 9705.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19482328 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10115.7
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17488171 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0249s; samplesPerSecond = 10048.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15164433 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0281s; samplesPerSecond = 8901.2
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12142463 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0222s; samplesPerSecond = 11279.0
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15287631 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10489.7
05/03/2016 15:29:55: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19475469 * 10000; EvalClassificationError = 0.07830000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.964496s
05/03/2016 15:29:55: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
05/03/2016 15:29:55: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:29:55: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10717578 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0253s; samplesPerSecond = 9869.7
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17521929 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10701.1
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14088211 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0250s; samplesPerSecond = 9986.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16281337 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10287.6
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11778386 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0234s; samplesPerSecond = 10666.9
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16295400 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0266s; samplesPerSecond = 9385.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16287201 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0233s; samplesPerSecond = 10746.2
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19482140 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20113689 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0235s; samplesPerSecond = 10643.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13748570 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10484.4
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20080420 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10600.9
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17730590 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0268s; samplesPerSecond = 9342.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15851029 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0233s; samplesPerSecond = 10743.0
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16257260 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0250s; samplesPerSecond = 10012.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19772537 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0224s; samplesPerSecond = 11143.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10259204 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0235s; samplesPerSecond = 10626.1
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17093073 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0244s; samplesPerSecond = 10230.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16628544 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9936.8
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12690716 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0246s; samplesPerSecond = 10171.7
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11894288 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0233s; samplesPerSecond = 10718.1
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12815907 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0246s; samplesPerSecond = 10151.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18265773 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0225s; samplesPerSecond = 11131.9
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13388730 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0231s; samplesPerSecond = 10807.5
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19787903 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0251s; samplesPerSecond = 9951.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15563315 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0241s; samplesPerSecond = 10373.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11837055 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13732942 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10689.7
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20012115 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0253s; samplesPerSecond = 9872.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19086846 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10525.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16492589 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10272.8
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12141157 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10509.5
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16335481 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15923900 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0241s; samplesPerSecond = 10358.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12315803 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10617.1
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13481532 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0260s; samplesPerSecond = 9612.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20958008 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0223s; samplesPerSecond = 11232.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519713 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0255s; samplesPerSecond = 9814.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14990733 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0239s; samplesPerSecond = 10481.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16508552 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0255s; samplesPerSecond = 9789.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16941540 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0240s; samplesPerSecond = 10435.4
05/03/2016 15:29:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15791792 * 10000; EvalErrorPrediction = 0.07460000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.970059s
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10717578 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0253s; samplesPerSecond = 9869.7
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17521929 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10701.1
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14088211 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0250s; samplesPerSecond = 9986.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16281337 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10287.6
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11778386 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0234s; samplesPerSecond = 10666.9
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16295400 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0266s; samplesPerSecond = 9385.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16287201 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0233s; samplesPerSecond = 10746.2
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19482140 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20113689 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0235s; samplesPerSecond = 10643.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13748570 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10484.4
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20080420 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10600.9
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17730590 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0268s; samplesPerSecond = 9342.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15851029 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0233s; samplesPerSecond = 10743.0
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16257260 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0250s; samplesPerSecond = 10012.8
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19772537 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0224s; samplesPerSecond = 11143.3
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10259204 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0235s; samplesPerSecond = 10626.1
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17093073 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0244s; samplesPerSecond = 10230.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16628544 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9936.8
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12690716 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0246s; samplesPerSecond = 10171.7
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11894288 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0233s; samplesPerSecond = 10718.1
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12815907 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0246s; samplesPerSecond = 10151.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18265773 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0225s; samplesPerSecond = 11131.9
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13388730 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0231s; samplesPerSecond = 10807.5
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19787903 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0251s; samplesPerSecond = 9951.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15563315 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0241s; samplesPerSecond = 10373.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11837055 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13732942 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10689.7
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20012115 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0253s; samplesPerSecond = 9872.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19086846 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10525.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16492589 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10272.8
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12141157 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10509.5
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16335481 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15923900 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0241s; samplesPerSecond = 10358.0
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12315803 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10617.1
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13481532 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0260s; samplesPerSecond = 9612.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20958008 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0223s; samplesPerSecond = 11232.4
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519713 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0255s; samplesPerSecond = 9814.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14990733 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0239s; samplesPerSecond = 10481.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16508552 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0255s; samplesPerSecond = 9789.3
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16941540 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0240s; samplesPerSecond = 10435.4
05/03/2016 15:29:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15791792 * 10000; EvalClassificationError = 0.07460000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.970059s
05/03/2016 15:29:56: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
05/03/2016 15:29:56: CNTKCommandTrainEnd: Multigpu_Demo_Train
@ -622,7 +622,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -651,7 +651,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -675,7 +675,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0000005743925BB0: {[HLast Value[2 x 1 x *1]] }
0000005743925D90: {[MVNormalizedFeatures Value[2 x *1]] }
0000005743925E30: {[CrossEntropyWithSoftmax Value[1]] }
@ -688,7 +688,7 @@ Memory Sharing Structure:
00000057439265B0: {[W0*features+B0 Value[50 x 1 x *1]] }
0000005743926650: {[W1*H1 Value[50 x 1 x *1]] }
0000005743926970: {[H2 Value[50 x 1 x *1]] }
0000005743926AB0: {[EvalErrorPrediction Value[1]] }
0000005743926AB0: {[EvalClassificationError Value[1]] }
000000574B7FAD10: {[W0 Value[50 x 2]] }
000000574B7FB170: {[InvStdOfFeatures Value[2]] }
000000574B7FB210: {[MeanOfFeatures Value[2]] }
@ -700,7 +700,7 @@ Memory Sharing Structure:
000000574D960E50: {[B2 Value[2 x 1]] }
000000574D9610D0: {[B0 Value[50 x 1]] }
05/03/2016 15:29:56: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12022919 * 603; perplexity = 1.12775529
05/03/2016 15:29:56: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12022919 * 603; perplexity = 1.12775529
05/03/2016 15:29:56: Action "test" complete.

6
Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/testcases.yml
Просмотреть файл

@ -21,7 +21,7 @@ testCases:
patterns:
- Finished Epoch[{{integer}} of {{integer}}]
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}
- totalSamplesSeen = {{integer}}
- learningRatePerSample = {{float,tolerance=0.1%}}
@ -29,10 +29,10 @@ testCases:
patterns:
- Epoch[{{integer}} of {{integer}}]-Minibatch[{{integer}}-{{integer}}
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}
Final test results must match:
patterns:
- "Final Results: Minibatch[{{integer}}-{{integer}}]"
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}

278
Tests/EndToEndTests/Examples/Other/Simple2d/Simple/baseline.linux.cpu.txt
Просмотреть файл

@ -58,7 +58,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -157,7 +157,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -300,7 +300,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -355,7 +355,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -384,7 +384,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -408,14 +408,14 @@ Post-processing network complete.
05/03/2016 15:21:15: Evaluation criterion node(s):
05/03/2016 15:21:15: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:21:15: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
(nil): {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0x2e7f338: {[features Value[2 x *]] }
0x2e82908: {[MeanOfFeatures Value[2]] }
0x2e84f08: {[InvStdOfFeatures Value[2]] }
@ -427,7 +427,7 @@ Memory Sharing Structure:
0x2e8b718: {[B2 Value[2 x 1]] }
0x2e8c1e8: {[labels Value[2 x *]] }
0x2e8cf38: {[Prior Value[2]] }
0x2e926f8: {[EvalErrorPrediction Value[1]] }
0x2e926f8: {[EvalClassificationError Value[1]] }
0x2e92858: {[ScaledLogLikelihood Value[2 x 1 x *]] }
0x2e929b8: {[CrossEntropyWithSoftmax Value[1]] }
0x2e93218: {[LogOfPrior Value[2]] }
@ -458,139 +458,139 @@ Memory Sharing Structure:
05/03/2016 15:21:17: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:17: Starting minibatch loop.
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0806s; samplesPerSecond = 3103.4
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0489s; samplesPerSecond = 5107.5
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0598s; samplesPerSecond = 4180.0
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0581s; samplesPerSecond = 4306.3
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0618s; samplesPerSecond = 4045.4
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0579s; samplesPerSecond = 4314.7
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.2699s; samplesPerSecond = 926.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0620s; samplesPerSecond = 4035.0
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0826s; samplesPerSecond = 3027.2
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0811s; samplesPerSecond = 3082.2
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0895s; samplesPerSecond = 2793.1
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0482s; samplesPerSecond = 5187.9
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0567s; samplesPerSecond = 4408.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalErrorPrediction = 0.54000000 * 250; time = 0.0586s; samplesPerSecond = 4266.7
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0546s; samplesPerSecond = 4575.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0640s; samplesPerSecond = 3907.4
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0756s; samplesPerSecond = 3307.9
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0598s; samplesPerSecond = 4178.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0813s; samplesPerSecond = 3075.3
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0811s; samplesPerSecond = 3082.9
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0673s; samplesPerSecond = 3717.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0819s; samplesPerSecond = 3052.5
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0909s; samplesPerSecond = 2749.3
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0752s; samplesPerSecond = 3323.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0734s; samplesPerSecond = 3406.2
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0869s; samplesPerSecond = 2875.4
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0893s; samplesPerSecond = 2798.7
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0814s; samplesPerSecond = 3072.2
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0812s; samplesPerSecond = 3077.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0895s; samplesPerSecond = 2792.1
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0831s; samplesPerSecond = 3008.7
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0818s; samplesPerSecond = 3056.5
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.2681s; samplesPerSecond = 932.5
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0513s; samplesPerSecond = 4869.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalErrorPrediction = 0.30400000 * 250; time = 0.0680s; samplesPerSecond = 3678.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalErrorPrediction = 0.27200000 * 250; time = 0.0758s; samplesPerSecond = 3296.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0664s; samplesPerSecond = 3763.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0505s; samplesPerSecond = 4955.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0515s; samplesPerSecond = 4855.7
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0517s; samplesPerSecond = 4834.4
05/03/2016 15:21:20: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalErrorPrediction = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.21314s
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0806s; samplesPerSecond = 3103.4
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0489s; samplesPerSecond = 5107.5
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0598s; samplesPerSecond = 4180.0
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0581s; samplesPerSecond = 4306.3
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalClassificationError = 0.57600000 * 250; time = 0.0618s; samplesPerSecond = 4045.4
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0579s; samplesPerSecond = 4314.7
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.2699s; samplesPerSecond = 926.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0620s; samplesPerSecond = 4035.0
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0826s; samplesPerSecond = 3027.2
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0811s; samplesPerSecond = 3082.2
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0895s; samplesPerSecond = 2793.1
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0482s; samplesPerSecond = 5187.9
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0567s; samplesPerSecond = 4408.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalClassificationError = 0.54000000 * 250; time = 0.0586s; samplesPerSecond = 4266.7
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0546s; samplesPerSecond = 4575.3
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0640s; samplesPerSecond = 3907.4
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0756s; samplesPerSecond = 3307.9
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0598s; samplesPerSecond = 4178.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0813s; samplesPerSecond = 3075.3
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0811s; samplesPerSecond = 3082.9
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0673s; samplesPerSecond = 3717.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalClassificationError = 0.48400000 * 250; time = 0.0819s; samplesPerSecond = 3052.5
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0909s; samplesPerSecond = 2749.3
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0752s; samplesPerSecond = 3323.1
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalClassificationError = 0.43600000 * 250; time = 0.0734s; samplesPerSecond = 3406.2
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0869s; samplesPerSecond = 2875.4
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0893s; samplesPerSecond = 2798.7
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalClassificationError = 0.55600000 * 250; time = 0.0814s; samplesPerSecond = 3072.2
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0812s; samplesPerSecond = 3077.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0895s; samplesPerSecond = 2792.1
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0831s; samplesPerSecond = 3008.7
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0818s; samplesPerSecond = 3056.5
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalClassificationError = 0.44800000 * 250; time = 0.2681s; samplesPerSecond = 932.5
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalClassificationError = 0.49200000 * 250; time = 0.0513s; samplesPerSecond = 4869.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalClassificationError = 0.30400000 * 250; time = 0.0680s; samplesPerSecond = 3678.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalClassificationError = 0.27200000 * 250; time = 0.0758s; samplesPerSecond = 3296.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0664s; samplesPerSecond = 3763.4
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0505s; samplesPerSecond = 4955.3
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0515s; samplesPerSecond = 4855.7
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0517s; samplesPerSecond = 4834.4
05/03/2016 15:21:20: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalClassificationError = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.21314s
05/03/2016 15:21:20: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.1'
05/03/2016 15:21:20: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:20: Starting minibatch loop.
05/03/2016 15:21:20: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20732678 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0782s; samplesPerSecond = 3196.0
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19684015 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0812s; samplesPerSecond = 3079.4
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16083588 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0796s; samplesPerSecond = 3141.3
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13558752 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0811s; samplesPerSecond = 3083.5
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17992950 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0814s; samplesPerSecond = 3070.9
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17858063 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0812s; samplesPerSecond = 3079.3
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16847546 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0688s; samplesPerSecond = 3631.6
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16359399 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0547s; samplesPerSecond = 4572.7
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19534705 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0521s; samplesPerSecond = 4796.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19363660 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0758s; samplesPerSecond = 3297.5
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12703638 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0682s; samplesPerSecond = 3667.7
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18622827 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0576s; samplesPerSecond = 4344.0
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11595044 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0599s; samplesPerSecond = 4171.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16689380 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0650s; samplesPerSecond = 3845.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15822559 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0631s; samplesPerSecond = 3964.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18381909 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0638s; samplesPerSecond = 3920.5
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18274048 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3893.2
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18638428 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0564s; samplesPerSecond = 4431.5
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20111572 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0528s; samplesPerSecond = 4733.8
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13185034 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0504s; samplesPerSecond = 4962.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13692554 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0559s; samplesPerSecond = 4468.8
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15396802 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0672s; samplesPerSecond = 3719.4
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15347241 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0818s; samplesPerSecond = 3057.6
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14583887 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.2662s; samplesPerSecond = 939.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12333276 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0738s; samplesPerSecond = 3389.0
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13958154 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0778s; samplesPerSecond = 3211.3
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12539844 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0772s; samplesPerSecond = 3239.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.19014404 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0475s; samplesPerSecond = 5259.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17959521 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0780s; samplesPerSecond = 3206.4
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18899121 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0469s; samplesPerSecond = 5333.6
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17525586 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0625s; samplesPerSecond = 4003.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14735645 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0940s; samplesPerSecond = 2658.9
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13705518 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0543s; samplesPerSecond = 4600.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13610693 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0752s; samplesPerSecond = 3324.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13555811 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0583s; samplesPerSecond = 4291.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14883594 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0598s; samplesPerSecond = 4180.7
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14724707 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0599s; samplesPerSecond = 4172.4
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13130469 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0664s; samplesPerSecond = 3764.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19636084 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0644s; samplesPerSecond = 3884.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15681836 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0651s; samplesPerSecond = 3841.0
05/03/2016 15:21:23: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16173864 * 10000; EvalErrorPrediction = 0.07520000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=2.87283s
05/03/2016 15:21:20: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20732678 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0782s; samplesPerSecond = 3196.0
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19684015 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0812s; samplesPerSecond = 3079.4
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16083588 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0796s; samplesPerSecond = 3141.3
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13558752 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0811s; samplesPerSecond = 3083.5
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17992950 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0814s; samplesPerSecond = 3070.9
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17858063 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0812s; samplesPerSecond = 3079.3
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16847546 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0688s; samplesPerSecond = 3631.6
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16359399 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0547s; samplesPerSecond = 4572.7
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19534705 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0521s; samplesPerSecond = 4796.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19363660 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0758s; samplesPerSecond = 3297.5
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12703638 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0682s; samplesPerSecond = 3667.7
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18622827 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0576s; samplesPerSecond = 4344.0
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11595044 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0599s; samplesPerSecond = 4171.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16689380 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0650s; samplesPerSecond = 3845.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15822559 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0631s; samplesPerSecond = 3964.2
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18381909 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0638s; samplesPerSecond = 3920.5
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18274048 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3893.2
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18638428 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0564s; samplesPerSecond = 4431.5
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20111572 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0528s; samplesPerSecond = 4733.8
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13185034 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0504s; samplesPerSecond = 4962.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13692554 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0559s; samplesPerSecond = 4468.8
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15396802 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0672s; samplesPerSecond = 3719.4
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15347241 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0818s; samplesPerSecond = 3057.6
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14583887 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.2662s; samplesPerSecond = 939.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12333276 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0738s; samplesPerSecond = 3389.0
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13958154 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0778s; samplesPerSecond = 3211.3
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12539844 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0772s; samplesPerSecond = 3239.1
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.19014404 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0475s; samplesPerSecond = 5259.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17959521 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0780s; samplesPerSecond = 3206.4
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18899121 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0469s; samplesPerSecond = 5333.6
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17525586 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0625s; samplesPerSecond = 4003.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14735645 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0940s; samplesPerSecond = 2658.9
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13705518 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0543s; samplesPerSecond = 4600.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13610693 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0752s; samplesPerSecond = 3324.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13555811 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0583s; samplesPerSecond = 4291.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14883594 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0598s; samplesPerSecond = 4180.7
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14724707 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0599s; samplesPerSecond = 4172.4
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13130469 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0664s; samplesPerSecond = 3764.2
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19636084 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0644s; samplesPerSecond = 3884.1
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15681836 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0651s; samplesPerSecond = 3841.0
05/03/2016 15:21:23: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16173864 * 10000; EvalClassificationError = 0.07520000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=2.87283s
05/03/2016 15:21:23: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.2'
05/03/2016 15:21:23: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:23: Starting minibatch loop.
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18214960 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0604s; samplesPerSecond = 4138.7
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13526825 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0622s; samplesPerSecond = 4020.6
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14344995 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0640s; samplesPerSecond = 3906.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12557471 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0628s; samplesPerSecond = 3978.7
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17627924 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0639s; samplesPerSecond = 3914.6
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17585291 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0644s; samplesPerSecond = 3884.2
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14716791 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0628s; samplesPerSecond = 3979.1
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16757751 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0643s; samplesPerSecond = 3885.5
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10314917 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0642s; samplesPerSecond = 3895.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20322217 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0650s; samplesPerSecond = 3848.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16604797 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3892.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15105725 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0651s; samplesPerSecond = 3839.4
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19206934 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0640s; samplesPerSecond = 3903.9
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13667065 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.2688s; samplesPerSecond = 930.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20713037 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0472s; samplesPerSecond = 5299.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12862158 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0625s; samplesPerSecond = 3998.5
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17174683 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0465s; samplesPerSecond = 5381.7
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16493628 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0526s; samplesPerSecond = 4753.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14843726 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0505s; samplesPerSecond = 4952.5
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12574292 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0505s; samplesPerSecond = 4951.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13455151 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0614s; samplesPerSecond = 4072.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16762988 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0495s; samplesPerSecond = 5055.0
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22347461 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0523s; samplesPerSecond = 4780.1
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18213623 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0542s; samplesPerSecond = 4611.6
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19970923 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0539s; samplesPerSecond = 4638.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22695947 * 250; EvalErrorPrediction = 0.12800000 * 250; time = 0.0542s; samplesPerSecond = 4609.7
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12664502 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0541s; samplesPerSecond = 4625.3
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15838037 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0538s; samplesPerSecond = 4648.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11555566 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0581s; samplesPerSecond = 4305.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14157520 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0544s; samplesPerSecond = 4595.2
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18558350 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0541s; samplesPerSecond = 4622.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15083594 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0540s; samplesPerSecond = 4632.9
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12831787 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0541s; samplesPerSecond = 4624.1
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17656494 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0545s; samplesPerSecond = 4587.6
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14956396 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0625s; samplesPerSecond = 4000.3
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11451660 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0496s; samplesPerSecond = 5040.3
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16392383 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0496s; samplesPerSecond = 5036.0
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811230 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0505s; samplesPerSecond = 4955.0
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16003760 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0588s; samplesPerSecond = 4255.2
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17969775 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0482s; samplesPerSecond = 5185.4
05/03/2016 15:21:26: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15964808 * 10000; EvalErrorPrediction = 0.07750000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=2.49695s
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18214960 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0604s; samplesPerSecond = 4138.7
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13526825 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0622s; samplesPerSecond = 4020.6
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14344995 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0640s; samplesPerSecond = 3906.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12557471 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0628s; samplesPerSecond = 3978.7
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17627924 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0639s; samplesPerSecond = 3914.6
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17585291 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0644s; samplesPerSecond = 3884.2
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14716791 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0628s; samplesPerSecond = 3979.1
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16757751 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0643s; samplesPerSecond = 3885.5
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10314917 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0642s; samplesPerSecond = 3895.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20322217 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0650s; samplesPerSecond = 3848.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16604797 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3892.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15105725 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0651s; samplesPerSecond = 3839.4
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19206934 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0640s; samplesPerSecond = 3903.9
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13667065 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.2688s; samplesPerSecond = 930.0
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20713037 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0472s; samplesPerSecond = 5299.3
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12862158 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0625s; samplesPerSecond = 3998.5
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17174683 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0465s; samplesPerSecond = 5381.7
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16493628 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0526s; samplesPerSecond = 4753.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14843726 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0505s; samplesPerSecond = 4952.5
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12574292 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0505s; samplesPerSecond = 4951.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13455151 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0614s; samplesPerSecond = 4072.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16762988 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0495s; samplesPerSecond = 5055.0
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22347461 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0523s; samplesPerSecond = 4780.1
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18213623 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0542s; samplesPerSecond = 4611.6
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19970923 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0539s; samplesPerSecond = 4638.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22695947 * 250; EvalClassificationError = 0.12800000 * 250; time = 0.0542s; samplesPerSecond = 4609.7
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12664502 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0541s; samplesPerSecond = 4625.3
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15838037 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0538s; samplesPerSecond = 4648.8
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11555566 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0581s; samplesPerSecond = 4305.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14157520 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0544s; samplesPerSecond = 4595.2
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18558350 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0541s; samplesPerSecond = 4622.4
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15083594 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0540s; samplesPerSecond = 4632.9
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12831787 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0541s; samplesPerSecond = 4624.1
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17656494 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0545s; samplesPerSecond = 4587.6
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14956396 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0625s; samplesPerSecond = 4000.3
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11451660 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0496s; samplesPerSecond = 5040.3
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16392383 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0496s; samplesPerSecond = 5036.0
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811230 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0505s; samplesPerSecond = 4955.0
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16003760 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0588s; samplesPerSecond = 4255.2
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17969775 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0482s; samplesPerSecond = 5185.4
05/03/2016 15:21:26: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15964808 * 10000; EvalClassificationError = 0.07750000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=2.49695s
05/03/2016 15:21:26: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn'
05/03/2016 15:21:26: CNTKCommandTrainEnd: Simple_Demo_Train
@ -608,7 +608,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -637,7 +637,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -661,7 +661,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0x2e83eb8: {[W2 Value[2 x 50]] }
0x2e87ac8: {[MVNormalizedFeatures Value[2 x *1]] }
0x2e87e78: {[W0*features Value[50 x *1]] }
@ -676,7 +676,7 @@ Memory Sharing Structure:
0x2e8d298: {[B2 Value[2 x 1]] }
0x2e8f2c8: {[labels Value[2 x *1]] }
0x2e8f8e8: {[MeanOfFeatures Value[2]] }
0x2e91598: {[EvalErrorPrediction Value[1]] }
0x2e91598: {[EvalClassificationError Value[1]] }
0x2e916f8: {[CrossEntropyWithSoftmax Value[1]] }
0x2e91bb8: {[LogOfPrior Value[2]] }
0x2e93758: {[B0 Value[50 x 1]] }
@ -686,7 +686,7 @@ Memory Sharing Structure:
0x2e985f8: {[W1 Value[50 x 50]] }
0x2e99178: {[features Value[2 x *1]] }
05/03/2016 15:21:26: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13085309 * 603; perplexity = 1.13980032
05/03/2016 15:21:26: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13085309 * 603; perplexity = 1.13980032
05/03/2016 15:21:26: Action "test" complete.
@ -702,7 +702,7 @@ Post-processing network...
8 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -732,7 +732,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *2] -> [2 x 1 x *2]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *2], [2 x 1] -> [2 x 1 x *2]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *2] -> [2 x 1 x *2]
Validating --> Prior = Mean (labels) : [2 x *2] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -755,7 +755,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalClassificationError Gradient[1]] [EvalClassificationError Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
0x2e82858: {[PosteriorProb Value[2 x 1 x *2]] }
0x2e83b58: {[labels Value[2 x *2]] }
0x2e84318: {[MeanOfFeatures Value[2]] }

278
Tests/EndToEndTests/Examples/Other/Simple2d/Simple/baseline.linux.gpu.txt
Просмотреть файл

@ -58,7 +58,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -157,7 +157,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -300,7 +300,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -356,7 +356,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -385,7 +385,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -409,14 +409,14 @@ Post-processing network complete.
05/03/2016 15:21:27: Evaluation criterion node(s):
05/03/2016 15:21:27: EvalErrorPrediction = ErrorPrediction
05/03/2016 15:21:27: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
(nil): {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0x1ef9338: {[features Value[2 x *]] }
0x2b32ad8: {[MeanOfFeatures Value[2]] }
0x2b32fe8: {[InvStdOfFeatures Value[2]] }
@ -429,7 +429,7 @@ Memory Sharing Structure:
0x3185898: {[Prior Value[2]] }
0x3186bd8: {[LogOfPrior Value[2]] }
0x318b378: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
0x318b498: {[EvalErrorPrediction Value[1]] }
0x318b498: {[EvalClassificationError Value[1]] }
0x318b798: {[ScaledLogLikelihood Value[2 x 1 x *]] }
0x318b8f8: {[CrossEntropyWithSoftmax Value[1]] }
0x3191148: {[B0 Value[50 x 1]] }
@ -459,139 +459,139 @@ Memory Sharing Structure:
05/03/2016 15:21:28: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:28: Starting minibatch loop.
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0055s; samplesPerSecond = 45495.9
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54241.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54549.4
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0046s; samplesPerSecond = 54136.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0046s; samplesPerSecond = 54359.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54194.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0046s; samplesPerSecond = 54501.9
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54300.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54644.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54716.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54336.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0046s; samplesPerSecond = 54288.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54218.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54454.4
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54609.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalErrorPrediction = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.1879s
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0055s; samplesPerSecond = 45495.9
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54241.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54549.4
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalClassificationError = 0.57600000 * 250; time = 0.0046s; samplesPerSecond = 54136.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0046s; samplesPerSecond = 54359.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54194.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0046s; samplesPerSecond = 54501.9
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54300.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalClassificationError = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54644.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54716.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54336.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalClassificationError = 0.43600000 * 250; time = 0.0046s; samplesPerSecond = 54288.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalClassificationError = 0.55600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54218.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54454.4
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalClassificationError = 0.44400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54609.0
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalClassificationError = 0.12000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalClassificationError = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.1879s
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.1'
05/03/2016 15:21:28: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:28: Starting minibatch loop.
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27895840 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 53902.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24395615 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19587115 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16368213 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 55126.8
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19700140 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19580530 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54585.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18257983 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17520911 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20164514 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19787024 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13437573 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0045s; samplesPerSecond = 55090.3
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19004956 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12287280 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16975903 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55175.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16102686 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54513.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18611646 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18469507 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55334.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18472339 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20064648 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54597.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13324683 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13878418 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15587354 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15337378 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54812.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14797070 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55199.8
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12512891 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0046s; samplesPerSecond = 54383.3
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14058545 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12611963 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18970605 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17965479 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18866455 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17539941 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14742432 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13789502 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13652100 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13619336 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14909424 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54478.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14762256 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55139.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13142578 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54860.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19570459 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15718604 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55005.5
05/03/2016 15:21:28: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16901047 * 10000; EvalErrorPrediction = 0.07510000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.184798s
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27895840 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 53902.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24395615 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19587115 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16368213 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 55126.8
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19700140 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19580530 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54585.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18257983 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17520911 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20164514 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19787024 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13437573 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0045s; samplesPerSecond = 55090.3
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19004956 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12287280 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16975903 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55175.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16102686 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54513.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18611646 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18469507 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55334.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18472339 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20064648 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54597.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13324683 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13878418 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15587354 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15337378 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54812.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14797070 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55199.8
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12512891 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0046s; samplesPerSecond = 54383.3
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14058545 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12611963 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18970605 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17965479 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18866455 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17539941 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14742432 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13789502 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13652100 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13619336 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14909424 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54478.1
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14762256 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55139.0
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13142578 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54860.7
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19570459 * 250; EvalClassificationError = 0.11600000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15718604 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55005.5
05/03/2016 15:21:28: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16901047 * 10000; EvalClassificationError = 0.07510000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.184798s
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.2'
05/03/2016 15:21:28: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 15:21:28: Starting minibatch loop.
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18133401 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13605756 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14345651 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54668.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12512610 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17690991 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17504150 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14723834 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16752893 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10317773 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20306372 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16637036 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55066.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15126868 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19167224 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13687085 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55420.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20709912 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12918774 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17185107 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55322.0
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16523242 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14880249 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54728.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12590967 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13443018 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16726147 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22407422 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55041.8
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18191553 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19983057 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22728223 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12720459 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15842871 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11558691 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14163428 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18560596 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15099561 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12822461 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54395.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17662500 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 55309.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14950781 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11450977 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16386768 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0045s; samplesPerSecond = 55260.8
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811523 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16021143 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17989551 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
05/03/2016 15:21:28: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15971016 * 10000; EvalErrorPrediction = 0.07740000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.184406s
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18133401 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13605756 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14345651 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54668.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12512610 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17690991 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17504150 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14723834 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16752893 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10317773 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20306372 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16637036 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55066.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15126868 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19167224 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13687085 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55420.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20709912 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12918774 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17185107 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55322.0
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16523242 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14880249 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54728.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12590967 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13443018 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16726147 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22407422 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55041.8
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18191553 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19983057 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22728223 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12720459 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15842871 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11558691 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14163428 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18560596 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15099561 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12822461 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54395.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17662500 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 55309.7
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14950781 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54945.1
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11450977 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16386768 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0045s; samplesPerSecond = 55260.8
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811523 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16021143 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17989551 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
05/03/2016 15:21:28: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15971016 * 10000; EvalClassificationError = 0.07740000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.184406s
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn'
05/03/2016 15:21:29: CNTKCommandTrainEnd: Simple_Demo_Train
@ -609,7 +609,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -638,7 +638,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -662,11 +662,11 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0x1efcc08: {[B2 Value[2 x 1]] }
0x1efd8c8: {[W0 Value[50 x 2]] }
0x1efee68: {[InvStdOfFeatures Value[2]] }
0x2b337e8: {[EvalErrorPrediction Value[1]] }
0x2b337e8: {[EvalClassificationError Value[1]] }
0x2b33948: {[CrossEntropyWithSoftmax Value[1]] }
0x2b33f08: {[LogOfPrior Value[2]] }
0x31808e8: {[W2 Value[2 x 50]] }
@ -687,7 +687,7 @@ Memory Sharing Structure:
0x7273058: {[W2*H1 Value[2 x 1 x *1]] }
0x7273218: {[HLast Value[2 x 1 x *1]] }
05/03/2016 15:21:29: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13093129 * 603; perplexity = 1.13988946
05/03/2016 15:21:29: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13093129 * 603; perplexity = 1.13988946
05/03/2016 15:21:29: Action "test" complete.
@ -703,7 +703,7 @@ Post-processing network...
8 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -733,7 +733,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *2] -> [2 x 1 x *2]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *2], [2 x 1] -> [2 x 1 x *2]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *2] -> [2 x 1 x *2]
Validating --> Prior = Mean (labels) : [2 x *2] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -756,7 +756,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalClassificationError Gradient[1]] [EvalClassificationError Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
0x1efcef8: {[features Value[2 x *2]] }
0x1efe2c8: {[labels Value[2 x *2]] }
0x1eff188: {[PosteriorProb Value[2 x 1 x *2]] }

278
Tests/EndToEndTests/Examples/Other/Simple2d/Simple/baseline.windows.cpu.txt
Просмотреть файл

@ -56,7 +56,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -155,7 +155,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -298,7 +298,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -353,7 +353,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -382,7 +382,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -406,14 +406,14 @@ Post-processing network complete.
05/03/2016 13:12:46: Evaluation criterion node(s):
05/03/2016 13:12:46: EvalErrorPrediction = ErrorPrediction
05/03/2016 13:12:46: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
000000702B410E90: {[features Value[2 x *]] }
000000702B44E0C0: {[W0 Value[50 x 2]] }
000000702B4D76F0: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
@ -428,7 +428,7 @@ Memory Sharing Structure:
000000702B4D8690: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
000000702B4D8730: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
000000702B4D89B0: {[CrossEntropyWithSoftmax Value[1]] }
000000702B4D8AF0: {[EvalErrorPrediction Value[1]] }
000000702B4D8AF0: {[EvalClassificationError Value[1]] }
000000702B4D8B90: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
000000702B4D8F50: {[B2 Gradient[2 x 1]] }
000000702B4D91D0: {[ScaledLogLikelihood Value[2 x 1 x *]] }
@ -456,139 +456,139 @@ Memory Sharing Structure:
05/03/2016 13:12:47: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:12:47: Starting minibatch loop.
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0327s; samplesPerSecond = 7657.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9726.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0248s; samplesPerSecond = 10096.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0245s; samplesPerSecond = 10210.3
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0249s; samplesPerSecond = 10032.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0248s; samplesPerSecond = 10065.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0256s; samplesPerSecond = 9766.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0259s; samplesPerSecond = 9662.6
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0239s; samplesPerSecond = 10469.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0255s; samplesPerSecond = 9802.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0248s; samplesPerSecond = 10100.6
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0255s; samplesPerSecond = 9808.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0261s; samplesPerSecond = 9593.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0255s; samplesPerSecond = 9807.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0249s; samplesPerSecond = 10049.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0247s; samplesPerSecond = 10117.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0254s; samplesPerSecond = 9834.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0243s; samplesPerSecond = 10275.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0255s; samplesPerSecond = 9802.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0246s; samplesPerSecond = 10146.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0243s; samplesPerSecond = 10286.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0252s; samplesPerSecond = 9909.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0244s; samplesPerSecond = 10227.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0244s; samplesPerSecond = 10243.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0248s; samplesPerSecond = 10081.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0254s; samplesPerSecond = 9844.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0258s; samplesPerSecond = 9679.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0255s; samplesPerSecond = 9798.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0260s; samplesPerSecond = 9609.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9431.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9722.7
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0259s; samplesPerSecond = 9647.3
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalErrorPrediction = 0.37200000 * 250; time = 0.0267s; samplesPerSecond = 9364.7
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0258s; samplesPerSecond = 9700.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.0260s; samplesPerSecond = 9608.0
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalErrorPrediction = 0.36400000 * 250; time = 0.0263s; samplesPerSecond = 9515.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0229s; samplesPerSecond = 10907.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0239s; samplesPerSecond = 10479.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0229s; samplesPerSecond = 10917.0
05/03/2016 13:12:48: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalErrorPrediction = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.01718s
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0327s; samplesPerSecond = 7657.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9726.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0248s; samplesPerSecond = 10096.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalClassificationError = 0.56000000 * 250; time = 0.0245s; samplesPerSecond = 10210.3
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0249s; samplesPerSecond = 10032.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0248s; samplesPerSecond = 10065.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0256s; samplesPerSecond = 9766.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0259s; samplesPerSecond = 9662.6
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0239s; samplesPerSecond = 10469.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0255s; samplesPerSecond = 9802.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0248s; samplesPerSecond = 10100.6
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0255s; samplesPerSecond = 9808.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0261s; samplesPerSecond = 9593.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0255s; samplesPerSecond = 9807.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0249s; samplesPerSecond = 10049.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0247s; samplesPerSecond = 10117.4
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0254s; samplesPerSecond = 9834.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0243s; samplesPerSecond = 10275.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0255s; samplesPerSecond = 9802.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0246s; samplesPerSecond = 10146.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0243s; samplesPerSecond = 10286.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0252s; samplesPerSecond = 9909.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0244s; samplesPerSecond = 10227.0
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0244s; samplesPerSecond = 10243.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0248s; samplesPerSecond = 10081.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0254s; samplesPerSecond = 9844.5
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0258s; samplesPerSecond = 9679.8
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0255s; samplesPerSecond = 9798.2
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0260s; samplesPerSecond = 9609.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9431.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9722.7
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0259s; samplesPerSecond = 9647.3
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalClassificationError = 0.37200000 * 250; time = 0.0267s; samplesPerSecond = 9364.7
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0258s; samplesPerSecond = 9700.1
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalClassificationError = 0.44800000 * 250; time = 0.0260s; samplesPerSecond = 9608.0
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalClassificationError = 0.36400000 * 250; time = 0.0263s; samplesPerSecond = 9515.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0229s; samplesPerSecond = 10907.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0239s; samplesPerSecond = 10479.5
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0229s; samplesPerSecond = 10917.0
05/03/2016 13:12:48: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalClassificationError = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.01718s
05/03/2016 13:12:48: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.1'
05/03/2016 13:12:48: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:12:48: Starting minibatch loop.
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28579105 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0228s; samplesPerSecond = 10943.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.27768619 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0230s; samplesPerSecond = 10860.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23309790 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0223s; samplesPerSecond = 11187.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.20937585 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0221s; samplesPerSecond = 11327.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20192059 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0225s; samplesPerSecond = 11116.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21303992 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0232s; samplesPerSecond = 10762.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17823340 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10120.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18892688 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0231s; samplesPerSecond = 10816.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14161328 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0225s; samplesPerSecond = 11100.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15813574 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0226s; samplesPerSecond = 11077.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21082446 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10728.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16117041 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0229s; samplesPerSecond = 10928.0
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15665234 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0223s; samplesPerSecond = 11195.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13067773 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0226s; samplesPerSecond = 11047.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16602710 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0212s; samplesPerSecond = 11796.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.14975708 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0215s; samplesPerSecond = 11641.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22351709 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0214s; samplesPerSecond = 11708.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18010474 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0207s; samplesPerSecond = 12085.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15341577 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12072.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17195337 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0209s; samplesPerSecond = 11976.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15546069 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0217s; samplesPerSecond = 11534.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16008325 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11689.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15944043 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0209s; samplesPerSecond = 11981.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15336865 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0207s; samplesPerSecond = 12102.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14822266 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0212s; samplesPerSecond = 11766.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14999512 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0211s; samplesPerSecond = 11833.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15481982 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0208s; samplesPerSecond = 11992.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17656738 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0204s; samplesPerSecond = 12229.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22373242 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0213s; samplesPerSecond = 11738.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16403760 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0211s; samplesPerSecond = 11856.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17322168 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0211s; samplesPerSecond = 11868.0
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13165430 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0205s; samplesPerSecond = 12202.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14016992 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 11993.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18369678 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0214s; samplesPerSecond = 11657.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15161035 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0215s; samplesPerSecond = 11612.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18919824 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0215s; samplesPerSecond = 11632.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17373975 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11818.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033740 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 12036.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12107568 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12075.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15386328 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0227s; samplesPerSecond = 10997.7
05/03/2016 13:12:48: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17515541 * 10000; EvalErrorPrediction = 0.07440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.87149s
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28579105 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0228s; samplesPerSecond = 10943.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.27768619 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0230s; samplesPerSecond = 10860.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23309790 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0223s; samplesPerSecond = 11187.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.20937585 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0221s; samplesPerSecond = 11327.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20192059 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0225s; samplesPerSecond = 11116.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21303992 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0232s; samplesPerSecond = 10762.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17823340 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10120.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18892688 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0231s; samplesPerSecond = 10816.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14161328 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0225s; samplesPerSecond = 11100.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15813574 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0226s; samplesPerSecond = 11077.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21082446 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10728.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16117041 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0229s; samplesPerSecond = 10928.0
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15665234 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0223s; samplesPerSecond = 11195.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13067773 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0226s; samplesPerSecond = 11047.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16602710 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0212s; samplesPerSecond = 11796.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.14975708 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0215s; samplesPerSecond = 11641.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22351709 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0214s; samplesPerSecond = 11708.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18010474 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0207s; samplesPerSecond = 12085.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15341577 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12072.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17195337 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0209s; samplesPerSecond = 11976.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15546069 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0217s; samplesPerSecond = 11534.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16008325 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11689.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15944043 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0209s; samplesPerSecond = 11981.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15336865 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0207s; samplesPerSecond = 12102.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14822266 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0212s; samplesPerSecond = 11766.4
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14999512 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0211s; samplesPerSecond = 11833.2
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15481982 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0208s; samplesPerSecond = 11992.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17656738 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0204s; samplesPerSecond = 12229.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22373242 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0213s; samplesPerSecond = 11738.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16403760 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0211s; samplesPerSecond = 11856.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17322168 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0211s; samplesPerSecond = 11868.0
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13165430 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0205s; samplesPerSecond = 12202.3
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14016992 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 11993.9
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18369678 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0214s; samplesPerSecond = 11657.7
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15161035 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0215s; samplesPerSecond = 11612.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18919824 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0215s; samplesPerSecond = 11632.8
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17373975 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11818.1
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033740 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 12036.6
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12107568 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12075.5
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15386328 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0227s; samplesPerSecond = 10997.7
05/03/2016 13:12:48: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17515541 * 10000; EvalClassificationError = 0.07440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.87149s
05/03/2016 13:12:48: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.2'
05/03/2016 13:12:48: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:12:48: Starting minibatch loop.
05/03/2016 13:12:48: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10671188 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0217s; samplesPerSecond = 11511.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17609265 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0205s; samplesPerSecond = 12183.8
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14152701 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0208s; samplesPerSecond = 12001.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348053 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0213s; samplesPerSecond = 11748.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11764551 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0219s; samplesPerSecond = 11435.4
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16246954 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11811.4
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16140149 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0207s; samplesPerSecond = 12078.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19747632 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0202s; samplesPerSecond = 12391.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20041309 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0214s; samplesPerSecond = 11659.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13657080 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0208s; samplesPerSecond = 12033.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20124377 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0203s; samplesPerSecond = 12293.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17898120 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0206s; samplesPerSecond = 12144.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16037830 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0232s; samplesPerSecond = 10779.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16276050 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0214s; samplesPerSecond = 11704.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19882275 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0218s; samplesPerSecond = 11454.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10263354 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0208s; samplesPerSecond = 12041.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17038770 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0213s; samplesPerSecond = 11725.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16624731 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0209s; samplesPerSecond = 11958.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12664160 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0213s; samplesPerSecond = 11723.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11944995 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0213s; samplesPerSecond = 11733.8
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12949756 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0208s; samplesPerSecond = 11996.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18147778 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0222s; samplesPerSecond = 11242.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13172412 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0233s; samplesPerSecond = 10719.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19600269 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0238s; samplesPerSecond = 10521.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15840479 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0226s; samplesPerSecond = 11084.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11888281 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0225s; samplesPerSecond = 11129.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13710742 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0222s; samplesPerSecond = 11251.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20026318 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0233s; samplesPerSecond = 10730.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18824951 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0223s; samplesPerSecond = 11227.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16653223 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0225s; samplesPerSecond = 11096.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11935254 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10918.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16085400 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0225s; samplesPerSecond = 11132.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16112646 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0219s; samplesPerSecond = 11439.6
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12345313 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10904.6
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13502686 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0226s; samplesPerSecond = 11075.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20874756 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0224s; samplesPerSecond = 11185.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16650537 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0227s; samplesPerSecond = 11009.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14995752 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0206s; samplesPerSecond = 12134.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16497070 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0209s; samplesPerSecond = 11953.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16843018 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0210s; samplesPerSecond = 11912.1
05/03/2016 13:12:49: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15794755 * 10000; EvalErrorPrediction = 0.07480000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.871499s
05/03/2016 13:12:48: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10671188 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0217s; samplesPerSecond = 11511.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17609265 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0205s; samplesPerSecond = 12183.8
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14152701 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0208s; samplesPerSecond = 12001.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348053 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0213s; samplesPerSecond = 11748.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11764551 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0219s; samplesPerSecond = 11435.4
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16246954 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11811.4
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16140149 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0207s; samplesPerSecond = 12078.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19747632 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0202s; samplesPerSecond = 12391.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20041309 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0214s; samplesPerSecond = 11659.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13657080 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0208s; samplesPerSecond = 12033.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20124377 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0203s; samplesPerSecond = 12293.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17898120 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0206s; samplesPerSecond = 12144.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16037830 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0232s; samplesPerSecond = 10779.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16276050 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0214s; samplesPerSecond = 11704.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19882275 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0218s; samplesPerSecond = 11454.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10263354 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0208s; samplesPerSecond = 12041.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17038770 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0213s; samplesPerSecond = 11725.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16624731 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0209s; samplesPerSecond = 11958.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12664160 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0213s; samplesPerSecond = 11723.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11944995 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0213s; samplesPerSecond = 11733.8
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12949756 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0208s; samplesPerSecond = 11996.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18147778 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0222s; samplesPerSecond = 11242.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13172412 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0233s; samplesPerSecond = 10719.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19600269 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0238s; samplesPerSecond = 10521.0
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15840479 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0226s; samplesPerSecond = 11084.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11888281 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0225s; samplesPerSecond = 11129.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13710742 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0222s; samplesPerSecond = 11251.1
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20026318 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0233s; samplesPerSecond = 10730.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18824951 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0223s; samplesPerSecond = 11227.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16653223 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0225s; samplesPerSecond = 11096.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11935254 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10918.5
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16085400 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0225s; samplesPerSecond = 11132.9
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16112646 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0219s; samplesPerSecond = 11439.6
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12345313 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10904.6
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13502686 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0226s; samplesPerSecond = 11075.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20874756 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0224s; samplesPerSecond = 11185.2
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16650537 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0227s; samplesPerSecond = 11009.3
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14995752 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0206s; samplesPerSecond = 12134.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16497070 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0209s; samplesPerSecond = 11953.7
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16843018 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0210s; samplesPerSecond = 11912.1
05/03/2016 13:12:49: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15794755 * 10000; EvalClassificationError = 0.07480000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.871499s
05/03/2016 13:12:49: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn'
05/03/2016 13:12:49: CNTKCommandTrainEnd: Simple_Demo_Train
@ -606,7 +606,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -635,7 +635,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -659,7 +659,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
00000070343C5200: {[InvStdOfFeatures Value[2]] }
00000070343C5340: {[Prior Value[2]] }
00000070343C53E0: {[W0 Value[50 x 2]] }
@ -671,7 +671,7 @@ Memory Sharing Structure:
000000703442D030: {[HLast Value[2 x 1 x *1]] }
000000703442D0D0: {[W0*features Value[50 x *1]] }
000000703442D170: {[W1*H1+B1 Value[50 x 1 x *1]] }
000000703442D2B0: {[EvalErrorPrediction Value[1]] }
000000703442D2B0: {[EvalClassificationError Value[1]] }
000000703442D530: {[CrossEntropyWithSoftmax Value[1]] }
000000703442D5D0: {[W2 Value[2 x 50]] }
000000703442D670: {[LogOfPrior Value[2]] }
@ -684,7 +684,7 @@ Memory Sharing Structure:
0000007034432340: {[B0 Value[50 x 1]] }
0000007034432480: {[B2 Value[2 x 1]] }
05/03/2016 13:12:50: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12474995 * 603; perplexity = 1.13286515
05/03/2016 13:12:50: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12474995 * 603; perplexity = 1.13286515
05/03/2016 13:12:50: Action "test" complete.
@ -700,7 +700,7 @@ Post-processing network...
8 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -730,7 +730,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *2] -> [2 x 1 x *2]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *2], [2 x 1] -> [2 x 1 x *2]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *2] -> [2 x 1 x *2]
Validating --> Prior = Mean (labels) : [2 x *2] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -753,7 +753,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalClassificationError Gradient[1]] [EvalClassificationError Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
000000702E3275E0: {[H2 Value[50 x 1 x *2]] }
000000702E327680: {[W2*H1 Value[2 x 1 x *2]] }
000000702E3277C0: {[LogOfPrior Value[2]] }

278
Tests/EndToEndTests/Examples/Other/Simple2d/Simple/baseline.windows.gpu.txt
Просмотреть файл

@ -56,7 +56,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -155,7 +155,7 @@ Simple_Demo_Train = [
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -298,7 +298,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
SimpleNetworkBuilder = [
layerSizes = 2:50*2:2
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
initValueScale = 1.0
applyMeanVarNorm = true
@ -354,7 +354,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -383,7 +383,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
Validating --> Prior = Mean (labels) : [2 x *] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -407,14 +407,14 @@ Post-processing network complete.
05/03/2016 13:01:59: Evaluation criterion node(s):
05/03/2016 13:01:59: EvalErrorPrediction = ErrorPrediction
05/03/2016 13:01:59: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
000000501A590FF0: {[W2 Value[2 x 50]] }
000000501A591090: {[W0 Value[50 x 2]] }
000000501A5919F0: {[B1 Value[50 x 1]] }
@ -427,7 +427,7 @@ Memory Sharing Structure:
000000501A5A1180: {[ScaledLogLikelihood Value[2 x 1 x *]] }
000000501A5A1220: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
000000501A5A17C0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
000000501A5A1900: {[EvalErrorPrediction Value[1]] }
000000501A5A1900: {[EvalClassificationError Value[1]] }
000000501A5A19A0: {[W0*features Value[50 x *]] }
000000501A5A1A40: {[W2*H1 Gradient[2 x 1 x *]] }
000000501A5A1F40: {[MVNormalizedFeatures Value[2 x *]] }
@ -457,139 +457,139 @@ Memory Sharing Structure:
05/03/2016 13:01:59: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:01:59: Starting minibatch loop.
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0123s; samplesPerSecond = 20247.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0112s; samplesPerSecond = 22393.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0126s; samplesPerSecond = 19907.6
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0113s; samplesPerSecond = 22042.0
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0131s; samplesPerSecond = 19088.3
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0138s; samplesPerSecond = 18059.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0148s; samplesPerSecond = 16917.0
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0164s; samplesPerSecond = 15236.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0123s; samplesPerSecond = 20321.9
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0167s; samplesPerSecond = 14944.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0131s; samplesPerSecond = 19105.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0132s; samplesPerSecond = 18886.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0128s; samplesPerSecond = 19574.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0128s; samplesPerSecond = 19467.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0123s; samplesPerSecond = 20391.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0143s; samplesPerSecond = 17465.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0117s; samplesPerSecond = 21367.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0137s; samplesPerSecond = 18200.3
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21782.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0131s; samplesPerSecond = 19017.2
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0137s; samplesPerSecond = 18191.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0109s; samplesPerSecond = 22891.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0133s; samplesPerSecond = 18739.2
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0130s; samplesPerSecond = 19291.6
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalErrorPrediction = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19230.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0118s; samplesPerSecond = 21168.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0128s; samplesPerSecond = 19577.1
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0129s; samplesPerSecond = 19432.6
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0126s; samplesPerSecond = 19767.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0121s; samplesPerSecond = 20736.6
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalErrorPrediction = 0.40800000 * 250; time = 0.0124s; samplesPerSecond = 20109.4
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19727.0
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0127s; samplesPerSecond = 19615.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0117s; samplesPerSecond = 21292.9
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0127s; samplesPerSecond = 19624.8
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalErrorPrediction = 0.16000000 * 250; time = 0.0130s; samplesPerSecond = 19157.1
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalErrorPrediction = 0.19200000 * 250; time = 0.0143s; samplesPerSecond = 17521.7
05/03/2016 13:02:00: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.526194s
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0123s; samplesPerSecond = 20247.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0112s; samplesPerSecond = 22393.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0126s; samplesPerSecond = 19907.6
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalClassificationError = 0.56000000 * 250; time = 0.0113s; samplesPerSecond = 22042.0
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalClassificationError = 0.52800000 * 250; time = 0.0131s; samplesPerSecond = 19088.3
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0138s; samplesPerSecond = 18059.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalClassificationError = 0.43200000 * 250; time = 0.0148s; samplesPerSecond = 16917.0
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0164s; samplesPerSecond = 15236.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0123s; samplesPerSecond = 20321.9
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalClassificationError = 0.47200000 * 250; time = 0.0167s; samplesPerSecond = 14944.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0131s; samplesPerSecond = 19105.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0132s; samplesPerSecond = 18886.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0128s; samplesPerSecond = 19574.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0128s; samplesPerSecond = 19467.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0123s; samplesPerSecond = 20391.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0143s; samplesPerSecond = 17465.4
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0117s; samplesPerSecond = 21367.5
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0137s; samplesPerSecond = 18200.3
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalClassificationError = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21782.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0131s; samplesPerSecond = 19017.2
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0137s; samplesPerSecond = 18191.1
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0109s; samplesPerSecond = 22891.7
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0133s; samplesPerSecond = 18739.2
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0130s; samplesPerSecond = 19291.6
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalClassificationError = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19230.8
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0118s; samplesPerSecond = 21168.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0128s; samplesPerSecond = 19577.1
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalClassificationError = 0.53200000 * 250; time = 0.0129s; samplesPerSecond = 19432.6
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0126s; samplesPerSecond = 19767.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0121s; samplesPerSecond = 20736.6
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalClassificationError = 0.40800000 * 250; time = 0.0124s; samplesPerSecond = 20109.4
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalClassificationError = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19727.0
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalClassificationError = 0.48000000 * 250; time = 0.0127s; samplesPerSecond = 19615.5
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0117s; samplesPerSecond = 21292.9
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalClassificationError = 0.20400000 * 250; time = 0.0127s; samplesPerSecond = 19624.8
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalClassificationError = 0.16000000 * 250; time = 0.0130s; samplesPerSecond = 19157.1
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalClassificationError = 0.19200000 * 250; time = 0.0143s; samplesPerSecond = 17521.7
05/03/2016 13:02:00: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalClassificationError = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.526194s
05/03/2016 13:02:00: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.1'
05/03/2016 13:02:00: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:02:00: Starting minibatch loop.
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.44832977 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0124s; samplesPerSecond = 20205.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40085291 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0142s; samplesPerSecond = 17631.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.33487201 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0129s; samplesPerSecond = 19405.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29081885 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0125s; samplesPerSecond = 20016.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26279236 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0118s; samplesPerSecond = 21188.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25220630 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0138s; samplesPerSecond = 18158.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.20988293 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0129s; samplesPerSecond = 19447.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21577441 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0148s; samplesPerSecond = 16846.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16622900 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0157s; samplesPerSecond = 15967.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17637866 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0144s; samplesPerSecond = 17315.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22185278 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0123s; samplesPerSecond = 20366.6
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17055811 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0151s; samplesPerSecond = 16564.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16481055 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0140s; samplesPerSecond = 17910.9
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13871704 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0156s; samplesPerSecond = 16005.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16922363 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0143s; samplesPerSecond = 17454.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15403345 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0135s; samplesPerSecond = 18485.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22255859 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0108s; samplesPerSecond = 23079.8
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18146851 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0133s; samplesPerSecond = 18843.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15611523 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19081.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17320215 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0137s; samplesPerSecond = 18192.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15727930 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0117s; samplesPerSecond = 21404.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16195410 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0119s; samplesPerSecond = 21088.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16121338 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0128s; samplesPerSecond = 19546.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15427100 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0125s; samplesPerSecond = 20011.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14844775 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0141s; samplesPerSecond = 17743.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15055713 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0108s; samplesPerSecond = 23067.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15467627 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0132s; samplesPerSecond = 18965.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17615869 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0140s; samplesPerSecond = 17872.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22356104 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0121s; samplesPerSecond = 20650.9
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16514209 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0109s; samplesPerSecond = 22946.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17355859 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0129s; samplesPerSecond = 19372.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13117578 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0138s; samplesPerSecond = 18151.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13956104 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0121s; samplesPerSecond = 20743.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18397363 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0105s; samplesPerSecond = 23741.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15222656 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19909.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18856396 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0145s; samplesPerSecond = 17207.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17513330 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0130s; samplesPerSecond = 19199.8
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15008252 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0108s; samplesPerSecond = 23043.6
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12125342 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0127s; samplesPerSecond = 19668.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15408496 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0141s; samplesPerSecond = 17788.5
05/03/2016 13:02:00: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19333879 * 10000; EvalErrorPrediction = 0.07700000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.525411s
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.44832977 * 250; EvalClassificationError = 0.15200000 * 250; time = 0.0124s; samplesPerSecond = 20205.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40085291 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0142s; samplesPerSecond = 17631.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.33487201 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0129s; samplesPerSecond = 19405.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29081885 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0125s; samplesPerSecond = 20016.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26279236 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0118s; samplesPerSecond = 21188.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25220630 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0138s; samplesPerSecond = 18158.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.20988293 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0129s; samplesPerSecond = 19447.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21577441 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0148s; samplesPerSecond = 16846.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16622900 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0157s; samplesPerSecond = 15967.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17637866 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0144s; samplesPerSecond = 17315.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22185278 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0123s; samplesPerSecond = 20366.6
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17055811 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0151s; samplesPerSecond = 16564.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16481055 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0140s; samplesPerSecond = 17910.9
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13871704 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0156s; samplesPerSecond = 16005.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16922363 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0143s; samplesPerSecond = 17454.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15403345 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0135s; samplesPerSecond = 18485.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22255859 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0108s; samplesPerSecond = 23079.8
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18146851 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0133s; samplesPerSecond = 18843.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15611523 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19081.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17320215 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0137s; samplesPerSecond = 18192.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15727930 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0117s; samplesPerSecond = 21404.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16195410 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0119s; samplesPerSecond = 21088.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16121338 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0128s; samplesPerSecond = 19546.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15427100 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0125s; samplesPerSecond = 20011.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14844775 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0141s; samplesPerSecond = 17743.1
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15055713 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0108s; samplesPerSecond = 23067.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15467627 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0132s; samplesPerSecond = 18965.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17615869 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0140s; samplesPerSecond = 17872.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22356104 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0121s; samplesPerSecond = 20650.9
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16514209 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0109s; samplesPerSecond = 22946.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17355859 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0129s; samplesPerSecond = 19372.3
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13117578 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0138s; samplesPerSecond = 18151.5
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13956104 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0121s; samplesPerSecond = 20743.4
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18397363 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0105s; samplesPerSecond = 23741.7
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15222656 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19909.2
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18856396 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0145s; samplesPerSecond = 17207.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17513330 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0130s; samplesPerSecond = 19199.8
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15008252 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0108s; samplesPerSecond = 23043.6
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12125342 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0127s; samplesPerSecond = 19668.0
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15408496 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0141s; samplesPerSecond = 17788.5
05/03/2016 13:02:00: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19333879 * 10000; EvalClassificationError = 0.07700000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.525411s
05/03/2016 13:02:00: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.2'
05/03/2016 13:02:00: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
05/03/2016 13:02:00: Starting minibatch loop.
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10746781 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19806.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17648278 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0122s; samplesPerSecond = 20429.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14106094 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19838.1
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348077 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11767151 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0110s; samplesPerSecond = 22787.3
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16217944 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0137s; samplesPerSecond = 18292.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16171204 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0147s; samplesPerSecond = 16977.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19844067 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0130s; samplesPerSecond = 19285.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19984509 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0116s; samplesPerSecond = 21585.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13727051 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0133s; samplesPerSecond = 18839.5
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20126648 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0150s; samplesPerSecond = 16709.0
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17913672 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0138s; samplesPerSecond = 18066.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15983582 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0138s; samplesPerSecond = 18131.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16260010 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0126s; samplesPerSecond = 19798.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19813428 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0122s; samplesPerSecond = 20453.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10295117 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0124s; samplesPerSecond = 20091.6
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17117065 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0127s; samplesPerSecond = 19762.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16661938 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0127s; samplesPerSecond = 19620.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12718042 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0108s; samplesPerSecond = 23156.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11923853 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0139s; samplesPerSecond = 17989.5
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12890332 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0129s; samplesPerSecond = 19340.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18205469 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0124s; samplesPerSecond = 20182.4
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13154199 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0111s; samplesPerSecond = 22599.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19668359 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0139s; samplesPerSecond = 17922.4
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15817578 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0126s; samplesPerSecond = 19915.6
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11871240 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0136s; samplesPerSecond = 18378.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13730908 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0107s; samplesPerSecond = 23384.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20024854 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0134s; samplesPerSecond = 18719.6
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18850244 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0131s; samplesPerSecond = 19151.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16640479 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0108s; samplesPerSecond = 23086.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11872168 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0107s; samplesPerSecond = 23347.0
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16090430 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0127s; samplesPerSecond = 19730.1
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16162939 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0137s; samplesPerSecond = 18205.7
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12408594 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0109s; samplesPerSecond = 22839.4
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13544434 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19893.4
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20890771 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0129s; samplesPerSecond = 19366.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16674365 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0146s; samplesPerSecond = 17116.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033398 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19152.7
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16547705 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0120s; samplesPerSecond = 20752.1
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16792480 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0129s; samplesPerSecond = 19450.7
05/03/2016 13:02:01: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15806136 * 10000; EvalErrorPrediction = 0.07470000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.511151s
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10746781 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19806.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17648278 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0122s; samplesPerSecond = 20429.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14106094 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19838.1
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348077 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11767151 * 250; EvalClassificationError = 0.04000000 * 250; time = 0.0110s; samplesPerSecond = 22787.3
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16217944 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0137s; samplesPerSecond = 18292.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16171204 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0147s; samplesPerSecond = 16977.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19844067 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0130s; samplesPerSecond = 19285.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19984509 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0116s; samplesPerSecond = 21585.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13727051 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0133s; samplesPerSecond = 18839.5
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20126648 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0150s; samplesPerSecond = 16709.0
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17913672 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0138s; samplesPerSecond = 18066.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15983582 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0138s; samplesPerSecond = 18131.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16260010 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0126s; samplesPerSecond = 19798.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19813428 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0122s; samplesPerSecond = 20453.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10295117 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0124s; samplesPerSecond = 20091.6
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17117065 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0127s; samplesPerSecond = 19762.8
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16661938 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0127s; samplesPerSecond = 19620.2
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12718042 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0108s; samplesPerSecond = 23156.7
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11923853 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0139s; samplesPerSecond = 17989.5
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12890332 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0129s; samplesPerSecond = 19340.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18205469 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0124s; samplesPerSecond = 20182.4
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13154199 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0111s; samplesPerSecond = 22599.9
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19668359 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0139s; samplesPerSecond = 17922.4
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15817578 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0126s; samplesPerSecond = 19915.6
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11871240 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0136s; samplesPerSecond = 18378.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13730908 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0107s; samplesPerSecond = 23384.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20024854 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0134s; samplesPerSecond = 18719.6
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18850244 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0131s; samplesPerSecond = 19151.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16640479 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0108s; samplesPerSecond = 23086.2
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11872168 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0107s; samplesPerSecond = 23347.0
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16090430 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0127s; samplesPerSecond = 19730.1
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16162939 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0137s; samplesPerSecond = 18205.7
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12408594 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0109s; samplesPerSecond = 22839.4
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13544434 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19893.4
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20890771 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0129s; samplesPerSecond = 19366.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16674365 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0146s; samplesPerSecond = 17116.3
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033398 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19152.7
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16547705 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0120s; samplesPerSecond = 20752.1
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16792480 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0129s; samplesPerSecond = 19450.7
05/03/2016 13:02:01: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15806136 * 10000; EvalClassificationError = 0.07470000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.511151s
05/03/2016 13:02:01: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn'
05/03/2016 13:02:01: CNTKCommandTrainEnd: Simple_Demo_Train
@ -607,7 +607,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -636,7 +636,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -660,7 +660,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalClassificationError Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
000000501A591090: {[W0*features+B0 Value[50 x 1 x *1]] }
000000501A591130: {[W1*H1 Value[50 x 1 x *1]] }
000000501A5916D0: {[W1*H1+B1 Value[50 x 1 x *1]] }
@ -672,7 +672,7 @@ Memory Sharing Structure:
000000501A592850: {[LogOfPrior Value[2]] }
000000501A5928F0: {[H2 Value[50 x 1 x *1]] }
000000501A592B70: {[W2 Value[2 x 50]] }
000000501A592D50: {[EvalErrorPrediction Value[1]] }
000000501A592D50: {[EvalClassificationError Value[1]] }
000000501A592DF0: {[CrossEntropyWithSoftmax Value[1]] }
0000005024E60C70: {[W1 Value[50 x 50]] }
0000005024E613F0: {[W0 Value[50 x 2]] }
@ -685,7 +685,7 @@ Memory Sharing Structure:
0000005024E62430: {[features Value[2 x *1]] }
0000005024E624D0: {[B1 Value[50 x 1]] }
05/03/2016 13:02:01: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12740351 * 603; perplexity = 1.13587526
05/03/2016 13:02:01: Final Results: Minibatch[1-1]: EvalClassificationError = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12740351 * 603; perplexity = 1.13587526
05/03/2016 13:02:01: Action "test" complete.
@ -701,7 +701,7 @@ Post-processing network...
8 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -731,7 +731,7 @@ Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *2] -> [2 x 1 x *2]
Validating --> B2 = LearnableParameter() : -> [2 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *2], [2 x 1] -> [2 x 1 x *2]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *2] -> [2 x 1 x *2]
Validating --> Prior = Mean (labels) : [2 x *2] -> [2]
Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
@ -754,7 +754,7 @@ Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalClassificationError Gradient[1]] [EvalClassificationError Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
000000501A5914F0: {[InvStdOfFeatures Value[2]] }
000000501A591590: {[MeanOfFeatures Value[2]] }
000000501A5916D0: {[labels Value[2 x *2]] }

6
Tests/EndToEndTests/Examples/Other/Simple2d/Simple/testcases.yml
Просмотреть файл

@ -21,7 +21,7 @@ testCases:
patterns:
- Finished Epoch[{{integer}} of {{integer}}]
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}
- totalSamplesSeen = {{integer}}
- learningRatePerSample = {{float,tolerance=0.1%}}
@ -29,10 +29,10 @@ testCases:
patterns:
- Epoch[{{integer}} of {{integer}}]-Minibatch[{{integer}}-{{integer}}
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}
Final test results must match:
patterns:
- "Final Results: Minibatch[{{integer}}-{{integer}}]"
- CrossEntropyWithSoftmax = {{float,tolerance=0.05}} * {{integer}}
- EvalErrorPrediction = {{float,tolerance=0.05}} * {{integer}}
- EvalClassificationError = {{float,tolerance=0.05}} * {{integer}}

18
Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/baseline.windows.release.cpu.txt
Просмотреть файл

@ -65,7 +65,7 @@ speechTrain = [
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -139,7 +139,7 @@ speechTrain = [
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -214,7 +214,7 @@ configparameters: FeedForward.cntk:speechTrain=[
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -290,7 +290,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -319,7 +319,7 @@ Validating --> W2*H1 = Times (W2, H2) : [132 x 512], [512 x 1 x *] -> [132 x 1 x
Validating --> B2 = LearnableParameter() : -> [132 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [132 x 1 x *] -> [132 x 1 x *]
Validating --> Prior = Mean (labels) : [132 x *] -> [132]
Validating --> LogOfPrior = Log (Prior) : [132] -> [132]
@ -343,14 +343,14 @@ Post-processing network complete.
05/03/2016 13:22:23: Evaluation criterion node(s):
05/03/2016 13:22:23: EvalErrorPrediction = ErrorPrediction
05/03/2016 13:22:23: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
000000BDD334C430: {[features Value[363 x *]] }
000000BDD334C4D0: {[W0 Value[512 x 363]] }
000000BDD334C610: {[MeanOfFeatures Value[363]] }
@ -359,7 +359,7 @@ Memory Sharing Structure:
000000BDD334CE30: {[B1 Value[512 x 1]] }
000000BDD334D1F0: {[InvStdOfFeatures Value[363]] }
000000BDD5BCA080: {[Prior Value[132]] }
000000BDD5BCA120: {[EvalErrorPrediction Value[1]] }
000000BDD5BCA120: {[EvalClassificationError Value[1]] }
000000BDD5BCA260: {[W2 Value[132 x 512]] }
000000BDD5BCA440: {[labels Value[132 x *]] }
000000BDD5BCA6C0: {[MVNormalizedFeatures Value[363 x *]] }
@ -396,7 +396,7 @@ requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 1
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
05/03/2016 13:22:24: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
05/03/2016 13:22:25: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.48531419 * 2048; EvalErrorPrediction = 0.90722656 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.288909s
05/03/2016 13:22:25: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.48531419 * 2048; EvalClassificationError = 0.90722656 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.288909s
05/03/2016 13:22:25: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn'
05/03/2016 13:22:25: CNTKCommandTrainEnd: speechTrain

18
Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/baseline.windows.release.gpu.txt
Просмотреть файл

@ -65,7 +65,7 @@ speechTrain = [
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -139,7 +139,7 @@ speechTrain = [
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -214,7 +214,7 @@ configparameters: FeedForward.cntk:speechTrain=[
SimpleNetworkBuilder = [
layerSizes = 363:512:512:132
trainingCriterion = "CrossEntropyWithSoftmax"
evalCriterion = "ErrorPrediction"
evalCriterion = "ClassificationError"
layerTypes = "Sigmoid"
applyMeanVarNorm = true
needPrior = true
@ -291,7 +291,7 @@ Post-processing network...
7 roots:
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
EvalErrorPrediction = ErrorPrediction()
EvalClassificationError = ClassificationError()
InvStdOfFeatures = InvStdDev()
MeanOfFeatures = Mean()
PosteriorProb = Softmax()
@ -320,7 +320,7 @@ Validating --> W2*H1 = Times (W2, H2) : [132 x 512], [512 x 1 x *] -> [132 x 1 x
Validating --> B2 = LearnableParameter() : -> [132 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [132 x 1 x *] -> [132 x 1 x *]
Validating --> Prior = Mean (labels) : [132 x *] -> [132]
Validating --> LogOfPrior = Log (Prior) : [132] -> [132]
@ -344,14 +344,14 @@ Post-processing network complete.
05/03/2016 13:22:26: Evaluation criterion node(s):
05/03/2016 13:22:26: EvalErrorPrediction = ErrorPrediction
05/03/2016 13:22:26: EvalClassificationError = ClassificationError
Allocating matrices for forward and/or backward propagation.
Memory Sharing Structure:
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
0000000000000000: {[EvalClassificationError Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
00000087D360C610: {[features Value[363 x *]] }
00000087EB4FEEF0: {[W0 Value[512 x 363]] }
00000087EB4FF530: {[B1 Value[512 x 1]] }
@ -368,7 +368,7 @@ Memory Sharing Structure:
00000087EDA2B8D0: {[W0 Gradient[512 x 363]] [W0*features+B0 Value[512 x 1 x *]] }
00000087EDA2BB50: {[CrossEntropyWithSoftmax Value[1]] }
00000087EDA2BC90: {[W0*features+B0 Gradient[512 x 1 x *]] [W1*H1 Value[512 x 1 x *]] }
00000087EDA2C0F0: {[EvalErrorPrediction Value[1]] }
00000087EDA2C0F0: {[EvalClassificationError Value[1]] }
00000087EDA2C190: {[W0*features Value[512 x *]] }
00000087EDA2C2D0: {[H1 Value[512 x 1 x *]] [W0*features Gradient[512 x *]] }
00000087EDA2C370: {[W2*H1 Gradient[132 x 1 x *]] }
@ -397,7 +397,7 @@ requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 1
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
05/03/2016 13:22:27: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
05/03/2016 13:22:27: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.42832291 * 2048; EvalErrorPrediction = 0.91357422 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.052947s
05/03/2016 13:22:27: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.42832291 * 2048; EvalClassificationError = 0.91357422 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.052947s
05/03/2016 13:22:27: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn'
05/03/2016 13:22:27: CNTKCommandTrainEnd: speechTrain

6
Tests/EndToEndTests/Examples/Speech/AN4/LSTM/baseline.windows.release.cpu.txt
Просмотреть файл

@ -241,7 +241,7 @@ Post-processing network...
6 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
featNorm.xMean = Mean()
featNorm.xStdDev = InvStdDev()
logPrior.prior = Mean()
@ -385,7 +385,7 @@ Validating --> unnamed174 = Times (W, LSTMoutput3.output) : [132 x 512], [512 x
Validating --> b = LearnableParameter() : -> [132 x 1]
Validating --> LSTMoutputW = Plus (unnamed174, b) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
@ -426,7 +426,7 @@ Post-processing network complete.
05/03/2016 13:22:29: Evaluation criterion node(s):
05/03/2016 13:22:29: err = ErrorPrediction
05/03/2016 13:22:29: err = ClassificationError
Allocating matrices for forward and/or backward propagation.

6
Tests/EndToEndTests/Examples/Speech/AN4/LSTM/baseline.windows.release.gpu.txt
Просмотреть файл

@ -242,7 +242,7 @@ Post-processing network...
6 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
featNorm.xMean = Mean()
featNorm.xStdDev = InvStdDev()
logPrior.prior = Mean()
@ -386,7 +386,7 @@ Validating --> unnamed174 = Times (W, LSTMoutput3.output) : [132 x 512], [512 x
Validating --> b = LearnableParameter() : -> [132 x 1]
Validating --> LSTMoutputW = Plus (unnamed174, b) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
@ -427,7 +427,7 @@ Post-processing network complete.
05/03/2016 13:22:43: Evaluation criterion node(s):
05/03/2016 13:22:43: err = ErrorPrediction
05/03/2016 13:22:43: err = ClassificationError
Allocating matrices for forward and/or backward propagation.

2
Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
Просмотреть файл

@ -103,6 +103,6 @@ DNN=[
ol = DNNLastLayer(hiddenDim, labelDim, h2_d, fc3WScale, fc3BValue)
ce = CrossEntropyWithSoftmax(labels, ol, tag = "criterion")
err = ErrorPrediction(labels, ol, tag = "evaluation")
err = ClassificationError(labels, ol, tag = "evaluation")
OutputNodes = ol
]

2
Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
Просмотреть файл

@ -1,6 +1,6 @@
m1 = LoadModel("$curModel$", format="cntk")
SetDefaultModel(m1)
errTop5 = ErrorPrediction(labels, outputNodes.z, Const(5), tag="evaluation")
errTop5 = ClassificationError(labels, outputNodes.z, Const(5), tag="evaluation")
SaveModel(m1, "$newModel$", format="cntk")

66
Tests/EndToEndTests/Image/AlexNet/baseline.linux.debug.gpu.txt
Просмотреть файл

@ -320,10 +320,10 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus
CE = CrossEntropyWithSoftmax
Err = ErrorPrediction
Err = ClassificationError
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
Validating for node OutputNodes.z. 45 nodes to process in pass 1.
@ -674,7 +674,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -724,7 +724,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -774,7 +774,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -786,7 +786,7 @@ Training criterion node(s):
CE = CrossEntropyWithSoftmax
Evaluation criterion node(s):
Err = ErrorPrediction
Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -821,10 +821,10 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax
Err = ErrorPrediction
Err = ClassificationError
OutputNodes.z = Plus
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
@ -1029,7 +1029,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -1079,7 +1079,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -1129,7 +1129,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -1286,12 +1286,12 @@ Post-processing network...
4 roots:
CE = CrossEntropyWithSoftmax
Err = ErrorPrediction
errTop5 = ErrorPrediction
Err = ClassificationError
errTop5 = ClassificationError
OutputNodes.z = Plus
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
@ -1496,7 +1496,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -1546,7 +1546,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -1596,7 +1596,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -1650,7 +1650,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5. 29 nodes to process in pass 2.
@ -1701,7 +1701,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5, final verification.
@ -1752,7 +1752,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
18 out of 48 nodes do not share the minibatch layout with the input data.
@ -1909,12 +1909,12 @@ Post-processing network...
4 roots:
CE = CrossEntropyWithSoftmax
errTop5 = ErrorPrediction
Err = ErrorPrediction
errTop5 = ClassificationError
Err = ClassificationError
OutputNodes.z = Plus
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
@ -2120,7 +2120,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5. 29 nodes to process in pass 2.
@ -2171,7 +2171,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5, final verification.
@ -2222,7 +2222,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
18 out of 48 nodes do not share the minibatch layout with the input data.
@ -2275,7 +2275,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -2325,7 +2325,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -2375,7 +2375,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -2531,6 +2531,6 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
Allocating matrices for forward and/or backward propagation.
Minibatch[1-32]: Samples Seen = 500 Err: ErrorPrediction/Sample = 0.998 errTop5: ErrorPrediction/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9591762
Final Results: Minibatch[1-32]: Samples Seen = 500 Err: ErrorPrediction/Sample = 0.998 errTop5: ErrorPrediction/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9591762 perplexity = 1052.766
Minibatch[1-32]: Samples Seen = 500 Err: ClassificationError/Sample = 0.998 errTop5: ClassificationError/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9591762
Final Results: Minibatch[1-32]: Samples Seen = 500 Err: ClassificationError/Sample = 0.998 errTop5: ClassificationError/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9591762 perplexity = 1052.766
__COMPLETED__

26
Tests/EndToEndTests/Image/AlexNet/baseline.linux.release.gpu.txt
Просмотреть файл

@ -341,7 +341,7 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 48 nodes to process in pass 1.
@ -392,7 +392,7 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
Validating --> labels = InputValue() : -> [1000 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating network. 30 nodes to process in pass 2.
@ -428,7 +428,7 @@ Post-processing network complete.
05/03/2016 18:06:53: Evaluation criterion node(s):
05/03/2016 18:06:53: err = ErrorPrediction
05/03/2016 18:06:53: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -535,7 +535,7 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 48 nodes to process in pass 1.
@ -586,7 +586,7 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1], [1000] -> [1000 x *1]
Validating --> labels = InputValue() : -> [1000 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating network. 30 nodes to process in pass 2.
@ -621,8 +621,8 @@ Post-processing network...
4 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop5 = ErrorPrediction()
err = ClassificationError()
errTop5 = ClassificationError()
Validating network. 50 nodes to process in pass 1.
@ -673,9 +673,9 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1], [1000] -> [1000 x *1]
Validating --> labels = InputValue() : -> [1000 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
Validating --> errTop5 = ClassificationError (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
Validating network. 31 nodes to process in pass 2.
@ -704,8 +704,8 @@ Post-processing network...
4 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop5 = ErrorPrediction()
err = ClassificationError()
errTop5 = ClassificationError()
Validating network. 50 nodes to process in pass 1.
@ -756,9 +756,9 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
Validating --> labels = InputValue() : -> [1000 x *2]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
Validating --> errTop5 = ClassificationError (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
Validating network. 31 nodes to process in pass 2.

66
Tests/EndToEndTests/Image/AlexNet/baseline.windows.debug.gpu.txt
Просмотреть файл

@ -319,10 +319,10 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus
Err = ErrorPrediction
Err = ClassificationError
CE = CrossEntropyWithSoftmax
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
@ -521,7 +521,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -571,7 +571,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -621,7 +621,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -786,7 +786,7 @@ Training criterion node(s):
CE = CrossEntropyWithSoftmax
Evaluation criterion node(s):
Err = ErrorPrediction
Err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -821,10 +821,10 @@ Post-processing network...
3 roots:
CE = CrossEntropyWithSoftmax
Err = ErrorPrediction
Err = ClassificationError
OutputNodes.z = Plus
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
@ -1029,7 +1029,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -1079,7 +1079,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -1129,7 +1129,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -1286,12 +1286,12 @@ Post-processing network...
4 roots:
CE = CrossEntropyWithSoftmax
errTop5 = ErrorPrediction
Err = ErrorPrediction
errTop5 = ClassificationError
Err = ClassificationError
OutputNodes.z = Plus
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
@ -1497,7 +1497,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5. 29 nodes to process in pass 2.
@ -1548,7 +1548,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5, final verification.
@ -1599,7 +1599,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
18 out of 48 nodes do not share the minibatch layout with the input data.
@ -1652,7 +1652,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -1702,7 +1702,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -1752,7 +1752,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -1908,12 +1908,12 @@ Post-processing network complete.
Post-processing network...
4 roots:
Err = ErrorPrediction
errTop5 = ErrorPrediction
Err = ClassificationError
errTop5 = ClassificationError
OutputNodes.z = Plus
CE = CrossEntropyWithSoftmax
FormNestedNetwork: WARNING: Was called twice for Err ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ErrorPrediction operation
FormNestedNetwork: WARNING: Was called twice for Err ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for errTop5 ClassificationError operation
FormNestedNetwork: WARNING: Was called twice for OutputNodes.z Plus operation
FormNestedNetwork: WARNING: Was called twice for CE CrossEntropyWithSoftmax operation
@ -1966,7 +1966,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err. 29 nodes to process in pass 2.
@ -2016,7 +2016,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating for node Err, final verification.
@ -2066,7 +2066,7 @@ Validating --> h2_d = Dropout(h2.y[4096, MBSize 0]) -> [4096 [4096 {1}], MBSize
Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSize 0]) -> [1000 [1000 {1}], MBSize 0]
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> Err = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
Validating --> Err = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0]) -> [1 [1 {1}], 1]
17 out of 47 nodes do not share the minibatch layout with the input data.
@ -2120,7 +2120,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5. 29 nodes to process in pass 2.
@ -2171,7 +2171,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating for node errTop5, final verification.
@ -2222,7 +2222,7 @@ Validating --> OutputNodes.t = Times(OutputNodes.W[1000, 4096], h2_d[4096, MBSiz
Validating --> OutputNodes.b = LearnableParameter -> [1000 [1000 {1}], 1]
Validating --> OutputNodes.z = Plus(OutputNodes.t[1000, MBSize 0], OutputNodes.b[1000, 1]) -> [1000 [1000 {1}], MBSize 0]
Validating --> unnamed125 = LearnableParameter -> [1 [1 {1}], 1]
Validating --> errTop5 = ErrorPrediction(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
Validating --> errTop5 = ClassificationError(labels[1000, MBSize 0], OutputNodes.z[1000, MBSize 0], unnamed125[1, 1]) -> [1 [1 {1}], 1]
18 out of 48 nodes do not share the minibatch layout with the input data.
@ -2979,6 +2979,6 @@ CUDA error 11 [c:\tools\cub-1.4.1\cub\device\dispatch/dispatch_radix_sort.cuh, 7
CUDA error 11 [c:\tools\cub-1.4.1\cub\device\dispatch/dispatch_radix_sort.cuh, 796]: invalid argument
CUDA error 11 [c:\tools\cub-1.4.1\cub\device\dispatch/dispatch_radix_sort.cuh, 796]: invalid argument
CUDA error 11 [c:\tools\cub-1.4.1\cub\device\dispatch/dispatch_radix_sort.cuh, 796]: invalid argument
Minibatch[1-32]: Samples Seen = 500 Err: ErrorPrediction/Sample = 1 errTop5: ErrorPrediction/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9566009
Final Results: Minibatch[1-32]: Samples Seen = 500 Err: ErrorPrediction/Sample = 1 errTop5: ErrorPrediction/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9566009 perplexity = 1050.0582
Minibatch[1-32]: Samples Seen = 500 Err: ClassificationError/Sample = 1 errTop5: ClassificationError/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9566009
Final Results: Minibatch[1-32]: Samples Seen = 500 Err: ClassificationError/Sample = 1 errTop5: ClassificationError/Sample = 0.992 CE: CrossEntropyWithSoftmax/Sample = 6.9566009 perplexity = 1050.0582
__COMPLETED__

26
Tests/EndToEndTests/Image/AlexNet/baseline.windows.release.gpu.txt
Просмотреть файл

@ -339,7 +339,7 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 48 nodes to process in pass 1.
@ -390,7 +390,7 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
Validating --> labels = InputValue() : -> [1000 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *], [1000 x *] -> [1]
Validating network. 30 nodes to process in pass 2.
@ -426,7 +426,7 @@ Post-processing network complete.
05/03/2016 14:11:02: Evaluation criterion node(s):
05/03/2016 14:11:02: err = ErrorPrediction
05/03/2016 14:11:02: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -533,7 +533,7 @@ Post-processing network...
3 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 48 nodes to process in pass 1.
@ -584,7 +584,7 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1], [1000] -> [1000 x *1]
Validating --> labels = InputValue() : -> [1000 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating network. 30 nodes to process in pass 2.
@ -619,8 +619,8 @@ Post-processing network...
4 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop5 = ErrorPrediction()
err = ClassificationError()
errTop5 = ClassificationError()
Validating network. 50 nodes to process in pass 1.
@ -671,9 +671,9 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1], [1000] -> [1000 x *1]
Validating --> labels = InputValue() : -> [1000 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
Validating --> errTop5 = ClassificationError (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
Validating network. 31 nodes to process in pass 2.
@ -702,8 +702,8 @@ Post-processing network...
4 roots:
OutputNodes.z = Plus()
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
errTop5 = ErrorPrediction()
err = ClassificationError()
errTop5 = ClassificationError()
Validating network. 50 nodes to process in pass 1.
@ -754,9 +754,9 @@ Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
Validating --> labels = InputValue() : -> [1000 x *2]
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> err = ClassificationError (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
Validating --> errTop5 = ClassificationError (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
Validating network. 31 nodes to process in pass 2.

32
Tests/EndToEndTests/Image/QuickE2E/baseline.linux.txt
Просмотреть файл

@ -107,7 +107,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -227,7 +227,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -372,7 +372,7 @@ configparameters: Image_QuickE2E.cntk:train=[
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -421,7 +421,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -451,7 +451,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -479,7 +479,7 @@ Post-processing network complete.
05/03/2016 18:05:56: Evaluation criterion node(s):
05/03/2016 18:05:56: err = ErrorPrediction
05/03/2016 18:05:56: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -572,7 +572,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -602,7 +602,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -775,7 +775,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -896,7 +896,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -1043,7 +1043,7 @@ configparameters: Image_QuickE2E.cntk:train=[
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -1092,7 +1092,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -1122,7 +1122,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -1150,7 +1150,7 @@ Post-processing network complete.
05/03/2016 18:05:56: Evaluation criterion node(s):
05/03/2016 18:05:56: err = ErrorPrediction
05/03/2016 18:05:56: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -1215,7 +1215,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -1245,7 +1245,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.

36
Tests/EndToEndTests/Image/QuickE2E/baseline.windows.txt
Просмотреть файл

@ -105,7 +105,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -224,7 +224,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -368,7 +368,7 @@ configparameters: Image_QuickE2E.cntk:train=[
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -416,7 +416,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -446,7 +446,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -474,7 +474,7 @@ Post-processing network complete.
04/20/2016 13:14:17: Evaluation criterion node(s):
04/20/2016 13:14:17: err = ErrorPrediction
04/20/2016 13:14:17: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -530,7 +530,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -560,7 +560,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -585,7 +585,7 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
Allocating matrices for forward and/or backward propagation.
Final Results: Minibatch[1-1]: * 100 err: ErrorPrediction/Sample = 0.9 ce: CrossEntropyWithSoftmax/Sample = 2.3315085 perplexity = 10.293457
Final Results: Minibatch[1-1]: * 100 err: ClassificationError/Sample = 0.9 ce: CrossEntropyWithSoftmax/Sample = 2.3315085 perplexity = 10.293457
04/20/2016 13:14:19: Action "test" complete.
@ -699,7 +699,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -819,7 +819,7 @@ train = [
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -965,7 +965,7 @@ configparameters: Image_QuickE2E.cntk:train=[
h1 = DNNSigmoidLayer((4 : 4 : cMap2/*cudnn: CHW*/), h1Dim, pool2, 1).out
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
outputNodes = ol
]
SGD = [
@ -1013,7 +1013,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -1043,7 +1043,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *]
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *], [10 x 1 x *] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -1071,7 +1071,7 @@ Post-processing network complete.
04/20/2016 13:14:20: Evaluation criterion node(s):
04/20/2016 13:14:20: err = ErrorPrediction
04/20/2016 13:14:20: err = ClassificationError
Allocating matrices for forward and/or backward propagation.
@ -1100,7 +1100,7 @@ Post-processing network...
2 roots:
ce = CrossEntropyWithSoftmax()
err = ErrorPrediction()
err = ClassificationError()
Validating network. 27 nodes to process in pass 1.
@ -1130,7 +1130,7 @@ Validating --> ol.out.PlusArgs[0] = Times (ol.W, h1.out) : [10 x 128], [128 x *1
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
Validating --> ol.out = Plus (ol.out.PlusArgs[0], ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ErrorPrediction (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> err = ClassificationError (labels, ol.out) : [10 x *1], [10 x 1 x *1] -> [1]
Validating network. 16 nodes to process in pass 2.
@ -1155,7 +1155,7 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
Allocating matrices for forward and/or backward propagation.
Final Results: Minibatch[1-1]: * 100 err: ErrorPrediction/Sample = 0.9 ce: CrossEntropyWithSoftmax/Sample = 2.3327824 perplexity = 10.306579
Final Results: Minibatch[1-1]: * 100 err: ClassificationError/Sample = 0.9 ce: CrossEntropyWithSoftmax/Sample = 2.3327824 perplexity = 10.306579
04/20/2016 13:14:21: Action "test" complete.

2
Tests/EndToEndTests/Image/QuickE2E/cntk.cntk
Просмотреть файл

@ -87,7 +87,7 @@ train = [
ol = DNNLayer(h1Dim, labelDim, h1, 1).out
ce = CrossEntropyWithSoftmax(labels, ol, tag="criterion")
err = ErrorPrediction(labels, ol, tag="evaluation")
err = ClassificationError(labels, ol, tag="evaluation")
]
SGD = [

1376
Tests/EndToEndTests/ParallelTraining/NoQuantization/DoublePrecision/baseline.cpu.txt
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1376
Tests/EndToEndTests/ParallelTraining/NoQuantization/DoublePrecision/baseline.gpu.txt
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Tests/EndToEndTests/ParallelTraining/NoQuantization/DoublePrecision/baseline.windows.cpu.txt
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Tests/EndToEndTests/ParallelTraining/NoQuantization/DoublePrecision/baseline.windows.gpu.txt
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4
Tests/EndToEndTests/ParallelTraining/NoQuantization/DoublePrecision/testcases.yml
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@ -19,7 +19,7 @@ testCases:
- ^MPI Rank {{integer}}
- Finished Epoch[{{integer}} of {{integer}}]
- CrossEntropyWithSoftmax = {{float,tolerance=0%}}
- EvalErrorPrediction = {{float,tolerance=0%}}
- EvalClassificationError = {{float,tolerance=0%}}
- learningRatePerSample = {{float,tolerance=0.001%}}
Per-minibatch training results must match for each MPI Rank:
@ -28,7 +28,7 @@ testCases:
- Epoch[{{integer}} of {{integer}}]-Minibatch[{{integer}}-{{integer}}
- " * {{integer}}; "
- CrossEntropyWithSoftmax = {{float,tolerance=0%}}
- EvalErrorPrediction = {{float,tolerance=0%}}
- EvalClassificationError = {{float,tolerance=0%}}
DataParallelSGD training parameters must match for each MPI Rank:
patterns:

1376
Tests/EndToEndTests/ParallelTraining/NoQuantization/SinglePrecision/baseline.cpu.txt
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Tests/EndToEndTests/ParallelTraining/NoQuantization/SinglePrecision/baseline.windows.cpu.txt
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