CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
    Hardware threads: 24
    Total Memory: 268381192 kB
-------------------------------------------------------------------
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated/02_Convolution_ndl_deprecated.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu DeviceId=0 timestamping=true forceDeterministicAlgorithms=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn" stderr=-
-------------------------------------------------------------------
Build info: 

		Built time: Sep 13 2016 08:34:18
		Last modified date: Tue Sep 13 08:14:18 2016
		Build type: Release
		Build target: GPU
		Math lib: mkl
		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
		CUB_PATH: C:\src\cub-1.4.1
		CUDNN_PATH: c:\NVIDIA\cudnn-5.1\cuda
		Build Branch: HEAD
		Build SHA1: dc27f22964e37ed721e445787ed58be17efe8f24
		Built by svcphil on liana-08-w
		Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
-------------------------------------------------------------------
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData
09/13/2016 08:52:41: Redirecting stderr to file -_train_test.log
09/13/2016 08:52:41: -------------------------------------------------------------------
09/13/2016 08:52:41: Build info: 

09/13/2016 08:52:41: 		Built time: Sep 13 2016 08:34:18
09/13/2016 08:52:41: 		Last modified date: Tue Sep 13 08:14:18 2016
09/13/2016 08:52:41: 		Build type: Release
09/13/2016 08:52:41: 		Build target: GPU
09/13/2016 08:52:41: 		Math lib: mkl
09/13/2016 08:52:41: 		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
09/13/2016 08:52:41: 		CUB_PATH: C:\src\cub-1.4.1
09/13/2016 08:52:41: 		CUDNN_PATH: c:\NVIDIA\cudnn-5.1\cuda
09/13/2016 08:52:41: 		Build Branch: HEAD
09/13/2016 08:52:41: 		Build SHA1: dc27f22964e37ed721e445787ed58be17efe8f24
09/13/2016 08:52:41: 		Built by svcphil on liana-08-w
09/13/2016 08:52:41: 		Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
09/13/2016 08:52:41: -------------------------------------------------------------------
09/13/2016 08:52:43: -------------------------------------------------------------------
09/13/2016 08:52:43: GPU info:

09/13/2016 08:52:43: 		Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/13/2016 08:52:43: 		Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/13/2016 08:52:43: 		Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/13/2016 08:52:43: -------------------------------------------------------------------

09/13/2016 08:52:43: Running on DPHAIM-22 at 2016/09/13 08:52:43
09/13/2016 08:52:43: Command line: 
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated/02_Convolution_ndl_deprecated.cntk  currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu  DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu  DeviceId=0  timestamping=true  forceDeterministicAlgorithms=true  train=[SGD=[maxEpochs=3]]  imageLayout="cudnn"  stderr=-


Configuration After Processing and Variable Resolution:

configparameters: 02_Convolution_ndl_deprecated.cntk:command=train:test
configparameters: 02_Convolution_ndl_deprecated.cntk:configDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated
configparameters: 02_Convolution_ndl_deprecated.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData
configparameters: 02_Convolution_ndl_deprecated.cntk:dataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData
configparameters: 02_Convolution_ndl_deprecated.cntk:deviceId=0
configparameters: 02_Convolution_ndl_deprecated.cntk:forceDeterministicAlgorithms=true
configparameters: 02_Convolution_ndl_deprecated.cntk:imageLayout=cudnn
configparameters: 02_Convolution_ndl_deprecated.cntk:modelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models
configparameters: 02_Convolution_ndl_deprecated.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models/02_Convolution
configparameters: 02_Convolution_ndl_deprecated.cntk:numMBsToShowResult=500
configparameters: 02_Convolution_ndl_deprecated.cntk:outputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu
configparameters: 02_Convolution_ndl_deprecated.cntk:precision=float
configparameters: 02_Convolution_ndl_deprecated.cntk:rootDir=..
configparameters: 02_Convolution_ndl_deprecated.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu
configparameters: 02_Convolution_ndl_deprecated.cntk:stderr=-
configparameters: 02_Convolution_ndl_deprecated.cntk:test=[
    action = test
    minibatchSize = 1024
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData/Test-28x28_cntk_text.txt"
        input = [
            features = [
            dim = 784
            format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]
]

configparameters: 02_Convolution_ndl_deprecated.cntk:timestamping=true
configparameters: 02_Convolution_ndl_deprecated.cntk:traceLevel=1
configparameters: 02_Convolution_ndl_deprecated.cntk:train=[
    action = "train"
    NDLNetworkBuilder = [
        imageLayout = "cudnn"
        initOnCPUOnly = true
        ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated/Macros.ndl"
        networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config\Ndl_deprecated/02_Convolution.ndl"
    ]
    SGD = [
        epochSize = 60000
        minibatchSize = 32
        learningRatesPerMB = 0.1*5:0.3
        momentumPerMB = 0*10:0.7
        maxEpochs = 15
    ]
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu\TestData/Train-28x28_cntk_text.txt"
        input = [
            features = [
                dim = 784
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
] [SGD=[maxEpochs=3]]

09/13/2016 08:52:43: Commands: train test
09/13/2016 08:52:43: Precision = "float"
09/13/2016 08:52:43: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models/02_Convolution
09/13/2016 08:52:43: CNTKCommandTrainInfo: train : 3
09/13/2016 08:52:43: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3

09/13/2016 08:52:43: ##############################################################################
09/13/2016 08:52:43: #                                                                            #
09/13/2016 08:52:43: # Action "train"                                                             #
09/13/2016 08:52:43: #                                                                            #
09/13/2016 08:52:43: ##############################################################################

09/13/2016 08:52:43: CNTKCommandTrainBegin: train

09/13/2016 08:52:43: Creating virgin network.
NDLBuilder Using GPU 0
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- 0.000000.
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 0.000000.
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- 0.000000.
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- 0.000000.
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- 0.000000.
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- uniform(seed=1, init dims=[16 x 25], range=0.050000*10.000000, onCPU=true).
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 1.000000.
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- uniform(seed=2, init dims=[32 x 400], range=0.050000*10.000000, onCPU=true).
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 1.000000.
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- uniform(seed=3, init dims=[128 x 1568], range=0.050000*1.000000, onCPU=true).
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- uniform(seed=4, init dims=[128 x 1], range=0.050000*1.000000, onCPU=true).
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- uniform(seed=5, init dims=[10 x 128], range=0.050000*1.000000, onCPU=true).
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=6, init dims=[10 x 1], range=0.050000*1.000000, onCPU=true).

Post-processing network...

3 roots:
	ce = CrossEntropyWithSoftmax()
	errs = ClassificationError()
	ol.z = Plus()

Validating network. 27 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *]
Validating --> ol.W = LearnableParameter() :  -> [10 x 128]
Validating --> h1.W = LearnableParameter() :  -> [128 x 7 x 7 x 32]
Validating --> conv2.w.W = LearnableParameter() :  -> [32 x 400]
Validating --> conv1.w.W = LearnableParameter() :  -> [16 x 25]
Validating --> featScale = LearnableParameter() :  -> [1 x 1]
Validating --> features = InputValue() :  -> [28 x 28 x 1 x *]
Validating --> featScaled = ElementTimes (featScale, features) : [1 x 1], [28 x 28 x 1 x *] -> [28 x 28 x 1 x *]
Validating --> conv1.c.c = Convolution (conv1.w.W, featScaled) : [16 x 25], [28 x 28 x 1 x *] -> [28 x 28 x 16 x *]
Validating --> conv1.b.b = LearnableParameter() :  -> [1 x 1 x 16]
Validating --> conv1.cpb = Plus (conv1.c.c, conv1.b.b) : [28 x 28 x 16 x *], [1 x 1 x 16] -> [28 x 28 x 16 x *]
Validating --> conv1.out = RectifiedLinear (conv1.cpb) : [28 x 28 x 16 x *] -> [28 x 28 x 16 x *]
Validating --> pool1 = MaxPooling (conv1.out) : [28 x 28 x 16 x *] -> [14 x 14 x 16 x *]
Validating --> conv2.c.c = Convolution (conv2.w.W, pool1) : [32 x 400], [14 x 14 x 16 x *] -> [14 x 14 x 32 x *]
Validating --> conv2.b.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv2.cpb = Plus (conv2.c.c, conv2.b.b) : [14 x 14 x 32 x *], [1 x 1 x 32] -> [14 x 14 x 32 x *]
Validating --> conv2.out = RectifiedLinear (conv2.cpb) : [14 x 14 x 32 x *] -> [14 x 14 x 32 x *]
Validating --> pool2.p = Pooling (conv2.out) : [14 x 14 x 32 x *] -> [7 x 7 x 32 x *]
Validating --> h1.t = Times (h1.W, pool2.p) : [128 x 7 x 7 x 32], [7 x 7 x 32 x *] -> [128 x *]
Validating --> h1.b = LearnableParameter() :  -> [128 x 1]
Validating --> h1.z = Plus (h1.t, h1.b) : [128 x *], [128 x 1] -> [128 x 1 x *]
Validating --> h1.y = Sigmoid (h1.z) : [128 x 1 x *] -> [128 x 1 x *]
Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *] -> [10 x 1 x *]
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 --> errs = ClassificationError (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]

Validating network. 16 nodes to process in pass 2.


Validating network, final pass.

conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.


11 out of 27 nodes do not share the minibatch layout with the input data.

Post-processing network complete.

09/13/2016 08:52:44: Created model with 27 nodes on GPU 0.

09/13/2016 08:52:44: Training criterion node(s):
09/13/2016 08:52:44: 	ce = CrossEntropyWithSoftmax

09/13/2016 08:52:44: Evaluation criterion node(s):
09/13/2016 08:52:44: 	errs = ClassificationError


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 49 matrices, 29 are shared as 13, and 20 are not shared.

	{ conv1.cpb : [28 x 28 x 16 x *]
	  conv1.w.W : [16 x 25] (gradient) }
	{ conv1.c.c : [28 x 28 x 16 x *] (gradient)
	  conv1.out : [28 x 28 x 16 x *] }
	{ conv1.cpb : [28 x 28 x 16 x *] (gradient)
	  pool1 : [14 x 14 x 16 x *] }
	{ conv1.b.b : [1 x 1 x 16] (gradient)
	  conv1.out : [28 x 28 x 16 x *] (gradient) }
	{ conv2.cpb : [14 x 14 x 32 x *]
	  conv2.w.W : [32 x 400] (gradient) }
	{ conv2.c.c : [14 x 14 x 32 x *] (gradient)
	  conv2.out : [14 x 14 x 32 x *] }
	{ conv2.cpb : [14 x 14 x 32 x *] (gradient)
	  pool1 : [14 x 14 x 16 x *] (gradient)
	  pool2.p : [7 x 7 x 32 x *] }
	{ conv2.b.b : [1 x 1 x 32] (gradient)
	  conv2.out : [14 x 14 x 32 x *] (gradient)
	  h1.t : [128 x *] }
	{ h1.W : [128 x 7 x 7 x 32] (gradient)
	  h1.z : [128 x 1 x *] }
	{ ol.W : [10 x 128] (gradient)
	  ol.z : [10 x 1 x *] (gradient) }
	{ h1.z : [128 x 1 x *] (gradient)
	  ol.t : [10 x 1 x *]
	  pool2.p : [7 x 7 x 32 x *] (gradient) }
	{ h1.t : [128 x *] (gradient)
	  h1.y : [128 x 1 x *] }
	{ h1.b : [128 x 1] (gradient)
	  h1.y : [128 x 1 x *] (gradient) }


09/13/2016 08:52:44: Training 215370 parameters in 8 out of 8 parameter tensors and 22 nodes with gradient:

09/13/2016 08:52:44: 	Node 'conv1.b.b' (LearnableParameter operation) : [1 x 1 x 16]
09/13/2016 08:52:44: 	Node 'conv1.w.W' (LearnableParameter operation) : [16 x 25]
09/13/2016 08:52:44: 	Node 'conv2.b.b' (LearnableParameter operation) : [1 x 1 x 32]
09/13/2016 08:52:44: 	Node 'conv2.w.W' (LearnableParameter operation) : [32 x 400]
09/13/2016 08:52:44: 	Node 'h1.W' (LearnableParameter operation) : [128 x 7 x 7 x 32]
09/13/2016 08:52:44: 	Node 'h1.b' (LearnableParameter operation) : [128 x 1]
09/13/2016 08:52:44: 	Node 'ol.W' (LearnableParameter operation) : [10 x 128]
09/13/2016 08:52:44: 	Node 'ol.b' (LearnableParameter operation) : [10 x 1]

09/13/2016 08:52:44: No PreCompute nodes found, or all already computed. Skipping pre-computation step.

09/13/2016 08:52:44: Starting Epoch 1: learning rate per sample = 0.003125  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/13/2016 08:52:44: Starting minibatch loop.
09/13/2016 08:52:49:  Epoch[ 1 of 3]-Minibatch[   1- 500, 26.67%]: ce = 1.29986121 * 16000; errs = 44.825% * 16000; time = 4.7630s; samplesPerSecond = 3359.2
09/13/2016 08:52:51:  Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.19019495 * 16000; errs = 5.319% * 16000; time = 2.7931s; samplesPerSecond = 5728.4
09/13/2016 08:52:54:  Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.11099622 * 16000; errs = 3.281% * 16000; time = 2.7892s; samplesPerSecond = 5736.5
09/13/2016 08:52:56: Finished Epoch[ 1 of 3]: [Training] ce = 0.44494518 * 60000; errs = 14.805% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=12.4582s
09/13/2016 08:52:56: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models/02_Convolution.1'

09/13/2016 08:52:56: Starting Epoch 2: learning rate per sample = 0.003125  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/13/2016 08:52:56: Starting minibatch loop.
09/13/2016 08:52:59:  Epoch[ 2 of 3]-Minibatch[   1- 500, 26.67%]: ce = 0.07434936 * 16000; errs = 2.288% * 16000; time = 2.7891s; samplesPerSecond = 5736.6
09/13/2016 08:53:02:  Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.06040998 * 16000; errs = 1.825% * 16000; time = 2.7892s; samplesPerSecond = 5736.5
09/13/2016 08:53:05:  Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06375415 * 16000; errs = 1.975% * 16000; time = 2.7887s; samplesPerSecond = 5737.4
09/13/2016 08:53:07: Finished Epoch[ 2 of 3]: [Training] ce = 0.06537900 * 60000; errs = 1.988% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=10.4691s
09/13/2016 08:53:07: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models/02_Convolution.2'

09/13/2016 08:53:07: Starting Epoch 3: learning rate per sample = 0.003125  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/13/2016 08:53:07: Starting minibatch loop.
09/13/2016 08:53:10:  Epoch[ 3 of 3]-Minibatch[   1- 500, 26.67%]: ce = 0.04688654 * 16000; errs = 1.475% * 16000; time = 2.7885s; samplesPerSecond = 5737.8
09/13/2016 08:53:12:  Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.04112575 * 16000; errs = 1.250% * 16000; time = 2.7875s; samplesPerSecond = 5739.9
09/13/2016 08:53:15:  Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.04371809 * 16000; errs = 1.281% * 16000; time = 2.7867s; samplesPerSecond = 5741.6
09/13/2016 08:53:17: Finished Epoch[ 3 of 3]: [Training] ce = 0.04367646 * 60000; errs = 1.347% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=10.4617s
09/13/2016 08:53:17: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160913085011.264477\Examples\Image\MNIST_02_Convolution_ndl@release_gpu/Models/02_Convolution'
09/13/2016 08:53:17: CNTKCommandTrainEnd: train

09/13/2016 08:53:17: Action "train" complete.


09/13/2016 08:53:17: ##############################################################################
09/13/2016 08:53:17: #                                                                            #
09/13/2016 08:53:17: # Action "test"                                                              #
09/13/2016 08:53:17: #                                                                            #
09/13/2016 08:53:17: ##############################################################################


Post-processing network...

3 roots:
	ce = CrossEntropyWithSoftmax()
	errs = ClassificationError()
	ol.z = Plus()

Validating network. 27 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *1]
Validating --> ol.W = LearnableParameter() :  -> [10 x 128]
Validating --> h1.W = LearnableParameter() :  -> [128 x 7 x 7 x 32]
Validating --> conv2.w.W = LearnableParameter() :  -> [32 x 400]
Validating --> conv1.w.W = LearnableParameter() :  -> [16 x 25]
Validating --> featScale = LearnableParameter() :  -> [1 x 1]
Validating --> features = InputValue() :  -> [28 x 28 x 1 x *1]
Validating --> featScaled = ElementTimes (featScale, features) : [1 x 1], [28 x 28 x 1 x *1] -> [28 x 28 x 1 x *1]
Validating --> conv1.c.c = Convolution (conv1.w.W, featScaled) : [16 x 25], [28 x 28 x 1 x *1] -> [28 x 28 x 16 x *1]
Validating --> conv1.b.b = LearnableParameter() :  -> [1 x 1 x 16]
Validating --> conv1.cpb = Plus (conv1.c.c, conv1.b.b) : [28 x 28 x 16 x *1], [1 x 1 x 16] -> [28 x 28 x 16 x *1]
Validating --> conv1.out = RectifiedLinear (conv1.cpb) : [28 x 28 x 16 x *1] -> [28 x 28 x 16 x *1]
Validating --> pool1 = MaxPooling (conv1.out) : [28 x 28 x 16 x *1] -> [14 x 14 x 16 x *1]
Validating --> conv2.c.c = Convolution (conv2.w.W, pool1) : [32 x 400], [14 x 14 x 16 x *1] -> [14 x 14 x 32 x *1]
Validating --> conv2.b.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv2.cpb = Plus (conv2.c.c, conv2.b.b) : [14 x 14 x 32 x *1], [1 x 1 x 32] -> [14 x 14 x 32 x *1]
Validating --> conv2.out = RectifiedLinear (conv2.cpb) : [14 x 14 x 32 x *1] -> [14 x 14 x 32 x *1]
Validating --> pool2.p = Pooling (conv2.out) : [14 x 14 x 32 x *1] -> [7 x 7 x 32 x *1]
Validating --> h1.t = Times (h1.W, pool2.p) : [128 x 7 x 7 x 32], [7 x 7 x 32 x *1] -> [128 x *1]
Validating --> h1.b = LearnableParameter() :  -> [128 x 1]
Validating --> h1.z = Plus (h1.t, h1.b) : [128 x *1], [128 x 1] -> [128 x 1 x *1]
Validating --> h1.y = Sigmoid (h1.z) : [128 x 1 x *1] -> [128 x 1 x *1]
Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *1] -> [10 x 1 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 --> errs = ClassificationError (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]

Validating network. 16 nodes to process in pass 2.


Validating network, final pass.

conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.


11 out of 27 nodes do not share the minibatch layout with the input data.

Post-processing network complete.

evalNodeNames are not specified, using all the default evalnodes and training criterion nodes.


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 27 matrices, 0 are shared as 0, and 27 are not shared.


09/13/2016 08:53:18: Minibatch[1-10]: errs = 1.320% * 10000; ce = 0.04335406 * 10000
09/13/2016 08:53:18: Final Results: Minibatch[1-10]: errs = 1.320% * 10000; ce = 0.04335406 * 10000; perplexity = 1.04430758

09/13/2016 08:53:18: Action "test" complete.

09/13/2016 08:53:18: __COMPLETED__
