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\GettingStarted/02_OneConv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\GettingStarted OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu DeviceId=0 timestamping=true forceDeterministicAlgorithms=true stderr=- trainNetwork=[SGD=[maxEpochs=3]]
CNTK 1.7+ (HEAD 216029, Sep 22 2016 16:13:35) on DPHAIM-22 at 2016/09/22 16:26:57

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\GettingStarted/02_OneConv.cntk  currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu  DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\GettingStarted  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu  DeviceId=0  timestamping=true  forceDeterministicAlgorithms=true  stderr=-  trainNetwork=[SGD=[maxEpochs=3]]
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData
09/22/2016 16:27:00: Redirecting stderr to file -_trainNetwork_testNetwork.log
09/22/2016 16:27:00: -------------------------------------------------------------------
09/22/2016 16:27:00: Build info: 

09/22/2016 16:27:00: 		Built time: Sep 22 2016 16:13:35
09/22/2016 16:27:00: 		Last modified date: Thu Sep 22 13:24:23 2016
09/22/2016 16:27:00: 		Build type: Release
09/22/2016 16:27:00: 		Build target: GPU
09/22/2016 16:27:00: 		Math lib: mkl
09/22/2016 16:27:00: 		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
09/22/2016 16:27:00: 		CUB_PATH: C:\src\cub-1.4.1
09/22/2016 16:27:00: 		CUDNN_PATH: c:\NVIDIA\cudnn-5.1\cuda
09/22/2016 16:27:00: 		Build Branch: HEAD
09/22/2016 16:27:00: 		Build SHA1: 216029bfedd92253fd45034da1d1cc68c4d4c7f1
09/22/2016 16:27:00: 		Built by svcphil on liana-08-w
09/22/2016 16:27:00: 		Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
09/22/2016 16:27:00: -------------------------------------------------------------------
09/22/2016 16:27:02: -------------------------------------------------------------------
09/22/2016 16:27:02: GPU info:

09/22/2016 16:27:02: 		Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/22/2016 16:27:02: 		Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/22/2016 16:27:02: 		Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/22/2016 16:27:02: 		Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
09/22/2016 16:27:02: -------------------------------------------------------------------

Configuration After Processing and Variable Resolution:

configparameters: 02_OneConv.cntk:command=trainNetwork:testNetwork
configparameters: 02_OneConv.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\GettingStarted
configparameters: 02_OneConv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData
configparameters: 02_OneConv.cntk:dataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData
configparameters: 02_OneConv.cntk:deviceId=0
configparameters: 02_OneConv.cntk:forceDeterministicAlgorithms=true
configparameters: 02_OneConv.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu/Models/02_OneConv
configparameters: 02_OneConv.cntk:outputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu
configparameters: 02_OneConv.cntk:precision=float
configparameters: 02_OneConv.cntk:rootDir=..
configparameters: 02_OneConv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu
configparameters: 02_OneConv.cntk:stderr=-
configparameters: 02_OneConv.cntk:testNetwork={
    action = "test"
minibatchSize = 1024    
    reader = {
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData/Test-28x28_cntk_text.txt"
        input = {
            features = { dim = 784 ; format = "dense" }
            labels =   { dim = 10  ; format = "dense" }
        }
    }
}

configparameters: 02_OneConv.cntk:timestamping=true
configparameters: 02_OneConv.cntk:traceLevel=1
configparameters: 02_OneConv.cntk:trainNetwork={
    action = "train"
    BrainScriptNetworkBuilder = {
imageShape = 28:28:1                        
labelDim = 10                               
        featScale = 1/256
        Scale{f} = x => Constant(f) .* x
        model = Sequential (
            Scale {featScale} :
            ConvolutionalLayer {16, (5:5), pad = true} : ReLU : 
            MaxPoolingLayer    {(2:2), stride=(2:2)} :
            DenseLayer {64} : ReLU : 
            LinearLayer {labelDim}
        )
        features = Input {imageShape}
        labels = Input (labelDim)
        ol = model (features)
        ce   = CrossEntropyWithSoftmax (labels, ol)
        errs = ClassificationError (labels, ol)
        featureNodes    = (features)
        labelNodes      = (labels)
        criterionNodes  = (ce)
        evaluationNodes = (errs)
        outputNodes     = (ol)
    }
    SGD = {
        epochSize = 60000
        minibatchSize = 64
        maxEpochs = 15
        learningRatesPerSample = 0.001*5:0.0005
        momentumAsTimeConstant = 0
        numMBsToShowResult = 500
    }
    reader = {
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu\TestData/Train-28x28_cntk_text.txt"
        input = {
            features = { dim = 784 ; format = "dense" }
            labels =   { dim = 10  ; format = "dense" }
        }
    }   
} [SGD=[maxEpochs=3]]

09/22/2016 16:27:02: Commands: trainNetwork testNetwork
09/22/2016 16:27:02: precision = "float"

09/22/2016 16:27:02: ##############################################################################
09/22/2016 16:27:02: #                                                                            #
09/22/2016 16:27:02: # trainNetwork command (train action)                                        #
09/22/2016 16:27:02: #                                                                            #
09/22/2016 16:27:02: ##############################################################################

09/22/2016 16:27:02: 
Creating virgin network.
Node '<placeholder>' (LearnableParameter operation): Initializating Parameter[10 x 0] as glorotUniform later when dimensions are fully known.
Node '<placeholder>' (LearnableParameter operation): Initializating Parameter[64 x 0] as glorotUniform later when dimensions are fully known.
Node '<placeholder>' (LearnableParameter operation): Initializating Parameter[5 x 5 x 0 x 16] as glorotUniform later when dimensions are fully known.

Post-processing network...

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

Validating network. 21 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *]
Validating --> model.arrayOfFunctions[6].W = LearnableParameter() :  -> [10 x 0]
Validating --> model.arrayOfFunctions[4].arrayOfFunctions[0].W = LearnableParameter() :  -> [64 x 0]
Validating --> model.arrayOfFunctions[1].W = LearnableParameter() :  -> [5 x 5 x 0 x 16]
Validating --> ol.x._.x.x._.x.ElementTimesArgs[0] = LearnableParameter() :  -> [1 x 1]
Validating --> features = InputValue() :  -> [28 x 28 x 1 x *]
Validating --> ol.x._.x.x._.x = ElementTimes (ol.x._.x.x._.x.ElementTimesArgs[0], features) : [1 x 1], [28 x 28 x 1 x *] -> [28 x 28 x 1 x *]
Node 'model.arrayOfFunctions[1].W' (LearnableParameter operation) operation: Tensor shape was inferred as [5 x 5 x 1 x 16].
Node 'model.arrayOfFunctions[1].W' (LearnableParameter operation): Initializing Parameter[5 x 5 x 1 x 16] <- glorotUniform(seed=3, init dims=[400 x 25], range=0.118818*1.000000, onCPU=true) { -0.01709269, ... }
.
Validating --> ol.x._.x.x._.c = Convolution (model.arrayOfFunctions[1].W, ol.x._.x.x._.x) : [5 x 5 x 1 x 16], [28 x 28 x 1 x *] -> [28 x 28 x 16 x *]
Validating --> model.arrayOfFunctions[1].b = LearnableParameter() :  -> [1 x 1 x 16]
Validating --> ol.x._.x.x._.res.x = Plus (ol.x._.x.x._.c, model.arrayOfFunctions[1].b) : [28 x 28 x 16 x *], [1 x 1 x 16] -> [28 x 28 x 16 x *]
Validating --> ol.x._.x.x = RectifiedLinear (ol.x._.x.x._.res.x) : [28 x 28 x 16 x *] -> [28 x 28 x 16 x *]
Validating --> _ol.x._.x = Pooling (ol.x._.x.x) : [28 x 28 x 16 x *] -> [14 x 14 x 16 x *]
Node 'model.arrayOfFunctions[4].arrayOfFunctions[0].W' (LearnableParameter operation) operation: Tensor shape was inferred as [64 x 14 x 14 x 16].
Node 'model.arrayOfFunctions[4].arrayOfFunctions[0].W' (LearnableParameter operation): Initializing Parameter[64 x 14 x 14 x 16] <- glorotUniform(seed=2, init dims=[64 x 3136], range=0.043301*1.000000, onCPU=true) { -0.01858653, ... }
.
Validating --> ol.x._.x.PlusArgs[0] = Times (model.arrayOfFunctions[4].arrayOfFunctions[0].W, _ol.x._.x) : [64 x 14 x 14 x 16], [14 x 14 x 16 x *] -> [64 x *]
Validating --> model.arrayOfFunctions[4].arrayOfFunctions[0].b = LearnableParameter() :  -> [64]
Validating --> ol.x._.x = Plus (ol.x._.x.PlusArgs[0], model.arrayOfFunctions[4].arrayOfFunctions[0].b) : [64 x *], [64] -> [64 x *]
Validating --> ol.x = RectifiedLinear (ol.x._.x) : [64 x *] -> [64 x *]
Node 'model.arrayOfFunctions[6].W' (LearnableParameter operation) operation: Tensor shape was inferred as [10 x 64].
Node 'model.arrayOfFunctions[6].W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- glorotUniform(seed=1, init dims=[10 x 64], range=0.284747*1.000000, onCPU=true) { -0.20348585, ... }
.
Validating --> ol.PlusArgs[0] = Times (model.arrayOfFunctions[6].W, ol.x) : [10 x 64], [64 x *] -> [10 x *]
Validating --> model.arrayOfFunctions[6].b = LearnableParameter() :  -> [10]
Validating --> ol = Plus (ol.PlusArgs[0], model.arrayOfFunctions[6].b) : [10 x *], [10] -> [10 x *]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol) : [10 x *], [10 x *] -> [1]
Validating --> errs = ClassificationError (labels, ol) : [10 x *], [10 x *] -> [1]

Validating network. 12 nodes to process in pass 2.


Validating network, final pass.

ol.x._.x.x._.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: 16, Stride: 1 x 1 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
_ol.x._.x: 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, 1, 1), AutoPad: (0, 0, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.



Post-processing network complete.

09/22/2016 16:27:04: 
Model has 21 nodes. Using GPU 0.

09/22/2016 16:27:04: Training criterion:   ce = CrossEntropyWithSoftmax
09/22/2016 16:27:04: Evaluation criterion: errs = ClassificationError


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 37 matrices, 20 are shared as 9, and 17 are not shared.

	{ model.arrayOfFunctions[4].arrayOfFunctions[0].W : [64 x 14 x 14 x 16] (gradient)
	  ol.x._.x : [64 x *] }
	{ model.arrayOfFunctions[6].W : [10 x 64] (gradient)
	  ol : [10 x *] (gradient) }
	{ _ol.x._.x : [14 x 14 x 16 x *] (gradient)
	  ol.PlusArgs[0] : [10 x *]
	  ol.x._.x : [64 x *] (gradient) }
	{ model.arrayOfFunctions[1].W : [5 x 5 x 1 x 16] (gradient)
	  ol.x._.x.x._.res.x : [28 x 28 x 16 x *] }
	{ model.arrayOfFunctions[4].arrayOfFunctions[0].b : [64] (gradient)
	  ol.x : [64 x *] (gradient) }
	{ ol.x._.x.x : [28 x 28 x 16 x *]
	  ol.x._.x.x._.c : [28 x 28 x 16 x *] (gradient) }
	{ model.arrayOfFunctions[1].b : [1 x 1 x 16] (gradient)
	  ol.x._.x.PlusArgs[0] : [64 x *]
	  ol.x._.x.x : [28 x 28 x 16 x *] (gradient) }
	{ ol.x : [64 x *]
	  ol.x._.x.PlusArgs[0] : [64 x *] (gradient) }
	{ _ol.x._.x : [14 x 14 x 16 x *]
	  ol.x._.x.x._.res.x : [28 x 28 x 16 x *] (gradient) }


09/22/2016 16:27:04: Training 201834 parameters in 6 out of 6 parameter tensors and 16 nodes with gradient:

09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[1].W' (LearnableParameter operation) : [5 x 5 x 1 x 16]
09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[1].b' (LearnableParameter operation) : [1 x 1 x 16]
09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[4].arrayOfFunctions[0].W' (LearnableParameter operation) : [64 x 14 x 14 x 16]
09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[4].arrayOfFunctions[0].b' (LearnableParameter operation) : [64]
09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[6].W' (LearnableParameter operation) : [10 x 64]
09/22/2016 16:27:04: 	Node 'model.arrayOfFunctions[6].b' (LearnableParameter operation) : [10]

09/22/2016 16:27:04: No PreCompute nodes found, or all already computed. Skipping pre-computation step.

09/22/2016 16:27:04: Starting Epoch 1: learning rate per sample = 0.001000  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/22/2016 16:27:04: Starting minibatch loop.
09/22/2016 16:27:07:  Epoch[ 1 of 3]-Minibatch[   1- 500, 53.33%]: ce = 0.42854120 * 32000; errs = 12.831% * 32000; time = 3.2001s; samplesPerSecond = 9999.8
09/22/2016 16:27:08: Finished Epoch[ 1 of 3]: [Training] ce = 0.29932327 * 60000; errs = 8.903% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.001; epochTime=4.55283s
09/22/2016 16:27:08: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu/Models/02_OneConv.1'

09/22/2016 16:27:08: Starting Epoch 2: learning rate per sample = 0.001000  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/22/2016 16:27:08: Starting minibatch loop.
09/22/2016 16:27:10:  Epoch[ 2 of 3]-Minibatch[   1- 500, 53.33%]: ce = 0.10526356 * 32000; errs = 3.119% * 32000; time = 1.5226s; samplesPerSecond = 21016.6
09/22/2016 16:27:11: Finished Epoch[ 2 of 3]: [Training] ce = 0.09716760 * 60000; errs = 2.878% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.001; epochTime=2.85802s
09/22/2016 16:27:11: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu/Models/02_OneConv.2'

09/22/2016 16:27:11: Starting Epoch 3: learning rate per sample = 0.001000  effective momentum = 0.000000  momentum as time constant = 0.0 samples

09/22/2016 16:27:11: Starting minibatch loop.
09/22/2016 16:27:13:  Epoch[ 3 of 3]-Minibatch[   1- 500, 53.33%]: ce = 0.06875948 * 32000; errs = 2.044% * 32000; time = 1.5330s; samplesPerSecond = 20874.4
09/22/2016 16:27:14: Finished Epoch[ 3 of 3]: [Training] ce = 0.06673716 * 60000; errs = 2.002% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.001; epochTime=2.87023s
09/22/2016 16:27:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160922162518.374503\Examples\Image\GettingStarted_02_OneConv@release_gpu/Models/02_OneConv'

09/22/2016 16:27:14: Action "train" complete.


09/22/2016 16:27:14: ##############################################################################
09/22/2016 16:27:14: #                                                                            #
09/22/2016 16:27:14: # testNetwork command (test action)                                          #
09/22/2016 16:27:14: #                                                                            #
09/22/2016 16:27:14: ##############################################################################


Post-processing network...

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

Validating network. 21 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *1]
Validating --> model.arrayOfFunctions[6].W = LearnableParameter() :  -> [10 x 64]
Validating --> model.arrayOfFunctions[4].arrayOfFunctions[0].W = LearnableParameter() :  -> [64 x 14 x 14 x 16]
Validating --> model.arrayOfFunctions[1].W = LearnableParameter() :  -> [5 x 5 x 1 x 16]
Validating --> ol.x._.x.x._.x.ElementTimesArgs[0] = LearnableParameter() :  -> [1 x 1]
Validating --> features = InputValue() :  -> [28 x 28 x 1 x *1]
Validating --> ol.x._.x.x._.x = ElementTimes (ol.x._.x.x._.x.ElementTimesArgs[0], features) : [1 x 1], [28 x 28 x 1 x *1] -> [28 x 28 x 1 x *1]
Validating --> ol.x._.x.x._.c = Convolution (model.arrayOfFunctions[1].W, ol.x._.x.x._.x) : [5 x 5 x 1 x 16], [28 x 28 x 1 x *1] -> [28 x 28 x 16 x *1]
Validating --> model.arrayOfFunctions[1].b = LearnableParameter() :  -> [1 x 1 x 16]
Validating --> ol.x._.x.x._.res.x = Plus (ol.x._.x.x._.c, model.arrayOfFunctions[1].b) : [28 x 28 x 16 x *1], [1 x 1 x 16] -> [28 x 28 x 16 x *1]
Validating --> ol.x._.x.x = RectifiedLinear (ol.x._.x.x._.res.x) : [28 x 28 x 16 x *1] -> [28 x 28 x 16 x *1]
Validating --> _ol.x._.x = Pooling (ol.x._.x.x) : [28 x 28 x 16 x *1] -> [14 x 14 x 16 x *1]
Validating --> ol.x._.x.PlusArgs[0] = Times (model.arrayOfFunctions[4].arrayOfFunctions[0].W, _ol.x._.x) : [64 x 14 x 14 x 16], [14 x 14 x 16 x *1] -> [64 x *1]
Validating --> model.arrayOfFunctions[4].arrayOfFunctions[0].b = LearnableParameter() :  -> [64]
Validating --> ol.x._.x = Plus (ol.x._.x.PlusArgs[0], model.arrayOfFunctions[4].arrayOfFunctions[0].b) : [64 x *1], [64] -> [64 x *1]
Validating --> ol.x = RectifiedLinear (ol.x._.x) : [64 x *1] -> [64 x *1]
Validating --> ol.PlusArgs[0] = Times (model.arrayOfFunctions[6].W, ol.x) : [10 x 64], [64 x *1] -> [10 x *1]
Validating --> model.arrayOfFunctions[6].b = LearnableParameter() :  -> [10]
Validating --> ol = Plus (ol.PlusArgs[0], model.arrayOfFunctions[6].b) : [10 x *1], [10] -> [10 x *1]
Validating --> ce = CrossEntropyWithSoftmax (labels, ol) : [10 x *1], [10 x *1] -> [1]
Validating --> errs = ClassificationError (labels, ol) : [10 x *1], [10 x *1] -> [1]

Validating network. 12 nodes to process in pass 2.


Validating network, final pass.

ol.x._.x.x._.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: 16, Stride: 1 x 1 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
_ol.x._.x: 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, 1, 1), AutoPad: (0, 0, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.



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 21 matrices, 0 are shared as 0, and 21 are not shared.


09/22/2016 16:27:15: Minibatch[1-10]: errs = 1.890% * 10000; ce = 0.05781048 * 10000
09/22/2016 16:27:15: Final Results: Minibatch[1-10]: errs = 1.890% * 10000; ce = 0.05781048 * 10000; perplexity = 1.05951418

09/22/2016 16:27:15: Action "test" complete.

09/22/2016 16:27:15: __COMPLETED__
