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03_OneConvDropout.cntk
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03_OneConvDropout.cntk
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# Parameters can be overwritten on the command line
# for example: cntk configFile=myConfigFile RootDir=../..
# For running from Visual Studio add
# currentDirectory=$(SolutionDir)/<path to corresponding data folder>
command = trainNetwork:testNetwork
precision = "float"; traceLevel = 1 ; deviceId = "auto"
rootDir = ".." ; dataDir = "$rootDir$/DataSets/MNIST" ;
outputDir = "./Output" ;
modelPath = "$outputDir$/Models/03_OneConvDropout"
#stderr = "$outputDir$/03_OneConvDropout_bs_out"
# TRAINING CONFIG
trainNetwork = {
action = "train"
BrainScriptNetworkBuilder = {
imageShape = 28:28:1 # image dimensions, 1 channel only
labelDim = 10 # number of distinct labels
featScale = 1/256
Scale{f} = x => Constant(f) .* x
model = Sequential (
Scale {featScale} :
ConvolutionalLayer {16, (5:5), pad = true} : ReLU :
MaxPoolingLayer {(2:2), stride=(2:2)} : Dropout :
DenseLayer {64} : ReLU :
LinearLayer {labelDim}
)
# inputs
features = Input {imageShape}
labels = Input (labelDim)
# apply model to features
ol = model (features)
# loss and error computation
ce = CrossEntropyWithSoftmax (labels, ol)
errs = ClassificationError (labels, ol)
# declare special nodes
featureNodes = (features)
labelNodes = (labels)
criterionNodes = (ce)
evaluationNodes = (errs)
outputNodes = (ol)
}
SGD = {
epochSize = 60000
minibatchSize = 64
maxEpochs = 15
learningRatesPerSample = 0.001*5:0.0005
momentumAsTimeConstant = 0
dropoutRate = 0.5
numMBsToShowResult = 500
}
reader = {
readerType = "CNTKTextFormatReader"
# See ../README.md for details on getting the data (Train-28x28_cntk_text.txt).
file = "$DataDir$/Train-28x28_cntk_text.txt"
input = {
features = { dim = 784 ; format = "dense" }
labels = { dim = 10 ; format = "dense" }
}
}
}
# TEST CONFIG
testNetwork = {
action = "test"
minibatchSize = 1024 # reduce this if you run out of memory
reader = {
readerType = "CNTKTextFormatReader"
file = "$DataDir$/Test-28x28_cntk_text.txt"
input = {
features = { dim = 784 ; format = "dense" }
labels = { dim = 10 ; format = "dense" }
}
}
}