Revision 615a046fa54348b3d89faae21c254155dd4da058 authored by Bowen Bao on 28 June 2018, 01:40:27 UTC, committed by Bowen Bao on 28 June 2018, 01:47:01 UTC
1 parent 1314647
ImageHandsOn_Task6.cntk
# CNTK Configuration File for training a simple CIFAR-10 convnet.
# During the hands-on tutorial, this will be fleshed out into a ResNet-20 model.
command = TrainConvNet:Eval
makeMode = false ; traceLevel = 0 ; deviceId = "auto"
rootDir = "." ; dataDir = "$rootDir$" ; modelDir = "$rootDir$/Models"
modelPath = "$modelDir$/cifar10.cmf"
# Training action for a convolutional network
TrainConvNet = {
action = "train"
BrainScriptNetworkBuilder = {
imageShape = 32:32:3
labelDim = 10
MySubSampleBN (x, depth, stride) =
{
s = Splice ((MaxPoolingLayer {(1:1), stride=(stride:stride)} (x) : ConstantTensor (0, (1:1:depth/stride))), axis = 3) # sub-sample and pad: [W x H x depth/2] --> [W/2 x H/2 x depth]
b = BatchNormalizationLayer {spatialRank=2} (s)
}.b
MyConvBN (x, depth, stride) =
{
c = ConvolutionalLayer {depth, (3:3), pad=true, stride=(stride:stride), bias=false} (x)
b = BatchNormalizationLayer {spatialRank=2} (c)
}.b
ResNetNode (x, depth) =
{
c1 = MyConvBN (x, depth, 1)
r1 = ReLU (c1)
c2 = MyConvBN (r1, depth, 1)
r = ReLU (x + c2)
}.r
ResNetIncNode (x, depth) =
{
c1 = MyConvBN (x, depth, 2) # note the 2
r1 = ReLU (c1)
c2 = MyConvBN (r1, depth, 1)
xs = MySubSampleBN (x, depth, 2)
r = ReLU (xs + c2)
}.r
ResNetNodeStack (x, depth, L) =
{
r = if L == 0
then x
else ResNetNode (ResNetNodeStack (x, depth, L-1), depth)
}.r
model (features) =
{
conv1 = ReLU (MyConvBN (features, 16, 1))
rn1 = ResNetNodeStack (conv1, 16, 3) ##### replaced
rn2_1 = ResNetIncNode (rn1, 32)
rn2 = ResNetNodeStack (rn2_1, 32, 2) ##### replaced
rn3_1 = ResNetIncNode (rn2, 64)
rn3 = ResNetNodeStack (rn3_1, 64, 2) ##### replaced
pool = AveragePoolingLayer {(8:8)} (rn3)
z = LinearLayer {labelDim} (pool)
}.z
# inputs
features = Input {imageShape}
labels = Input {labelDim}
# apply model to features
z = model (features)
# connect to system
ce = CrossEntropyWithSoftmax (labels, z)
errs = ClassificationError (labels, z)
featureNodes = (features)
labelNodes = (labels)
criterionNodes = (ce)
evaluationNodes = (errs)
outputNodes = (z)
}
SGD = {
epochSize = 50000
maxEpochs = 160 ; minibatchSize = 128
learningRatesPerSample = 0.0078125*80:0.00078125*40:0.000078125
momentumAsTimeConstant = 1200
L2RegWeight = 0.0001
firstMBsToShowResult = 10 ; numMBsToShowResult = 500
ParallelTrain = {
parallelizationMethod = "DataParallelSGD"
parallelizationStartEpoch = 1
distributedMBReading = true
dataParallelSGD = { gradientBits = 2:1 }
}
AutoAdjust = {
autoAdjustMinibatch = true # enable automatic growing of minibatch size
minibatchSizeTuningFrequency = 10 # try to enlarge after this many epochs
numMiniBatch4LRSearch = 200
minibatchSizeTuningMax = 15000 # out of memory above this
}
}
reader = {
verbosity = 0 ; randomize = true
deserializers = ({
type = "ImageDeserializer" ; module = "ImageReader"
file = "$dataDir$/cifar-10-batches-py/train_map.txt"
input = {
features = { transforms = (
{ type = "Crop" ; cropType = "RandomSide" ; sideRatio = 0.8 ; jitterType = "UniRatio" } :
{ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } :
{ type = "Transpose" }
)}
labels = { labelDim = 10 }
}
})
}
}
# Eval action
Eval = {
action = "eval"
minibatchSize = 16
evalNodeNames = errs
reader = {
verbosity = 0 ; randomize = true
deserializers = ({
type = "ImageDeserializer" ; module = "ImageReader"
file = "$dataDir$/cifar-10-batches-py/test_map.txt"
input = {
features = { transforms = (
{ type = "Scale" ; width = 32 ; height = 32 ; channels = 3 ; interpolations = "linear" } :
{ type = "Transpose" }
)}
labels = { labelDim = 10 }
}
})
}
}
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