Revision 7e54db0d330c73fb27f7c1845303898385ade190 authored by Project Philly on 17 October 2016, 11:13:52 UTC, committed by Project Philly on 17 October 2016, 11:13:52 UTC
lr_bs_broken.cntk.txt
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
# logistic regression cntk script -- using BrainScript network description language
featureDim = 2 # input features data dimensions
labelDim = 1 # label data dimensions
command=TrainLRModel:TestModel # which commands to run
modelPath = "Models/LR_reg.dnn" # where to write the model to
deviceId = "auto" # -1 means CPU; use 0 for first GPU, 1 for second etc.
makeMode = false # start from scratch; do not resume from earlier runs
parallelTrain = false # Enable parallel training
# training config
TrainLRModel = { # command=TrainLRModel --> CNTK looks for a section named TrainLRModel
action = "train" # execute CNTK's 'train' routine
# network description
BrainScriptNetworkBuilder = [
# Logistic regression model definition
model(features) =
{
# parameters to learn
b = ParameterTensor{$labelDim$} # bias
w = ParameterTensor{($featureDim$ : $labelDim$)} # projection
# operations
z = Sigmoid(w * features + b)
}.z
# inputs
features = Input($featureDim$)
labels = Input($labelDim$)
# apply model to features
z = model(features)
# connect to model training system
lr = Logistic(labels, z) # optimization criterion
err = SquareError (labels, z) # model evaluation
featureNodes = (features)
labelNodes = (labels)
criterionNodes = (lr)
evaluationNodes = (err)
outputNodes = (z)
]
# configuration parameters of the SGD training procedure
SGD = {
epochSize = 0 # 0 means size of the training set
minibatchSize = 25
learningRatesPerSample = 0.04 # gradient contribution from each sample
maxEpochs = 10
# Parallel training specific options; only applicable when 'parallelTrain=true' at top level
parallelTrain = {
parallelizationMethod = "DataParallelSGD"
dataParallelSGD = { gradientBits = 1 }
}
}
# configuration of data reading
reader = {
randomize = true
deserializers = ({
type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
file = "Train_cntk_text.txt"
input = {
features = { dim = $featureDim$ ; format = "dense" }
labels = { dim = $labelDim$ ; format = "dense" }
}
})
}
}
# test config
TestModel = {
action = "eval"
reader = {
randomize = true
deserializers = ({
type = "CNTKTextFormatDeserializer" ; module = "CNTKTextFormatReader"
file = "Test_cntk_text.txt"
input = {
features = { dim = $featureDim$ ; format = "dense" }
labels = { dim = $labelDim$ ; format = "dense" }
}
})
}
}
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