CNTK/Tutorials/HelloWorld-LogisticRegression/lr_bs.cntk

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# 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 network description language BrainScript
# which commands to run
command=Train:Output:DumpNodeInfo:Test
# required...
modelPath = "Models/LR_reg.dnn" # where to write the model to
deviceId = -1 # -1 means CPU; use 0 for your first GPU, 1 for the second etc.
dimension = 2 # input data dimensions
# training config
Train = [ # command=Train --> CNTK will look for a parameter named Train
action = "train" # execute CNTK's 'train' routine
# network description
BrainScriptNetworkBuilder = [
# sample and label dimensions
SDim = $dimension$
LDim = 1
features = Input (SDim)
labels = Input (LDim)
# parameters to learn
b = Parameter (LDim, 1) # bias
w = Parameter (LDim, SDim) # weights
# operations
p = Sigmoid (w * features + b)
lr = Logistic (labels, p)
err = SquareError (labels, p)
# root nodes
featureNodes = (features)
labelNodes = (labels)
criterionNodes = (lr)
evaluationNodes = (err)
outputNodes = (p)
]
# configuration parameters of the SGD procedure
SGD = [
epochSize = 0 # =0 means size of the training set
minibatchSize = 25
learningRatesPerSample = 0.04 # gradient contribution from each sample
maxEpochs = 50
]
# configuration of data reading
reader = [
readerType = "CNTKTextFormatReader"
file = "Train_cntk_text.txt"
input = [
features = [
dim = $dimension$
format = "dense"
]
labels = [
dim = 1
format = "dense"
]
]
]
]
# test
Test = [
action = "test"
reader = [
readerType = "CNTKTextFormatReader"
file = "Test_cntk_text.txt"
input = [
features = [
dim = $dimension$
format = "dense"
]
labels = [
dim = 1
format = "dense"
]
]
]
]
# output the results
Output = [
action = "write"
reader = [
readerType = "CNTKTextFormatReader"
file = "Test_cntk_text.txt"
input = [
features = [
dim = $dimension$ # $$ means variable substitution
format = "dense"
]
labels = [
dim = 1 # label has 1 dimension
format = "dense"
]
]
]
outputPath = "LR.txt" # dump the output to this text file
]
# dump parameter values
DumpNodeInfo = [
action = "dumpNode"
printValues = true
]