85 строки
2.5 KiB
Plaintext
85 строки
2.5 KiB
Plaintext
# CNTK Configuration File for creating a slot tagger and an intent tagger.
|
|
|
|
command = TrainTagger:TestTagger
|
|
|
|
makeMode = false ; traceLevel = 0 ; deviceId = "auto"
|
|
|
|
rootDir = "." ; dataDir = "$rootDir$" ; modelDir = "$rootDir$/Models"
|
|
|
|
modelPath = "$modelDir$/slu.cmf"
|
|
|
|
vocabSize = 943 ; numLabels = 129 ; numIntents = 26 # number of words in vocab, slot labels, and intent labels
|
|
|
|
# The command to train the LSTM model
|
|
TrainTagger = {
|
|
action = "train"
|
|
BrainScriptNetworkBuilder = {
|
|
inputDim = $vocabSize$
|
|
labelDim = $numLabels$
|
|
embDim = 150
|
|
hiddenDim = 300
|
|
|
|
model = Sequential (
|
|
EmbeddingLayer {embDim} : # embedding
|
|
RecurrentLSTMLayer {hiddenDim, goBackwards=false} : # LSTM
|
|
DenseLayer {labelDim} # output layer
|
|
)
|
|
|
|
# features
|
|
query = Input {inputDim}
|
|
slotLabels = Input {labelDim}
|
|
|
|
# model application
|
|
z = model (query)
|
|
|
|
# loss and metric
|
|
ce = CrossEntropyWithSoftmax (slotLabels, z)
|
|
errs = ClassificationError (slotLabels, z)
|
|
|
|
featureNodes = (query)
|
|
labelNodes = (slotLabels)
|
|
criterionNodes = (ce)
|
|
evaluationNodes = (errs)
|
|
outputNodes = (z)
|
|
}
|
|
|
|
SGD = {
|
|
maxEpochs = 8 ; epochSize = 36000
|
|
|
|
minibatchSize = 70
|
|
|
|
learningRatesPerSample = 0.003*2:0.0015*12:0.0003
|
|
gradUpdateType = "fsAdaGrad"
|
|
gradientClippingWithTruncation = true ; clippingThresholdPerSample = 15.0
|
|
|
|
firstMBsToShowResult = 10 ; numMBsToShowResult = 100
|
|
}
|
|
|
|
reader = {
|
|
readerType = "CNTKTextFormatReader"
|
|
file = "$DataDir$/atis.train.ctf"
|
|
randomize = true
|
|
input = {
|
|
query = { alias = "S0" ; dim = $vocabSize$ ; format = "sparse" }
|
|
intentLabels = { alias = "S1" ; dim = $numIntents$ ; format = "sparse" }
|
|
slotLabels = { alias = "S2" ; dim = $numLabels$ ; format = "sparse" }
|
|
}
|
|
}
|
|
}
|
|
|
|
# Test the model's accuracy (as an error count)
|
|
TestTagger = {
|
|
action = "eval"
|
|
modelPath = $modelPath$
|
|
reader = {
|
|
readerType = "CNTKTextFormatReader"
|
|
file = "$DataDir$/atis.test.ctf"
|
|
randomize = false
|
|
input = {
|
|
query = { alias = "S0" ; dim = $vocabSize$ ; format = "sparse" }
|
|
intentLabels = { alias = "S1" ; dim = $numIntents$ ; format = "sparse" }
|
|
slotLabels = { alias = "S2" ; dim = $numLabels$ ; format = "sparse" }
|
|
}
|
|
}
|
|
}
|