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