def main():
full_path = os.path.realpath(__file__)
path, filename = os.path.split(full_path)
logging.config.fileConfig(os.path.join(path, 'logging.conf'), defaults={})
log = logging.getLogger(__name__)
if len(sys.argv) != 2:
log.error("Missing argument: <JSON config file>")
exit(1)
argsDict = JsonArgParser(PARAMETERS).parse(sys.argv[1])
args = dict2obj(argsDict, 'ShortDocArguments')
logging.getLogger(__name__).info(argsDict)
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
lr = args.lr
startSymbol = args.start_symbol
endSymbol = args.end_symbol
numEpochs = args.num_epochs
shuffle = args.shuffle
normalizeMethod = args.normalization
wordWindowSize = args.word_window_size
hiddenLayerSize = args.hidden_size
convSize = args.conv_size
# Load classes for filters.
filters = []
for filterName in args.filters:
moduleName, className = filterName.rsplit('.', 1)
log.info("Filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
W1 = None
b1 = None
W2 = None
b2 = None
wordEmbedding = None
if args.word_embedding:
log.info("Reading W2v File")
(wordLexicon, wordEmbedding) = Embedding.fromWord2Vec(args.word_embedding, unknownSymbol="__UNKNOWN__")
wordLexicon.stopAdd()
elif args.word_lexicon and args.word_emb_size:
wordLexicon = Lexicon.fromTextFile(args.word_lexicon, hasUnknowSymbol=False)
wordEmbedding = Embedding(wordLexicon, embeddingSize=args.word_emb_size)
wordLexicon.stopAdd()
else:
log.error("You must provide argument word_embedding or word_lexicon and word_emb_size")
# Create the lexicon of labels.
labelLexicon = None
if args.labels is not None:
if args.label_lexicon is not None:
log.error("Only one of the parameters label_lexicon and labels can be provided!")
exit(1)
labelLexicon = Lexicon.fromList(args.labels, hasUnknowSymbol=False)
elif args.label_lexicon is not None:
labelLexicon = Lexicon.fromTextFile(args.label_lexicon, hasUnknowSymbol=False)
else:
log.error("One of the parameters label_lexicon or labels must be provided!")
exit(1)
#
# Build the network model (Theano graph).
#
# TODO: debug
# theano.config.compute_test_value = 'warn'
# ex = trainIterator.next()
# inWords.tag.test_value = ex[0][0]
# outLabel.tag.test_value = ex[1][0]
# Matriz de entrada. Cada linha representa um token da oferta. Cada token é
# representado por uma janela de tokens (token central e alguns tokens
# próximos). Cada valor desta matriz corresponde a um índice que representa
# um token no embedding.
inWords = tensor.lmatrix("inWords")
# Categoria correta de uma oferta.
outLabel = tensor.lscalar("outLabel")
# List of input tensors. One for each input layer.
inputTensors = [inWords]
# Whether the word embedding will be updated during training.
embLayerTrainable = not args.fix_word_embedding
if not embLayerTrainable:
log.info("Not updating the word embedding!")
# Lookup table for word features.
embeddingLayer = EmbeddingLayer(inWords, wordEmbedding.getEmbeddingMatrix(), trainable=embLayerTrainable)
# if not args.train and args.load_wordEmbedding:
# attrs = np.load(args.load_wordEmbedding)
# embeddingLayer.load(attrs)
# log.info("Loaded word embedding (shape %s) from file %s" % (
# str(attrs[0].shape), args.load_wordEmbedding))
# A saída da lookup table possui 3 dimensões (numTokens, szWindow, szEmbedding).
# Esta camada dá um flat nas duas últimas dimensões, produzindo uma saída
# com a forma (numTokens, szWindow * szEmbedding).
flattenInput = FlattenLayer(embeddingLayer)
# Random weight initialization procedure.
weightInit = GlorotUniform()
# Convolution layer. Convolução no texto de uma oferta.
convW = None
convb = None
if not args.train and args.load_conv:
convNPY = np.load(args.load_conv)
convW = convNPY[0]
convb = convNPY[1]
log.info("Loaded convolutional layer (shape %s) from file %s" % (str(convW.shape), args.load_conv))
convLinear = LinearLayer(flattenInput,
wordWindowSize * wordEmbedding.getEmbeddingSize(),
convSize, W=convW, b=convb,
weightInitialization=weightInit)
if args.conv_act:
convOut = ActivationLayer(convLinear, tanh)
else:
convOut = convLinear
# Max pooling layer.
maxPooling = MaxPoolingLayer(convOut)
# Hidden layer.
if not args.train and args.load_hiddenLayer:
hiddenNPY = np.load(args.load_hiddenLayer)
W1 = hiddenNPY[0]
b1 = hiddenNPY[1]
log.info("Loaded hidden layer (shape %s) from file %s" % (str(W1.shape), args.load_hiddenLayer))
hiddenLinear = LinearLayer(maxPooling,
convSize,
hiddenLayerSize,
W=W1, b=b1,
weightInitialization=weightInit)
hiddenAct = ActivationLayer(hiddenLinear, tanh)
# Entrada linear da camada softmax.
if not args.train and args.load_softmax:
hiddenNPY = np.load(args.load_softmax)
W2 = hiddenNPY[0]
b2 = hiddenNPY[1]
log.info("Loaded softmax layer (shape %s) from file %s" % (str(W2.shape), args.load_softmax))
sotmaxLinearInput = LinearLayer(hiddenAct,
hiddenLayerSize,
labelLexicon.getLen(),
W=W2, b=b2,
weightInitialization=ZeroWeightGenerator())
# Softmax.
# softmaxAct = ReshapeLayer(ActivationLayer(sotmaxLinearInput, softmax), (1, -1))
softmaxAct = ActivationLayer(sotmaxLinearInput, softmax)
# Prediction layer (argmax).
prediction = ArgmaxPrediction(None).predict(softmaxAct.getOutput())
# Loss function.
if args.label_weights is not None and len(args.label_weights) != labelLexicon.getLen():
log.error("Number of label weights (%d) is different from number of labels (%d)!" % (
len(args.label_weights), labelLexicon.getLen()))
nlloe = NegativeLogLikelihoodOneExample(weights=args.label_weights)
loss = nlloe.calculateError(softmaxAct.getOutput()[0], prediction, outLabel)
# Input generators: word window.
inputGenerators = [WordWindowGenerator(wordWindowSize, wordLexicon, filters, startSymbol, endSymbol)]
# Output generator: generate one label per offer.
outputGenerators = [TextLabelGenerator(labelLexicon)]
# outputGenerators = [lambda label: labelLexicon.put(label)]
evalPerIteration = None
if args.train:
trainDatasetReader = ShortDocReader(args.train)
if args.load_method == "sync":
log.info("Reading training examples...")
trainIterator = SyncBatchIterator(trainDatasetReader,
inputGenerators,
outputGenerators,
- 1,
shuffle=shuffle)
wordLexicon.stopAdd()
elif args.load_method == "async":
log.info("Examples will be asynchronously loaded.")
trainIterator = AsyncBatchIterator(trainDatasetReader,
inputGenerators,
outputGenerators,
- 1,
shuffle=shuffle,
maxqSize=1000)
else:
log.error("The argument 'load_method' has an invalid value: %s." % args.load_method)
sys.exit(1)
labelLexicon.stopAdd()
# Get dev inputs and output
dev = args.dev
evalPerIteration = args.eval_per_iteration
if not dev and evalPerIteration > 0:
log.error("Argument eval_per_iteration cannot be used without a dev argument.")
sys.exit(1)
if dev:
log.info("Reading development examples")
devReader = ShortDocReader(args.dev)
devIterator = SyncBatchIterator(devReader,
inputGenerators,
outputGenerators,
- 1,
shuffle=False)
else:
devIterator = None
else:
trainIterator = None
devIterator = None
if normalizeMethod == "minmax":
log.info("Normalization: minmax")
wordEmbedding.minMaxNormalization()
elif normalizeMethod == "mean":
log.info("Normalization: mean normalization")
wordEmbedding.meanNormalization()
elif normalizeMethod == "zscore":
log.info("Normalization: zscore normalization")
wordEmbedding.zscoreNormalization()
elif normalizeMethod:
log.error("Normalization: unknown value %s" % normalizeMethod)
sys.exit(1)
# Decaimento da taxa de aprendizado.
decay = None
if args.decay == "none":
decay = 0.0
elif args.decay == "linear":
decay = 1.0
else:
log.error("Unknown decay parameter %s." % args.decay)
exit(1)
# Algoritmo de aprendizado.
if args.alg == "adagrad":
log.info("Using Adagrad")
opt = Adagrad(lr=lr, decay=decay)
elif args.alg == "sgd":
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
else:
log.error("Unknown algorithm: %s." % args.alg)
sys.exit(1)
# TODO: debug
# opt.lr.tag.test_value = 0.05
# Printing embedding information.
dictionarySize = wordEmbedding.getNumberOfVectors()
embeddingSize = wordEmbedding.getEmbeddingSize()
log.info("Dictionary size: %d" % dictionarySize)
log.info("Embedding size: %d" % embeddingSize)
log.info("Number of categories: %d" % labelLexicon.getLen())
# Train metrics.
trainMetrics = None
if trainIterator:
trainMetrics = [
LossMetric("TrainLoss", loss),
AccuracyMetric("TrainAccuracy", outLabel, prediction)
]
# Evaluation metrics.
evalMetrics = None
if devIterator:
evalMetrics = [
LossMetric("EvalLoss", loss),
AccuracyMetric("EvalAccuracy", outLabel, prediction),
FMetric("EvalFMetric", outLabel, prediction, labels=labelLexicon.getLexiconDict().values())
]
# Test metrics.
testMetrics = None
if args.test:
testMetrics = [
LossMetric("TestLoss", loss),
AccuracyMetric("TestAccuracy", outLabel, prediction),
FMetric("TestFMetric", outLabel, prediction, labels=labelLexicon.getLexiconDict().values())
]
# TODO: debug
# mode = theano.compile.debugmode.DebugMode(optimizer=None)
mode = None
model = BasicModel(x=inputTensors,
y=[outLabel],
allLayers=softmaxAct.getLayerSet(),
optimizer=opt,
prediction=prediction,
loss=loss,
trainMetrics=trainMetrics,
evalMetrics=evalMetrics,
testMetrics=testMetrics,
mode=mode)
# Training
if trainIterator:
log.info("Training")
model.train(trainIterator, numEpochs, devIterator, evalPerIteration=evalPerIteration)
# Saving model after training
if args.save_wordEmbedding:
embeddingLayer.saveAsW2V(args.save_wordEmbedding, lexicon=wordLexicon)
log.info("Saved word to vector to file: %s" % (args.save_wordEmbedding))
if args.save_conv:
convLinear.save(args.save_conv)
log.info("Saved convolution layer to file: %s" % (args.save_conv))
if args.save_hiddenLayer:
hiddenLinear.save(args.save_hiddenLayer)
log.info("Saved hidden layer to file: %s" % (args.save_hiddenLayer))
if args.save_softmax:
sotmaxLinearInput.save(args.save_softmax)
log.info("Saved softmax to file: %s" % (args.save_softmax))
# Testing
if args.test:
log.info("Reading test examples")
testReader = ShortDocReader(args.test)
testIterator = SyncBatchIterator(testReader,
inputGenerators,
outputGenerators,
- 1,
shuffle=False)
log.info("Testing")
model.test(testIterator)
def main(**kwargs):
log = logging.getLogger(__name__)
log.info(kwargs)
if kwargs["seed"] != None:
random.seed(kwargs["seed"])
np.random.seed(kwargs["seed"])
filters = []
for filterName in kwargs["filters"]:
moduleName, className = filterName.rsplit('.', 1)
log.info("Usando o filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
wordWindowSize = kwargs["word_window_size"]
hiddenLayerSize = kwargs["hidden_size"]
batchSize = kwargs["batch_size"]
startSymbol = kwargs["start_symbol"]
endSymbol = kwargs["end_symbol"]
numEpochs = kwargs["num_epochs"]
lr = kwargs["lr"]
tagLexicon = createLexiconUsingFile(kwargs["label_file"])
# _lambda = theano.shared(kwargs["lambda"], "lambda")
_lambda = theano.shared(0.0, "lambda")
useAdagrad = kwargs["adagrad"]
shuffle = kwargs["shuffle"]
supHiddenLayerSize = kwargs["hidden_size_supervised_part"]
unsupHiddenLayerSize = kwargs["hidden_size_unsupervised_part"]
normalization = kwargs["normalization"]
activationHiddenExtractor = kwargs["activation_hidden_extractor"]
withCharWNN = kwargs["with_charwnn"]
convSize = kwargs["conv_size"]
charEmbeddingSize = kwargs["char_emb_size"]
charWindowSize = kwargs["char_window_size"]
startSymbolChar = "</s>"
if kwargs["charwnn_with_act"]:
charAct = tanh
else:
charAct = None
# TODO: the maximum number of characters of word is fixed in 20.
numMaxChar = 20
if kwargs["decay"].lower() == "normal":
decay = 0.0
elif kwargs["decay"].lower() == "divide_epoch":
decay = 1.0
# Add the lexicon of target
domainLexicon = Lexicon()
domainLexicon.put("0")
domainLexicon.put("1")
domainLexicon.stopAdd()
log.info("Reading W2v File1")
wordEmbedding = EmbeddingFactory().createFromW2V(kwargs["word_embedding"],
RandomUnknownStrategy())
log.info("Reading training examples")
# Generators
inputGenerators = [
WordWindowGenerator(wordWindowSize, wordEmbedding, filters,
startSymbol)
]
outputGeneratorTag = LabelGenerator(tagLexicon)
if withCharWNN:
# Create the character embedding
charEmbedding = EmbeddingFactory().createRandomEmbedding(
charEmbeddingSize)
# Insert the padding of the character window
charEmbedding.put(startSymbolChar)
# Insert the character that will be used to fill the matrix
# with a dimension lesser than chosen dimension.This enables that the convolution is performed by a matrix multiplication.
artificialChar = "ART_CHAR"
charEmbedding.put(artificialChar)
inputGenerators.append(
CharacterWindowGenerator(charEmbedding,
numMaxChar,
charWindowSize,
wordWindowSize,
artificialChar,
startSymbolChar,
startPaddingWrd=startSymbol,
endPaddingWrd=endSymbol))
unsupervisedLabelSource = ConstantLabel(domainLexicon, "0")
# Reading supervised and unsupervised data sets.
trainSupervisedDatasetReader = TokenLabelReader(
kwargs["train_source"], kwargs["token_label_separator"])
trainSupervisedBatch = SyncBatchIterator(
trainSupervisedDatasetReader,
inputGenerators, [outputGeneratorTag, unsupervisedLabelSource],
batchSize[0],
shuffle=shuffle)
# Get Unsupervised Input
unsupervisedLabelTarget = ConstantLabel(domainLexicon, "1")
trainUnsupervisedDatasetReader = TokenReader(kwargs["train_target"])
trainUnsupervisedDatasetBatch = SyncBatchIterator(
trainUnsupervisedDatasetReader,
inputGenerators, [unsupervisedLabelTarget],
batchSize[1],
shuffle=shuffle)
# Stopping to add new words, labels and chars
wordEmbedding.stopAdd()
tagLexicon.stopAdd()
domainLexicon.stopAdd()
if withCharWNN:
charEmbedding.stopAdd()
# Printing embedding information
dictionarySize = wordEmbedding.getNumberOfVectors()
embeddingSize = wordEmbedding.getEmbeddingSize()
log.info("Size of word dictionary and word embedding size: %d and %d" %
(dictionarySize, embeddingSize))
log.info(
"Size of char dictionary and char embedding size: %d and %d" %
(charEmbedding.getNumberOfVectors(), charEmbedding.getEmbeddingSize()))
# Word Embedding Normalization
if normalization == "zscore":
wordEmbedding.zscoreNormalization()
elif normalization == "minmax":
wordEmbedding.minMaxNormalization()
elif normalization == "mean":
wordEmbedding.meanNormalization()
elif normalization == "none" or not normalization:
pass
else:
raise Exception()
# Source input
wordWindowSource = T.lmatrix(name="windowSource")
sourceInput = [wordWindowSource]
# Create the layers related with the extractor of features
embeddingLayerSrc = EmbeddingLayer(wordWindowSource,
wordEmbedding.getEmbeddingMatrix(),
trainable=True)
flattenSrc = FlattenLayer(embeddingLayerSrc)
if withCharWNN:
log.info("Using charwnn")
# Create the charwn
charWindowIdxSrc = T.ltensor4(name="char_window_idx_source")
sourceInput.append(charWindowIdxSrc)
charEmbeddingConvLayerSrc = EmbeddingConvolutionalLayer(
charWindowIdxSrc, charEmbedding.getEmbeddingMatrix(), numMaxChar,
convSize, charWindowSize, charEmbeddingSize, charAct)
layerBeforeLinearSrc = ConcatenateLayer(
[flattenSrc, charEmbeddingConvLayerSrc])
sizeLayerBeforeLinearSrc = wordWindowSize * (
wordEmbedding.getEmbeddingSize() + convSize)
else:
layerBeforeLinearSrc = flattenSrc
sizeLayerBeforeLinearSrc = wordWindowSize * wordEmbedding.getEmbeddingSize(
)
if activationHiddenExtractor == "tanh":
log.info("Using tanh in the hidden layer of extractor")
linear1 = LinearLayer(layerBeforeLinearSrc,
sizeLayerBeforeLinearSrc,
hiddenLayerSize,
weightInitialization=GlorotUniform())
act1 = ActivationLayer(linear1, tanh)
elif activationHiddenExtractor == "sigmoid":
log.info("Using sigmoid in the hidden layer of extractor")
linear1 = LinearLayer(layerBeforeLinearSrc,
sizeLayerBeforeLinearSrc,
hiddenLayerSize,
weightInitialization=SigmoidGenerator())
act1 = ActivationLayer(linear1, sigmoid)
else:
raise Exception()
# Create the layers with the Tagger
if supHiddenLayerSize == 0:
layerBeforeSupSoftmax = act1
layerSizeBeforeSupSoftmax = hiddenLayerSize
log.info("It didn't insert the layer before the supervised softmax.")
else:
linear2 = LinearLayer(act1,
hiddenLayerSize,
supHiddenLayerSize,
weightInitialization=GlorotUniform())
act2 = ActivationLayer(linear2, tanh)
layerBeforeSupSoftmax = act2
layerSizeBeforeSupSoftmax = supHiddenLayerSize
log.info("It inserted the layer before the supervised softmax.")
supervisedLinear = LinearLayer(layerBeforeSupSoftmax,
layerSizeBeforeSupSoftmax,
tagLexicon.getLen(),
weightInitialization=ZeroWeightGenerator())
supervisedSoftmax = ActivationLayer(supervisedLinear, softmax)
# Create the layers with the domain classifier
gradientReversalSource = GradientReversalLayer(act1, _lambda)
if unsupHiddenLayerSize == 0:
layerBeforeUnsupSoftmax = gradientReversalSource
layerSizeBeforeUnsupSoftmax = hiddenLayerSize
log.info("It didn't insert the layer before the unsupervised softmax.")
else:
unsupervisedSourceLinearBf = LinearLayer(
gradientReversalSource,
hiddenLayerSize,
unsupHiddenLayerSize,
weightInitialization=GlorotUniform())
actUnsupervisedSourceBf = ActivationLayer(unsupervisedSourceLinearBf,
tanh)
layerBeforeUnsupSoftmax = actUnsupervisedSourceBf
layerSizeBeforeUnsupSoftmax = unsupHiddenLayerSize
log.info("It inserted the layer before the unsupervised softmax.")
unsupervisedSourceLinear = LinearLayer(
layerBeforeUnsupSoftmax,
layerSizeBeforeUnsupSoftmax,
domainLexicon.getLen(),
weightInitialization=ZeroWeightGenerator())
unsupervisedSourceSoftmax = ActivationLayer(unsupervisedSourceLinear,
softmax)
## Target Part
windowTarget = T.lmatrix(name="windowTarget")
targetInput = [windowTarget]
# Create the layers related with the extractor of features
embeddingLayerUnsuper1 = EmbeddingLayer(
windowTarget, embeddingLayerSrc.getParameters()[0], trainable=True)
flattenUnsuper1 = FlattenLayer(embeddingLayerUnsuper1)
if withCharWNN:
log.info("Using charwnn")
# Create the charwn
charWindowIdxTgt = T.ltensor4(name="char_window_idx_target")
targetInput.append(charWindowIdxTgt)
charEmbeddingConvLayerTgt = EmbeddingConvolutionalLayer(
charWindowIdxTgt,
charEmbeddingConvLayerSrc.getParameters()[0],
numMaxChar,
convSize,
charWindowSize,
charEmbeddingSize,
charAct,
trainable=True)
layerBeforeLinearTgt = ConcatenateLayer(
[flattenUnsuper1, charEmbeddingConvLayerTgt])
sizeLayerBeforeLinearTgt = wordWindowSize * (
wordEmbedding.getEmbeddingSize() + convSize)
else:
layerBeforeLinearTgt = flattenUnsuper1
sizeLayerBeforeLinearTgt = wordWindowSize * wordEmbedding.getEmbeddingSize(
)
w, b = linear1.getParameters()
linearUnsuper1 = LinearLayer(layerBeforeLinearTgt,
sizeLayerBeforeLinearTgt,
hiddenLayerSize,
W=w,
b=b,
trainable=True)
if activationHiddenExtractor == "tanh":
log.info("Using tanh in the hidden layer of extractor")
actUnsupervised1 = ActivationLayer(linearUnsuper1, tanh)
elif activationHiddenExtractor == "sigmoid":
log.info("Using sigmoid in the hidden layer of extractor")
actUnsupervised1 = ActivationLayer(linearUnsuper1, sigmoid)
else:
raise Exception()
# Create the layers with the domain classifier
grandientReversalTarget = GradientReversalLayer(actUnsupervised1, _lambda)
if unsupHiddenLayerSize == 0:
layerBeforeUnsupSoftmax = grandientReversalTarget
layerSizeBeforeUnsupSoftmax = hiddenLayerSize
log.info("It didn't insert the layer before the unsupervised softmax.")
else:
w, b = unsupervisedSourceLinearBf.getParameters()
unsupervisedTargetLinearBf = LinearLayer(grandientReversalTarget,
hiddenLayerSize,
unsupHiddenLayerSize,
W=w,
b=b,
trainable=True)
actUnsupervisedTargetLinearBf = ActivationLayer(
unsupervisedTargetLinearBf, tanh)
layerBeforeUnsupSoftmax = actUnsupervisedTargetLinearBf
layerSizeBeforeUnsupSoftmax = unsupHiddenLayerSize
log.info("It inserted the layer before the unsupervised softmax.")
w, b = unsupervisedSourceLinear.getParameters()
unsupervisedTargetLinear = LinearLayer(layerBeforeUnsupSoftmax,
layerSizeBeforeUnsupSoftmax,
domainLexicon.getLen(),
W=w,
b=b,
trainable=True)
unsupervisedTargetSoftmax = ActivationLayer(unsupervisedTargetLinear,
softmax)
# Set loss and prediction and retrieve all layers
supervisedLabel = T.lvector("supervisedLabel")
unsupervisedLabelSource = T.lvector("unsupervisedLabelSource")
unsupervisedLabelTarget = T.lvector("unsupervisedLabelTarget")
supervisedOutput = supervisedSoftmax.getOutput()
supervisedPrediction = ArgmaxPrediction(1).predict(supervisedOutput)
supervisedLoss = NegativeLogLikelihood().calculateError(
supervisedOutput, supervisedPrediction, supervisedLabel)
unsupervisedOutputSource = unsupervisedSourceSoftmax.getOutput()
unsupervisedPredSource = ArgmaxPrediction(1).predict(
unsupervisedOutputSource)
unsupervisedLossSource = NegativeLogLikelihood().calculateError(
unsupervisedOutputSource, None, unsupervisedLabelSource)
unsupervisedOutputTarget = unsupervisedTargetSoftmax.getOutput()
unsupervisedPredTarget = ArgmaxPrediction(1).predict(
unsupervisedOutputTarget)
unsupervisedLossTarget = NegativeLogLikelihood().calculateError(
unsupervisedOutputTarget, None, unsupervisedLabelTarget)
# Creates model
if useAdagrad:
log.info("Using ADAGRAD")
opt = Adagrad(lr=lr, decay=decay)
else:
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
allLayersSource = supervisedSoftmax.getLayerSet(
) | unsupervisedSourceSoftmax.getLayerSet()
allLayersTarget = unsupervisedTargetSoftmax.getLayerSet()
unsupervisedLossTarget *= float(
trainSupervisedBatch.size()) / trainUnsupervisedDatasetBatch.size()
supervisedTrainMetrics = [
LossMetric("TrainSupervisedLoss", supervisedLoss),
AccuracyMetric("TrainSupervisedAcc", supervisedLabel,
supervisedPrediction),
LossMetric("TrainUnsupervisedLoss", unsupervisedLossSource),
AccuracyMetric("TrainUnsupervisedAccuracy", unsupervisedLabelSource,
unsupervisedPredSource)
]
unsupervisedTrainMetrics = [
LossMetric("TrainUnsupervisedLoss", unsupervisedLossTarget),
AccuracyMetric("TrainUnsupervisedAccuracy", unsupervisedLabelTarget,
unsupervisedPredTarget)
]
evalMetrics = [
AccuracyMetric("EvalAcc", supervisedLabel, supervisedPrediction)
]
testMetrics = [
AccuracyMetric("TestAcc", supervisedLabel, supervisedPrediction)
]
#TODO: Não tive tempo de testar o código depois das modificações
GradientReversalModel(sourceInput,
targetInput,
supervisedLabel,
unsupervisedLabelSource,
unsupervisedLabelTarget,
allLayersSource,
allLayersTarget,
opt,
supervisedPrediction,
supervisedLoss,
unsupervisedLossSource,
unsupervisedLossTarget,
supervisedTrainMetrics,
unsupervisedTrainMetrics,
evalMetrics,
testMetrics,
mode=None)
# Get dev inputs and output
log.info("Reading development examples")
devDatasetReader = TokenLabelReader(kwargs["dev"],
kwargs["token_label_separator"])
devReader = SyncBatchIterator(devDatasetReader,
inputGenerators, [outputGeneratorTag],
sys.maxint,
shuffle=False)
callbacks = []
# log.info("Usando lambda fixo: " + str(_lambda.get_value()))
log.info("Usando lambda variado. alpha=" + str(kwargs["alpha"]) +
" height=" + str(kwargs["height"]))
callbacks.append(
ChangeLambda(_lambda, kwargs["alpha"], numEpochs, kwargs["height"]))
if kwargs["additional_dev"]:
callbacks.append(
AdditionalDevDataset(model, kwargs["additional_dev"],
kwargs["token_label_separator"],
inputGenerators, outputGeneratorTag))
# Training Model
model.train([trainSupervisedBatch, trainUnsupervisedDatasetBatch],
numEpochs,
devReader,
callbacks=callbacks)
def trainNetwork(args, log, trainIterator, devIterator, wordEmbedding, charEmbedding, borrow, labelLexicon):
# Build neural network.
wordWindow = T.lmatrix("word_window")
inputModel = [wordWindow]
wordEmbeddingLayer = EmbeddingLayer(wordWindow, wordEmbedding.getEmbeddingMatrix(), borrow=borrow,
structGrad=args.struct_grad, trainable=True, name="word_embedding_layer")
flatWordEmbedding = FlattenLayer(wordEmbeddingLayer)
charWindowIdxs = T.ltensor4(name="char_window_idx")
inputModel.append(charWindowIdxs)
# # TODO: debug
# theano.config.compute_test_value = 'warn'
# ex = trainIterator.next()
# inWords.tag.test_value = ex[0][0]
# outLabel.tag.test_value = ex[1][0]
charEmbeddingConvLayer = EmbeddingConvolutionalLayer(charWindowIdxs, charEmbedding.getEmbeddingMatrix(), 20,
args.conv_size, args.char_window_size, args.char_emb_size,
tanh, structGrad=args.char_struct_grad,
name="char_convolution_layer", borrow=borrow)
layerBeforeLinear = ConcatenateLayer([flatWordEmbedding, charEmbeddingConvLayer])
sizeLayerBeforeLinear = args.word_window_size * (wordEmbedding.getEmbeddingSize() + args.conv_size)
hiddenActFunction = method_name(args.hidden_activation_function)
weightInit = SigmoidGlorot() if hiddenActFunction == sigmoid else GlorotUniform()
linearHidden = LinearLayer(layerBeforeLinear, sizeLayerBeforeLinear, args.hidden_size,
weightInitialization=weightInit, name="linear1")
actHidden = ActivationLayer(linearHidden, hiddenActFunction)
linearSoftmax = LinearLayer(actHidden, args.hidden_size, labelLexicon.getLen(),
weightInitialization=ZeroWeightGenerator(), name="linear_softmax")
actSoftmax = ActivationLayer(linearSoftmax, softmax)
prediction = ArgmaxPrediction(1).predict(actSoftmax.getOutput())
# Output symbolic tensor variable.
y = T.lvector("y")
if args.decay.lower() == "normal":
decay = 0.0
elif args.decay.lower() == "divide_epoch":
decay = 1.0
else:
log.error("Unknown decay argument: %s" % args.decay)
sys.exit(1)
if args.adagrad:
log.info("Training algorithm: Adagrad")
opt = Adagrad(lr=args.lr, decay=decay)
else:
log.info("Training algorithm: SGD")
opt = SGD(lr=args.lr, decay=decay)
# Training loss function.
loss = NegativeLogLikelihood().calculateError(actSoftmax.getOutput(), prediction, y)
# L2 regularization.
if args.l2:
loss += args.l2 * (T.sum(T.square(linearHidden.getParameters()[0])))
# # TODO: debug
# opt.lr.tag.test_value = 0.02
# Metrics.
trainMetrics = [
LossMetric("LossTrain", loss, True),
AccuracyMetric("AccTrain", y, prediction)
]
evalMetrics = None
if args.dev:
evalMetrics = [
LossMetric("LossDev", loss, True),
AccuracyMetric("AccDev", y, prediction),
CustomMetric("CustomMetricDev", y, prediction)
]
testMetrics = None
if args.test:
testMetrics = [
CustomMetric("CustomMetricTest", y, prediction)
]
log.info("Compiling the network...")
# # TODO: debug
# mode = theano.compile.debugmode.DebugMode(optimizer=None)
mode = None
wnnModel = BasicModel(inputModel, [y], actSoftmax.getLayerSet(), opt, prediction, loss, trainMetrics=trainMetrics,
evalMetrics=evalMetrics, testMetrics=testMetrics, mode=mode)
log.info("Training...")
wnnModel.train(trainIterator, args.num_epochs, devIterator)
def optimizer(optim_name, epsilon=None, gamma=None):
if optim_name == 'SGD':
optim = SGD(gamma=gamma, epsilon=epsilon)
else:
raise NotImplementedError
return optim
def main(args):
log = logging.getLogger(__name__)
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
lr = args.lr
startSymbol = args.start_symbol
endSymbol = args.end_symbol
numEpochs = args.num_epochs
shuffle = args.shuffle
normalizeMethod = args.normalization
wordWindowSize = args.word_window_size
hiddenLayerSize = args.hidden_size
convSize = args.conv_size
# Load classes for filters.
filters = []
for filterName in args.filters:
moduleName, className = filterName.rsplit('.', 1)
log.info("Filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
loadPath = args.load_model
if loadPath:
with codecs.open(loadPath + ".param", "r",
encoding="utf-8") as paramsFile:
param = json.load(paramsFile, encoding="utf-8")
hiddenActFunctionName = param['hiddenActFunction']
hiddenActFunction = method_name(hiddenActFunctionName)
# Loading Embedding
log.info("Loading Model")
wordEmbedding = EmbeddingFactory().createFromW2V(
loadPath + ".wv", ChosenUnknownStrategy(param["unknown"]))
labelLexicon = Lexicon()
for l in param["labels"]:
labelLexicon.put(l)
labelLexicon.stopAdd()
# Loading model
labelWeights = np.load(loadPath + ".npy").item(0)
W1 = labelWeights["W_Hidden"]
b1 = labelWeights["b_Hidden"]
W2 = labelWeights["W_Softmax"]
b2 = labelWeights["b_Softmax"]
hiddenLayerSize = b1.shape[0]
else:
W1 = None
b1 = None
W2 = None
b2 = None
hiddenActFunctionName = args.hidden_activation_function
hiddenActFunction = method_name(hiddenActFunctionName)
if args.word_embedding:
log.info("Reading W2v File")
wordEmbedding = EmbeddingFactory().createFromW2V(
args.word_embedding, RandomUnknownStrategy())
wordEmbedding.stopAdd()
elif args.hash_lex_size:
wordEmbedding = RandomEmbedding(args.word_emb_size,
RandomUnknownStrategy(),
HashLexicon(args.hash_lex_size))
else:
wordEmbedding = EmbeddingFactory().createRandomEmbedding(
args.word_emb_size)
# Get the inputs and output
if args.labels:
labelLexicon = createLexiconUsingFile(args.labels)
else:
labelLexicon = Lexicon()
if args.load_hidden_layer:
# Loading Hidden Layer
log.info("Loading Hidden Layer")
hl = np.load(args.load_hidden_layer).item(0)
W1 = hl["W_Encoder"]
b1 = hl["b_Encoder"]
hiddenLayerSize = b1.shape[0]
#
# Build the network model (Theano graph).
#
# TODO: debug
# theano.config.compute_test_value = 'warn'
# ex = trainIterator.next()
# inWords.tag.test_value = ex[0][0]
# outLabel.tag.test_value = ex[1][0]
# Matriz de entrada. Cada linha representa um token da oferta. Cada token é
# representado por uma janela de tokens (token central e alguns tokens
# próximos). Cada valor desta matriz corresponde a um índice que representa
# um token no embedding.
inWords = T.lmatrix("inWords")
# Categoria correta de uma oferta.
outLabel = T.lscalar("outLabel")
# List of input tensors. One for each input layer.
inputTensors = [inWords]
# Whether the word embedding will be updated during training.
embLayerTrainable = not args.fix_word_embedding
if not embLayerTrainable:
log.info("Not updating the word embedding!")
# Lookup table for word features.
embeddingLayer = EmbeddingLayer(inWords,
wordEmbedding.getEmbeddingMatrix(),
trainable=embLayerTrainable)
# A saída da lookup table possui 3 dimensões (numTokens, szWindow, szEmbedding).
# Esta camada dá um flat nas duas últimas dimensões, produzindo uma saída
# com a forma (numTokens, szWindow * szEmbedding).
flattenInput = FlattenLayer(embeddingLayer)
# Random weight initialization procedure.
weightInit = SigmoidGlorot(
) if hiddenActFunction == sigmoid else GlorotUniform()
# Convolution layer. Convolução no texto de uma oferta.
convLinear = LinearLayer(flattenInput,
wordWindowSize * wordEmbedding.getEmbeddingSize(),
convSize,
W=None,
b=None,
weightInitialization=weightInit)
# Max pooling layer.
maxPooling = MaxPoolingLayer(convLinear)
# List of input layers (will be concatenated).
inputLayers = [maxPooling]
# Generate word windows.
wordWindowFeatureGenerator = WordWindowGenerator(wordWindowSize,
wordEmbedding, filters,
startSymbol, endSymbol)
# List of input generators.
inputGenerators = [
lambda offer: wordWindowFeatureGenerator(offer["tokens"])
]
concatenatedSize = convSize
# Additional features.
if args.categorical_features is not None:
log.info("Using categorical features: %s" %
str([ftr[0] for ftr in args.categorical_features]))
for ftr in args.categorical_features:
concatenatedSize += ftr[2]
ftrLexicon = createLexiconUsingFile(ftr[1])
ftrEmbedding = RandomEmbedding(
embeddingSize=ftr[2],
unknownGenerateStrategy=RandomUnknownStrategy(),
lexicon=ftrLexicon,
)
ftrInput = T.lscalar("in_" + ftr[0])
ftrLayer = EmbeddingLayer(ftrInput,
ftrEmbedding.getEmbeddingMatrix())
inputGenerators.append(
lambda offer: ftrLexicon.put(offer[ftr[0]].strip().lower()))
inputTensors.append(ftrInput)
inputLayers.append(ftrLayer)
log.info("Input layers: %s" % str(inputLayers))
# Concatenate all input layers, when there are more thean one input layer.
concatenatedInLayers = maxPooling if len(
inputLayers) == 1 else ConcatenateLayer(inputLayers, axis=0)
if args.include_hidden_layer:
# Hidden layer.
hiddenLinear = LinearLayer(concatenatedInLayers,
concatenatedSize,
hiddenLayerSize,
W=W1,
b=b1,
weightInitialization=weightInit)
hiddenAct = ActivationLayer(hiddenLinear, hiddenActFunction)
else:
# Do not use a hidden layer.
log.info("Not using hidden layer!")
hiddenAct = concatenatedInLayers
hiddenLayerSize = concatenatedSize
# Entrada linear da camada softmax.
sotmaxLinearInput = LinearLayer(hiddenAct,
hiddenLayerSize,
labelLexicon.getLen(),
W=W2,
b=b2,
weightInitialization=ZeroWeightGenerator())
# Softmax.
# softmaxAct = ReshapeLayer(ActivationLayer(sotmaxLinearInput, softmax), (1, -1))
softmaxAct = ActivationLayer(sotmaxLinearInput, softmax)
# Prediction layer (argmax).
prediction = ArgmaxPrediction(None).predict(softmaxAct.getOutput())
# Class weights.
labelWeights = None
if args.labels_probs:
numLabels = labelLexicon.getLen()
labelWeights = np.zeros(numLabels, dtype=theano.config.floatX)
if args.labels_probs.startswith("@"):
# Load the dictionary from a JSON file.
with codecs.open(args.labels_probs[1:], mode="r",
encoding="utf8") as f:
labelDistribution = json.load(f)
else:
# The argument value is already a JSON.
labelDistribution = json.loads(args.labels_probs)
for k, v in labelDistribution.items():
# The weight of a class is inversely-proportional to its frequency.
labelWeights[labelLexicon.getLexiconIndex(k)] = 1.0 / v
if args.labels_weights_log:
# Attenuate weights for highly unbalanced classes.
labelWeights = np.log(labelWeights)
log.info("Label weights: " + str(labelWeights))
# Loss function.
loss = NegativeLogLikelihoodOneExample(labelWeights).calculateError(
softmaxAct.getOutput()[0], prediction, outLabel)
# Output generator: generate one label per offer.
outputGenerators = [TextLabelGenerator(labelLexicon)]
if args.train:
trainDatasetReader = OfertasReader(args.train)
if args.load_method == "sync":
log.info("Reading training examples...")
trainIterator = SyncBatchIterator(trainDatasetReader,
intputGenerators,
outputGenerators,
-1,
shuffle=shuffle)
wordEmbedding.stopAdd()
elif args.load_method == "async":
log.info("Examples will be asynchronously loaded.")
trainIterator = AsyncBatchIterator(trainDatasetReader,
inputGenerators,
outputGenerators,
-1,
shuffle=shuffle,
maxqSize=1000)
else:
log.error("The argument 'load_method' has an invalid value: %s." %
args.load_method)
sys.exit(1)
labelLexicon.stopAdd()
# Get dev inputs and output
dev = args.dev
evalPerIteration = args.eval_per_iteration
if not dev and evalPerIteration > 0:
log.error(
"Argument eval_per_iteration cannot be used without a dev argument."
)
sys.exit(1)
if dev:
log.info("Reading development examples")
devReader = OfertasReader(args.dev)
devIterator = SyncBatchIterator(devReader,
inputGenerators,
outputGenerators,
-1,
shuffle=False)
else:
devIterator = None
else:
trainIterator = None
devIterator = None
if normalizeMethod == "minmax":
log.info("Normalization: minmax")
wordEmbedding.minMaxNormalization()
elif normalizeMethod == "mean":
log.info("Normalization: mean normalization")
wordEmbedding.meanNormalization()
elif normalizeMethod == "zscore":
log.info("Normalization: zscore normalization")
wordEmbedding.zscoreNormalization()
elif normalizeMethod:
log.error("Normalization: unknown value %s" % normalizeMethod)
sys.exit(1)
if normalizeMethod is not None and loadPath is not None:
log.warn(
"The word embedding of model was normalized. This can change the result of test."
)
# if kwargs["lambda"]:
# _lambda = kwargs["lambda"]
# log.info("Using L2 with lambda= %.2f", _lambda)
# loss += _lambda * (T.sum(T.square(hiddenLinear.getParameters()[0])))
# Decaimento da taxa de aprendizado.
decay = 0.0
if args.decay == "linear":
decay = 1.0
# Algoritmo de aprendizado.
if args.alg == "adagrad":
log.info("Using Adagrad")
opt = Adagrad(lr=lr, decay=decay)
elif args.alg == "sgd":
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
else:
log.error(
"Unknown algorithm: %s. Expected values are: adagrad or sgd." %
args.alg)
sys.exit(1)
# TODO: debug
# opt.lr.tag.test_value = 0.05
# Printing embedding information.
dictionarySize = wordEmbedding.getNumberOfVectors()
embeddingSize = wordEmbedding.getEmbeddingSize()
log.info("Dictionary size: %d" % dictionarySize)
log.info("Embedding size: %d" % embeddingSize)
log.info("Number of categories: %d" % labelLexicon.getLen())
# Train metrics.
trainMetrics = None
if trainIterator:
trainMetrics = [
LossMetric("TrainLoss", loss),
AccuracyMetric("TrainAccuracy", outLabel, prediction)
]
# Evaluation metrics.
evalMetrics = None
if devIterator:
evalMetrics = [
LossMetric("EvalLoss", loss),
AccuracyMetric("EvalAccuracy", outLabel, prediction),
FMetric("EvalFMetric",
outLabel,
prediction,
labels=labelLexicon.getLexiconDict().values())
]
# Test metrics.
testMetrics = None
if args.test:
testMetrics = [
LossMetric("TestLoss", loss),
AccuracyMetric("TestAccuracy", outLabel, prediction),
FMetric("TestFMetric",
outLabel,
prediction,
labels=labelLexicon.getLexiconDict().values())
]
if args.test_probs:
# Append predicted probabilities for the test set.
testMetrics.append(
PredictedProbabilities("TestProbs", softmaxAct.getOutput()))
else:
if args.test_probs:
log.error(
"The option test_probs requires a test dataset (option test).")
sys.exit(1)
# TODO: debug
# mode = theano.compile.debugmode.DebugMode(optimizer=None)
mode = None
model = Model(x=inputTensors,
y=[outLabel],
allLayers=softmaxAct.getLayerSet(),
optimizer=opt,
prediction=prediction,
loss=loss,
trainMetrics=trainMetrics,
evalMetrics=evalMetrics,
testMetrics=testMetrics,
mode=mode)
# Training
if trainIterator:
callback = []
if args.save_model:
savePath = args.save_model
modelWriter = OfertasModelWritter(savePath, embeddingLayer,
hiddenLinear, sotmaxLinearInput,
wordEmbedding, labelLexicon,
hiddenActFunctionName)
callback.append(SaveModelCallback(modelWriter, "eval_acc", True))
log.info("Training")
model.train(trainIterator,
numEpochs,
devIterator,
evalPerIteration=evalPerIteration,
callbacks=callback)
# Testing
if args.test:
log.info("Reading test examples")
testReader = OfertasReader(args.test)
testIterator = SyncBatchIterator(testReader,
inputGenerators,
outputGenerators,
-1,
shuffle=False)
log.info("Testing")
model.test(testIterator)
learning_rate = 0.01
# OPTIMIZER
OPTIMIZER = 'SGD'
assert DATA_NAME in ['Contracept', 'Heart', 'Yeast']
assert OPTIMIZER == 'SGD'
# Load dataset, model and evaluation metric
train_data, test_data, logistic_regression = _initialize(DATA_NAME)
train_x, train_y = train_data
num_data, num_features = train_x.shape
num_class = len(np.unique(train_y))
optim = SGD()
model = LogisticRegression(num_features)
loss, epoch = model.train(train_x, train_y, batch_size, num_epochs,
learning_rate, optim)
test_x, test_y = test_data
hypo, pred = model.predict(test_x)
pred = np.squeeze(pred)
# ======== Edit here =========
result = evaluation_test1(test_y, pred, at=90)
# =============================
for key in result.keys():
print(key, '\t\t\t:\t\t %.6f' % result[key])
def main():
full_path = os.path.realpath(__file__)
path, filename = os.path.split(full_path)
logging.config.fileConfig(os.path.join(path, 'logging.conf'), defaults={})
log = logging.getLogger(__name__)
if len(sys.argv) != 3:
log.error("Missing argument: <JSON config file> or/and <Input file>")
exit(1)
argsDict = JsonArgParser(PARAMETERS).parse(sys.argv[1])
args = dict2obj(argsDict, 'ShortDocArguments')
logging.getLogger(__name__).info(argsDict)
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
lr = args.lr
startSymbol = args.start_symbol
endSymbol = args.end_symbol
numEpochs = args.num_epochs
shuffle = args.shuffle
normalizeMethod = args.normalization
wordWindowSize = args.word_window_size
hiddenLayerSize = args.hidden_size
convSize = args.conv_size
# Load classes for filters.
filters = []
for filterName in args.filters:
moduleName, className = filterName.rsplit('.', 1)
log.info("Filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
W1 = None
b1 = None
W2 = None
b2 = None
wordEmbedding = None
if args.word_embedding:
log.info("Reading W2v File")
(wordLexicon,
wordEmbedding) = Embedding.fromWord2Vec(args.word_embedding,
unknownSymbol="__UNKNOWN__")
wordLexicon.stopAdd()
elif args.word_lexicon and args.word_emb_size:
wordLexicon = Lexicon.fromTextFile(args.word_lexicon,
hasUnknowSymbol=False)
wordEmbedding = Embedding(wordLexicon,
embeddingSize=args.word_emb_size)
wordLexicon.stopAdd()
else:
log.error(
"You must provide argument word_embedding or word_lexicon and word_emb_size"
)
# Create the lexicon of labels.
labelLexicon = None
if args.labels is not None:
if args.label_lexicon is not None:
log.error(
"Only one of the parameters label_lexicon and labels can be provided!"
)
exit(1)
labelLexicon = Lexicon.fromList(args.labels, hasUnknowSymbol=False)
elif args.label_lexicon is not None:
labelLexicon = Lexicon.fromTextFile(args.label_lexicon,
hasUnknowSymbol=False)
else:
log.error(
"One of the parameters label_lexicon or labels must be provided!")
exit(1)
#
# Build the network model (Theano graph).
#
# TODO: debug
# theano.config.compute_test_value = 'warn'
# ex = trainIterator.next()
# inWords.tag.test_value = ex[0][0]
# outLabel.tag.test_value = ex[1][0]
# Matriz de entrada. Cada linha representa um token da oferta. Cada token é
# representado por uma janela de tokens (token central e alguns tokens
# próximos). Cada valor desta matriz corresponde a um índice que representa
# um token no embedding.
inWords = tensor.lmatrix("inWords")
# Categoria correta de uma oferta.
outLabel = tensor.lscalar("outLabel")
# List of input tensors. One for each input layer.
inputTensors = [inWords]
# Whether the word embedding will be updated during training.
embLayerTrainable = not args.fix_word_embedding
if not embLayerTrainable:
log.info("Not updating the word embedding!")
# Lookup table for word features.
embeddingLayer = EmbeddingLayer(inWords,
wordEmbedding.getEmbeddingMatrix(),
trainable=embLayerTrainable)
# if not args.train and args.load_wordEmbedding:
# attrs = np.load(args.load_wordEmbedding)
# embeddingLayer.load(attrs)
# log.info("Loaded word embedding (shape %s) from file %s" % (
# str(attrs[0].shape), args.load_wordEmbedding))
# A saída da lookup table possui 3 dimensões (numTokens, szWindow, szEmbedding).
# Esta camada dá um flat nas duas últimas dimensões, produzindo uma saída
# com a forma (numTokens, szWindow * szEmbedding).
flattenInput = FlattenLayer(embeddingLayer)
# Random weight initialization procedure.
weightInit = GlorotUniform()
# Convolution layer. Convolução no texto de uma oferta.
convW = None
convb = None
if not args.train and args.load_conv:
convNPY = np.load(args.load_conv)
convW = convNPY[0]
convb = convNPY[1]
log.info("Loaded convolutional layer (shape %s) from file %s" %
(str(convW.shape), args.load_conv))
convLinear = LinearLayer(flattenInput,
wordWindowSize * wordEmbedding.getEmbeddingSize(),
convSize,
W=convW,
b=convb,
weightInitialization=weightInit)
# Max pooling layer.
maxPooling = MaxPoolingLayer(convLinear)
# Hidden layer.
if not args.train and args.load_hiddenLayer:
hiddenNPY = np.load(args.load_hiddenLayer)
W1 = hiddenNPY[0]
b1 = hiddenNPY[1]
log.info("Loaded hidden layer (shape %s) from file %s" %
(str(W1.shape), args.load_hiddenLayer))
hiddenLinear = LinearLayer(maxPooling,
convSize,
hiddenLayerSize,
W=W1,
b=b1,
weightInitialization=weightInit)
hiddenAct = ActivationLayer(hiddenLinear, tanh)
# Entrada linear da camada softmax.
if not args.train and args.load_softmax:
hiddenNPY = np.load(args.load_softmax)
W2 = hiddenNPY[0]
b2 = hiddenNPY[1]
log.info("Loaded softmax layer (shape %s) from file %s" %
(str(W2.shape), args.load_softmax))
sotmaxLinearInput = LinearLayer(hiddenAct,
hiddenLayerSize,
labelLexicon.getLen(),
W=W2,
b=b2,
weightInitialization=ZeroWeightGenerator())
# Softmax.
# softmaxAct = ReshapeLayer(ActivationLayer(sotmaxLinearInput, softmax), (1, -1))
softmaxAct = ActivationLayer(sotmaxLinearInput, softmax)
# Prediction layer (argmax).
prediction = ArgmaxPrediction(None).predict(softmaxAct.getOutput())
# Loss function.
if args.label_weights is not None and len(
args.label_weights) != labelLexicon.getLen():
log.error(
"Number of label weights (%d) is different from number of labels (%d)!"
% (len(args.label_weights), labelLexicon.getLen()))
nlloe = NegativeLogLikelihoodOneExample(weights=args.label_weights)
loss = nlloe.calculateError(softmaxAct.getOutput()[0], prediction,
outLabel)
# Input generators: word window.
inputGenerators = [
WordWindowGenerator(wordWindowSize, wordLexicon, filters, startSymbol,
endSymbol)
]
# Output generator: generate one label per offer.
outputGenerators = [TextLabelGenerator(labelLexicon)]
# outputGenerators = [lambda label: labelLexicon.put(label)]
evalPerIteration = None
if normalizeMethod == "minmax":
log.info("Normalization: minmax")
wordEmbedding.minMaxNormalization()
elif normalizeMethod == "mean":
log.info("Normalization: mean normalization")
wordEmbedding.meanNormalization()
elif normalizeMethod == "zscore":
log.info("Normalization: zscore normalization")
wordEmbedding.zscoreNormalization()
elif normalizeMethod:
log.error("Normalization: unknown value %s" % normalizeMethod)
sys.exit(1)
# Decaimento da taxa de aprendizado.
decay = None
if args.decay == "none":
decay = 0.0
elif args.decay == "linear":
decay = 1.0
else:
log.error("Unknown decay parameter %s." % args.decay)
exit(1)
# Algoritmo de aprendizado.
if args.alg == "adagrad":
log.info("Using Adagrad")
opt = Adagrad(lr=lr, decay=decay)
elif args.alg == "sgd":
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
else:
log.error("Unknown algorithm: %s." % args.alg)
sys.exit(1)
# TODO: debug
# opt.lr.tag.test_value = 0.05
# Printing embedding information.
dictionarySize = wordEmbedding.getNumberOfVectors()
embeddingSize = wordEmbedding.getEmbeddingSize()
log.info("Dictionary size: %d" % dictionarySize)
log.info("Embedding size: %d" % embeddingSize)
log.info("Number of categories: %d" % labelLexicon.getLen())
# TODO: debug
# mode = theano.compile.debugmode.DebugMode(optimizer=None)
mode = None
model = BasicModel(x=inputTensors,
y=[outLabel],
allLayers=softmaxAct.getLayerSet(),
optimizer=opt,
prediction=prediction,
loss=loss,
mode=mode)
wordWindow = WordWindowGenerator(wordWindowSize, wordLexicon, filters,
startSymbol, endSymbol)
# GETS HIDDEN LAYER:
# graph = EmbeddingGraph([inWords], [hiddenAct.getOutput()], wordWindow)
# GRAPH FOR PREDICTION LAYER
graph = EmbeddingGraph(inputTensors, prediction, wordWindow, mode)
lblTxt = ["Sim", "Nao"]
tweets = []
with open(sys.argv[2]) as inputFile:
content = inputFile.readlines()
for line in content:
tweets.append(line.decode('utf-8').encode('utf-8'))
#print tweets
# graph.getResultsFor(t) retorna a predição para dado Tweet t
try:
output_file = open("Output.txt", "w")
except:
print "Falha em criar o arquivo de saida\n"
try:
for t in tweets:
output_file.write(
t.replace('\n', '').replace('\t', '') + "\t " +
lblTxt[graph.getResultsFor(t)] + "\n")
print "Resultados gerados com sucesso!\n"
except:
print "Erro na geração de resultados\n"
def main(args):
log = logging.getLogger(__name__)
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
lr = args.lr
startSymbol = args.start_symbol
endSymbol = args.end_symbol
numEpochs = args.num_epochs
shuffle = args.shuffle
normalizeMethod = args.wv_normalization
wordWindowSize = args.word_window_size
hiddenLayerSize = args.hidden_size
convSize = args.conv_size
# Load classes for filters.
filters = []
for filterName in args.filters:
moduleName, className = filterName.rsplit('.', 1)
log.info("Filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
W1 = None
b1 = None
W2 = None
b2 = None
hiddenActFunction = tanh
if args.word_embedding:
log.info("Reading W2v File")
(lexicon,
wordEmbedding) = Embedding.fromWord2Vec(args.word_embedding,
unknownSymbol='unknown')
lexicon.stopAdd()
else:
wordEmbedding = EmbeddingFactory().createRandomEmbedding(
args.word_emb_size)
# Get the inputs and output
if args.labels:
labelLexicon = Lexicon.fromTextFile(args.labels, hasUnknowSymbol=False)
else:
labelLexicon = Lexicon()
#
# Build the network model (Theano graph).
#
# TODO: debug
# theano.config.compute_test_value = 'warn'
# ex = trainIterator.next()
# inWords.tag.test_value = ex[0][0]
# outLabel.tag.test_value = ex[1][0]
# Matriz de entrada. Cada linha representa um token da oferta. Cada token é
# representado por uma janela de tokens (token central e alguns tokens
# próximos). Cada valor desta matriz corresponde a um índice que representa
# um token no embedding.
inWords = T.lmatrix("inWords")
# Categoria correta de uma oferta.
outLabel = T.lscalar("outLabel")
# List of input tensors. One for each input layer.
inputTensors = [inWords]
# Whether the word embedding will be updated during training.
embLayerTrainable = not args.fix_word_embedding
if not embLayerTrainable:
log.info("Not updating the word embedding!")
# Lookup table for word features.
embeddingLayer = EmbeddingLayer(inWords,
wordEmbedding.getEmbeddingMatrix(),
trainable=embLayerTrainable)
# A saída da lookup table possui 3 dimensões (numTokens, szWindow, szEmbedding).
# Esta camada dá um flat nas duas últimas dimensões, produzindo uma saída
# com a forma (numTokens, szWindow * szEmbedding).
flattenInput = FlattenLayer(embeddingLayer)
# Random weight initialization procedure.
weightInit = SigmoidGlorot(
) if hiddenActFunction == sigmoid else GlorotUniform()
# Convolution layer. Convolução no texto de um documento.
convLinear = LinearLayer(flattenInput,
wordWindowSize * wordEmbedding.getEmbeddingSize(),
convSize,
W=None,
b=None,
weightInitialization=weightInit)
# Max pooling layer.
maxPooling = MaxPoolingLayer(convLinear)
# Generate word windows.
wordWindowFeatureGenerator = WordWindowGenerator(wordWindowSize, lexicon,
filters, startSymbol,
endSymbol)
# List of input generators.
inputGenerators = [wordWindowFeatureGenerator]
# Hidden layer.
hiddenLinear = LinearLayer(maxPooling,
convSize,
hiddenLayerSize,
W=W1,
b=b1,
weightInitialization=weightInit)
hiddenAct = ActivationLayer(hiddenLinear, hiddenActFunction)
# Entrada linear da camada softmax.
sotmaxLinearInput = LinearLayer(hiddenAct,
hiddenLayerSize,
labelLexicon.getLen(),
W=W2,
b=b2,
weightInitialization=ZeroWeightGenerator())
# Softmax.
# softmaxAct = ReshapeLayer(ActivationLayer(sotmaxLinearInput, softmax), (1, -1))
softmaxAct = ActivationLayer(sotmaxLinearInput, softmax)
# Prediction layer (argmax).
prediction = ArgmaxPrediction(None).predict(softmaxAct.getOutput())
# Loss function.
loss = NegativeLogLikelihoodOneExample().calculateError(
softmaxAct.getOutput()[0], prediction, outLabel)
# Output generator: generate one label per offer.
outputGenerators = [TextLabelGenerator(labelLexicon)]
if args.train:
trainDatasetReader = DocReader(args.train)
log.info("Reading training examples...")
trainIterator = SyncBatchIterator(trainDatasetReader,
inputGenerators,
outputGenerators,
-1,
shuffle=shuffle)
lexicon.stopAdd()
labelLexicon.stopAdd()
# Get dev inputs and output
dev = args.dev
evalPerIteration = args.eval_per_iteration
if not dev and evalPerIteration > 0:
log.error(
"Argument eval_per_iteration cannot be used without a dev argument."
)
sys.exit(1)
if dev:
log.info("Reading development examples")
devReader = DocReader(args.dev)
devIterator = SyncBatchIterator(devReader,
inputGenerators,
outputGenerators,
-1,
shuffle=False)
else:
devIterator = None
else:
trainIterator = None
devIterator = None
if normalizeMethod == "minmax":
log.info("Normalization: minmax")
wordEmbedding.minMaxNormalization()
elif normalizeMethod == "mean":
log.info("Normalization: mean normalization")
wordEmbedding.meanNormalization()
elif normalizeMethod == "zscore":
log.info("Normalization: zscore normalization")
wordEmbedding.zscoreNormalization()
elif normalizeMethod:
log.error("Normalization: unknown value %s" % normalizeMethod)
sys.exit(1)
# Decaimento da taxa de aprendizado.
if args.decay == "linear":
decay = 1.0
elif args.decay == "none":
decay = 0.0
else:
log.error("Unknown decay strategy %s. Expected: none or linear." %
args.decay)
sys.exit(1)
# Algoritmo de aprendizado.
if args.alg == "adagrad":
log.info("Using Adagrad")
opt = Adagrad(lr=lr, decay=decay)
elif args.alg == "sgd":
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
else:
log.error(
"Unknown algorithm: %s. Expected values are: adagrad or sgd." %
args.alg)
sys.exit(1)
# TODO: debug
# opt.lr.tag.test_value = 0.05
# Printing embedding information.
dictionarySize = wordEmbedding.getNumberOfVectors()
embeddingSize = wordEmbedding.getEmbeddingSize()
log.info("Dictionary size: %d" % dictionarySize)
log.info("Embedding size: %d" % embeddingSize)
log.info("Number of categories: %d" % labelLexicon.getLen())
# Train metrics.
trainMetrics = None
if trainIterator:
trainMetrics = [
LossMetric("TrainLoss", loss),
AccuracyMetric("TrainAccuracy", outLabel, prediction)
]
# Evaluation metrics.
evalMetrics = None
if devIterator:
evalMetrics = [
LossMetric("EvalLoss", loss),
AccuracyMetric("EvalAccuracy", outLabel, prediction)
]
# Test metrics.
testMetrics = None
if args.test:
testMetrics = [
LossMetric("TestLoss", loss),
AccuracyMetric("TestAccuracy", outLabel, prediction)
]
# TODO: debug
# mode = theano.compile.debugmode.DebugMode(optimizer=None)
mode = None
model = BasicModel(x=inputTensors,
y=[outLabel],
allLayers=softmaxAct.getLayerSet(),
optimizer=opt,
prediction=prediction,
loss=loss,
trainMetrics=trainMetrics,
evalMetrics=evalMetrics,
testMetrics=testMetrics,
mode=mode)
# Training
if trainIterator:
log.info("Training")
model.train(trainIterator,
numEpochs,
devIterator,
evalPerIteration=evalPerIteration)
# Testing
if args.test:
log.info("Reading test examples")
testReader = DocReader(args.test)
testIterator = SyncBatchIterator(testReader,
inputGenerators,
outputGenerators,
-1,
shuffle=False)
log.info("Testing")
model.test(testIterator)
def main(**kwargs):
log = logging.getLogger(__name__)
log.info(kwargs)
if kwargs["seed"] != None:
random.seed(kwargs["seed"])
np.random.seed(kwargs["seed"])
filters = []
for filterName in kwargs["filters"]:
moduleName, className = filterName.rsplit('.', 1)
log.info("Usando o filtro: " + moduleName + " " + className)
module_ = importlib.import_module(moduleName)
filters.append(getattr(module_, className)())
# Get the inputs and output
wordWindowSize = kwargs["word_window_size"]
hiddenLayerSize = kwargs["hidden_size"]
batchSize = kwargs["batch_size"]
startSymbol = kwargs["start_symbol"]
numEpochs = kwargs["num_epochs"]
lr = kwargs["lr"]
labelLexicon = createLexiconUsingFile(kwargs["label_file"])
log.info("Reading training examples")
log.info("Reading W2v File1")
embedding1 = EmbeddingFactory().createFromW2V(kwargs["word_embedding1"],
RandomUnknownStrategy())
# Supervised part
# Learner1
input1 = T.lmatrix(name="input1")
embeddingLayer1 = EmbeddingLayer(input1,
embedding1.getEmbeddingMatrix(),
trainable=True)
flatten1 = FlattenLayer(embeddingLayer1)
linear11 = LinearLayer(flatten1,
wordWindowSize * embedding1.getEmbeddingSize(),
hiddenLayerSize,
weightInitialization=GlorotUniform())
act11 = ActivationLayer(linear11, tanh)
linear12 = LinearLayer(act11,
hiddenLayerSize,
labelLexicon.getLen(),
weightInitialization=ZeroWeightGenerator())
act12 = ActivationLayer(linear12, softmax)
## Learner2
log.info("Reading W2v File2")
embedding2 = EmbeddingFactory().createFromW2V(kwargs["word_embedding2"],
RandomUnknownStrategy())
input2 = T.lmatrix(name="input2")
embeddingLayer2 = EmbeddingLayer(input2,
embedding2.getEmbeddingMatrix(),
trainable=True)
flatten2 = FlattenLayer(embeddingLayer2)
linear21 = LinearLayer(flatten2,
wordWindowSize * embedding2.getEmbeddingSize(),
hiddenLayerSize,
weightInitialization=GlorotUniform())
act21 = ActivationLayer(linear21, tanh)
linear22 = LinearLayer(act21,
hiddenLayerSize,
labelLexicon.getLen(),
weightInitialization=ZeroWeightGenerator())
act22 = ActivationLayer(linear22, softmax)
y = T.lvector("y")
# Set loss and prediction and retrieve all layers
output1 = act12.getOutput()
prediction1 = ArgmaxPrediction(1).predict(output1)
loss1 = NegativeLogLikelihood().calculateError(output1, prediction1, y)
if kwargs["l2"][0]:
_lambda1 = kwargs["l2"][0]
log.info("Using L2 with lambda= %.2f", _lambda1)
loss1 += _lambda1 * (T.sum(T.square(linear11.getParameters()[0])))
output2 = act22.getOutput()
prediction2 = ArgmaxPrediction(1).predict(output2)
loss2 = NegativeLogLikelihood().calculateError(output2, prediction2, y)
if kwargs["l2"][1]:
_lambda2 = kwargs["l2"][1]
log.info("Using L2 with lambda= %.2f", _lambda2)
loss2 += _lambda2 * (T.sum(T.square(linear21.getParameters()[0])))
loss = loss1 + loss2
## CoLearningPrediction
output = T.stack([linear12.getOutput(), linear22.getOutput()])
# return T.argmax(output, 2)[T.argmax(T.max(output, 2), 0),T.arange(output.shape[1])]
average = T.mean(output, 0)
prediction = ArgmaxPrediction(1).predict(
ActivationLayer(average, softmax).getOutput())
# prediction = CoLearningWnnPrediction().predict([output1, output2])
supervisedModeUnit = ModelUnit("supervised_wnn", [input1, input2],
y,
loss,
prediction=prediction)
# Unsupervised part
## Learner1
inputUnsuper1 = T.lmatrix(name="input_unsupervised_1")
embeddingLayerUnsuper1 = EmbeddingLayer(inputUnsuper1,
embeddingLayer1.getParameters()[0],
trainable=True)
flattenUnsuper1 = FlattenLayer(embeddingLayerUnsuper1)
w, b = linear11.getParameters()
linearUnsuper11 = LinearLayer(flattenUnsuper1,
wordWindowSize *
embedding1.getEmbeddingSize(),
hiddenLayerSize,
W=w,
b=b)
actUnsupervised11 = ActivationLayer(linearUnsuper11, tanh)
w, b = linear12.getParameters()
linearUnsuper12 = LinearLayer(actUnsupervised11,
hiddenLayerSize,
labelLexicon.getLen(),
W=w,
b=b)
actUnsuper12 = ActivationLayer(linearUnsuper12, softmax)
## Learner2
inputUnsuper2 = T.lmatrix(name="input_unsupervised_2")
embeddingLayerUnsuper2 = EmbeddingLayer(inputUnsuper2,
embeddingLayer2.getParameters()[0],
trainable=True)
flattenUnsuper2 = FlattenLayer(embeddingLayerUnsuper2)
w, b = linear21.getParameters()
linearUnsuper21 = LinearLayer(flattenUnsuper2,
wordWindowSize *
embedding2.getEmbeddingSize(),
hiddenLayerSize,
W=w,
b=b)
actUnsuper21 = ActivationLayer(linearUnsuper21, tanh)
w, b = linear22.getParameters()
linearUnsuper22 = LinearLayer(actUnsuper21,
hiddenLayerSize,
labelLexicon.getLen(),
W=w,
b=b)
actUnsuper22 = ActivationLayer(linearUnsuper22, softmax)
# Set loss and prediction and retrieve all layers
outputUns1 = actUnsuper12.getOutput()
predictionUns1 = ArgmaxPrediction(1).predict(outputUns1)
outputUns2 = actUnsuper22.getOutput()
predictionUns2 = ArgmaxPrediction(1).predict(outputUns2)
#
# unsupervisedLoss = kwargs["lambda"] * (
# NegativeLogLikelihood().calculateError(outputUns1, predictionUns1, predictionUns2) +
# NegativeLogLikelihood().calculateError(outputUns2, predictionUns2, predictionUns1))
_lambdaShared = theano.shared(value=kwargs["lambda"],
name='lambda',
borrow=True)
unsupervisedLoss = _lambdaShared * (NegativeLogLikelihood().calculateError(
outputUns1, predictionUns1, predictionUns2) + NegativeLogLikelihood(
).calculateError(outputUns2, predictionUns2, predictionUns1))
unsupervisedUnit = ModelUnit("unsupervised_wnn",
[inputUnsuper1, inputUnsuper2],
None,
unsupervisedLoss,
yWillBeReceived=False)
# Creates model
model = CoLearningModel(kwargs["loss_uns_epoch"])
model.addTrainingModelUnit(supervisedModeUnit, metrics=["loss", "acc"])
model.addTrainingModelUnit(unsupervisedUnit, metrics=["loss"])
model.setEvaluatedModelUnit(supervisedModeUnit, metrics=["acc"])
# Compile Model
opt1 = SGD(lr=lr[0], decay=1.0)
opt2 = SGD(lr=lr[1], decay=1.0)
log.info("Compiling the model")
model.compile([(opt1, {
supervisedModeUnit: act12.getLayerSet(),
unsupervisedUnit: actUnsuper12.getLayerSet()
}),
(opt2, {
supervisedModeUnit: act22.getLayerSet(),
unsupervisedUnit: actUnsuper22.getLayerSet()
})])
# Generators
inputGenerator1 = WordWindowGenerator(wordWindowSize, embedding1, filters,
startSymbol)
inputGenerator2 = WordWindowGenerator(wordWindowSize, embedding2, filters,
startSymbol)
outputGenerator = LabelGenerator(labelLexicon)
# Reading supervised and unsupervised data sets.
trainSupervisedDatasetReader = TokenLabelReader(
kwargs["train_supervised"], kwargs["token_label_separator"])
trainSupervisedDatasetReader = SyncBatchIterator(
trainSupervisedDatasetReader, [inputGenerator1, inputGenerator2],
[outputGenerator], batchSize[0])
trainUnsupervisedDataset = TokenReader(kwargs["train_unsupervised"])
trainUnsupervisedDatasetReader = SyncBatchIterator(
trainUnsupervisedDataset, [inputGenerator1, inputGenerator2], None,
batchSize[1])
embedding1.stopAdd()
embedding2.stopAdd()
labelLexicon.stopAdd()
# Get dev inputs and output
log.info("Reading development examples")
devDatasetReader = TokenLabelReader(kwargs["dev"],
kwargs["token_label_separator"])
devReader = SyncBatchIterator(devDatasetReader,
[inputGenerator1, inputGenerator2],
[outputGenerator],
sys.maxint,
shuffle=False)
lambdaChange = ChangeLambda(_lambdaShared, kwargs["lambda"],
kwargs["loss_uns_epoch"])
lossCallback = LossCallback(loss1, loss2, input1, input2, y)
# trainUnsupervisedDatasetReaderAcc = SyncBatchIterator(trainUnsupervisedDataset,
# [inputGenerator1, inputGenerator2],
# [outputGenerator], sys.maxint)
# accCallBack = AccCallBack(prediction1, prediction2, input1, input2,
# unsurpervisedDataset=trainUnsupervisedDatasetReaderAcc)
# Training Model
model.train([trainSupervisedDatasetReader, trainUnsupervisedDatasetReader],
numEpochs,
devReader,
callbacks=[lambdaChange, lossCallback])
def mainWnnNegativeSampling(args):
# Reading parameters
embeddingMatrix = None
wordEmbeddingSize = args.word_embedding_size
windowSize = args.window_size
hiddenLayerSize = args.hidden_size
startSymbol = args.start_symbol
# endSymbol = args.end_symbol
endSymbol = startSymbol
noiseRate = args.noise_rate
# todo: o algoritmo não suporta mini batch. Somente treinamento estocástico.
batchSize = 1
shuffle = args.shuffle
lr = args.lr
numEpochs = args.num_epochs
power = args.power
minLr = args.min_lr
numExUpdLr = args.num_examples_updt_lr
log = logging.getLogger(__name__)
log.info(str(args))
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
#
# if args.decay.lower() == "normal":
# decay = 0.0
# elif args.decay.lower() == "divide_epoch":
# decay = 1.0
parametersToSaveOrLoad = {"hidden_size", "window_size", "start_symbol"}
# Calculate the frequency of each word
trainReader = TokenReader(args.train)
wordLexicon = Lexicon("UUKNNN", "lexicon")
wordLexicon.put(startSymbol, False)
totalNumOfTokens = 0
for tokens, labels in trainReader.read():
# we don't count the </s>, because this token is only insert in the sentence to count its frequency.
totalNumOfTokens += len(tokens)
# Word2vec considers that the number of lines is the frequency of </s>
tokens += [startSymbol]
for token in tokens:
wordLexicon.put(token)
# Prune the words with the frequency less than min_count
wordLexicon.prune(args.min_count)
wordLexicon.stopAdd()
# Calculte the unigram distribution
frequency = np.power(wordLexicon.getFrequencyOfAllWords(), power)
total = float(frequency.sum())
# # Print the distribution of all words
# for _ in xrange(len(frequency)):
# print "%s\t%d\t%.4f" % (wordLexicon.getLexicon(_), frequency[_],frequency[_]/float(total))
sampler = Sampler(frequency / float(total))
# Create a random embedding for each word
wordEmbedding = Embedding(wordLexicon, None, wordEmbeddingSize)
log.info("Lexicon size: %d" % (wordLexicon.getLen()))
# Create NN
x = T.lmatrix("word_window")
y = T.lvector("labels")
wordEmbeddingLayer = EmbeddingLayer(x,
wordEmbedding.getEmbeddingMatrix(),
name="embedding")
flatten = FlattenLayer(wordEmbeddingLayer)
linear1 = LinearLayer(flatten,
wordEmbeddingSize * windowSize,
hiddenLayerSize,
name="linear1")
act1 = ActivationLayer(linear1, tanh)
# Softmax regression. It's like a logistic regression
linear2 = LinearLayer(act1,
hiddenLayerSize,
1,
weightInitialization=ZeroWeightGenerator(),
name="linear_softmax_regresion")
act2 = ActivationLayer(linear2, sigmoid)
# We clip the output of -sigmoid, because this output can be 0 and ln(0) is infinite, which can cause problems.
output = T.flatten(T.clip(act2.getOutput(), 10**-5, 1 - 10**-5))
# Loss Functions
negativeSamplingLoss = T.nnet.binary_crossentropy(output, y).sum()
# Set training
inputGenerators = [
WordWindowGenerator(windowSize, wordLexicon, [], startSymbol,
endSymbol)
]
outputGenerators = [ConstantLabel(labelLexicon=None, label=1)]
trainIterator = SyncBatchIterator(trainReader, inputGenerators,
outputGenerators, batchSize, shuffle)
trainMetrics = [LossMetric("lossTrain", negativeSamplingLoss)]
allLayers = act2.getLayerSet()
# opt = SGD(lr=lr, decay=decay)
opt = SGD(lr=lr)
model = NegativeSamplingModel(args.t, noiseRate, sampler, minLr,
numExUpdLr, totalNumOfTokens, numEpochs, [x],
[y], allLayers, opt, negativeSamplingLoss,
trainMetrics)
# Save Model
if args.save_model:
savePath = args.save_model
objsToSave = list(act2.getLayerSet()) + [wordLexicon]
modelWriter = ModelWriter(savePath, objsToSave, args,
parametersToSaveOrLoad)
# Training
model.train(trainIterator, numEpochs=numEpochs, callbacks=[])
if args.save_model:
modelWriter.save()
def mainWnn(args):
################################################
# Initializing parameters
##############################################
log = logging.getLogger(__name__)
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
parametersToSaveOrLoad = {"word_filters", "suffix_filters", "char_filters", "cap_filters",
"alg", "hidden_activation_function", "word_window_size", "char_window_size",
"hidden_size", "with_charwnn", "conv_size", "charwnn_with_act", "suffix_size",
"use_capitalization", "start_symbol", "end_symbol", "with_hidden"}
# Load parameters of the saving model
if args.load_model:
persistentManager = H5py(args.load_model)
savedParameters = json.loads(persistentManager.getAttribute("parameters"))
if savedParameters.get("charwnn_filters", None) != None:
savedParameters["char_filters"] = savedParameters["charwnn_filters"]
savedParameters.pop("charwnn_filters")
print savedParameters
log.info("Loading parameters of the model")
args = args._replace(**savedParameters)
log.info(str(args))
# Read the parameters
lr = args.lr
startSymbol = args.start_symbol
endSymbol = args.end_symbol
numEpochs = args.num_epochs
shuffle = args.shuffle
normalizeMethod = args.normalization.lower() if args.normalization is not None else None
wordWindowSize = args.word_window_size
hiddenLayerSize = args.hidden_size
hiddenActFunctionName = args.hidden_activation_function
embeddingSize = args.word_emb_size
withCharWNN = args.with_charwnn
charEmbeddingSize = args.char_emb_size
charWindowSize = args.char_window_size
startSymbolChar = "</s>"
suffixEmbSize = args.suffix_emb_size
capEmbSize = args.cap_emb_size
useSuffixFeatures = args.suffix_size > 0
useCapFeatures = args.use_capitalization
# Insert the character that will be used to fill the matrix
# with a dimension lesser than chosen dimension.This enables that the convolution is performed by a matrix multiplication.
artificialChar = "ART_CHAR"
# TODO: the maximum number of characters of word is fixed in 20.
numMaxChar = 20
if args.alg == "window_stn":
isSentenceModel = True
elif args.alg == "window_word":
isSentenceModel = False
else:
raise Exception("The value of model_type isn't valid.")
batchSize = -1 if isSentenceModel else args.batch_size
wordFilters = []
# Lendo Filtros do wnn
log.info("Lendo filtros básicos")
wordFilters = getFilters(args.word_filters, log)
# Lendo Filtros do charwnn
log.info("Lendo filtros do charwnn")
charFilters = getFilters(args.char_filters, log)
# Lendo Filtros do suffix
log.info("Lendo filtros do sufixo")
suffixFilters = getFilters(args.suffix_filters, log)
# Lendo Filtros da capitalização
log.info("Lendo filtros da capitalização")
capFilters = getFilters(args.cap_filters, log)
################################################
# Create the lexicon and go out after this
################################################
if args.create_only_lexicon:
inputGenerators = []
lexiconsToSave = []
if args.word_lexicon and not os.path.exists(args.word_lexicon):
wordLexicon = Lexicon("UUUNKKK", "labelLexicon")
inputGenerators.append(
WordWindowGenerator(wordWindowSize, wordLexicon, wordFilters, startSymbol, endSymbol))
lexiconsToSave.append((wordLexicon, args.word_lexicon))
if not os.path.exists(args.label_file):
labelLexicon = Lexicon(None, "labelLexicon")
outputGenerator = [LabelGenerator(labelLexicon)]
lexiconsToSave.append((labelLexicon, args.label_file))
else:
outputGenerator = None
if args.char_lexicon and not os.path.exists(args.char_lexicon):
charLexicon = Lexicon("UUUNKKK", "charLexicon")
charLexicon.put(startSymbolChar)
charLexicon.put(artificialChar)
inputGenerators.append(
CharacterWindowGenerator(charLexicon, numMaxChar, charWindowSize, wordWindowSize, artificialChar,
startSymbolChar, startPaddingWrd=startSymbol, endPaddingWrd=endSymbol,
filters=charFilters))
lexiconsToSave.append((charLexicon, args.char_lexicon))
if args.suffix_lexicon and not os.path.exists(args.suffix_lexicon):
suffixLexicon = Lexicon("UUUNKKK", "suffixLexicon")
if args.suffix_size <= 0:
raise Exception(
"Unable to generate the suffix lexicon because the suffix is less than or equal to 0.")
inputGenerators.append(
SuffixFeatureGenerator(args.suffix_size, wordWindowSize, suffixLexicon, suffixFilters))
lexiconsToSave.append((suffixLexicon, args.suffix_lexicon))
if args.cap_lexicon and not os.path.exists(args.cap_lexicon):
capLexicon = Lexicon("UUUNKKK", "capitalizationLexicon")
inputGenerators.append(CapitalizationFeatureGenerator(wordWindowSize, capLexicon, capFilters))
lexiconsToSave.append((capLexicon, args.cap_lexicon))
if len(inputGenerators) == 0:
inputGenerators = None
if not (inputGenerators or outputGenerator):
log.info("All lexicons have been generated.")
return
trainDatasetReader = TokenLabelReader(args.train, args.token_label_separator)
trainReader = SyncBatchIterator(trainDatasetReader, inputGenerators, outputGenerator, batchSize,
shuffle=shuffle)
for lexicon, pathToSave in lexiconsToSave:
lexicon.save(pathToSave)
log.info("Lexicons were generated with success!")
return
################################################
# Starting training
###########################################
if withCharWNN and (useSuffixFeatures or useCapFeatures):
raise Exception("It's impossible to use hand-crafted features with Charwnn.")
# Read word lexicon and create word embeddings
if args.load_model:
wordLexicon = Lexicon.fromPersistentManager(persistentManager, "word_lexicon")
vectors = EmbeddingLayer.getEmbeddingFromPersistenceManager(persistentManager, "word_embedding_layer")
wordEmbedding = Embedding(wordLexicon, vectors)
elif args.word_embedding:
wordLexicon, wordEmbedding = Embedding.fromWord2Vec(args.word_embedding, "UUUNKKK", "word_lexicon")
elif args.word_lexicon:
wordLexicon = Lexicon.fromTextFile(args.word_lexicon, True, "word_lexicon")
wordEmbedding = Embedding(wordLexicon, vectors=None, embeddingSize=embeddingSize)
else:
log.error("You need to set one of these parameters: load_model, word_embedding or word_lexicon")
return
# Read char lexicon and create char embeddings
if withCharWNN:
if args.load_model:
charLexicon = Lexicon.fromPersistentManager(persistentManager, "char_lexicon")
vectors = EmbeddingConvolutionalLayer.getEmbeddingFromPersistenceManager(persistentManager,
"char_convolution_layer")
charEmbedding = Embedding(charLexicon, vectors)
elif args.char_lexicon:
charLexicon = Lexicon.fromTextFile(args.char_lexicon, True, "char_lexicon")
charEmbedding = Embedding(charLexicon, vectors=None, embeddingSize=charEmbeddingSize)
else:
log.error("You need to set one of these parameters: load_model or char_lexicon")
return
else:
# Read suffix lexicon if suffix size is greater than 0
if useSuffixFeatures:
if args.load_model:
suffixLexicon = Lexicon.fromPersistentManager(persistentManager, "suffix_lexicon")
vectors = EmbeddingConvolutionalLayer.getEmbeddingFromPersistenceManager(persistentManager,
"suffix_embedding")
suffixEmbedding = Embedding(suffixLexicon, vectors)
elif args.suffix_lexicon:
suffixLexicon = Lexicon.fromTextFile(args.suffix_lexicon, True, "suffix_lexicon")
suffixEmbedding = Embedding(suffixLexicon, vectors=None, embeddingSize=suffixEmbSize)
else:
log.error("You need to set one of these parameters: load_model or suffix_lexicon")
return
# Read capitalization lexicon
if useCapFeatures:
if args.load_model:
capLexicon = Lexicon.fromPersistentManager(persistentManager, "cap_lexicon")
vectors = EmbeddingConvolutionalLayer.getEmbeddingFromPersistenceManager(persistentManager,
"cap_embedding")
capEmbedding = Embedding(capLexicon, vectors)
elif args.cap_lexicon:
capLexicon = Lexicon.fromTextFile(args.cap_lexicon, True, "cap_lexicon")
capEmbedding = Embedding(capLexicon, vectors=None, embeddingSize=capEmbSize)
else:
log.error("You need to set one of these parameters: load_model or cap_lexicon")
return
# Read labels
if args.load_model:
labelLexicon = Lexicon.fromPersistentManager(persistentManager, "label_lexicon")
elif args.label_file:
labelLexicon = Lexicon.fromTextFile(args.label_file, False, lexiconName="label_lexicon")
else:
log.error("You need to set one of these parameters: load_model, word_embedding or word_lexicon")
return
# Normalize the word embedding
if not normalizeMethod:
pass
elif normalizeMethod == "minmax":
log.info("Normalization: minmax")
wordEmbedding.minMaxNormalization()
elif normalizeMethod == "mean":
log.info("Normalization: mean normalization")
wordEmbedding.meanNormalization()
else:
log.error("Unknown normalization method: %s" % normalizeMethod)
sys.exit(1)
if normalizeMethod is not None and args.load_model is not None:
log.warn("The word embedding of model was normalized. This can change the result of test.")
# Build neural network
if isSentenceModel:
raise NotImplementedError("Sentence model is not implemented!")
else:
wordWindow = T.lmatrix("word_window")
inputModel = [wordWindow]
wordEmbeddingLayer = EmbeddingLayer(wordWindow, wordEmbedding.getEmbeddingMatrix(), trainable=True,
name="word_embedding_layer")
flatten = FlattenLayer(wordEmbeddingLayer)
if withCharWNN:
# Use the convolution
log.info("Using charwnn")
convSize = args.conv_size
if args.charwnn_with_act:
charAct = tanh
else:
charAct = None
charWindowIdxs = T.ltensor4(name="char_window_idx")
inputModel.append(charWindowIdxs)
charEmbeddingConvLayer = EmbeddingConvolutionalLayer(charWindowIdxs, charEmbedding.getEmbeddingMatrix(),
numMaxChar, convSize, charWindowSize,
charEmbeddingSize, charAct,
name="char_convolution_layer")
layerBeforeLinear = ConcatenateLayer([flatten, charEmbeddingConvLayer])
sizeLayerBeforeLinear = wordWindowSize * (wordEmbedding.getEmbeddingSize() + convSize)
elif useSuffixFeatures or useCapFeatures:
# Use hand-crafted features
concatenateInputs = [flatten]
nmFetauresByWord = wordEmbedding.getEmbeddingSize()
if useSuffixFeatures:
log.info("Using suffix features")
suffixInput = T.lmatrix("suffix_input")
suffixEmbLayer = EmbeddingLayer(suffixInput, suffixEmbedding.getEmbeddingMatrix(),
name="suffix_embedding")
suffixFlatten = FlattenLayer(suffixEmbLayer)
concatenateInputs.append(suffixFlatten)
nmFetauresByWord += suffixEmbedding.getEmbeddingSize()
inputModel.append(suffixInput)
if useCapFeatures:
log.info("Using capitalization features")
capInput = T.lmatrix("capitalization_input")
capEmbLayer = EmbeddingLayer(capInput, capEmbedding.getEmbeddingMatrix(),
name="cap_embedding")
capFlatten = FlattenLayer(capEmbLayer)
concatenateInputs.append(capFlatten)
nmFetauresByWord += capEmbedding.getEmbeddingSize()
inputModel.append(capInput)
layerBeforeLinear = ConcatenateLayer(concatenateInputs)
sizeLayerBeforeLinear = wordWindowSize * nmFetauresByWord
else:
# Use only the word embeddings
layerBeforeLinear = flatten
sizeLayerBeforeLinear = wordWindowSize * wordEmbedding.getEmbeddingSize()
# The rest of the NN
if args.with_hidden:
hiddenActFunction = method_name(hiddenActFunctionName)
weightInit = SigmoidGlorot() if hiddenActFunction == sigmoid else GlorotUniform()
linear1 = LinearLayer(layerBeforeLinear, sizeLayerBeforeLinear, hiddenLayerSize,
weightInitialization=weightInit, name="linear1")
act1 = ActivationLayer(linear1, hiddenActFunction)
layerBeforeSoftmax = act1
sizeLayerBeforeSoftmax = hiddenLayerSize
log.info("Using hidden layer")
else:
layerBeforeSoftmax = layerBeforeLinear
sizeLayerBeforeSoftmax = sizeLayerBeforeLinear
log.info("Not using hidden layer")
linear2 = LinearLayer(layerBeforeSoftmax, sizeLayerBeforeSoftmax, labelLexicon.getLen(),
weightInitialization=ZeroWeightGenerator(),
name="linear_softmax")
act2 = ActivationLayer(linear2, softmax)
prediction = ArgmaxPrediction(1).predict(act2.getOutput())
# Load the model
if args.load_model:
alreadyLoaded = set([wordEmbeddingLayer])
for o in (act2.getLayerSet() - alreadyLoaded):
if o.getName():
persistentManager.load(o)
# Set the input and output
inputGenerators = [WordWindowGenerator(wordWindowSize, wordLexicon, wordFilters, startSymbol, endSymbol)]
if withCharWNN:
inputGenerators.append(
CharacterWindowGenerator(charLexicon, numMaxChar, charWindowSize, wordWindowSize, artificialChar,
startSymbolChar, startPaddingWrd=startSymbol, endPaddingWrd=endSymbol,
filters=charFilters))
else:
if useSuffixFeatures:
inputGenerators.append(
SuffixFeatureGenerator(args.suffix_size, wordWindowSize, suffixLexicon, suffixFilters))
if useCapFeatures:
inputGenerators.append(CapitalizationFeatureGenerator(wordWindowSize, capLexicon, capFilters))
outputGenerator = LabelGenerator(labelLexicon)
if args.train:
log.info("Reading training examples")
trainDatasetReader = TokenLabelReader(args.train, args.token_label_separator)
trainReader = SyncBatchIterator(trainDatasetReader, inputGenerators, [outputGenerator], batchSize,
shuffle=shuffle)
# Get dev inputs and output
dev = args.dev
if dev:
log.info("Reading development examples")
devDatasetReader = TokenLabelReader(args.dev, args.token_label_separator)
devReader = SyncBatchIterator(devDatasetReader, inputGenerators, [outputGenerator], sys.maxint,
shuffle=False)
else:
devReader = None
else:
trainReader = None
devReader = None
y = T.lvector("y")
if args.decay.lower() == "normal":
decay = 0.0
elif args.decay.lower() == "divide_epoch":
decay = 1.0
if args.adagrad:
log.info("Using Adagrad")
opt = Adagrad(lr=lr, decay=decay)
else:
log.info("Using SGD")
opt = SGD(lr=lr, decay=decay)
# Printing embedding information
dictionarySize = wordEmbedding.getNumberOfVectors()
log.info("Size of word dictionary and word embedding size: %d and %d" % (dictionarySize, embeddingSize))
if withCharWNN:
log.info("Size of char dictionary and char embedding size: %d and %d" % (
charEmbedding.getNumberOfVectors(), charEmbedding.getEmbeddingSize()))
if useSuffixFeatures:
log.info("Size of suffix dictionary and suffix embedding size: %d and %d" % (
suffixEmbedding.getNumberOfVectors(), suffixEmbedding.getEmbeddingSize()))
if useCapFeatures:
log.info("Size of capitalization dictionary and capitalization embedding size: %d and %d" % (
capEmbedding.getNumberOfVectors(), capEmbedding.getEmbeddingSize()))
# Compiling
loss = NegativeLogLikelihood().calculateError(act2.getOutput(), prediction, y)
if args.lambda_L2:
_lambda = args.lambda_L2
log.info("Using L2 with lambda= %.2f", _lambda)
loss += _lambda * (T.sum(T.square(linear1.getParameters()[0])))
trainMetrics = [
LossMetric("LossTrain", loss, True),
AccuracyMetric("AccTrain", y, prediction),
]
evalMetrics = [
LossMetric("LossDev", loss, True),
AccuracyMetric("AccDev", y, prediction),
]
testMetrics = [
LossMetric("LossTest", loss, True),
AccuracyMetric("AccTest", y, prediction),
]
wnnModel = BasicModel(inputModel, [y], act2.getLayerSet(), opt, prediction, loss, trainMetrics=trainMetrics,
evalMetrics=evalMetrics, testMetrics=testMetrics, mode=None)
# Training
if trainReader:
callback = []
if args.save_model:
savePath = args.save_model
objsToSave = list(act2.getLayerSet()) + [wordLexicon, labelLexicon]
if withCharWNN:
objsToSave.append(charLexicon)
if useSuffixFeatures:
objsToSave.append(suffixLexicon)
if useCapFeatures:
objsToSave.append(capLexicon)
modelWriter = ModelWriter(savePath, objsToSave, args, parametersToSaveOrLoad)
# Save the model with best acc in dev
if args.save_by_acc:
callback.append(SaveModelCallback(modelWriter, evalMetrics[1], "accuracy", True))
log.info("Training")
wnnModel.train(trainReader, numEpochs, devReader, callbacks=callback)
# Save the model at the end of training
if args.save_model and not args.save_by_acc:
modelWriter.save()
# Testing
if args.test:
log.info("Reading test examples")
testDatasetReader = TokenLabelReader(args.test, args.token_label_separator)
testReader = SyncBatchIterator(testDatasetReader, inputGenerators, [outputGenerator], sys.maxint, shuffle=False)
log.info("Testing")
wnnModel.test(testReader)
if args.print_prediction:
f = codecs.open(args.print_prediction, "w", encoding="utf-8")
for x, labels in testReader:
inputs = x
predictions = wnnModel.prediction(inputs)
for prediction in predictions:
f.write(labelLexicon.getLexicon(prediction))
f.write("\n")