def getNamesFromParameters(red):
parameters = []
for definition in red.find_all("def"):
for argument in definition.arguments:
parameters.append(argument.target)
return parameters
def __init__(self, fileEmbeddings, wordEmbeddings, weights=None, contextSize=None, negative=None):
filesCount, fileEmbeddingSize = fileEmbeddings.shape
wordsCount, wordEmbeddingSize = wordEmbeddings.shape
trainWeights = weights is None
if trainWeights:
weights = rnd2(fileEmbeddingSize + contextSize * wordEmbeddingSize, wordsCount)
else:
featuresCount, activationsCount = weights.shape
contextSize = (featuresCount - fileEmbeddingSize) / wordEmbeddingSize
negative = activationsCount - 1
self.fileEmbeddings = theano.shared(asfx(fileEmbeddings), 'fileEmbeddings', borrow=False)
self.wordEmbeddings = theano.shared(asfx(wordEmbeddings), 'wordEmbeddings', borrow=False)
self.weights = theano.shared(asfx(weights), 'weights', borrow=False)
fileIndexOffset = 0
wordIndicesOffset = fileIndexOffset + 1
indicesOffset = wordIndicesOffset + contextSize
contexts = T.imatrix('contexts')
fileIndices = contexts[:,fileIndexOffset:wordIndicesOffset]
wordIndices = contexts[:,wordIndicesOffset:indicesOffset]
indices = contexts[:,indicesOffset:indicesOffset + negative]
files = self.fileEmbeddings[fileIndices]
fileFeatures = T.flatten(files, outdim=2)
words = self.wordEmbeddings[wordIndices]
wordFeatures = T.flatten(words, outdim=2)
features = T.concatenate([fileFeatures, wordFeatures], axis=1)
subWeights = self.weights[:,indices].dimshuffle(1, 0, 2)
probabilities = T.batched_dot(features, subWeights)
parameters = [self.fileEmbeddings]
subParameters = [files]
consider_constant = [self.wordEmbeddings]
if trainWeights:
parameters.append(self.weights)
subParameters.append(subWeights)
else:
consider_constant.append(self.weights)
# cost = -T.mean(T.log(T.nnet.sigmoid(probabilities[:,0])) + T.sum(T.log(T.nnet.sigmoid(-probabilities[:,1:])), dtype=floatX, acc_dtype=floatX), dtype=floatX, acc_dtype=floatX)
cost = -T.log(T.nnet.sigmoid(probabilities[:,0])) - T.sum(T.log(T.nnet.sigmoid(-probabilities[:,1:])), dtype=floatX, acc_dtype=floatX)
learningRate = T.scalar('learningRate', dtype=floatX)
updates = []
for p, subP in zip(parameters, subParameters):
if subP is not None:
gradient = T.jacobian(cost, wrt=subP)
update = (p, T.inc_subtensor(subP, -learningRate * gradient))
else:
gradient = T.jacobian(cost, wrt=p)
update = (p, p - learningRate * gradient)
updates.append(update)
contextIndex = T.iscalar('contextIndex')
self.trainingContexts = theano.shared(empty(1,1,1), 'trainingContexts', borrow=False)
self.trainModel = theano.function(
inputs=[contextIndex, learningRate],
outputs=cost,
updates=updates,
givens={
contexts: self.trainingContexts[:,contextIndex]
}
)
def __init__(self, fileEmbeddings, wordEmbeddings, weights=None, contextSize=None, negative=None):
filesCount, fileEmbeddingSize = fileEmbeddings.shape
wordsCount, wordEmbeddingSize = wordEmbeddings.shape
if weights is not None:
featuresCount, activationsCount = weights.shape
contextSize = (featuresCount - fileEmbeddingSize) / wordEmbeddingSize
negative = activationsCount - 1
else:
weights = rnd2(fileEmbeddingSize + contextSize * wordEmbeddingSize, wordsCount)
self.fileEmbeddings = theano.shared(asfx(fileEmbeddings), 'fileEmbeddings', borrow=False)
self.wordEmbeddings = theano.shared(asfx(wordEmbeddings), 'wordEmbeddings', borrow=False)
self.weights = theano.shared(asfx(weights), 'weights', borrow=False)
fileIndexOffset = 0
wordIndicesOffset = fileIndexOffset + 1
indicesOffset = wordIndicesOffset + contextSize
contexts = T.imatrix('contexts')
context = T.flatten(contexts)
fileIndex = context[fileIndexOffset:wordIndicesOffset]
wordIndices = context[wordIndicesOffset:indicesOffset]
indices = context[indicesOffset:indicesOffset + negative]
file = self.fileEmbeddings[fileIndex]
fileFeatures = T.flatten(file, outdim=1)
words = self.wordEmbeddings[wordIndices]
wordFeatures = T.flatten(words, outdim=1)
features = T.concatenate([fileFeatures, wordFeatures], axis=0)
subWeights = self.weights[:,indices]
probabilities = T.dot(features, subWeights)
parameters = [self.fileEmbeddings]
subParameters = [file]
consider_constant = [self.wordEmbeddings]
if weights is not None:
consider_constant.append(self.weights)
else:
parameters.append(self.weights)
subParameters.append(subWeights)
cost = -T.mean(T.log(T.nnet.sigmoid(probabilities[0])) + T.sum(T.log(T.nnet.sigmoid(-probabilities[1:]))))
learningRate = T.scalar('learningRate', dtype=floatX)
gradients = [T.grad(cost, wrt=subP, consider_constant=consider_constant) for subP in subParameters]
updates = [(p, T.inc_subtensor(subP, -learningRate * g)) for p, subP, g in zip(parameters, subParameters, gradients)]
contextIndex = T.iscalar('batchIndex')
self.trainingContexts = theano.shared(empty(1,1), 'trainingContexts', borrow=False)
self.trainModel = theano.function(
inputs=[contextIndex, learningRate],
outputs=cost,
updates=updates,
givens={
contexts: self.trainingContexts[contextIndex:contextIndex + 1]
}
)
