def work():
print "Started!"
print "Loading data."
cr = CorpusReader(minDocSentenceNum=5, minSentenceWordNum=5, dataset="data/split", labelset="data/traindataset2zgb")
# valid_cr = CorpusReader(minDocSentenceNum=5, minSentenceWordNum=5, dataset="data/valid/split", labelset="data/valid/label.txt")
validDocMatrixes, validDocSentenceNums, validSentenceWordNums, validLabels = cr.getCorpus([800, 870])
print "Data loaded."
mr = numpy.max([cr.getMaxDocSentenceNum(), cr.getMaxDocSentenceNum()])
learning_rate = 0.01
docSentenceCount = T.fvector("docSentenceCount")
sentenceWordCount = T.fmatrix("sentenceWordCount")
corpus = T.ftensor4("corpus")
docLabel = T.ivector('docLabel')
rng = numpy.random.RandomState(23455)
batchSize = 40
# for list-type data
layer0 = DocEmbeddingNN(corpus, docSentenceCount, sentenceWordCount, rng, wordEmbeddingDim=200, \
maxRandge=mr, \
sentenceLayerNodesNum=100, \
sentenceLayerNodesSize=[5, 200], \
docLayerNodesNum=100, \
docLayerNodesSize=[3, 100])
# for padding data
# layer0 = DocEmbeddingNN(corpus, docSentenceCount, sentenceWordCount, rng, corpus_shape=(batchSize, cr.getMaxDocSentenceNum(), cr.getMaxSentenceWordNum(), cr.getDim()), \
# maxRandge=mr, \
# sentenceLayerNodesNum=100, \
# sentenceLayerNodesSize=5, \
# docLayerNodesNum=200, \
# docLayerNodesSize=3)
layer1 = HiddenLayer(
rng,
input=layer0.output,
n_in=layer0.outputDimension,
n_out=100,
activation=T.tanh
)
layer2 = LogisticRegression(input=layer1.output, n_in=100, n_out=2)
error = layer2.errors(docLabel)
cost = layer2.negative_log_likelihood(docLabel)
# construct the parameter array.
params = layer2.params + layer1.params + layer0.params
# Load the parameters last time, optionally.
loadParamsVal(params)
grads = T.grad(cost, params)
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
print "Compiling computing graph."
# for list-type data
train_model = theano.function(
[corpus, docSentenceCount, sentenceWordCount, docLabel],
[cost, error],
updates=updates,
)
valid_model = theano.function(
[corpus, docSentenceCount, sentenceWordCount, docLabel],
[cost, error]
)
# for padding data
# train_model = theano.function(
# [corpus, docLabel],
# [cost, error],
# updates=updates,
# )
print "Compiled."
print "Start to train."
epoch = 0
n_epochs = 200
ite = 0
while (epoch < n_epochs):
epoch = epoch + 1
# ####Validate the model####
# for list-type data
costNum, errorNum = valid_model(validDocMatrixes, validDocSentenceNums, validSentenceWordNums, validLabels)
# for padding data
# costNum, errorNum = train_model(docMatrixes, labels)
print "Valid current model:"
print "Cost: ", costNum
print "Error: ", errorNum
#######################
for i in range(1000):
# if(i * batchSize >= 800):
# break
docInfo = cr.getCorpus([i * batchSize, numpy.min([ (i + 1) * batchSize, 800])])
if(docInfo is None):
break
ite = ite + 1
docMatrixes, docSentenceNums, sentenceWordNums, labels = docInfo
# for list-type data
costNum, errorNum = train_model(docMatrixes, docSentenceNums, sentenceWordNums, labels)
# for padding data
# costNum, errorNum = train_model(docMatrixes, labels)
del docMatrixes, docSentenceNums, sentenceWordNums, labels
# print ".",
if(ite % 1 == 0):
print
print "@iter: ", ite
print "Cost: ", costNum
print "Error: ", errorNum
print "Saving parameters."
saveParamsVal(params)
print "Saved."
print "All finished!"
